diff --git "a/compiled/UnetRefiner.mlmodelc/model.mil" "b/compiled/UnetRefiner.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/compiled/UnetRefiner.mlmodelc/model.mil" @@ -0,0 +1,8800 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.5"}, {"coremlc-version", "1877.40.3"}, {"coremltools-component-torch", "2.0.1"}, {"coremltools-version", "7.0"}})] +{ + func main(tensor encoder_hidden_states, tensor sample, tensor text_embeds, tensor time_ids, tensor timestep) { + tensor var_28 = const()[name = tensor("op_28"), val = tensor(-1)]; + tensor var_45_axes_0 = const()[name = tensor("op_45_axes_0"), val = tensor([1])]; + tensor var_45_cast = expand_dims(axes = var_45_axes_0, x = timestep)[name = tensor("op_45_cast")]; + tensor var_47_to_fp16 = const()[name = tensor("op_47_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast = mul(x = var_45_cast, y = var_47_to_fp16)[name = tensor("emb_3_cast")]; + tensor var_52_cast = sin(x = emb_3_cast)[name = tensor("op_52_cast")]; + tensor var_53_cast = cos(x = emb_3_cast)[name = tensor("op_53_cast")]; + tensor emb_7_interleave_0 = const()[name = tensor("emb_7_interleave_0"), val = tensor(false)]; + tensor emb_7_cast = concat(axis = var_28, interleave = emb_7_interleave_0, values = (var_52_cast, var_53_cast))[name = tensor("emb_7_cast")]; + tensor var_57_begin_0 = const()[name = tensor("op_57_begin_0"), val = tensor([0, 192])]; + tensor var_57_end_0 = const()[name = tensor("op_57_end_0"), val = tensor([2, 384])]; + tensor var_57_end_mask_0 = const()[name = tensor("op_57_end_mask_0"), val = tensor([true, true])]; + tensor var_57_cast = slice_by_index(begin = var_57_begin_0, end = var_57_end_0, end_mask = var_57_end_mask_0, x = emb_7_cast)[name = tensor("op_57_cast")]; + tensor var_59_begin_0 = const()[name = tensor("op_59_begin_0"), val = tensor([0, 0])]; + tensor var_59_end_0 = const()[name = tensor("op_59_end_0"), val = tensor([2, 192])]; + tensor var_59_end_mask_0 = const()[name = tensor("op_59_end_mask_0"), val = tensor([true, false])]; + tensor var_59_cast = slice_by_index(begin = var_59_begin_0, end = var_59_end_0, end_mask = var_59_end_mask_0, x = emb_7_cast)[name = tensor("op_59_cast")]; + tensor sample_3_interleave_0 = const()[name = tensor("sample_3_interleave_0"), val = tensor(false)]; + tensor sample_3_cast = concat(axis = var_28, interleave = sample_3_interleave_0, values = (var_57_cast, var_59_cast))[name = tensor("sample_3_cast")]; + tensor var_62 = const()[name = tensor("op_62"), val = tensor(1)]; + tensor var_69_axes_0 = const()[name = tensor("op_69_axes_0"), val = tensor([-1])]; + tensor var_69_cast = expand_dims(axes = var_69_axes_0, x = sample_3_cast)[name = tensor("op_69_cast")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast = expand_dims(axes = input_1_axes_0, x = var_69_cast)[name = tensor("input_1_cast")]; + tensor var_73 = const()[name = tensor("op_73"), val = tensor([1, 1])]; + tensor var_75 = const()[name = tensor("op_75"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16 = const()[name = tensor("time_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512)))]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180224)))]; + tensor input_3_cast = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_75, groups = var_62, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_73, weight = time_embedding_linear_1_weight_to_fp16, x = input_1_cast)[name = tensor("input_3_cast")]; + tensor input_5_cast = silu(x = input_3_cast)[name = tensor("input_5_cast")]; + tensor var_81 = const()[name = tensor("op_81"), val = tensor([1, 1])]; + tensor var_83 = const()[name = tensor("op_83"), val = tensor([1, 1])]; + tensor emb_pad_type_0 = const()[name = tensor("emb_pad_type_0"), val = tensor("custom")]; + tensor emb_pad_0 = const()[name = tensor("emb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16 = const()[name = tensor("time_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183360)))]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5902016)))]; + tensor emb_cast = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_83, groups = var_62, pad = emb_pad_0, pad_type = emb_pad_type_0, strides = var_81, weight = time_embedding_linear_2_weight_to_fp16, x = input_5_cast)[name = tensor("emb_cast")]; + tensor concat_0 = const()[name = tensor("concat_0"), val = tensor([10])]; + tensor timesteps_cast = reshape(shape = concat_0, x = time_ids)[name = tensor("timesteps_cast")]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor(-1)]; + tensor var_106_axes_0 = const()[name = tensor("op_106_axes_0"), val = tensor([1])]; + tensor var_106_cast = expand_dims(axes = var_106_axes_0, x = timesteps_cast)[name = tensor("op_106_cast")]; + tensor var_108_to_fp16 = const()[name = tensor("op_108_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5905152)))]; + tensor emb_11_cast = mul(x = var_106_cast, y = var_108_to_fp16)[name = tensor("emb_11_cast")]; + tensor var_113_cast = sin(x = emb_11_cast)[name = tensor("op_113_cast")]; + tensor var_114_cast = cos(x = emb_11_cast)[name = tensor("op_114_cast")]; + tensor emb_15_interleave_0 = const()[name = tensor("emb_15_interleave_0"), val = tensor(false)]; + tensor emb_15_cast = concat(axis = var_89, interleave = emb_15_interleave_0, values = (var_113_cast, var_114_cast))[name = tensor("emb_15_cast")]; + tensor var_118_begin_0 = const()[name = tensor("op_118_begin_0"), val = tensor([0, 128])]; + tensor var_118_end_0 = const()[name = tensor("op_118_end_0"), val = tensor([10, 256])]; + tensor var_118_end_mask_0 = const()[name = tensor("op_118_end_mask_0"), val = tensor([true, true])]; + tensor var_118_cast = slice_by_index(begin = var_118_begin_0, end = var_118_end_0, end_mask = var_118_end_mask_0, x = emb_15_cast)[name = tensor("op_118_cast")]; + tensor var_120_begin_0 = const()[name = tensor("op_120_begin_0"), val = tensor([0, 0])]; + tensor var_120_end_0 = const()[name = tensor("op_120_end_0"), val = tensor([10, 128])]; + tensor var_120_end_mask_0 = const()[name = tensor("op_120_end_mask_0"), val = tensor([true, false])]; + tensor var_120_cast = slice_by_index(begin = var_120_begin_0, end = var_120_end_0, end_mask = var_120_end_mask_0, x = emb_15_cast)[name = tensor("op_120_cast")]; + tensor time_embeds_1_interleave_0 = const()[name = tensor("time_embeds_1_interleave_0"), val = tensor(false)]; + tensor time_embeds_1_cast = concat(axis = var_89, interleave = time_embeds_1_interleave_0, values = (var_118_cast, var_120_cast))[name = tensor("time_embeds_1_cast")]; + tensor var_128 = const()[name = tensor("op_128"), val = tensor([2, -1])]; + tensor time_embeds_cast = reshape(shape = var_128, x = time_embeds_1_cast)[name = tensor("time_embeds_cast")]; + tensor var_131 = const()[name = tensor("op_131"), val = tensor(-1)]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast = concat(axis = var_131, interleave = sample_interleave_0, values = (text_embeds, time_embeds_cast))[name = tensor("sample_cast")]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor(1)]; + tensor var_140_axes_0 = const()[name = tensor("op_140_axes_0"), val = tensor([-1])]; + tensor var_140_cast = expand_dims(axes = var_140_axes_0, x = sample_cast)[name = tensor("op_140_cast")]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor input_7_cast = expand_dims(axes = input_7_axes_0, x = var_140_cast)[name = tensor("input_7_cast")]; + tensor var_144 = const()[name = tensor("op_144"), val = tensor([1, 1])]; + tensor var_146 = const()[name = tensor("op_146"), val = tensor([1, 1])]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_1_weight_to_fp16 = const()[name = tensor("add_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5905472)))]; + tensor add_embedding_linear_1_bias_to_fp16 = const()[name = tensor("add_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13769856)))]; + tensor input_9_cast = conv(bias = add_embedding_linear_1_bias_to_fp16, dilations = var_146, groups = var_133, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_144, weight = add_embedding_linear_1_weight_to_fp16, x = input_7_cast)[name = tensor("input_9_cast")]; + tensor input_11_cast = silu(x = input_9_cast)[name = tensor("input_11_cast")]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 1])]; + tensor var_154 = const()[name = tensor("op_154"), val = tensor([1, 1])]; + tensor aug_emb_pad_type_0 = const()[name = tensor("aug_emb_pad_type_0"), val = tensor("custom")]; + tensor aug_emb_pad_0 = const()[name = tensor("aug_emb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_2_weight_to_fp16 = const()[name = tensor("add_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13772992)))]; + tensor add_embedding_linear_2_bias_to_fp16 = const()[name = tensor("add_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18491648)))]; + tensor aug_emb_cast = conv(bias = add_embedding_linear_2_bias_to_fp16, dilations = var_154, groups = var_133, pad = aug_emb_pad_0, pad_type = aug_emb_pad_type_0, strides = var_152, weight = add_embedding_linear_2_weight_to_fp16, x = input_11_cast)[name = tensor("aug_emb_cast")]; + tensor input_19_cast = add(x = emb_cast, y = aug_emb_cast)[name = tensor("input_19_cast")]; + tensor var_162 = const()[name = tensor("op_162"), val = tensor(1)]; + tensor var_165 = const()[name = tensor("op_165"), val = tensor([1, 1])]; + tensor var_167 = const()[name = tensor("op_167"), val = tensor([1, 1])]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16 = const()[name = tensor("conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18494784)))]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18522496)))]; + tensor input_13_cast = conv(bias = conv_in_bias_to_fp16, dilations = var_167, groups = var_162, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_165, weight = conv_in_weight_to_fp16, x = sample)[name = tensor("input_13_cast")]; + tensor var_176 = const()[name = tensor("op_176"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_13_cast)[name = tensor("reshape_0_cast")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor("reduce_mean_2_cast")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18523328)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18524160)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18524992)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18525824)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast)[name = tensor("add_1_cast")]; + tensor input_17_cast = silu(x = add_1_cast)[name = tensor("input_17_cast")]; + tensor var_194 = const()[name = tensor("op_194"), val = tensor([1, 1])]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18526656)))]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21180928)))]; + tensor hidden_states_1_cast = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_196, groups = var_176, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_194, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_17_cast)[name = tensor("hidden_states_1_cast")]; + tensor input_21_cast = silu(x = input_19_cast)[name = tensor("input_21_cast")]; + tensor var_202 = const()[name = tensor("op_202"), val = tensor([1, 1])]; + tensor var_204 = const()[name = tensor("op_204"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21181760)))]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22361472)))]; + tensor temb_1_cast = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_204, groups = var_176, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_202, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_1_cast")]; + tensor input_23_cast = add(x = hidden_states_1_cast, y = temb_1_cast)[name = tensor("input_23_cast")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_23_cast)[name = tensor("reshape_4_cast")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor("reduce_mean_5_cast")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22362304)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22363136)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast)[name = tensor("add_3_cast")]; + tensor input_27_cast = silu(x = add_3_cast)[name = tensor("input_27_cast")]; + tensor var_214 = const()[name = tensor("op_214"), val = tensor([1, 1])]; + tensor var_216 = const()[name = tensor("op_216"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22363968)))]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25018240)))]; + tensor hidden_states_3_cast = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_216, groups = var_176, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_214, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_27_cast)[name = tensor("hidden_states_3_cast")]; + tensor input_29_cast = add(x = input_13_cast, y = hidden_states_3_cast)[name = tensor("input_29_cast")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = input_29_cast)[name = tensor("reshape_8_cast")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor("reduce_mean_8_cast")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25019072)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25019904)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor("add_5_cast")]; + tensor input_33_cast = silu(x = add_5_cast)[name = tensor("input_33_cast")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([1, 1])]; + tensor var_233 = const()[name = tensor("op_233"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25020736)))]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27675008)))]; + tensor hidden_states_5_cast = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_233, groups = var_176, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_231, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_33_cast)[name = tensor("hidden_states_5_cast")]; + tensor var_239 = const()[name = tensor("op_239"), val = tensor([1, 1])]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27675840)))]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28855552)))]; + tensor temb_3_cast = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_241, groups = var_176, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_239, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_3_cast")]; + tensor input_37_cast = add(x = hidden_states_5_cast, y = temb_3_cast)[name = tensor("input_37_cast")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_37_cast)[name = tensor("reshape_12_cast")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor("reduce_mean_11_cast")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28856384)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28857216)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast)[name = tensor("add_7_cast")]; + tensor input_41_cast = silu(x = add_7_cast)[name = tensor("input_41_cast")]; + tensor var_251 = const()[name = tensor("op_251"), val = tensor([1, 1])]; + tensor var_253 = const()[name = tensor("op_253"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28858048)))]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31512320)))]; + tensor hidden_states_7_cast = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_253, groups = var_176, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_251, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_41_cast)[name = tensor("hidden_states_7_cast")]; + tensor input_43_cast = add(x = input_29_cast, y = hidden_states_7_cast)[name = tensor("input_43_cast")]; + tensor var_260 = const()[name = tensor("op_260"), val = tensor([2, 2])]; + tensor var_262 = const()[name = tensor("op_262"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31513152)))]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34167424)))]; + tensor input_45_cast = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_262, groups = var_176, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_260, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = input_43_cast)[name = tensor("input_45_cast")]; + tensor var_270 = const()[name = tensor("op_270"), val = tensor(3)]; + tensor var_281 = const()[name = tensor("op_281"), val = tensor(true)]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor(1)]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 12, 64, 64])]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_45_cast)[name = tensor("reshape_16_cast")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor("reduce_mean_14_cast")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 384, 64, 64])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34168256)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34169088)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast)[name = tensor("add_9_cast")]; + tensor input_49_cast = silu(x = add_9_cast)[name = tensor("input_49_cast")]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([1, 1])]; + tensor var_311 = const()[name = tensor("op_311"), val = tensor([1, 1])]; + tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34169920)))]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39478400)))]; + tensor hidden_states_9_cast = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_311, groups = var_286, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_309, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_49_cast)[name = tensor("hidden_states_9_cast")]; + tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 1])]; + tensor var_319 = const()[name = tensor("op_319"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39480000)))]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41839360)))]; + tensor temb_5_cast = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_319, groups = var_286, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_317, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_5_cast")]; + tensor input_53_cast = add(x = hidden_states_9_cast, y = temb_5_cast)[name = tensor("input_53_cast")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = input_53_cast)[name = tensor("reshape_20_cast")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor("reduce_mean_17_cast")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41840960)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41842560)))]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41844160)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41845760)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast)[name = tensor("add_11_cast")]; + tensor input_57_cast = silu(x = add_11_cast)[name = tensor("input_57_cast")]; + tensor var_329 = const()[name = tensor("op_329"), val = tensor([1, 1])]; + tensor var_331 = const()[name = tensor("op_331"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41847360)))]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52464256)))]; + tensor hidden_states_11_cast = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_331, groups = var_286, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_329, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_57_cast)[name = tensor("hidden_states_11_cast")]; + tensor var_336 = const()[name = tensor("op_336"), val = tensor([1, 1])]; + tensor var_338 = const()[name = tensor("op_338"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52465856)))]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53055744)))]; + tensor x_1_cast = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_338, groups = var_286, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_336, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_45_cast)[name = tensor("x_1_cast")]; + tensor hidden_states_13_cast = add(x = x_1_cast, y = hidden_states_11_cast)[name = tensor("hidden_states_13_cast")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = hidden_states_13_cast)[name = tensor("reshape_24_cast")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor("reduce_mean_20_cast")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53057344)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53058944)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast)[name = tensor("add_13_cast")]; + tensor var_364 = const()[name = tensor("op_364"), val = tensor([1, 1])]; + tensor var_366 = const()[name = tensor("op_366"), val = tensor([1, 1])]; + tensor hidden_states_15_pad_type_0 = const()[name = tensor("hidden_states_15_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53060544)))]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54240256)))]; + tensor hidden_states_15_cast = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_366, groups = var_286, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_364, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_13_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_371 = const()[name = tensor("op_371"), val = tensor([2, 768, 1, 4096])]; + tensor inputs_1_cast = reshape(shape = var_371, x = hidden_states_15_cast)[name = tensor("inputs_1_cast")]; + tensor var_381 = const()[name = tensor("op_381"), val = tensor([1])]; + tensor channels_mean_1_cast = reduce_mean(axes = var_381, keep_dims = var_281, x = inputs_1_cast)[name = tensor("channels_mean_1_cast")]; + tensor zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast)[name = tensor("zero_mean_1_cast")]; + tensor zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast)[name = tensor("zero_mean_sq_1_cast")]; + tensor var_385 = const()[name = tensor("op_385"), val = tensor([1])]; + tensor var_386_cast = reduce_mean(axes = var_385, keep_dims = var_281, x = zero_mean_sq_1_cast)[name = tensor("op_386_cast")]; + tensor var_387_to_fp16 = const()[name = tensor("op_387_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_388_cast = add(x = var_386_cast, y = var_387_to_fp16)[name = tensor("op_388_cast")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_388_cast)[name = tensor("denom_1_cast")]; + tensor out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast)[name = tensor("out_1_cast")]; + tensor var_392_to_fp16 = const()[name = tensor("op_392_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54241856)))]; + tensor var_393_cast = add(x = out_1_cast, y = var_392_to_fp16)[name = tensor("op_393_cast")]; + tensor var_395_to_fp16 = const()[name = tensor("op_395_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54243456)))]; + tensor hidden_states_17_cast = mul(x = var_393_cast, y = var_395_to_fp16)[name = tensor("hidden_states_17_cast")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 1])]; + tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54245056)))]; + tensor q_1_cast = conv(dilations = var_404, groups = var_286, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_402, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("q_1_cast")]; + tensor var_408 = const()[name = tensor("op_408"), val = tensor([1, 1])]; + tensor var_410 = const()[name = tensor("op_410"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55424768)))]; + tensor k_1_cast = conv(dilations = var_410, groups = var_286, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_408, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("k_1_cast")]; + tensor var_414 = const()[name = tensor("op_414"), val = tensor([1, 1])]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56604480)))]; + tensor v_1_cast = conv(dilations = var_416, groups = var_286, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_414, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("v_1_cast")]; + tensor var_420 = const()[name = tensor("op_420"), val = tensor([2, 12, 64, -1])]; + tensor var_421_cast = reshape(shape = var_420, x = q_1_cast)[name = tensor("op_421_cast")]; + tensor var_422 = const()[name = tensor("op_422"), val = tensor([2, 12, 64, -1])]; + tensor var_423_cast = reshape(shape = var_422, x = k_1_cast)[name = tensor("op_423_cast")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([2, 12, 64, -1])]; + tensor var_425_cast = reshape(shape = var_424, x = v_1_cast)[name = tensor("op_425_cast")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_421_cast, y = var_423_cast)[name = tensor("attn_weights_1_cast")]; + tensor var_277_to_fp16 = const()[name = tensor("op_277_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_277_to_fp16)[name = tensor("attn_weights_3_cast")]; + tensor var_429_cast = softmax(axis = var_270, x = attn_weights_3_cast)[name = tensor("op_429_cast")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_425_cast, y = var_429_cast)[name = tensor("attn_1_cast")]; + tensor var_433 = const()[name = tensor("op_433"), val = tensor([2, 768, 1, -1])]; + tensor input_61_cast = reshape(shape = var_433, x = attn_1_cast)[name = tensor("input_61_cast")]; + tensor var_438 = const()[name = tensor("op_438"), val = tensor([1, 1])]; + tensor var_440 = const()[name = tensor("op_440"), val = tensor([1, 1])]; + tensor var_442_pad_type_0 = const()[name = tensor("op_442_pad_type_0"), val = tensor("custom")]; + tensor var_442_pad_0 = const()[name = tensor("op_442_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57784192)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58963904)))]; + tensor var_442_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_440, groups = var_286, pad = var_442_pad_0, pad_type = var_442_pad_type_0, strides = var_438, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_61_cast)[name = tensor("op_442_cast")]; + tensor inputs_3_cast = add(x = var_442_cast, y = inputs_1_cast)[name = tensor("inputs_3_cast")]; + tensor var_446 = const()[name = tensor("op_446"), val = tensor([1])]; + tensor channels_mean_3_cast = reduce_mean(axes = var_446, keep_dims = var_281, x = inputs_3_cast)[name = tensor("channels_mean_3_cast")]; + tensor zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast)[name = tensor("zero_mean_3_cast")]; + tensor zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast)[name = tensor("zero_mean_sq_3_cast")]; + tensor var_450 = const()[name = tensor("op_450"), val = tensor([1])]; + tensor var_451_cast = reduce_mean(axes = var_450, keep_dims = var_281, x = zero_mean_sq_3_cast)[name = tensor("op_451_cast")]; + tensor var_452_to_fp16 = const()[name = tensor("op_452_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_453_cast = add(x = var_451_cast, y = var_452_to_fp16)[name = tensor("op_453_cast")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_453_cast)[name = tensor("denom_3_cast")]; + tensor out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast)[name = tensor("out_3_cast")]; + tensor var_457_to_fp16 = const()[name = tensor("op_457_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58965504)))]; + tensor var_458_cast = add(x = out_3_cast, y = var_457_to_fp16)[name = tensor("op_458_cast")]; + tensor var_460_to_fp16 = const()[name = tensor("op_460_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58967104)))]; + tensor hidden_states_19_cast = mul(x = var_458_cast, y = var_460_to_fp16)[name = tensor("hidden_states_19_cast")]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; + tensor var_469 = const()[name = tensor("op_469"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58968704)))]; + tensor q_3_cast = conv(dilations = var_469, groups = var_286, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_467, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("q_3_cast")]; + tensor var_473 = const()[name = tensor("op_473"), val = tensor([1, 1])]; + tensor var_475 = const()[name = tensor("op_475"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60148416)))]; + tensor k_3_cast = conv(dilations = var_475, groups = var_286, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_473, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_3_cast")]; + tensor var_479 = const()[name = tensor("op_479"), val = tensor([1, 1])]; + tensor var_481 = const()[name = tensor("op_481"), val = tensor([1, 1])]; + tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62114560)))]; + tensor v_3_cast = conv(dilations = var_481, groups = var_286, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_479, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_3_cast")]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([2, 12, 64, -1])]; + tensor var_486_cast = reshape(shape = var_485, x = q_3_cast)[name = tensor("op_486_cast")]; + tensor var_487 = const()[name = tensor("op_487"), val = tensor([2, 12, 64, -1])]; + tensor var_488_cast = reshape(shape = var_487, x = k_3_cast)[name = tensor("op_488_cast")]; + tensor var_489 = const()[name = tensor("op_489"), val = tensor([2, 12, 64, -1])]; + tensor var_490_cast = reshape(shape = var_489, x = v_3_cast)[name = tensor("op_490_cast")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_486_cast, y = var_488_cast)[name = tensor("attn_weights_5_cast")]; + tensor attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_277_to_fp16)[name = tensor("attn_weights_7_cast")]; + tensor var_494_cast = softmax(axis = var_270, x = attn_weights_7_cast)[name = tensor("op_494_cast")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_490_cast, y = var_494_cast)[name = tensor("attn_3_cast")]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([2, 768, 1, -1])]; + tensor input_63_cast = reshape(shape = var_498, x = attn_3_cast)[name = tensor("input_63_cast")]; + tensor var_503 = const()[name = tensor("op_503"), val = tensor([1, 1])]; + tensor var_505 = const()[name = tensor("op_505"), val = tensor([1, 1])]; + tensor var_507_pad_type_0 = const()[name = tensor("op_507_pad_type_0"), val = tensor("custom")]; + tensor var_507_pad_0 = const()[name = tensor("op_507_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64080704)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65260416)))]; + tensor var_507_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_505, groups = var_286, pad = var_507_pad_0, pad_type = var_507_pad_type_0, strides = var_503, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_63_cast)[name = tensor("op_507_cast")]; + tensor inputs_5_cast = add(x = var_507_cast, y = inputs_3_cast)[name = tensor("inputs_5_cast")]; + tensor var_511 = const()[name = tensor("op_511"), val = tensor([1])]; + tensor channels_mean_5_cast = reduce_mean(axes = var_511, keep_dims = var_281, x = inputs_5_cast)[name = tensor("channels_mean_5_cast")]; + tensor zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast)[name = tensor("zero_mean_5_cast")]; + tensor zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast)[name = tensor("zero_mean_sq_5_cast")]; + tensor var_515 = const()[name = tensor("op_515"), val = tensor([1])]; + tensor var_516_cast = reduce_mean(axes = var_515, keep_dims = var_281, x = zero_mean_sq_5_cast)[name = tensor("op_516_cast")]; + tensor var_517_to_fp16 = const()[name = tensor("op_517_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_518_cast = add(x = var_516_cast, y = var_517_to_fp16)[name = tensor("op_518_cast")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_518_cast)[name = tensor("denom_5_cast")]; + tensor out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast)[name = tensor("out_5_cast")]; + tensor var_522_to_fp16 = const()[name = tensor("op_522_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65262016)))]; + tensor var_523_cast = add(x = out_5_cast, y = var_522_to_fp16)[name = tensor("op_523_cast")]; + tensor var_525_to_fp16 = const()[name = tensor("op_525_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65263616)))]; + tensor input_65_cast = mul(x = var_523_cast, y = var_525_to_fp16)[name = tensor("input_65_cast")]; + tensor var_533 = const()[name = tensor("op_533"), val = tensor([1, 1])]; + tensor var_535 = const()[name = tensor("op_535"), val = tensor([1, 1])]; + tensor var_537_pad_type_0 = const()[name = tensor("op_537_pad_type_0"), val = tensor("custom")]; + tensor var_537_pad_0 = const()[name = tensor("op_537_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65265216)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74702464)))]; + tensor var_537_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_535, groups = var_286, pad = var_537_pad_0, pad_type = var_537_pad_type_0, strides = var_533, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_65_cast)[name = tensor("op_537_cast")]; + tensor var_538_split_sizes_0 = const()[name = tensor("op_538_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_538_axis_0 = const()[name = tensor("op_538_axis_0"), val = tensor(1)]; + tensor var_538_cast_0, tensor var_538_cast_1 = split(axis = var_538_axis_0, split_sizes = var_538_split_sizes_0, x = var_537_cast)[name = tensor("op_538_cast")]; + tensor var_540_mode_0 = const()[name = tensor("op_540_mode_0"), val = tensor("EXACT")]; + tensor var_540_cast = gelu(mode = var_540_mode_0, x = var_538_cast_1)[name = tensor("op_540_cast")]; + tensor input_67_cast = mul(x = var_538_cast_0, y = var_540_cast)[name = tensor("input_67_cast")]; + tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, 1])]; + tensor var_546 = const()[name = tensor("op_546"), val = tensor([1, 1])]; + tensor var_548_pad_type_0 = const()[name = tensor("op_548_pad_type_0"), val = tensor("custom")]; + tensor var_548_pad_0 = const()[name = tensor("op_548_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74714816)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79433472)))]; + tensor var_548_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_546, groups = var_286, pad = var_548_pad_0, pad_type = var_548_pad_type_0, strides = var_544, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_67_cast)[name = tensor("op_548_cast")]; + tensor inputs_7_cast = add(x = var_548_cast, y = inputs_5_cast)[name = tensor("inputs_7_cast")]; + tensor var_558 = const()[name = tensor("op_558"), val = tensor([1])]; + tensor channels_mean_7_cast = reduce_mean(axes = var_558, keep_dims = var_281, x = inputs_7_cast)[name = tensor("channels_mean_7_cast")]; + tensor zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast)[name = tensor("zero_mean_7_cast")]; + tensor zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast)[name = tensor("zero_mean_sq_7_cast")]; + tensor var_562 = const()[name = tensor("op_562"), val = tensor([1])]; + tensor var_563_cast = reduce_mean(axes = var_562, keep_dims = var_281, x = zero_mean_sq_7_cast)[name = tensor("op_563_cast")]; + tensor var_564_to_fp16 = const()[name = tensor("op_564_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_565_cast = add(x = var_563_cast, y = var_564_to_fp16)[name = tensor("op_565_cast")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_565_cast)[name = tensor("denom_7_cast")]; + tensor out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast)[name = tensor("out_7_cast")]; + tensor var_569_to_fp16 = const()[name = tensor("op_569_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79435072)))]; + tensor var_570_cast = add(x = out_7_cast, y = var_569_to_fp16)[name = tensor("op_570_cast")]; + tensor var_572_to_fp16 = const()[name = tensor("op_572_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79436672)))]; + tensor hidden_states_23_cast = mul(x = var_570_cast, y = var_572_to_fp16)[name = tensor("hidden_states_23_cast")]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor var_581 = const()[name = tensor("op_581"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79438272)))]; + tensor q_5_cast = conv(dilations = var_581, groups = var_286, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_579, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("q_5_cast")]; + tensor var_585 = const()[name = tensor("op_585"), val = tensor([1, 1])]; + tensor var_587 = const()[name = tensor("op_587"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80617984)))]; + tensor k_5_cast = conv(dilations = var_587, groups = var_286, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_585, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("k_5_cast")]; + tensor var_591 = const()[name = tensor("op_591"), val = tensor([1, 1])]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81797696)))]; + tensor v_5_cast = conv(dilations = var_593, groups = var_286, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_591, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("v_5_cast")]; + tensor var_597 = const()[name = tensor("op_597"), val = tensor([2, 12, 64, -1])]; + tensor var_598_cast = reshape(shape = var_597, x = q_5_cast)[name = tensor("op_598_cast")]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([2, 12, 64, -1])]; + tensor var_600_cast = reshape(shape = var_599, x = k_5_cast)[name = tensor("op_600_cast")]; + tensor var_601 = const()[name = tensor("op_601"), val = tensor([2, 12, 64, -1])]; + tensor var_602_cast = reshape(shape = var_601, x = v_5_cast)[name = tensor("op_602_cast")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_598_cast, y = var_600_cast)[name = tensor("attn_weights_9_cast")]; + tensor attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_277_to_fp16)[name = tensor("attn_weights_11_cast")]; + tensor var_606_cast = softmax(axis = var_270, x = attn_weights_11_cast)[name = tensor("op_606_cast")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_602_cast, y = var_606_cast)[name = tensor("attn_5_cast")]; + tensor var_610 = const()[name = tensor("op_610"), val = tensor([2, 768, 1, -1])]; + tensor input_69_cast = reshape(shape = var_610, x = attn_5_cast)[name = tensor("input_69_cast")]; + tensor var_615 = const()[name = tensor("op_615"), val = tensor([1, 1])]; + tensor var_617 = const()[name = tensor("op_617"), val = tensor([1, 1])]; + tensor var_619_pad_type_0 = const()[name = tensor("op_619_pad_type_0"), val = tensor("custom")]; + tensor var_619_pad_0 = const()[name = tensor("op_619_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82977408)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84157120)))]; + tensor var_619_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_617, groups = var_286, pad = var_619_pad_0, pad_type = var_619_pad_type_0, strides = var_615, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_69_cast)[name = tensor("op_619_cast")]; + tensor inputs_9_cast = add(x = var_619_cast, y = inputs_7_cast)[name = tensor("inputs_9_cast")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([1])]; + tensor channels_mean_9_cast = reduce_mean(axes = var_623, keep_dims = var_281, x = inputs_9_cast)[name = tensor("channels_mean_9_cast")]; + tensor zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast)[name = tensor("zero_mean_9_cast")]; + tensor zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast)[name = tensor("zero_mean_sq_9_cast")]; + tensor var_627 = const()[name = tensor("op_627"), val = tensor([1])]; + tensor var_628_cast = reduce_mean(axes = var_627, keep_dims = var_281, x = zero_mean_sq_9_cast)[name = tensor("op_628_cast")]; + tensor var_629_to_fp16 = const()[name = tensor("op_629_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_630_cast = add(x = var_628_cast, y = var_629_to_fp16)[name = tensor("op_630_cast")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_630_cast)[name = tensor("denom_9_cast")]; + tensor out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast)[name = tensor("out_9_cast")]; + tensor var_634_to_fp16 = const()[name = tensor("op_634_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84158720)))]; + tensor var_635_cast = add(x = out_9_cast, y = var_634_to_fp16)[name = tensor("op_635_cast")]; + tensor var_637_to_fp16 = const()[name = tensor("op_637_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84160320)))]; + tensor hidden_states_25_cast = mul(x = var_635_cast, y = var_637_to_fp16)[name = tensor("hidden_states_25_cast")]; + tensor var_644 = const()[name = tensor("op_644"), val = tensor([1, 1])]; + tensor var_646 = const()[name = tensor("op_646"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84161920)))]; + tensor q_7_cast = conv(dilations = var_646, groups = var_286, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_644, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("q_7_cast")]; + tensor var_650 = const()[name = tensor("op_650"), val = tensor([1, 1])]; + tensor var_652 = const()[name = tensor("op_652"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85341632)))]; + tensor k_7_cast = conv(dilations = var_652, groups = var_286, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_650, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_7_cast")]; + tensor var_656 = const()[name = tensor("op_656"), val = tensor([1, 1])]; + tensor var_658 = const()[name = tensor("op_658"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87307776)))]; + tensor v_7_cast = conv(dilations = var_658, groups = var_286, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_656, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_7_cast")]; + tensor var_662 = const()[name = tensor("op_662"), val = tensor([2, 12, 64, -1])]; + tensor var_663_cast = reshape(shape = var_662, x = q_7_cast)[name = tensor("op_663_cast")]; + tensor var_664 = const()[name = tensor("op_664"), val = tensor([2, 12, 64, -1])]; + tensor var_665_cast = reshape(shape = var_664, x = k_7_cast)[name = tensor("op_665_cast")]; + tensor var_666 = const()[name = tensor("op_666"), val = tensor([2, 12, 64, -1])]; + tensor var_667_cast = reshape(shape = var_666, x = v_7_cast)[name = tensor("op_667_cast")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_663_cast, y = var_665_cast)[name = tensor("attn_weights_13_cast")]; + tensor attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_277_to_fp16)[name = tensor("attn_weights_15_cast")]; + tensor var_671_cast = softmax(axis = var_270, x = attn_weights_15_cast)[name = tensor("op_671_cast")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_667_cast, y = var_671_cast)[name = tensor("attn_7_cast")]; + tensor var_675 = const()[name = tensor("op_675"), val = tensor([2, 768, 1, -1])]; + tensor input_71_cast = reshape(shape = var_675, x = attn_7_cast)[name = tensor("input_71_cast")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([1, 1])]; + tensor var_682 = const()[name = tensor("op_682"), val = tensor([1, 1])]; + tensor var_684_pad_type_0 = const()[name = tensor("op_684_pad_type_0"), val = tensor("custom")]; + tensor var_684_pad_0 = const()[name = tensor("op_684_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89273920)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90453632)))]; + tensor var_684_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_682, groups = var_286, pad = var_684_pad_0, pad_type = var_684_pad_type_0, strides = var_680, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_71_cast)[name = tensor("op_684_cast")]; + tensor inputs_11_cast = add(x = var_684_cast, y = inputs_9_cast)[name = tensor("inputs_11_cast")]; + tensor var_688 = const()[name = tensor("op_688"), val = tensor([1])]; + tensor channels_mean_11_cast = reduce_mean(axes = var_688, keep_dims = var_281, x = inputs_11_cast)[name = tensor("channels_mean_11_cast")]; + tensor zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast)[name = tensor("zero_mean_11_cast")]; + tensor zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast)[name = tensor("zero_mean_sq_11_cast")]; + tensor var_692 = const()[name = tensor("op_692"), val = tensor([1])]; + tensor var_693_cast = reduce_mean(axes = var_692, keep_dims = var_281, x = zero_mean_sq_11_cast)[name = tensor("op_693_cast")]; + tensor var_694_to_fp16 = const()[name = tensor("op_694_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_695_cast = add(x = var_693_cast, y = var_694_to_fp16)[name = tensor("op_695_cast")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_695_cast)[name = tensor("denom_11_cast")]; + tensor out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast)[name = tensor("out_11_cast")]; + tensor var_699_to_fp16 = const()[name = tensor("op_699_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90455232)))]; + tensor var_700_cast = add(x = out_11_cast, y = var_699_to_fp16)[name = tensor("op_700_cast")]; + tensor var_702_to_fp16 = const()[name = tensor("op_702_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90456832)))]; + tensor input_73_cast = mul(x = var_700_cast, y = var_702_to_fp16)[name = tensor("input_73_cast")]; + tensor var_710 = const()[name = tensor("op_710"), val = tensor([1, 1])]; + tensor var_712 = const()[name = tensor("op_712"), val = tensor([1, 1])]; + tensor var_714_pad_type_0 = const()[name = tensor("op_714_pad_type_0"), val = tensor("custom")]; + tensor var_714_pad_0 = const()[name = tensor("op_714_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90458432)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99895680)))]; + tensor var_714_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_712, groups = var_286, pad = var_714_pad_0, pad_type = var_714_pad_type_0, strides = var_710, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_73_cast)[name = tensor("op_714_cast")]; + tensor var_715_split_sizes_0 = const()[name = tensor("op_715_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_715_axis_0 = const()[name = tensor("op_715_axis_0"), val = tensor(1)]; + tensor var_715_cast_0, tensor var_715_cast_1 = split(axis = var_715_axis_0, split_sizes = var_715_split_sizes_0, x = var_714_cast)[name = tensor("op_715_cast")]; + tensor var_717_mode_0 = const()[name = tensor("op_717_mode_0"), val = tensor("EXACT")]; + tensor var_717_cast = gelu(mode = var_717_mode_0, x = var_715_cast_1)[name = tensor("op_717_cast")]; + tensor input_75_cast = mul(x = var_715_cast_0, y = var_717_cast)[name = tensor("input_75_cast")]; + tensor var_721 = const()[name = tensor("op_721"), val = tensor([1, 1])]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor var_725_pad_type_0 = const()[name = tensor("op_725_pad_type_0"), val = tensor("custom")]; + tensor var_725_pad_0 = const()[name = tensor("op_725_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99908032)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104626688)))]; + tensor var_725_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_723, groups = var_286, pad = var_725_pad_0, pad_type = var_725_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_75_cast)[name = tensor("op_725_cast")]; + tensor inputs_13_cast = add(x = var_725_cast, y = inputs_11_cast)[name = tensor("inputs_13_cast")]; + tensor var_735 = const()[name = tensor("op_735"), val = tensor([1])]; + tensor channels_mean_13_cast = reduce_mean(axes = var_735, keep_dims = var_281, x = inputs_13_cast)[name = tensor("channels_mean_13_cast")]; + tensor zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast)[name = tensor("zero_mean_13_cast")]; + tensor zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast)[name = tensor("zero_mean_sq_13_cast")]; + tensor var_739 = const()[name = tensor("op_739"), val = tensor([1])]; + tensor var_740_cast = reduce_mean(axes = var_739, keep_dims = var_281, x = zero_mean_sq_13_cast)[name = tensor("op_740_cast")]; + tensor var_741_to_fp16 = const()[name = tensor("op_741_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_742_cast = add(x = var_740_cast, y = var_741_to_fp16)[name = tensor("op_742_cast")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_742_cast)[name = tensor("denom_13_cast")]; + tensor out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_cast)[name = tensor("out_13_cast")]; + tensor var_746_to_fp16 = const()[name = tensor("op_746_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104628288)))]; + tensor var_747_cast = add(x = out_13_cast, y = var_746_to_fp16)[name = tensor("op_747_cast")]; + tensor var_749_to_fp16 = const()[name = tensor("op_749_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104629888)))]; + tensor hidden_states_29_cast = mul(x = var_747_cast, y = var_749_to_fp16)[name = tensor("hidden_states_29_cast")]; + tensor var_756 = const()[name = tensor("op_756"), val = tensor([1, 1])]; + tensor var_758 = const()[name = tensor("op_758"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104631488)))]; + tensor q_9_cast = conv(dilations = var_758, groups = var_286, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_756, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_29_cast)[name = tensor("q_9_cast")]; + tensor var_762 = const()[name = tensor("op_762"), val = tensor([1, 1])]; + tensor var_764 = const()[name = tensor("op_764"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105811200)))]; + tensor k_9_cast = conv(dilations = var_764, groups = var_286, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_762, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_29_cast)[name = tensor("k_9_cast")]; + tensor var_768 = const()[name = tensor("op_768"), val = tensor([1, 1])]; + tensor var_770 = const()[name = tensor("op_770"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106990912)))]; + tensor v_9_cast = conv(dilations = var_770, groups = var_286, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_768, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_29_cast)[name = tensor("v_9_cast")]; + tensor var_774 = const()[name = tensor("op_774"), val = tensor([2, 12, 64, -1])]; + tensor var_775_cast = reshape(shape = var_774, x = q_9_cast)[name = tensor("op_775_cast")]; + tensor var_776 = const()[name = tensor("op_776"), val = tensor([2, 12, 64, -1])]; + tensor var_777_cast = reshape(shape = var_776, x = k_9_cast)[name = tensor("op_777_cast")]; + tensor var_778 = const()[name = tensor("op_778"), val = tensor([2, 12, 64, -1])]; + tensor var_779_cast = reshape(shape = var_778, x = v_9_cast)[name = tensor("op_779_cast")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_775_cast, y = var_777_cast)[name = tensor("attn_weights_17_cast")]; + tensor attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_277_to_fp16)[name = tensor("attn_weights_19_cast")]; + tensor var_783_cast = softmax(axis = var_270, x = attn_weights_19_cast)[name = tensor("op_783_cast")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_779_cast, y = var_783_cast)[name = tensor("attn_9_cast")]; + tensor var_787 = const()[name = tensor("op_787"), val = tensor([2, 768, 1, -1])]; + tensor input_77_cast = reshape(shape = var_787, x = attn_9_cast)[name = tensor("input_77_cast")]; + tensor var_792 = const()[name = tensor("op_792"), val = tensor([1, 1])]; + tensor var_794 = const()[name = tensor("op_794"), val = tensor([1, 1])]; + tensor var_796_pad_type_0 = const()[name = tensor("op_796_pad_type_0"), val = tensor("custom")]; + tensor var_796_pad_0 = const()[name = tensor("op_796_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108170624)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109350336)))]; + tensor var_796_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_794, groups = var_286, pad = var_796_pad_0, pad_type = var_796_pad_type_0, strides = var_792, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_77_cast)[name = tensor("op_796_cast")]; + tensor inputs_15_cast = add(x = var_796_cast, y = inputs_13_cast)[name = tensor("inputs_15_cast")]; + tensor var_800 = const()[name = tensor("op_800"), val = tensor([1])]; + tensor channels_mean_15_cast = reduce_mean(axes = var_800, keep_dims = var_281, x = inputs_15_cast)[name = tensor("channels_mean_15_cast")]; + tensor zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast)[name = tensor("zero_mean_15_cast")]; + tensor zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast)[name = tensor("zero_mean_sq_15_cast")]; + tensor var_804 = const()[name = tensor("op_804"), val = tensor([1])]; + tensor var_805_cast = reduce_mean(axes = var_804, keep_dims = var_281, x = zero_mean_sq_15_cast)[name = tensor("op_805_cast")]; + tensor var_806_to_fp16 = const()[name = tensor("op_806_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_807_cast = add(x = var_805_cast, y = var_806_to_fp16)[name = tensor("op_807_cast")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_807_cast)[name = tensor("denom_15_cast")]; + tensor out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_cast)[name = tensor("out_15_cast")]; + tensor var_811_to_fp16 = const()[name = tensor("op_811_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109351936)))]; + tensor var_812_cast = add(x = out_15_cast, y = var_811_to_fp16)[name = tensor("op_812_cast")]; + tensor var_814_to_fp16 = const()[name = tensor("op_814_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109353536)))]; + tensor hidden_states_31_cast = mul(x = var_812_cast, y = var_814_to_fp16)[name = tensor("hidden_states_31_cast")]; + tensor var_821 = const()[name = tensor("op_821"), val = tensor([1, 1])]; + tensor var_823 = const()[name = tensor("op_823"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109355136)))]; + tensor q_11_cast = conv(dilations = var_823, groups = var_286, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_821, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_31_cast)[name = tensor("q_11_cast")]; + tensor var_827 = const()[name = tensor("op_827"), val = tensor([1, 1])]; + tensor var_829 = const()[name = tensor("op_829"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110534848)))]; + tensor k_11_cast = conv(dilations = var_829, groups = var_286, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_827, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_11_cast")]; + tensor var_833 = const()[name = tensor("op_833"), val = tensor([1, 1])]; + tensor var_835 = const()[name = tensor("op_835"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112500992)))]; + tensor v_11_cast = conv(dilations = var_835, groups = var_286, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_833, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_11_cast")]; + tensor var_839 = const()[name = tensor("op_839"), val = tensor([2, 12, 64, -1])]; + tensor var_840_cast = reshape(shape = var_839, x = q_11_cast)[name = tensor("op_840_cast")]; + tensor var_841 = const()[name = tensor("op_841"), val = tensor([2, 12, 64, -1])]; + tensor var_842_cast = reshape(shape = var_841, x = k_11_cast)[name = tensor("op_842_cast")]; + tensor var_843 = const()[name = tensor("op_843"), val = tensor([2, 12, 64, -1])]; + tensor var_844_cast = reshape(shape = var_843, x = v_11_cast)[name = tensor("op_844_cast")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_840_cast, y = var_842_cast)[name = tensor("attn_weights_21_cast")]; + tensor attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_277_to_fp16)[name = tensor("attn_weights_23_cast")]; + tensor var_848_cast = softmax(axis = var_270, x = attn_weights_23_cast)[name = tensor("op_848_cast")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_844_cast, y = var_848_cast)[name = tensor("attn_11_cast")]; + tensor var_852 = const()[name = tensor("op_852"), val = tensor([2, 768, 1, -1])]; + tensor input_79_cast = reshape(shape = var_852, x = attn_11_cast)[name = tensor("input_79_cast")]; + tensor var_857 = const()[name = tensor("op_857"), val = tensor([1, 1])]; + tensor var_859 = const()[name = tensor("op_859"), val = tensor([1, 1])]; + tensor var_861_pad_type_0 = const()[name = tensor("op_861_pad_type_0"), val = tensor("custom")]; + tensor var_861_pad_0 = const()[name = tensor("op_861_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(114467136)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115646848)))]; + tensor var_861_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_859, groups = var_286, pad = var_861_pad_0, pad_type = var_861_pad_type_0, strides = var_857, weight = down_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_79_cast)[name = tensor("op_861_cast")]; + tensor inputs_17_cast = add(x = var_861_cast, y = inputs_15_cast)[name = tensor("inputs_17_cast")]; + tensor var_865 = const()[name = tensor("op_865"), val = tensor([1])]; + tensor channels_mean_17_cast = reduce_mean(axes = var_865, keep_dims = var_281, x = inputs_17_cast)[name = tensor("channels_mean_17_cast")]; + tensor zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast)[name = tensor("zero_mean_17_cast")]; + tensor zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast)[name = tensor("zero_mean_sq_17_cast")]; + tensor var_869 = const()[name = tensor("op_869"), val = tensor([1])]; + tensor var_870_cast = reduce_mean(axes = var_869, keep_dims = var_281, x = zero_mean_sq_17_cast)[name = tensor("op_870_cast")]; + tensor var_871_to_fp16 = const()[name = tensor("op_871_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_872_cast = add(x = var_870_cast, y = var_871_to_fp16)[name = tensor("op_872_cast")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_872_cast)[name = tensor("denom_17_cast")]; + tensor out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_cast)[name = tensor("out_17_cast")]; + tensor var_876_to_fp16 = const()[name = tensor("op_876_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115648448)))]; + tensor var_877_cast = add(x = out_17_cast, y = var_876_to_fp16)[name = tensor("op_877_cast")]; + tensor var_879_to_fp16 = const()[name = tensor("op_879_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115650048)))]; + tensor input_81_cast = mul(x = var_877_cast, y = var_879_to_fp16)[name = tensor("input_81_cast")]; + tensor var_887 = const()[name = tensor("op_887"), val = tensor([1, 1])]; + tensor var_889 = const()[name = tensor("op_889"), val = tensor([1, 1])]; + tensor var_891_pad_type_0 = const()[name = tensor("op_891_pad_type_0"), val = tensor("custom")]; + tensor var_891_pad_0 = const()[name = tensor("op_891_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115651648)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125088896)))]; + tensor var_891_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_889, groups = var_286, pad = var_891_pad_0, pad_type = var_891_pad_type_0, strides = var_887, weight = down_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_81_cast)[name = tensor("op_891_cast")]; + tensor var_892_split_sizes_0 = const()[name = tensor("op_892_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_892_axis_0 = const()[name = tensor("op_892_axis_0"), val = tensor(1)]; + tensor var_892_cast_0, tensor var_892_cast_1 = split(axis = var_892_axis_0, split_sizes = var_892_split_sizes_0, x = var_891_cast)[name = tensor("op_892_cast")]; + tensor var_894_mode_0 = const()[name = tensor("op_894_mode_0"), val = tensor("EXACT")]; + tensor var_894_cast = gelu(mode = var_894_mode_0, x = var_892_cast_1)[name = tensor("op_894_cast")]; + tensor input_83_cast = mul(x = var_892_cast_0, y = var_894_cast)[name = tensor("input_83_cast")]; + tensor var_898 = const()[name = tensor("op_898"), val = tensor([1, 1])]; + tensor var_900 = const()[name = tensor("op_900"), val = tensor([1, 1])]; + tensor var_902_pad_type_0 = const()[name = tensor("op_902_pad_type_0"), val = tensor("custom")]; + tensor var_902_pad_0 = const()[name = tensor("op_902_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125101248)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129819904)))]; + tensor var_902_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_900, groups = var_286, pad = var_902_pad_0, pad_type = var_902_pad_type_0, strides = var_898, weight = down_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_83_cast)[name = tensor("op_902_cast")]; + tensor inputs_19_cast = add(x = var_902_cast, y = inputs_17_cast)[name = tensor("inputs_19_cast")]; + tensor var_912 = const()[name = tensor("op_912"), val = tensor([1])]; + tensor channels_mean_19_cast = reduce_mean(axes = var_912, keep_dims = var_281, x = inputs_19_cast)[name = tensor("channels_mean_19_cast")]; + tensor zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast)[name = tensor("zero_mean_19_cast")]; + tensor zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast)[name = tensor("zero_mean_sq_19_cast")]; + tensor var_916 = const()[name = tensor("op_916"), val = tensor([1])]; + tensor var_917_cast = reduce_mean(axes = var_916, keep_dims = var_281, x = zero_mean_sq_19_cast)[name = tensor("op_917_cast")]; + tensor var_918_to_fp16 = const()[name = tensor("op_918_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_919_cast = add(x = var_917_cast, y = var_918_to_fp16)[name = tensor("op_919_cast")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_919_cast)[name = tensor("denom_19_cast")]; + tensor out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast)[name = tensor("out_19_cast")]; + tensor var_923_to_fp16 = const()[name = tensor("op_923_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129821504)))]; + tensor var_924_cast = add(x = out_19_cast, y = var_923_to_fp16)[name = tensor("op_924_cast")]; + tensor var_926_to_fp16 = const()[name = tensor("op_926_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129823104)))]; + tensor hidden_states_35_cast = mul(x = var_924_cast, y = var_926_to_fp16)[name = tensor("hidden_states_35_cast")]; + tensor var_933 = const()[name = tensor("op_933"), val = tensor([1, 1])]; + tensor var_935 = const()[name = tensor("op_935"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129824704)))]; + tensor q_13_cast = conv(dilations = var_935, groups = var_286, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_933, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_35_cast)[name = tensor("q_13_cast")]; + tensor var_939 = const()[name = tensor("op_939"), val = tensor([1, 1])]; + tensor var_941 = const()[name = tensor("op_941"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131004416)))]; + tensor k_13_cast = conv(dilations = var_941, groups = var_286, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_939, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_35_cast)[name = tensor("k_13_cast")]; + tensor var_945 = const()[name = tensor("op_945"), val = tensor([1, 1])]; + tensor var_947 = const()[name = tensor("op_947"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132184128)))]; + tensor v_13_cast = conv(dilations = var_947, groups = var_286, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_945, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_35_cast)[name = tensor("v_13_cast")]; + tensor var_951 = const()[name = tensor("op_951"), val = tensor([2, 12, 64, -1])]; + tensor var_952_cast = reshape(shape = var_951, x = q_13_cast)[name = tensor("op_952_cast")]; + tensor var_953 = const()[name = tensor("op_953"), val = tensor([2, 12, 64, -1])]; + tensor var_954_cast = reshape(shape = var_953, x = k_13_cast)[name = tensor("op_954_cast")]; + tensor var_955 = const()[name = tensor("op_955"), val = tensor([2, 12, 64, -1])]; + tensor var_956_cast = reshape(shape = var_955, x = v_13_cast)[name = tensor("op_956_cast")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_952_cast, y = var_954_cast)[name = tensor("attn_weights_25_cast")]; + tensor attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_277_to_fp16)[name = tensor("attn_weights_27_cast")]; + tensor var_960_cast = softmax(axis = var_270, x = attn_weights_27_cast)[name = tensor("op_960_cast")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_956_cast, y = var_960_cast)[name = tensor("attn_13_cast")]; + tensor var_964 = const()[name = tensor("op_964"), val = tensor([2, 768, 1, -1])]; + tensor input_85_cast = reshape(shape = var_964, x = attn_13_cast)[name = tensor("input_85_cast")]; + tensor var_969 = const()[name = tensor("op_969"), val = tensor([1, 1])]; + tensor var_971 = const()[name = tensor("op_971"), val = tensor([1, 1])]; + tensor var_973_pad_type_0 = const()[name = tensor("op_973_pad_type_0"), val = tensor("custom")]; + tensor var_973_pad_0 = const()[name = tensor("op_973_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133363840)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134543552)))]; + tensor var_973_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_971, groups = var_286, pad = var_973_pad_0, pad_type = var_973_pad_type_0, strides = var_969, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_85_cast)[name = tensor("op_973_cast")]; + tensor inputs_21_cast = add(x = var_973_cast, y = inputs_19_cast)[name = tensor("inputs_21_cast")]; + tensor var_977 = const()[name = tensor("op_977"), val = tensor([1])]; + tensor channels_mean_21_cast = reduce_mean(axes = var_977, keep_dims = var_281, x = inputs_21_cast)[name = tensor("channels_mean_21_cast")]; + tensor zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast)[name = tensor("zero_mean_21_cast")]; + tensor zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast)[name = tensor("zero_mean_sq_21_cast")]; + tensor var_981 = const()[name = tensor("op_981"), val = tensor([1])]; + tensor var_982_cast = reduce_mean(axes = var_981, keep_dims = var_281, x = zero_mean_sq_21_cast)[name = tensor("op_982_cast")]; + tensor var_983_to_fp16 = const()[name = tensor("op_983_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_984_cast = add(x = var_982_cast, y = var_983_to_fp16)[name = tensor("op_984_cast")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_984_cast)[name = tensor("denom_21_cast")]; + tensor out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast)[name = tensor("out_21_cast")]; + tensor var_988_to_fp16 = const()[name = tensor("op_988_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134545152)))]; + tensor var_989_cast = add(x = out_21_cast, y = var_988_to_fp16)[name = tensor("op_989_cast")]; + tensor var_991_to_fp16 = const()[name = tensor("op_991_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134546752)))]; + tensor hidden_states_37_cast = mul(x = var_989_cast, y = var_991_to_fp16)[name = tensor("hidden_states_37_cast")]; + tensor var_998 = const()[name = tensor("op_998"), val = tensor([1, 1])]; + tensor var_1000 = const()[name = tensor("op_1000"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134548352)))]; + tensor q_15_cast = conv(dilations = var_1000, groups = var_286, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_998, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("q_15_cast")]; + tensor var_1004 = const()[name = tensor("op_1004"), val = tensor([1, 1])]; + tensor var_1006 = const()[name = tensor("op_1006"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135728064)))]; + tensor k_15_cast = conv(dilations = var_1006, groups = var_286, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1004, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_15_cast")]; + tensor var_1010 = const()[name = tensor("op_1010"), val = tensor([1, 1])]; + tensor var_1012 = const()[name = tensor("op_1012"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137694208)))]; + tensor v_15_cast = conv(dilations = var_1012, groups = var_286, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1010, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_15_cast")]; + tensor var_1016 = const()[name = tensor("op_1016"), val = tensor([2, 12, 64, -1])]; + tensor var_1017_cast = reshape(shape = var_1016, x = q_15_cast)[name = tensor("op_1017_cast")]; + tensor var_1018 = const()[name = tensor("op_1018"), val = tensor([2, 12, 64, -1])]; + tensor var_1019_cast = reshape(shape = var_1018, x = k_15_cast)[name = tensor("op_1019_cast")]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([2, 12, 64, -1])]; + tensor var_1021_cast = reshape(shape = var_1020, x = v_15_cast)[name = tensor("op_1021_cast")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1017_cast, y = var_1019_cast)[name = tensor("attn_weights_29_cast")]; + tensor attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_277_to_fp16)[name = tensor("attn_weights_31_cast")]; + tensor var_1025_cast = softmax(axis = var_270, x = attn_weights_31_cast)[name = tensor("op_1025_cast")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1021_cast, y = var_1025_cast)[name = tensor("attn_15_cast")]; + tensor var_1029 = const()[name = tensor("op_1029"), val = tensor([2, 768, 1, -1])]; + tensor input_87_cast = reshape(shape = var_1029, x = attn_15_cast)[name = tensor("input_87_cast")]; + tensor var_1034 = const()[name = tensor("op_1034"), val = tensor([1, 1])]; + tensor var_1036 = const()[name = tensor("op_1036"), val = tensor([1, 1])]; + tensor var_1038_pad_type_0 = const()[name = tensor("op_1038_pad_type_0"), val = tensor("custom")]; + tensor var_1038_pad_0 = const()[name = tensor("op_1038_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139660352)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140840064)))]; + tensor var_1038_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_1036, groups = var_286, pad = var_1038_pad_0, pad_type = var_1038_pad_type_0, strides = var_1034, weight = down_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_87_cast)[name = tensor("op_1038_cast")]; + tensor inputs_23_cast = add(x = var_1038_cast, y = inputs_21_cast)[name = tensor("inputs_23_cast")]; + tensor var_1042 = const()[name = tensor("op_1042"), val = tensor([1])]; + tensor channels_mean_23_cast = reduce_mean(axes = var_1042, keep_dims = var_281, x = inputs_23_cast)[name = tensor("channels_mean_23_cast")]; + tensor zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast)[name = tensor("zero_mean_23_cast")]; + tensor zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast)[name = tensor("zero_mean_sq_23_cast")]; + tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1])]; + tensor var_1047_cast = reduce_mean(axes = var_1046, keep_dims = var_281, x = zero_mean_sq_23_cast)[name = tensor("op_1047_cast")]; + tensor var_1048_to_fp16 = const()[name = tensor("op_1048_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1049_cast = add(x = var_1047_cast, y = var_1048_to_fp16)[name = tensor("op_1049_cast")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1049_cast)[name = tensor("denom_23_cast")]; + tensor out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast)[name = tensor("out_23_cast")]; + tensor var_1053_to_fp16 = const()[name = tensor("op_1053_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140841664)))]; + tensor var_1054_cast = add(x = out_23_cast, y = var_1053_to_fp16)[name = tensor("op_1054_cast")]; + tensor var_1056_to_fp16 = const()[name = tensor("op_1056_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140843264)))]; + tensor input_89_cast = mul(x = var_1054_cast, y = var_1056_to_fp16)[name = tensor("input_89_cast")]; + tensor var_1064 = const()[name = tensor("op_1064"), val = tensor([1, 1])]; + tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, 1])]; + tensor var_1068_pad_type_0 = const()[name = tensor("op_1068_pad_type_0"), val = tensor("custom")]; + tensor var_1068_pad_0 = const()[name = tensor("op_1068_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140844864)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150282112)))]; + tensor var_1068_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_1066, groups = var_286, pad = var_1068_pad_0, pad_type = var_1068_pad_type_0, strides = var_1064, weight = down_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_89_cast)[name = tensor("op_1068_cast")]; + tensor var_1069_split_sizes_0 = const()[name = tensor("op_1069_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_1069_axis_0 = const()[name = tensor("op_1069_axis_0"), val = tensor(1)]; + tensor var_1069_cast_0, tensor var_1069_cast_1 = split(axis = var_1069_axis_0, split_sizes = var_1069_split_sizes_0, x = var_1068_cast)[name = tensor("op_1069_cast")]; + tensor var_1071_mode_0 = const()[name = tensor("op_1071_mode_0"), val = tensor("EXACT")]; + tensor var_1071_cast = gelu(mode = var_1071_mode_0, x = var_1069_cast_1)[name = tensor("op_1071_cast")]; + tensor input_91_cast = mul(x = var_1069_cast_0, y = var_1071_cast)[name = tensor("input_91_cast")]; + tensor var_1075 = const()[name = tensor("op_1075"), val = tensor([1, 1])]; + tensor var_1077 = const()[name = tensor("op_1077"), val = tensor([1, 1])]; + tensor var_1079_pad_type_0 = const()[name = tensor("op_1079_pad_type_0"), val = tensor("custom")]; + tensor var_1079_pad_0 = const()[name = tensor("op_1079_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150294464)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155013120)))]; + tensor var_1079_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_1077, groups = var_286, pad = var_1079_pad_0, pad_type = var_1079_pad_type_0, strides = var_1075, weight = down_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_91_cast)[name = tensor("op_1079_cast")]; + tensor hidden_states_41_cast = add(x = var_1079_cast, y = inputs_23_cast)[name = tensor("hidden_states_41_cast")]; + tensor var_1081 = const()[name = tensor("op_1081"), val = tensor([2, 768, 64, 64])]; + tensor input_93_cast = reshape(shape = var_1081, x = hidden_states_41_cast)[name = tensor("input_93_cast")]; + tensor var_1085 = const()[name = tensor("op_1085"), val = tensor([1, 1])]; + tensor var_1087 = const()[name = tensor("op_1087"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155014720)))]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156194432)))]; + tensor hidden_states_43_cast = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_1087, groups = var_286, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_93_cast)[name = tensor("hidden_states_43_cast")]; + tensor input_95_cast = add(x = hidden_states_43_cast, y = hidden_states_13_cast)[name = tensor("input_95_cast")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_95_cast)[name = tensor("reshape_28_cast")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor("reduce_mean_23_cast")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_cast")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156196032)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156197632)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast)[name = tensor("add_15_cast")]; + tensor input_99_cast = silu(x = add_15_cast)[name = tensor("input_99_cast")]; + tensor var_1102 = const()[name = tensor("op_1102"), val = tensor([1, 1])]; + tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([1, 1])]; + tensor hidden_states_45_pad_type_0 = const()[name = tensor("hidden_states_45_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_45_pad_0 = const()[name = tensor("hidden_states_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156199232)))]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166816128)))]; + tensor hidden_states_45_cast = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_1104, groups = var_286, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = var_1102, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_99_cast)[name = tensor("hidden_states_45_cast")]; + tensor var_1110 = const()[name = tensor("op_1110"), val = tensor([1, 1])]; + tensor var_1112 = const()[name = tensor("op_1112"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166817728)))]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169177088)))]; + tensor temb_7_cast = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1112, groups = var_286, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_1110, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_7_cast")]; + tensor input_103_cast = add(x = hidden_states_45_cast, y = temb_7_cast)[name = tensor("input_103_cast")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_103_cast)[name = tensor("reshape_32_cast")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor("reduce_mean_26_cast")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169178688)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169180288)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast)[name = tensor("add_17_cast")]; + tensor input_107_cast = silu(x = add_17_cast)[name = tensor("input_107_cast")]; + tensor var_1122 = const()[name = tensor("op_1122"), val = tensor([1, 1])]; + tensor var_1124 = const()[name = tensor("op_1124"), val = tensor([1, 1])]; + tensor hidden_states_47_pad_type_0 = const()[name = tensor("hidden_states_47_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_47_pad_0 = const()[name = tensor("hidden_states_47_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169181888)))]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179798784)))]; + tensor hidden_states_47_cast = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_1124, groups = var_286, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = var_1122, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_107_cast)[name = tensor("hidden_states_47_cast")]; + tensor hidden_states_49_cast = add(x = input_95_cast, y = hidden_states_47_cast)[name = tensor("hidden_states_49_cast")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = hidden_states_49_cast)[name = tensor("reshape_36_cast")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor("reduce_mean_29_cast")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179800384)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179801984)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_37_cast)[name = tensor("add_19_cast")]; + tensor var_1150 = const()[name = tensor("op_1150"), val = tensor([1, 1])]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor([1, 1])]; + tensor hidden_states_51_pad_type_0 = const()[name = tensor("hidden_states_51_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_51_pad_0 = const()[name = tensor("hidden_states_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179803584)))]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180983296)))]; + tensor hidden_states_51_cast = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_1152, groups = var_286, pad = hidden_states_51_pad_0, pad_type = hidden_states_51_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_19_cast)[name = tensor("hidden_states_51_cast")]; + tensor var_1157 = const()[name = tensor("op_1157"), val = tensor([2, 768, 1, 4096])]; + tensor inputs_25_cast = reshape(shape = var_1157, x = hidden_states_51_cast)[name = tensor("inputs_25_cast")]; + tensor var_1167 = const()[name = tensor("op_1167"), val = tensor([1])]; + tensor channels_mean_25_cast = reduce_mean(axes = var_1167, keep_dims = var_281, x = inputs_25_cast)[name = tensor("channels_mean_25_cast")]; + tensor zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast)[name = tensor("zero_mean_25_cast")]; + tensor zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast)[name = tensor("zero_mean_sq_25_cast")]; + tensor var_1171 = const()[name = tensor("op_1171"), val = tensor([1])]; + tensor var_1172_cast = reduce_mean(axes = var_1171, keep_dims = var_281, x = zero_mean_sq_25_cast)[name = tensor("op_1172_cast")]; + tensor var_1173_to_fp16 = const()[name = tensor("op_1173_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1174_cast = add(x = var_1172_cast, y = var_1173_to_fp16)[name = tensor("op_1174_cast")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1174_cast)[name = tensor("denom_25_cast")]; + tensor out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast)[name = tensor("out_25_cast")]; + tensor var_1178_to_fp16 = const()[name = tensor("op_1178_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180984896)))]; + tensor var_1179_cast = add(x = out_25_cast, y = var_1178_to_fp16)[name = tensor("op_1179_cast")]; + tensor var_1181_to_fp16 = const()[name = tensor("op_1181_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180986496)))]; + tensor hidden_states_53_cast = mul(x = var_1179_cast, y = var_1181_to_fp16)[name = tensor("hidden_states_53_cast")]; + tensor var_1188 = const()[name = tensor("op_1188"), val = tensor([1, 1])]; + tensor var_1190 = const()[name = tensor("op_1190"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180988096)))]; + tensor q_17_cast = conv(dilations = var_1190, groups = var_286, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1188, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_53_cast)[name = tensor("q_17_cast")]; + tensor var_1194 = const()[name = tensor("op_1194"), val = tensor([1, 1])]; + tensor var_1196 = const()[name = tensor("op_1196"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182167808)))]; + tensor k_17_cast = conv(dilations = var_1196, groups = var_286, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1194, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_53_cast)[name = tensor("k_17_cast")]; + tensor var_1200 = const()[name = tensor("op_1200"), val = tensor([1, 1])]; + tensor var_1202 = const()[name = tensor("op_1202"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183347520)))]; + tensor v_17_cast = conv(dilations = var_1202, groups = var_286, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1200, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_53_cast)[name = tensor("v_17_cast")]; + tensor var_1206 = const()[name = tensor("op_1206"), val = tensor([2, 12, 64, -1])]; + tensor var_1207_cast = reshape(shape = var_1206, x = q_17_cast)[name = tensor("op_1207_cast")]; + tensor var_1208 = const()[name = tensor("op_1208"), val = tensor([2, 12, 64, -1])]; + tensor var_1209_cast = reshape(shape = var_1208, x = k_17_cast)[name = tensor("op_1209_cast")]; + tensor var_1210 = const()[name = tensor("op_1210"), val = tensor([2, 12, 64, -1])]; + tensor var_1211_cast = reshape(shape = var_1210, x = v_17_cast)[name = tensor("op_1211_cast")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1207_cast, y = var_1209_cast)[name = tensor("attn_weights_33_cast")]; + tensor attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_277_to_fp16)[name = tensor("attn_weights_35_cast")]; + tensor var_1215_cast = softmax(axis = var_270, x = attn_weights_35_cast)[name = tensor("op_1215_cast")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1211_cast, y = var_1215_cast)[name = tensor("attn_17_cast")]; + tensor var_1219 = const()[name = tensor("op_1219"), val = tensor([2, 768, 1, -1])]; + tensor input_111_cast = reshape(shape = var_1219, x = attn_17_cast)[name = tensor("input_111_cast")]; + tensor var_1224 = const()[name = tensor("op_1224"), val = tensor([1, 1])]; + tensor var_1226 = const()[name = tensor("op_1226"), val = tensor([1, 1])]; + tensor var_1228_pad_type_0 = const()[name = tensor("op_1228_pad_type_0"), val = tensor("custom")]; + tensor var_1228_pad_0 = const()[name = tensor("op_1228_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184527232)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185706944)))]; + tensor var_1228_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1226, groups = var_286, pad = var_1228_pad_0, pad_type = var_1228_pad_type_0, strides = var_1224, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_111_cast)[name = tensor("op_1228_cast")]; + tensor inputs_27_cast = add(x = var_1228_cast, y = inputs_25_cast)[name = tensor("inputs_27_cast")]; + tensor var_1232 = const()[name = tensor("op_1232"), val = tensor([1])]; + tensor channels_mean_27_cast = reduce_mean(axes = var_1232, keep_dims = var_281, x = inputs_27_cast)[name = tensor("channels_mean_27_cast")]; + tensor zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast)[name = tensor("zero_mean_27_cast")]; + tensor zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast)[name = tensor("zero_mean_sq_27_cast")]; + tensor var_1236 = const()[name = tensor("op_1236"), val = tensor([1])]; + tensor var_1237_cast = reduce_mean(axes = var_1236, keep_dims = var_281, x = zero_mean_sq_27_cast)[name = tensor("op_1237_cast")]; + tensor var_1238_to_fp16 = const()[name = tensor("op_1238_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1239_cast = add(x = var_1237_cast, y = var_1238_to_fp16)[name = tensor("op_1239_cast")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1239_cast)[name = tensor("denom_27_cast")]; + tensor out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast)[name = tensor("out_27_cast")]; + tensor var_1243_to_fp16 = const()[name = tensor("op_1243_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185708544)))]; + tensor var_1244_cast = add(x = out_27_cast, y = var_1243_to_fp16)[name = tensor("op_1244_cast")]; + tensor var_1246_to_fp16 = const()[name = tensor("op_1246_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185710144)))]; + tensor hidden_states_55_cast = mul(x = var_1244_cast, y = var_1246_to_fp16)[name = tensor("hidden_states_55_cast")]; + tensor var_1253 = const()[name = tensor("op_1253"), val = tensor([1, 1])]; + tensor var_1255 = const()[name = tensor("op_1255"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185711744)))]; + tensor q_19_cast = conv(dilations = var_1255, groups = var_286, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1253, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_55_cast)[name = tensor("q_19_cast")]; + tensor var_1259 = const()[name = tensor("op_1259"), val = tensor([1, 1])]; + tensor var_1261 = const()[name = tensor("op_1261"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186891456)))]; + tensor k_19_cast = conv(dilations = var_1261, groups = var_286, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1259, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_19_cast")]; + tensor var_1265 = const()[name = tensor("op_1265"), val = tensor([1, 1])]; + tensor var_1267 = const()[name = tensor("op_1267"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188857600)))]; + tensor v_19_cast = conv(dilations = var_1267, groups = var_286, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1265, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_19_cast")]; + tensor var_1271 = const()[name = tensor("op_1271"), val = tensor([2, 12, 64, -1])]; + tensor var_1272_cast = reshape(shape = var_1271, x = q_19_cast)[name = tensor("op_1272_cast")]; + tensor var_1273 = const()[name = tensor("op_1273"), val = tensor([2, 12, 64, -1])]; + tensor var_1274_cast = reshape(shape = var_1273, x = k_19_cast)[name = tensor("op_1274_cast")]; + tensor var_1275 = const()[name = tensor("op_1275"), val = tensor([2, 12, 64, -1])]; + tensor var_1276_cast = reshape(shape = var_1275, x = v_19_cast)[name = tensor("op_1276_cast")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1272_cast, y = var_1274_cast)[name = tensor("attn_weights_37_cast")]; + tensor attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_277_to_fp16)[name = tensor("attn_weights_39_cast")]; + tensor var_1280_cast = softmax(axis = var_270, x = attn_weights_39_cast)[name = tensor("op_1280_cast")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1276_cast, y = var_1280_cast)[name = tensor("attn_19_cast")]; + tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([2, 768, 1, -1])]; + tensor input_113_cast = reshape(shape = var_1284, x = attn_19_cast)[name = tensor("input_113_cast")]; + tensor var_1289 = const()[name = tensor("op_1289"), val = tensor([1, 1])]; + tensor var_1291 = const()[name = tensor("op_1291"), val = tensor([1, 1])]; + tensor var_1293_pad_type_0 = const()[name = tensor("op_1293_pad_type_0"), val = tensor("custom")]; + tensor var_1293_pad_0 = const()[name = tensor("op_1293_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190823744)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192003456)))]; + tensor var_1293_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1291, groups = var_286, pad = var_1293_pad_0, pad_type = var_1293_pad_type_0, strides = var_1289, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_113_cast)[name = tensor("op_1293_cast")]; + tensor inputs_29_cast = add(x = var_1293_cast, y = inputs_27_cast)[name = tensor("inputs_29_cast")]; + tensor var_1297 = const()[name = tensor("op_1297"), val = tensor([1])]; + tensor channels_mean_29_cast = reduce_mean(axes = var_1297, keep_dims = var_281, x = inputs_29_cast)[name = tensor("channels_mean_29_cast")]; + tensor zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast)[name = tensor("zero_mean_29_cast")]; + tensor zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast)[name = tensor("zero_mean_sq_29_cast")]; + tensor var_1301 = const()[name = tensor("op_1301"), val = tensor([1])]; + tensor var_1302_cast = reduce_mean(axes = var_1301, keep_dims = var_281, x = zero_mean_sq_29_cast)[name = tensor("op_1302_cast")]; + tensor var_1303_to_fp16 = const()[name = tensor("op_1303_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1304_cast = add(x = var_1302_cast, y = var_1303_to_fp16)[name = tensor("op_1304_cast")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1304_cast)[name = tensor("denom_29_cast")]; + tensor out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast)[name = tensor("out_29_cast")]; + tensor var_1308_to_fp16 = const()[name = tensor("op_1308_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192005056)))]; + tensor var_1309_cast = add(x = out_29_cast, y = var_1308_to_fp16)[name = tensor("op_1309_cast")]; + tensor var_1311_to_fp16 = const()[name = tensor("op_1311_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192006656)))]; + tensor input_115_cast = mul(x = var_1309_cast, y = var_1311_to_fp16)[name = tensor("input_115_cast")]; + tensor var_1319 = const()[name = tensor("op_1319"), val = tensor([1, 1])]; + tensor var_1321 = const()[name = tensor("op_1321"), val = tensor([1, 1])]; + tensor var_1323_pad_type_0 = const()[name = tensor("op_1323_pad_type_0"), val = tensor("custom")]; + tensor var_1323_pad_0 = const()[name = tensor("op_1323_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192008256)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201445504)))]; + tensor var_1323_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1321, groups = var_286, pad = var_1323_pad_0, pad_type = var_1323_pad_type_0, strides = var_1319, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_115_cast)[name = tensor("op_1323_cast")]; + tensor var_1324_split_sizes_0 = const()[name = tensor("op_1324_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_1324_axis_0 = const()[name = tensor("op_1324_axis_0"), val = tensor(1)]; + tensor var_1324_cast_0, tensor var_1324_cast_1 = split(axis = var_1324_axis_0, split_sizes = var_1324_split_sizes_0, x = var_1323_cast)[name = tensor("op_1324_cast")]; + tensor var_1326_mode_0 = const()[name = tensor("op_1326_mode_0"), val = tensor("EXACT")]; + tensor var_1326_cast = gelu(mode = var_1326_mode_0, x = var_1324_cast_1)[name = tensor("op_1326_cast")]; + tensor input_117_cast = mul(x = var_1324_cast_0, y = var_1326_cast)[name = tensor("input_117_cast")]; + tensor var_1330 = const()[name = tensor("op_1330"), val = tensor([1, 1])]; + tensor var_1332 = const()[name = tensor("op_1332"), val = tensor([1, 1])]; + tensor var_1334_pad_type_0 = const()[name = tensor("op_1334_pad_type_0"), val = tensor("custom")]; + tensor var_1334_pad_0 = const()[name = tensor("op_1334_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201457856)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206176512)))]; + tensor var_1334_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1332, groups = var_286, pad = var_1334_pad_0, pad_type = var_1334_pad_type_0, strides = var_1330, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_117_cast)[name = tensor("op_1334_cast")]; + tensor inputs_31_cast = add(x = var_1334_cast, y = inputs_29_cast)[name = tensor("inputs_31_cast")]; + tensor var_1344 = const()[name = tensor("op_1344"), val = tensor([1])]; + tensor channels_mean_31_cast = reduce_mean(axes = var_1344, keep_dims = var_281, x = inputs_31_cast)[name = tensor("channels_mean_31_cast")]; + tensor zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast)[name = tensor("zero_mean_31_cast")]; + tensor zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast)[name = tensor("zero_mean_sq_31_cast")]; + tensor var_1348 = const()[name = tensor("op_1348"), val = tensor([1])]; + tensor var_1349_cast = reduce_mean(axes = var_1348, keep_dims = var_281, x = zero_mean_sq_31_cast)[name = tensor("op_1349_cast")]; + tensor var_1350_to_fp16 = const()[name = tensor("op_1350_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1351_cast = add(x = var_1349_cast, y = var_1350_to_fp16)[name = tensor("op_1351_cast")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1351_cast)[name = tensor("denom_31_cast")]; + tensor out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast)[name = tensor("out_31_cast")]; + tensor var_1355_to_fp16 = const()[name = tensor("op_1355_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206178112)))]; + tensor var_1356_cast = add(x = out_31_cast, y = var_1355_to_fp16)[name = tensor("op_1356_cast")]; + tensor var_1358_to_fp16 = const()[name = tensor("op_1358_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206179712)))]; + tensor hidden_states_59_cast = mul(x = var_1356_cast, y = var_1358_to_fp16)[name = tensor("hidden_states_59_cast")]; + tensor var_1365 = const()[name = tensor("op_1365"), val = tensor([1, 1])]; + tensor var_1367 = const()[name = tensor("op_1367"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206181312)))]; + tensor q_21_cast = conv(dilations = var_1367, groups = var_286, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1365, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_59_cast)[name = tensor("q_21_cast")]; + tensor var_1371 = const()[name = tensor("op_1371"), val = tensor([1, 1])]; + tensor var_1373 = const()[name = tensor("op_1373"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207361024)))]; + tensor k_21_cast = conv(dilations = var_1373, groups = var_286, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1371, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_59_cast)[name = tensor("k_21_cast")]; + tensor var_1377 = const()[name = tensor("op_1377"), val = tensor([1, 1])]; + tensor var_1379 = const()[name = tensor("op_1379"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208540736)))]; + tensor v_21_cast = conv(dilations = var_1379, groups = var_286, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1377, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_59_cast)[name = tensor("v_21_cast")]; + tensor var_1383 = const()[name = tensor("op_1383"), val = tensor([2, 12, 64, -1])]; + tensor var_1384_cast = reshape(shape = var_1383, x = q_21_cast)[name = tensor("op_1384_cast")]; + tensor var_1385 = const()[name = tensor("op_1385"), val = tensor([2, 12, 64, -1])]; + tensor var_1386_cast = reshape(shape = var_1385, x = k_21_cast)[name = tensor("op_1386_cast")]; + tensor var_1387 = const()[name = tensor("op_1387"), val = tensor([2, 12, 64, -1])]; + tensor var_1388_cast = reshape(shape = var_1387, x = v_21_cast)[name = tensor("op_1388_cast")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1384_cast, y = var_1386_cast)[name = tensor("attn_weights_41_cast")]; + tensor attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_277_to_fp16)[name = tensor("attn_weights_43_cast")]; + tensor var_1392_cast = softmax(axis = var_270, x = attn_weights_43_cast)[name = tensor("op_1392_cast")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1388_cast, y = var_1392_cast)[name = tensor("attn_21_cast")]; + tensor var_1396 = const()[name = tensor("op_1396"), val = tensor([2, 768, 1, -1])]; + tensor input_119_cast = reshape(shape = var_1396, x = attn_21_cast)[name = tensor("input_119_cast")]; + tensor var_1401 = const()[name = tensor("op_1401"), val = tensor([1, 1])]; + tensor var_1403 = const()[name = tensor("op_1403"), val = tensor([1, 1])]; + tensor var_1405_pad_type_0 = const()[name = tensor("op_1405_pad_type_0"), val = tensor("custom")]; + tensor var_1405_pad_0 = const()[name = tensor("op_1405_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209720448)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210900160)))]; + tensor var_1405_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1403, groups = var_286, pad = var_1405_pad_0, pad_type = var_1405_pad_type_0, strides = var_1401, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_119_cast)[name = tensor("op_1405_cast")]; + tensor inputs_33_cast = add(x = var_1405_cast, y = inputs_31_cast)[name = tensor("inputs_33_cast")]; + tensor var_1409 = const()[name = tensor("op_1409"), val = tensor([1])]; + tensor channels_mean_33_cast = reduce_mean(axes = var_1409, keep_dims = var_281, x = inputs_33_cast)[name = tensor("channels_mean_33_cast")]; + tensor zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast)[name = tensor("zero_mean_33_cast")]; + tensor zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast)[name = tensor("zero_mean_sq_33_cast")]; + tensor var_1413 = const()[name = tensor("op_1413"), val = tensor([1])]; + tensor var_1414_cast = reduce_mean(axes = var_1413, keep_dims = var_281, x = zero_mean_sq_33_cast)[name = tensor("op_1414_cast")]; + tensor var_1415_to_fp16 = const()[name = tensor("op_1415_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1416_cast = add(x = var_1414_cast, y = var_1415_to_fp16)[name = tensor("op_1416_cast")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1416_cast)[name = tensor("denom_33_cast")]; + tensor out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast)[name = tensor("out_33_cast")]; + tensor var_1420_to_fp16 = const()[name = tensor("op_1420_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210901760)))]; + tensor var_1421_cast = add(x = out_33_cast, y = var_1420_to_fp16)[name = tensor("op_1421_cast")]; + tensor var_1423_to_fp16 = const()[name = tensor("op_1423_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210903360)))]; + tensor hidden_states_61_cast = mul(x = var_1421_cast, y = var_1423_to_fp16)[name = tensor("hidden_states_61_cast")]; + tensor var_1430 = const()[name = tensor("op_1430"), val = tensor([1, 1])]; + tensor var_1432 = const()[name = tensor("op_1432"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210904960)))]; + tensor q_23_cast = conv(dilations = var_1432, groups = var_286, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1430, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_61_cast)[name = tensor("q_23_cast")]; + tensor var_1436 = const()[name = tensor("op_1436"), val = tensor([1, 1])]; + tensor var_1438 = const()[name = tensor("op_1438"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212084672)))]; + tensor k_23_cast = conv(dilations = var_1438, groups = var_286, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1436, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_23_cast")]; + tensor var_1442 = const()[name = tensor("op_1442"), val = tensor([1, 1])]; + tensor var_1444 = const()[name = tensor("op_1444"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214050816)))]; + tensor v_23_cast = conv(dilations = var_1444, groups = var_286, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1442, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_23_cast")]; + tensor var_1448 = const()[name = tensor("op_1448"), val = tensor([2, 12, 64, -1])]; + tensor var_1449_cast = reshape(shape = var_1448, x = q_23_cast)[name = tensor("op_1449_cast")]; + tensor var_1450 = const()[name = tensor("op_1450"), val = tensor([2, 12, 64, -1])]; + tensor var_1451_cast = reshape(shape = var_1450, x = k_23_cast)[name = tensor("op_1451_cast")]; + tensor var_1452 = const()[name = tensor("op_1452"), val = tensor([2, 12, 64, -1])]; + tensor var_1453_cast = reshape(shape = var_1452, x = v_23_cast)[name = tensor("op_1453_cast")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1449_cast, y = var_1451_cast)[name = tensor("attn_weights_45_cast")]; + tensor attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_277_to_fp16)[name = tensor("attn_weights_47_cast")]; + tensor var_1457_cast = softmax(axis = var_270, x = attn_weights_47_cast)[name = tensor("op_1457_cast")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1453_cast, y = var_1457_cast)[name = tensor("attn_23_cast")]; + tensor var_1461 = const()[name = tensor("op_1461"), val = tensor([2, 768, 1, -1])]; + tensor input_121_cast = reshape(shape = var_1461, x = attn_23_cast)[name = tensor("input_121_cast")]; + tensor var_1466 = const()[name = tensor("op_1466"), val = tensor([1, 1])]; + tensor var_1468 = const()[name = tensor("op_1468"), val = tensor([1, 1])]; + tensor var_1470_pad_type_0 = const()[name = tensor("op_1470_pad_type_0"), val = tensor("custom")]; + tensor var_1470_pad_0 = const()[name = tensor("op_1470_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216016960)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217196672)))]; + tensor var_1470_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1468, groups = var_286, pad = var_1470_pad_0, pad_type = var_1470_pad_type_0, strides = var_1466, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_121_cast)[name = tensor("op_1470_cast")]; + tensor inputs_35_cast = add(x = var_1470_cast, y = inputs_33_cast)[name = tensor("inputs_35_cast")]; + tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([1])]; + tensor channels_mean_35_cast = reduce_mean(axes = var_1474, keep_dims = var_281, x = inputs_35_cast)[name = tensor("channels_mean_35_cast")]; + tensor zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast)[name = tensor("zero_mean_35_cast")]; + tensor zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast)[name = tensor("zero_mean_sq_35_cast")]; + tensor var_1478 = const()[name = tensor("op_1478"), val = tensor([1])]; + tensor var_1479_cast = reduce_mean(axes = var_1478, keep_dims = var_281, x = zero_mean_sq_35_cast)[name = tensor("op_1479_cast")]; + tensor var_1480_to_fp16 = const()[name = tensor("op_1480_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1481_cast = add(x = var_1479_cast, y = var_1480_to_fp16)[name = tensor("op_1481_cast")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1481_cast)[name = tensor("denom_35_cast")]; + tensor out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast)[name = tensor("out_35_cast")]; + tensor var_1485_to_fp16 = const()[name = tensor("op_1485_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217198272)))]; + tensor var_1486_cast = add(x = out_35_cast, y = var_1485_to_fp16)[name = tensor("op_1486_cast")]; + tensor var_1488_to_fp16 = const()[name = tensor("op_1488_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217199872)))]; + tensor input_123_cast = mul(x = var_1486_cast, y = var_1488_to_fp16)[name = tensor("input_123_cast")]; + tensor var_1496 = const()[name = tensor("op_1496"), val = tensor([1, 1])]; + tensor var_1498 = const()[name = tensor("op_1498"), val = tensor([1, 1])]; + tensor var_1500_pad_type_0 = const()[name = tensor("op_1500_pad_type_0"), val = tensor("custom")]; + tensor var_1500_pad_0 = const()[name = tensor("op_1500_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217201472)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226638720)))]; + tensor var_1500_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_1498, groups = var_286, pad = var_1500_pad_0, pad_type = var_1500_pad_type_0, strides = var_1496, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_123_cast)[name = tensor("op_1500_cast")]; + tensor var_1501_split_sizes_0 = const()[name = tensor("op_1501_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_1501_axis_0 = const()[name = tensor("op_1501_axis_0"), val = tensor(1)]; + tensor var_1501_cast_0, tensor var_1501_cast_1 = split(axis = var_1501_axis_0, split_sizes = var_1501_split_sizes_0, x = var_1500_cast)[name = tensor("op_1501_cast")]; + tensor var_1503_mode_0 = const()[name = tensor("op_1503_mode_0"), val = tensor("EXACT")]; + tensor var_1503_cast = gelu(mode = var_1503_mode_0, x = var_1501_cast_1)[name = tensor("op_1503_cast")]; + tensor input_125_cast = mul(x = var_1501_cast_0, y = var_1503_cast)[name = tensor("input_125_cast")]; + tensor var_1507 = const()[name = tensor("op_1507"), val = tensor([1, 1])]; + tensor var_1509 = const()[name = tensor("op_1509"), val = tensor([1, 1])]; + tensor var_1511_pad_type_0 = const()[name = tensor("op_1511_pad_type_0"), val = tensor("custom")]; + tensor var_1511_pad_0 = const()[name = tensor("op_1511_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226651072)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231369728)))]; + tensor var_1511_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1509, groups = var_286, pad = var_1511_pad_0, pad_type = var_1511_pad_type_0, strides = var_1507, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_125_cast)[name = tensor("op_1511_cast")]; + tensor inputs_37_cast = add(x = var_1511_cast, y = inputs_35_cast)[name = tensor("inputs_37_cast")]; + tensor var_1521 = const()[name = tensor("op_1521"), val = tensor([1])]; + tensor channels_mean_37_cast = reduce_mean(axes = var_1521, keep_dims = var_281, x = inputs_37_cast)[name = tensor("channels_mean_37_cast")]; + tensor zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast)[name = tensor("zero_mean_37_cast")]; + tensor zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast)[name = tensor("zero_mean_sq_37_cast")]; + tensor var_1525 = const()[name = tensor("op_1525"), val = tensor([1])]; + tensor var_1526_cast = reduce_mean(axes = var_1525, keep_dims = var_281, x = zero_mean_sq_37_cast)[name = tensor("op_1526_cast")]; + tensor var_1527_to_fp16 = const()[name = tensor("op_1527_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1528_cast = add(x = var_1526_cast, y = var_1527_to_fp16)[name = tensor("op_1528_cast")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1528_cast)[name = tensor("denom_37_cast")]; + tensor out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast)[name = tensor("out_37_cast")]; + tensor var_1532_to_fp16 = const()[name = tensor("op_1532_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231371328)))]; + tensor var_1533_cast = add(x = out_37_cast, y = var_1532_to_fp16)[name = tensor("op_1533_cast")]; + tensor var_1535_to_fp16 = const()[name = tensor("op_1535_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231372928)))]; + tensor hidden_states_65_cast = mul(x = var_1533_cast, y = var_1535_to_fp16)[name = tensor("hidden_states_65_cast")]; + tensor var_1542 = const()[name = tensor("op_1542"), val = tensor([1, 1])]; + tensor var_1544 = const()[name = tensor("op_1544"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231374528)))]; + tensor q_25_cast = conv(dilations = var_1544, groups = var_286, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1542, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("q_25_cast")]; + tensor var_1548 = const()[name = tensor("op_1548"), val = tensor([1, 1])]; + tensor var_1550 = const()[name = tensor("op_1550"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232554240)))]; + tensor k_25_cast = conv(dilations = var_1550, groups = var_286, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1548, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("k_25_cast")]; + tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; + tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233733952)))]; + tensor v_25_cast = conv(dilations = var_1556, groups = var_286, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1554, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("v_25_cast")]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([2, 12, 64, -1])]; + tensor var_1561_cast = reshape(shape = var_1560, x = q_25_cast)[name = tensor("op_1561_cast")]; + tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([2, 12, 64, -1])]; + tensor var_1563_cast = reshape(shape = var_1562, x = k_25_cast)[name = tensor("op_1563_cast")]; + tensor var_1564 = const()[name = tensor("op_1564"), val = tensor([2, 12, 64, -1])]; + tensor var_1565_cast = reshape(shape = var_1564, x = v_25_cast)[name = tensor("op_1565_cast")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1561_cast, y = var_1563_cast)[name = tensor("attn_weights_49_cast")]; + tensor attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_277_to_fp16)[name = tensor("attn_weights_51_cast")]; + tensor var_1569_cast = softmax(axis = var_270, x = attn_weights_51_cast)[name = tensor("op_1569_cast")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1565_cast, y = var_1569_cast)[name = tensor("attn_25_cast")]; + tensor var_1573 = const()[name = tensor("op_1573"), val = tensor([2, 768, 1, -1])]; + tensor input_127_cast = reshape(shape = var_1573, x = attn_25_cast)[name = tensor("input_127_cast")]; + tensor var_1578 = const()[name = tensor("op_1578"), val = tensor([1, 1])]; + tensor var_1580 = const()[name = tensor("op_1580"), val = tensor([1, 1])]; + tensor var_1582_pad_type_0 = const()[name = tensor("op_1582_pad_type_0"), val = tensor("custom")]; + tensor var_1582_pad_0 = const()[name = tensor("op_1582_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234913664)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236093376)))]; + tensor var_1582_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_1580, groups = var_286, pad = var_1582_pad_0, pad_type = var_1582_pad_type_0, strides = var_1578, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_127_cast)[name = tensor("op_1582_cast")]; + tensor inputs_39_cast = add(x = var_1582_cast, y = inputs_37_cast)[name = tensor("inputs_39_cast")]; + tensor var_1586 = const()[name = tensor("op_1586"), val = tensor([1])]; + tensor channels_mean_39_cast = reduce_mean(axes = var_1586, keep_dims = var_281, x = inputs_39_cast)[name = tensor("channels_mean_39_cast")]; + tensor zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast)[name = tensor("zero_mean_39_cast")]; + tensor zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast)[name = tensor("zero_mean_sq_39_cast")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1])]; + tensor var_1591_cast = reduce_mean(axes = var_1590, keep_dims = var_281, x = zero_mean_sq_39_cast)[name = tensor("op_1591_cast")]; + tensor var_1592_to_fp16 = const()[name = tensor("op_1592_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1593_cast = add(x = var_1591_cast, y = var_1592_to_fp16)[name = tensor("op_1593_cast")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1593_cast)[name = tensor("denom_39_cast")]; + tensor out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast)[name = tensor("out_39_cast")]; + tensor var_1597_to_fp16 = const()[name = tensor("op_1597_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236094976)))]; + tensor var_1598_cast = add(x = out_39_cast, y = var_1597_to_fp16)[name = tensor("op_1598_cast")]; + tensor var_1600_to_fp16 = const()[name = tensor("op_1600_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236096576)))]; + tensor hidden_states_67_cast = mul(x = var_1598_cast, y = var_1600_to_fp16)[name = tensor("hidden_states_67_cast")]; + tensor var_1607 = const()[name = tensor("op_1607"), val = tensor([1, 1])]; + tensor var_1609 = const()[name = tensor("op_1609"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236098176)))]; + tensor q_27_cast = conv(dilations = var_1609, groups = var_286, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1607, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("q_27_cast")]; + tensor var_1613 = const()[name = tensor("op_1613"), val = tensor([1, 1])]; + tensor var_1615 = const()[name = tensor("op_1615"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237277888)))]; + tensor k_27_cast = conv(dilations = var_1615, groups = var_286, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1613, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_27_cast")]; + tensor var_1619 = const()[name = tensor("op_1619"), val = tensor([1, 1])]; + tensor var_1621 = const()[name = tensor("op_1621"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239244032)))]; + tensor v_27_cast = conv(dilations = var_1621, groups = var_286, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_1619, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_27_cast")]; + tensor var_1625 = const()[name = tensor("op_1625"), val = tensor([2, 12, 64, -1])]; + tensor var_1626_cast = reshape(shape = var_1625, x = q_27_cast)[name = tensor("op_1626_cast")]; + tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([2, 12, 64, -1])]; + tensor var_1628_cast = reshape(shape = var_1627, x = k_27_cast)[name = tensor("op_1628_cast")]; + tensor var_1629 = const()[name = tensor("op_1629"), val = tensor([2, 12, 64, -1])]; + tensor var_1630_cast = reshape(shape = var_1629, x = v_27_cast)[name = tensor("op_1630_cast")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_1626_cast, y = var_1628_cast)[name = tensor("attn_weights_53_cast")]; + tensor attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_277_to_fp16)[name = tensor("attn_weights_55_cast")]; + tensor var_1634_cast = softmax(axis = var_270, x = attn_weights_55_cast)[name = tensor("op_1634_cast")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1630_cast, y = var_1634_cast)[name = tensor("attn_27_cast")]; + tensor var_1638 = const()[name = tensor("op_1638"), val = tensor([2, 768, 1, -1])]; + tensor input_129_cast = reshape(shape = var_1638, x = attn_27_cast)[name = tensor("input_129_cast")]; + tensor var_1643 = const()[name = tensor("op_1643"), val = tensor([1, 1])]; + tensor var_1645 = const()[name = tensor("op_1645"), val = tensor([1, 1])]; + tensor var_1647_pad_type_0 = const()[name = tensor("op_1647_pad_type_0"), val = tensor("custom")]; + tensor var_1647_pad_0 = const()[name = tensor("op_1647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241210176)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242389888)))]; + tensor var_1647_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_1645, groups = var_286, pad = var_1647_pad_0, pad_type = var_1647_pad_type_0, strides = var_1643, weight = down_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_129_cast)[name = tensor("op_1647_cast")]; + tensor inputs_41_cast = add(x = var_1647_cast, y = inputs_39_cast)[name = tensor("inputs_41_cast")]; + tensor var_1651 = const()[name = tensor("op_1651"), val = tensor([1])]; + tensor channels_mean_41_cast = reduce_mean(axes = var_1651, keep_dims = var_281, x = inputs_41_cast)[name = tensor("channels_mean_41_cast")]; + tensor zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast)[name = tensor("zero_mean_41_cast")]; + tensor zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast)[name = tensor("zero_mean_sq_41_cast")]; + tensor var_1655 = const()[name = tensor("op_1655"), val = tensor([1])]; + tensor var_1656_cast = reduce_mean(axes = var_1655, keep_dims = var_281, x = zero_mean_sq_41_cast)[name = tensor("op_1656_cast")]; + tensor var_1657_to_fp16 = const()[name = tensor("op_1657_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1658_cast = add(x = var_1656_cast, y = var_1657_to_fp16)[name = tensor("op_1658_cast")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_1658_cast)[name = tensor("denom_41_cast")]; + tensor out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast)[name = tensor("out_41_cast")]; + tensor var_1662_to_fp16 = const()[name = tensor("op_1662_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242391488)))]; + tensor var_1663_cast = add(x = out_41_cast, y = var_1662_to_fp16)[name = tensor("op_1663_cast")]; + tensor var_1665_to_fp16 = const()[name = tensor("op_1665_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242393088)))]; + tensor input_131_cast = mul(x = var_1663_cast, y = var_1665_to_fp16)[name = tensor("input_131_cast")]; + tensor var_1673 = const()[name = tensor("op_1673"), val = tensor([1, 1])]; + tensor var_1675 = const()[name = tensor("op_1675"), val = tensor([1, 1])]; + tensor var_1677_pad_type_0 = const()[name = tensor("op_1677_pad_type_0"), val = tensor("custom")]; + tensor var_1677_pad_0 = const()[name = tensor("op_1677_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242394688)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251831936)))]; + tensor var_1677_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_1675, groups = var_286, pad = var_1677_pad_0, pad_type = var_1677_pad_type_0, strides = var_1673, weight = down_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_131_cast)[name = tensor("op_1677_cast")]; + tensor var_1678_split_sizes_0 = const()[name = tensor("op_1678_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_1678_axis_0 = const()[name = tensor("op_1678_axis_0"), val = tensor(1)]; + tensor var_1678_cast_0, tensor var_1678_cast_1 = split(axis = var_1678_axis_0, split_sizes = var_1678_split_sizes_0, x = var_1677_cast)[name = tensor("op_1678_cast")]; + tensor var_1680_mode_0 = const()[name = tensor("op_1680_mode_0"), val = tensor("EXACT")]; + tensor var_1680_cast = gelu(mode = var_1680_mode_0, x = var_1678_cast_1)[name = tensor("op_1680_cast")]; + tensor input_133_cast = mul(x = var_1678_cast_0, y = var_1680_cast)[name = tensor("input_133_cast")]; + tensor var_1684 = const()[name = tensor("op_1684"), val = tensor([1, 1])]; + tensor var_1686 = const()[name = tensor("op_1686"), val = tensor([1, 1])]; + tensor var_1688_pad_type_0 = const()[name = tensor("op_1688_pad_type_0"), val = tensor("custom")]; + tensor var_1688_pad_0 = const()[name = tensor("op_1688_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251844288)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256562944)))]; + tensor var_1688_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_1686, groups = var_286, pad = var_1688_pad_0, pad_type = var_1688_pad_type_0, strides = var_1684, weight = down_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_133_cast)[name = tensor("op_1688_cast")]; + tensor inputs_43_cast = add(x = var_1688_cast, y = inputs_41_cast)[name = tensor("inputs_43_cast")]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([1])]; + tensor channels_mean_43_cast = reduce_mean(axes = var_1698, keep_dims = var_281, x = inputs_43_cast)[name = tensor("channels_mean_43_cast")]; + tensor zero_mean_43_cast = sub(x = inputs_43_cast, y = channels_mean_43_cast)[name = tensor("zero_mean_43_cast")]; + tensor zero_mean_sq_43_cast = mul(x = zero_mean_43_cast, y = zero_mean_43_cast)[name = tensor("zero_mean_sq_43_cast")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([1])]; + tensor var_1703_cast = reduce_mean(axes = var_1702, keep_dims = var_281, x = zero_mean_sq_43_cast)[name = tensor("op_1703_cast")]; + tensor var_1704_to_fp16 = const()[name = tensor("op_1704_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1705_cast = add(x = var_1703_cast, y = var_1704_to_fp16)[name = tensor("op_1705_cast")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_1705_cast)[name = tensor("denom_43_cast")]; + tensor out_43_cast = mul(x = zero_mean_43_cast, y = denom_43_cast)[name = tensor("out_43_cast")]; + tensor var_1709_to_fp16 = const()[name = tensor("op_1709_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256564544)))]; + tensor var_1710_cast = add(x = out_43_cast, y = var_1709_to_fp16)[name = tensor("op_1710_cast")]; + tensor var_1712_to_fp16 = const()[name = tensor("op_1712_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256566144)))]; + tensor hidden_states_71_cast = mul(x = var_1710_cast, y = var_1712_to_fp16)[name = tensor("hidden_states_71_cast")]; + tensor var_1719 = const()[name = tensor("op_1719"), val = tensor([1, 1])]; + tensor var_1721 = const()[name = tensor("op_1721"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256567744)))]; + tensor q_29_cast = conv(dilations = var_1721, groups = var_286, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_1719, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("q_29_cast")]; + tensor var_1725 = const()[name = tensor("op_1725"), val = tensor([1, 1])]; + tensor var_1727 = const()[name = tensor("op_1727"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257747456)))]; + tensor k_29_cast = conv(dilations = var_1727, groups = var_286, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_1725, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("k_29_cast")]; + tensor var_1731 = const()[name = tensor("op_1731"), val = tensor([1, 1])]; + tensor var_1733 = const()[name = tensor("op_1733"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258927168)))]; + tensor v_29_cast = conv(dilations = var_1733, groups = var_286, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_1731, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("v_29_cast")]; + tensor var_1737 = const()[name = tensor("op_1737"), val = tensor([2, 12, 64, -1])]; + tensor var_1738_cast = reshape(shape = var_1737, x = q_29_cast)[name = tensor("op_1738_cast")]; + tensor var_1739 = const()[name = tensor("op_1739"), val = tensor([2, 12, 64, -1])]; + tensor var_1740_cast = reshape(shape = var_1739, x = k_29_cast)[name = tensor("op_1740_cast")]; + tensor var_1741 = const()[name = tensor("op_1741"), val = tensor([2, 12, 64, -1])]; + tensor var_1742_cast = reshape(shape = var_1741, x = v_29_cast)[name = tensor("op_1742_cast")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_1738_cast, y = var_1740_cast)[name = tensor("attn_weights_57_cast")]; + tensor attn_weights_59_cast = mul(x = attn_weights_57_cast, y = var_277_to_fp16)[name = tensor("attn_weights_59_cast")]; + tensor var_1746_cast = softmax(axis = var_270, x = attn_weights_59_cast)[name = tensor("op_1746_cast")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1742_cast, y = var_1746_cast)[name = tensor("attn_29_cast")]; + tensor var_1750 = const()[name = tensor("op_1750"), val = tensor([2, 768, 1, -1])]; + tensor input_135_cast = reshape(shape = var_1750, x = attn_29_cast)[name = tensor("input_135_cast")]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, 1])]; + tensor var_1757 = const()[name = tensor("op_1757"), val = tensor([1, 1])]; + tensor var_1759_pad_type_0 = const()[name = tensor("op_1759_pad_type_0"), val = tensor("custom")]; + tensor var_1759_pad_0 = const()[name = tensor("op_1759_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260106880)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261286592)))]; + tensor var_1759_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_1757, groups = var_286, pad = var_1759_pad_0, pad_type = var_1759_pad_type_0, strides = var_1755, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_135_cast)[name = tensor("op_1759_cast")]; + tensor inputs_45_cast = add(x = var_1759_cast, y = inputs_43_cast)[name = tensor("inputs_45_cast")]; + tensor var_1763 = const()[name = tensor("op_1763"), val = tensor([1])]; + tensor channels_mean_45_cast = reduce_mean(axes = var_1763, keep_dims = var_281, x = inputs_45_cast)[name = tensor("channels_mean_45_cast")]; + tensor zero_mean_45_cast = sub(x = inputs_45_cast, y = channels_mean_45_cast)[name = tensor("zero_mean_45_cast")]; + tensor zero_mean_sq_45_cast = mul(x = zero_mean_45_cast, y = zero_mean_45_cast)[name = tensor("zero_mean_sq_45_cast")]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1])]; + tensor var_1768_cast = reduce_mean(axes = var_1767, keep_dims = var_281, x = zero_mean_sq_45_cast)[name = tensor("op_1768_cast")]; + tensor var_1769_to_fp16 = const()[name = tensor("op_1769_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1770_cast = add(x = var_1768_cast, y = var_1769_to_fp16)[name = tensor("op_1770_cast")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_1770_cast)[name = tensor("denom_45_cast")]; + tensor out_45_cast = mul(x = zero_mean_45_cast, y = denom_45_cast)[name = tensor("out_45_cast")]; + tensor var_1774_to_fp16 = const()[name = tensor("op_1774_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261288192)))]; + tensor var_1775_cast = add(x = out_45_cast, y = var_1774_to_fp16)[name = tensor("op_1775_cast")]; + tensor var_1777_to_fp16 = const()[name = tensor("op_1777_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261289792)))]; + tensor hidden_states_73_cast = mul(x = var_1775_cast, y = var_1777_to_fp16)[name = tensor("hidden_states_73_cast")]; + tensor var_1784 = const()[name = tensor("op_1784"), val = tensor([1, 1])]; + tensor var_1786 = const()[name = tensor("op_1786"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261291392)))]; + tensor q_31_cast = conv(dilations = var_1786, groups = var_286, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_1784, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_73_cast)[name = tensor("q_31_cast")]; + tensor var_1790 = const()[name = tensor("op_1790"), val = tensor([1, 1])]; + tensor var_1792 = const()[name = tensor("op_1792"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262471104)))]; + tensor k_31_cast = conv(dilations = var_1792, groups = var_286, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_1790, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_31_cast")]; + tensor var_1796 = const()[name = tensor("op_1796"), val = tensor([1, 1])]; + tensor var_1798 = const()[name = tensor("op_1798"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264437248)))]; + tensor v_31_cast = conv(dilations = var_1798, groups = var_286, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_1796, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_31_cast")]; + tensor var_1802 = const()[name = tensor("op_1802"), val = tensor([2, 12, 64, -1])]; + tensor var_1803_cast = reshape(shape = var_1802, x = q_31_cast)[name = tensor("op_1803_cast")]; + tensor var_1804 = const()[name = tensor("op_1804"), val = tensor([2, 12, 64, -1])]; + tensor var_1805_cast = reshape(shape = var_1804, x = k_31_cast)[name = tensor("op_1805_cast")]; + tensor var_1806 = const()[name = tensor("op_1806"), val = tensor([2, 12, 64, -1])]; + tensor var_1807_cast = reshape(shape = var_1806, x = v_31_cast)[name = tensor("op_1807_cast")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_1803_cast, y = var_1805_cast)[name = tensor("attn_weights_61_cast")]; + tensor attn_weights_63_cast = mul(x = attn_weights_61_cast, y = var_277_to_fp16)[name = tensor("attn_weights_63_cast")]; + tensor var_1811_cast = softmax(axis = var_270, x = attn_weights_63_cast)[name = tensor("op_1811_cast")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1807_cast, y = var_1811_cast)[name = tensor("attn_31_cast")]; + tensor var_1815 = const()[name = tensor("op_1815"), val = tensor([2, 768, 1, -1])]; + tensor input_137_cast = reshape(shape = var_1815, x = attn_31_cast)[name = tensor("input_137_cast")]; + tensor var_1820 = const()[name = tensor("op_1820"), val = tensor([1, 1])]; + tensor var_1822 = const()[name = tensor("op_1822"), val = tensor([1, 1])]; + tensor var_1824_pad_type_0 = const()[name = tensor("op_1824_pad_type_0"), val = tensor("custom")]; + tensor var_1824_pad_0 = const()[name = tensor("op_1824_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266403392)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267583104)))]; + tensor var_1824_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_1822, groups = var_286, pad = var_1824_pad_0, pad_type = var_1824_pad_type_0, strides = var_1820, weight = down_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_137_cast)[name = tensor("op_1824_cast")]; + tensor inputs_47_cast = add(x = var_1824_cast, y = inputs_45_cast)[name = tensor("inputs_47_cast")]; + tensor var_1828 = const()[name = tensor("op_1828"), val = tensor([1])]; + tensor channels_mean_47_cast = reduce_mean(axes = var_1828, keep_dims = var_281, x = inputs_47_cast)[name = tensor("channels_mean_47_cast")]; + tensor zero_mean_47_cast = sub(x = inputs_47_cast, y = channels_mean_47_cast)[name = tensor("zero_mean_47_cast")]; + tensor zero_mean_sq_47_cast = mul(x = zero_mean_47_cast, y = zero_mean_47_cast)[name = tensor("zero_mean_sq_47_cast")]; + tensor var_1832 = const()[name = tensor("op_1832"), val = tensor([1])]; + tensor var_1833_cast = reduce_mean(axes = var_1832, keep_dims = var_281, x = zero_mean_sq_47_cast)[name = tensor("op_1833_cast")]; + tensor var_1834_to_fp16 = const()[name = tensor("op_1834_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1835_cast = add(x = var_1833_cast, y = var_1834_to_fp16)[name = tensor("op_1835_cast")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_1835_cast)[name = tensor("denom_47_cast")]; + tensor out_47_cast = mul(x = zero_mean_47_cast, y = denom_47_cast)[name = tensor("out_47_cast")]; + tensor var_1839_to_fp16 = const()[name = tensor("op_1839_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267584704)))]; + tensor var_1840_cast = add(x = out_47_cast, y = var_1839_to_fp16)[name = tensor("op_1840_cast")]; + tensor var_1842_to_fp16 = const()[name = tensor("op_1842_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267586304)))]; + tensor input_139_cast = mul(x = var_1840_cast, y = var_1842_to_fp16)[name = tensor("input_139_cast")]; + tensor var_1850 = const()[name = tensor("op_1850"), val = tensor([1, 1])]; + tensor var_1852 = const()[name = tensor("op_1852"), val = tensor([1, 1])]; + tensor var_1854_pad_type_0 = const()[name = tensor("op_1854_pad_type_0"), val = tensor("custom")]; + tensor var_1854_pad_0 = const()[name = tensor("op_1854_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267587904)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277025152)))]; + tensor var_1854_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_1852, groups = var_286, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1850, weight = down_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_139_cast)[name = tensor("op_1854_cast")]; + tensor var_1855_split_sizes_0 = const()[name = tensor("op_1855_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_1855_axis_0 = const()[name = tensor("op_1855_axis_0"), val = tensor(1)]; + tensor var_1855_cast_0, tensor var_1855_cast_1 = split(axis = var_1855_axis_0, split_sizes = var_1855_split_sizes_0, x = var_1854_cast)[name = tensor("op_1855_cast")]; + tensor var_1857_mode_0 = const()[name = tensor("op_1857_mode_0"), val = tensor("EXACT")]; + tensor var_1857_cast = gelu(mode = var_1857_mode_0, x = var_1855_cast_1)[name = tensor("op_1857_cast")]; + tensor input_141_cast = mul(x = var_1855_cast_0, y = var_1857_cast)[name = tensor("input_141_cast")]; + tensor var_1861 = const()[name = tensor("op_1861"), val = tensor([1, 1])]; + tensor var_1863 = const()[name = tensor("op_1863"), val = tensor([1, 1])]; + tensor var_1865_pad_type_0 = const()[name = tensor("op_1865_pad_type_0"), val = tensor("custom")]; + tensor var_1865_pad_0 = const()[name = tensor("op_1865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277037504)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281756160)))]; + tensor var_1865_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_1863, groups = var_286, pad = var_1865_pad_0, pad_type = var_1865_pad_type_0, strides = var_1861, weight = down_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_141_cast)[name = tensor("op_1865_cast")]; + tensor hidden_states_77_cast = add(x = var_1865_cast, y = inputs_47_cast)[name = tensor("hidden_states_77_cast")]; + tensor var_1867 = const()[name = tensor("op_1867"), val = tensor([2, 768, 64, 64])]; + tensor input_143_cast = reshape(shape = var_1867, x = hidden_states_77_cast)[name = tensor("input_143_cast")]; + tensor var_1871 = const()[name = tensor("op_1871"), val = tensor([1, 1])]; + tensor var_1873 = const()[name = tensor("op_1873"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_type_0 = const()[name = tensor("hidden_states_79_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_79_pad_0 = const()[name = tensor("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281757760)))]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282937472)))]; + tensor hidden_states_79_cast = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1873, groups = var_286, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1871, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_143_cast)[name = tensor("hidden_states_79_cast")]; + tensor input_145_cast = add(x = hidden_states_79_cast, y = hidden_states_49_cast)[name = tensor("input_145_cast")]; + tensor var_1880 = const()[name = tensor("op_1880"), val = tensor([2, 2])]; + tensor var_1882 = const()[name = tensor("op_1882"), val = tensor([1, 1])]; + tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("custom")]; + tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282939072)))]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293555968)))]; + tensor input_147_cast = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1882, groups = var_286, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = var_1880, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = input_145_cast)[name = tensor("input_147_cast")]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor(3)]; + tensor var_1901 = const()[name = tensor("op_1901"), val = tensor(true)]; + tensor var_1906 = const()[name = tensor("op_1906"), val = tensor(1)]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 24, 32, 32])]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_147_cast)[name = tensor("reshape_40_cast")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor("reduce_mean_32_cast")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 768, 32, 32])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293557568)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293559168)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_41_cast)[name = tensor("add_21_cast")]; + tensor input_151_cast = silu(x = add_21_cast)[name = tensor("input_151_cast")]; + tensor var_1929 = const()[name = tensor("op_1929"), val = tensor([1, 1])]; + tensor var_1931 = const()[name = tensor("op_1931"), val = tensor([1, 1])]; + tensor hidden_states_81_pad_type_0 = const()[name = tensor("hidden_states_81_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_81_pad_0 = const()[name = tensor("hidden_states_81_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293560768)))]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314794496)))]; + tensor hidden_states_81_cast = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1931, groups = var_1906, pad = hidden_states_81_pad_0, pad_type = hidden_states_81_pad_type_0, strides = var_1929, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_151_cast)[name = tensor("hidden_states_81_cast")]; + tensor var_1937 = const()[name = tensor("op_1937"), val = tensor([1, 1])]; + tensor var_1939 = const()[name = tensor("op_1939"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314797632)))]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319516288)))]; + tensor temb_9_cast = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1939, groups = var_1906, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1937, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_9_cast")]; + tensor input_155_cast = add(x = hidden_states_81_cast, y = temb_9_cast)[name = tensor("input_155_cast")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_155_cast)[name = tensor("reshape_44_cast")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor("reduce_mean_35_cast")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + tensor add_23_mean_0_to_fp16 = const()[name = tensor("add_23_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319519424)))]; + tensor add_23_variance_0_to_fp16 = const()[name = tensor("add_23_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319522560)))]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319525696)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319528832)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast)[name = tensor("add_23_cast")]; + tensor input_159_cast = silu(x = add_23_cast)[name = tensor("input_159_cast")]; + tensor var_1949 = const()[name = tensor("op_1949"), val = tensor([1, 1])]; + tensor var_1951 = const()[name = tensor("op_1951"), val = tensor([1, 1])]; + tensor hidden_states_83_pad_type_0 = const()[name = tensor("hidden_states_83_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_83_pad_0 = const()[name = tensor("hidden_states_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319531968)))]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361999360)))]; + tensor hidden_states_83_cast = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1951, groups = var_1906, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = var_1949, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_159_cast)[name = tensor("hidden_states_83_cast")]; + tensor var_1956 = const()[name = tensor("op_1956"), val = tensor([1, 1])]; + tensor var_1958 = const()[name = tensor("op_1958"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362002496)))]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364361856)))]; + tensor x_3_cast = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1958, groups = var_1906, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1956, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_147_cast)[name = tensor("x_3_cast")]; + tensor hidden_states_85_cast = add(x = x_3_cast, y = hidden_states_83_cast)[name = tensor("hidden_states_85_cast")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = hidden_states_85_cast)[name = tensor("reshape_48_cast")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor("reduce_mean_38_cast")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364364992)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364368128)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_49_cast)[name = tensor("add_25_cast")]; + tensor var_1984 = const()[name = tensor("op_1984"), val = tensor([1, 1])]; + tensor var_1986 = const()[name = tensor("op_1986"), val = tensor([1, 1])]; + tensor hidden_states_87_pad_type_0 = const()[name = tensor("hidden_states_87_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_87_pad_0 = const()[name = tensor("hidden_states_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364371264)))]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369089920)))]; + tensor hidden_states_87_cast = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1986, groups = var_1906, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = var_1984, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_25_cast)[name = tensor("hidden_states_87_cast")]; + tensor var_1991 = const()[name = tensor("op_1991"), val = tensor([2, 1536, 1, 1024])]; + tensor inputs_49_cast = reshape(shape = var_1991, x = hidden_states_87_cast)[name = tensor("inputs_49_cast")]; + tensor var_2001 = const()[name = tensor("op_2001"), val = tensor([1])]; + tensor channels_mean_49_cast = reduce_mean(axes = var_2001, keep_dims = var_1901, x = inputs_49_cast)[name = tensor("channels_mean_49_cast")]; + tensor zero_mean_49_cast = sub(x = inputs_49_cast, y = channels_mean_49_cast)[name = tensor("zero_mean_49_cast")]; + tensor zero_mean_sq_49_cast = mul(x = zero_mean_49_cast, y = zero_mean_49_cast)[name = tensor("zero_mean_sq_49_cast")]; + tensor var_2005 = const()[name = tensor("op_2005"), val = tensor([1])]; + tensor var_2006_cast = reduce_mean(axes = var_2005, keep_dims = var_1901, x = zero_mean_sq_49_cast)[name = tensor("op_2006_cast")]; + tensor var_2007_to_fp16 = const()[name = tensor("op_2007_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2008_cast = add(x = var_2006_cast, y = var_2007_to_fp16)[name = tensor("op_2008_cast")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2008_cast)[name = tensor("denom_49_cast")]; + tensor out_49_cast = mul(x = zero_mean_49_cast, y = denom_49_cast)[name = tensor("out_49_cast")]; + tensor var_2012_to_fp16 = const()[name = tensor("op_2012_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369093056)))]; + tensor var_2013_cast = add(x = out_49_cast, y = var_2012_to_fp16)[name = tensor("op_2013_cast")]; + tensor var_2015_to_fp16 = const()[name = tensor("op_2015_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369096192)))]; + tensor hidden_states_89_cast = mul(x = var_2013_cast, y = var_2015_to_fp16)[name = tensor("hidden_states_89_cast")]; + tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([1, 1])]; + tensor var_2024 = const()[name = tensor("op_2024"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369099328)))]; + tensor q_33_cast = conv(dilations = var_2024, groups = var_1906, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2022, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("q_33_cast")]; + tensor var_2028 = const()[name = tensor("op_2028"), val = tensor([1, 1])]; + tensor var_2030 = const()[name = tensor("op_2030"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(373817984)))]; + tensor k_33_cast = conv(dilations = var_2030, groups = var_1906, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2028, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("k_33_cast")]; + tensor var_2034 = const()[name = tensor("op_2034"), val = tensor([1, 1])]; + tensor var_2036 = const()[name = tensor("op_2036"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378536640)))]; + tensor v_33_cast = conv(dilations = var_2036, groups = var_1906, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2034, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("v_33_cast")]; + tensor var_2040 = const()[name = tensor("op_2040"), val = tensor([2, 24, 64, -1])]; + tensor var_2041_cast = reshape(shape = var_2040, x = q_33_cast)[name = tensor("op_2041_cast")]; + tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([2, 24, 64, -1])]; + tensor var_2043_cast = reshape(shape = var_2042, x = k_33_cast)[name = tensor("op_2043_cast")]; + tensor var_2044 = const()[name = tensor("op_2044"), val = tensor([2, 24, 64, -1])]; + tensor var_2045_cast = reshape(shape = var_2044, x = v_33_cast)[name = tensor("op_2045_cast")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2041_cast, y = var_2043_cast)[name = tensor("attn_weights_65_cast")]; + tensor var_1897_to_fp16 = const()[name = tensor("op_1897_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_67_cast = mul(x = attn_weights_65_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_67_cast")]; + tensor var_2049_cast = softmax(axis = var_1890, x = attn_weights_67_cast)[name = tensor("op_2049_cast")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2045_cast, y = var_2049_cast)[name = tensor("attn_33_cast")]; + tensor var_2053 = const()[name = tensor("op_2053"), val = tensor([2, 1536, 1, -1])]; + tensor input_163_cast = reshape(shape = var_2053, x = attn_33_cast)[name = tensor("input_163_cast")]; + tensor var_2058 = const()[name = tensor("op_2058"), val = tensor([1, 1])]; + tensor var_2060 = const()[name = tensor("op_2060"), val = tensor([1, 1])]; + tensor var_2062_pad_type_0 = const()[name = tensor("op_2062_pad_type_0"), val = tensor("custom")]; + tensor var_2062_pad_0 = const()[name = tensor("op_2062_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383255296)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387973952)))]; + tensor var_2062_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2060, groups = var_1906, pad = var_2062_pad_0, pad_type = var_2062_pad_type_0, strides = var_2058, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_163_cast)[name = tensor("op_2062_cast")]; + tensor inputs_51_cast = add(x = var_2062_cast, y = inputs_49_cast)[name = tensor("inputs_51_cast")]; + tensor var_2066 = const()[name = tensor("op_2066"), val = tensor([1])]; + tensor channels_mean_51_cast = reduce_mean(axes = var_2066, keep_dims = var_1901, x = inputs_51_cast)[name = tensor("channels_mean_51_cast")]; + tensor zero_mean_51_cast = sub(x = inputs_51_cast, y = channels_mean_51_cast)[name = tensor("zero_mean_51_cast")]; + tensor zero_mean_sq_51_cast = mul(x = zero_mean_51_cast, y = zero_mean_51_cast)[name = tensor("zero_mean_sq_51_cast")]; + tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([1])]; + tensor var_2071_cast = reduce_mean(axes = var_2070, keep_dims = var_1901, x = zero_mean_sq_51_cast)[name = tensor("op_2071_cast")]; + tensor var_2072_to_fp16 = const()[name = tensor("op_2072_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2073_cast = add(x = var_2071_cast, y = var_2072_to_fp16)[name = tensor("op_2073_cast")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2073_cast)[name = tensor("denom_51_cast")]; + tensor out_51_cast = mul(x = zero_mean_51_cast, y = denom_51_cast)[name = tensor("out_51_cast")]; + tensor var_2077_to_fp16 = const()[name = tensor("op_2077_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387977088)))]; + tensor var_2078_cast = add(x = out_51_cast, y = var_2077_to_fp16)[name = tensor("op_2078_cast")]; + tensor var_2080_to_fp16 = const()[name = tensor("op_2080_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387980224)))]; + tensor hidden_states_91_cast = mul(x = var_2078_cast, y = var_2080_to_fp16)[name = tensor("hidden_states_91_cast")]; + tensor var_2087 = const()[name = tensor("op_2087"), val = tensor([1, 1])]; + tensor var_2089 = const()[name = tensor("op_2089"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387983360)))]; + tensor q_35_cast = conv(dilations = var_2089, groups = var_1906, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2087, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_91_cast)[name = tensor("q_35_cast")]; + tensor var_2093 = const()[name = tensor("op_2093"), val = tensor([1, 1])]; + tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392702016)))]; + tensor k_35_cast = conv(dilations = var_2095, groups = var_1906, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2093, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_35_cast")]; + tensor var_2099 = const()[name = tensor("op_2099"), val = tensor([1, 1])]; + tensor var_2101 = const()[name = tensor("op_2101"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(396634240)))]; + tensor v_35_cast = conv(dilations = var_2101, groups = var_1906, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2099, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_35_cast")]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([2, 24, 64, -1])]; + tensor var_2106_cast = reshape(shape = var_2105, x = q_35_cast)[name = tensor("op_2106_cast")]; + tensor var_2107 = const()[name = tensor("op_2107"), val = tensor([2, 24, 64, -1])]; + tensor var_2108_cast = reshape(shape = var_2107, x = k_35_cast)[name = tensor("op_2108_cast")]; + tensor var_2109 = const()[name = tensor("op_2109"), val = tensor([2, 24, 64, -1])]; + tensor var_2110_cast = reshape(shape = var_2109, x = v_35_cast)[name = tensor("op_2110_cast")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2106_cast, y = var_2108_cast)[name = tensor("attn_weights_69_cast")]; + tensor attn_weights_71_cast = mul(x = attn_weights_69_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_71_cast")]; + tensor var_2114_cast = softmax(axis = var_1890, x = attn_weights_71_cast)[name = tensor("op_2114_cast")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2110_cast, y = var_2114_cast)[name = tensor("attn_35_cast")]; + tensor var_2118 = const()[name = tensor("op_2118"), val = tensor([2, 1536, 1, -1])]; + tensor input_165_cast = reshape(shape = var_2118, x = attn_35_cast)[name = tensor("input_165_cast")]; + tensor var_2123 = const()[name = tensor("op_2123"), val = tensor([1, 1])]; + tensor var_2125 = const()[name = tensor("op_2125"), val = tensor([1, 1])]; + tensor var_2127_pad_type_0 = const()[name = tensor("op_2127_pad_type_0"), val = tensor("custom")]; + tensor var_2127_pad_0 = const()[name = tensor("op_2127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(400566464)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405285120)))]; + tensor var_2127_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2125, groups = var_1906, pad = var_2127_pad_0, pad_type = var_2127_pad_type_0, strides = var_2123, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_165_cast)[name = tensor("op_2127_cast")]; + tensor inputs_53_cast = add(x = var_2127_cast, y = inputs_51_cast)[name = tensor("inputs_53_cast")]; + tensor var_2131 = const()[name = tensor("op_2131"), val = tensor([1])]; + tensor channels_mean_53_cast = reduce_mean(axes = var_2131, keep_dims = var_1901, x = inputs_53_cast)[name = tensor("channels_mean_53_cast")]; + tensor zero_mean_53_cast = sub(x = inputs_53_cast, y = channels_mean_53_cast)[name = tensor("zero_mean_53_cast")]; + tensor zero_mean_sq_53_cast = mul(x = zero_mean_53_cast, y = zero_mean_53_cast)[name = tensor("zero_mean_sq_53_cast")]; + tensor var_2135 = const()[name = tensor("op_2135"), val = tensor([1])]; + tensor var_2136_cast = reduce_mean(axes = var_2135, keep_dims = var_1901, x = zero_mean_sq_53_cast)[name = tensor("op_2136_cast")]; + tensor var_2137_to_fp16 = const()[name = tensor("op_2137_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2138_cast = add(x = var_2136_cast, y = var_2137_to_fp16)[name = tensor("op_2138_cast")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2138_cast)[name = tensor("denom_53_cast")]; + tensor out_53_cast = mul(x = zero_mean_53_cast, y = denom_53_cast)[name = tensor("out_53_cast")]; + tensor var_2142_to_fp16 = const()[name = tensor("op_2142_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405288256)))]; + tensor var_2143_cast = add(x = out_53_cast, y = var_2142_to_fp16)[name = tensor("op_2143_cast")]; + tensor var_2145_to_fp16 = const()[name = tensor("op_2145_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405291392)))]; + tensor input_167_cast = mul(x = var_2143_cast, y = var_2145_to_fp16)[name = tensor("input_167_cast")]; + tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; + tensor var_2155 = const()[name = tensor("op_2155"), val = tensor([1, 1])]; + tensor var_2157_pad_type_0 = const()[name = tensor("op_2157_pad_type_0"), val = tensor("custom")]; + tensor var_2157_pad_0 = const()[name = tensor("op_2157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405294528)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(443043328)))]; + tensor var_2157_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2155, groups = var_1906, pad = var_2157_pad_0, pad_type = var_2157_pad_type_0, strides = var_2153, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_167_cast)[name = tensor("op_2157_cast")]; + tensor var_2158_split_sizes_0 = const()[name = tensor("op_2158_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_2158_axis_0 = const()[name = tensor("op_2158_axis_0"), val = tensor(1)]; + tensor var_2158_cast_0, tensor var_2158_cast_1 = split(axis = var_2158_axis_0, split_sizes = var_2158_split_sizes_0, x = var_2157_cast)[name = tensor("op_2158_cast")]; + tensor var_2160_mode_0 = const()[name = tensor("op_2160_mode_0"), val = tensor("EXACT")]; + tensor var_2160_cast = gelu(mode = var_2160_mode_0, x = var_2158_cast_1)[name = tensor("op_2160_cast")]; + tensor input_169_cast = mul(x = var_2158_cast_0, y = var_2160_cast)[name = tensor("input_169_cast")]; + tensor var_2164 = const()[name = tensor("op_2164"), val = tensor([1, 1])]; + tensor var_2166 = const()[name = tensor("op_2166"), val = tensor([1, 1])]; + tensor var_2168_pad_type_0 = const()[name = tensor("op_2168_pad_type_0"), val = tensor("custom")]; + tensor var_2168_pad_0 = const()[name = tensor("op_2168_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(443067968)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(461942400)))]; + tensor var_2168_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2166, groups = var_1906, pad = var_2168_pad_0, pad_type = var_2168_pad_type_0, strides = var_2164, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_169_cast)[name = tensor("op_2168_cast")]; + tensor inputs_55_cast = add(x = var_2168_cast, y = inputs_53_cast)[name = tensor("inputs_55_cast")]; + tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1])]; + tensor channels_mean_55_cast = reduce_mean(axes = var_2178, keep_dims = var_1901, x = inputs_55_cast)[name = tensor("channels_mean_55_cast")]; + tensor zero_mean_55_cast = sub(x = inputs_55_cast, y = channels_mean_55_cast)[name = tensor("zero_mean_55_cast")]; + tensor zero_mean_sq_55_cast = mul(x = zero_mean_55_cast, y = zero_mean_55_cast)[name = tensor("zero_mean_sq_55_cast")]; + tensor var_2182 = const()[name = tensor("op_2182"), val = tensor([1])]; + tensor var_2183_cast = reduce_mean(axes = var_2182, keep_dims = var_1901, x = zero_mean_sq_55_cast)[name = tensor("op_2183_cast")]; + tensor var_2184_to_fp16 = const()[name = tensor("op_2184_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2185_cast = add(x = var_2183_cast, y = var_2184_to_fp16)[name = tensor("op_2185_cast")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2185_cast)[name = tensor("denom_55_cast")]; + tensor out_55_cast = mul(x = zero_mean_55_cast, y = denom_55_cast)[name = tensor("out_55_cast")]; + tensor var_2189_to_fp16 = const()[name = tensor("op_2189_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(461945536)))]; + tensor var_2190_cast = add(x = out_55_cast, y = var_2189_to_fp16)[name = tensor("op_2190_cast")]; + tensor var_2192_to_fp16 = const()[name = tensor("op_2192_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(461948672)))]; + tensor hidden_states_95_cast = mul(x = var_2190_cast, y = var_2192_to_fp16)[name = tensor("hidden_states_95_cast")]; + tensor var_2199 = const()[name = tensor("op_2199"), val = tensor([1, 1])]; + tensor var_2201 = const()[name = tensor("op_2201"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(461951808)))]; + tensor q_37_cast = conv(dilations = var_2201, groups = var_1906, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2199, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("q_37_cast")]; + tensor var_2205 = const()[name = tensor("op_2205"), val = tensor([1, 1])]; + tensor var_2207 = const()[name = tensor("op_2207"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(466670464)))]; + tensor k_37_cast = conv(dilations = var_2207, groups = var_1906, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2205, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("k_37_cast")]; + tensor var_2211 = const()[name = tensor("op_2211"), val = tensor([1, 1])]; + tensor var_2213 = const()[name = tensor("op_2213"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(471389120)))]; + tensor v_37_cast = conv(dilations = var_2213, groups = var_1906, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2211, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("v_37_cast")]; + tensor var_2217 = const()[name = tensor("op_2217"), val = tensor([2, 24, 64, -1])]; + tensor var_2218_cast = reshape(shape = var_2217, x = q_37_cast)[name = tensor("op_2218_cast")]; + tensor var_2219 = const()[name = tensor("op_2219"), val = tensor([2, 24, 64, -1])]; + tensor var_2220_cast = reshape(shape = var_2219, x = k_37_cast)[name = tensor("op_2220_cast")]; + tensor var_2221 = const()[name = tensor("op_2221"), val = tensor([2, 24, 64, -1])]; + tensor var_2222_cast = reshape(shape = var_2221, x = v_37_cast)[name = tensor("op_2222_cast")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2218_cast, y = var_2220_cast)[name = tensor("attn_weights_73_cast")]; + tensor attn_weights_75_cast = mul(x = attn_weights_73_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_75_cast")]; + tensor var_2226_cast = softmax(axis = var_1890, x = attn_weights_75_cast)[name = tensor("op_2226_cast")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2222_cast, y = var_2226_cast)[name = tensor("attn_37_cast")]; + tensor var_2230 = const()[name = tensor("op_2230"), val = tensor([2, 1536, 1, -1])]; + tensor input_171_cast = reshape(shape = var_2230, x = attn_37_cast)[name = tensor("input_171_cast")]; + tensor var_2235 = const()[name = tensor("op_2235"), val = tensor([1, 1])]; + tensor var_2237 = const()[name = tensor("op_2237"), val = tensor([1, 1])]; + tensor var_2239_pad_type_0 = const()[name = tensor("op_2239_pad_type_0"), val = tensor("custom")]; + tensor var_2239_pad_0 = const()[name = tensor("op_2239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476107776)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480826432)))]; + tensor var_2239_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_2237, groups = var_1906, pad = var_2239_pad_0, pad_type = var_2239_pad_type_0, strides = var_2235, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_171_cast)[name = tensor("op_2239_cast")]; + tensor inputs_57_cast = add(x = var_2239_cast, y = inputs_55_cast)[name = tensor("inputs_57_cast")]; + tensor var_2243 = const()[name = tensor("op_2243"), val = tensor([1])]; + tensor channels_mean_57_cast = reduce_mean(axes = var_2243, keep_dims = var_1901, x = inputs_57_cast)[name = tensor("channels_mean_57_cast")]; + tensor zero_mean_57_cast = sub(x = inputs_57_cast, y = channels_mean_57_cast)[name = tensor("zero_mean_57_cast")]; + tensor zero_mean_sq_57_cast = mul(x = zero_mean_57_cast, y = zero_mean_57_cast)[name = tensor("zero_mean_sq_57_cast")]; + tensor var_2247 = const()[name = tensor("op_2247"), val = tensor([1])]; + tensor var_2248_cast = reduce_mean(axes = var_2247, keep_dims = var_1901, x = zero_mean_sq_57_cast)[name = tensor("op_2248_cast")]; + tensor var_2249_to_fp16 = const()[name = tensor("op_2249_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2250_cast = add(x = var_2248_cast, y = var_2249_to_fp16)[name = tensor("op_2250_cast")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2250_cast)[name = tensor("denom_57_cast")]; + tensor out_57_cast = mul(x = zero_mean_57_cast, y = denom_57_cast)[name = tensor("out_57_cast")]; + tensor var_2254_to_fp16 = const()[name = tensor("op_2254_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480829568)))]; + tensor var_2255_cast = add(x = out_57_cast, y = var_2254_to_fp16)[name = tensor("op_2255_cast")]; + tensor var_2257_to_fp16 = const()[name = tensor("op_2257_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480832704)))]; + tensor hidden_states_97_cast = mul(x = var_2255_cast, y = var_2257_to_fp16)[name = tensor("hidden_states_97_cast")]; + tensor var_2264 = const()[name = tensor("op_2264"), val = tensor([1, 1])]; + tensor var_2266 = const()[name = tensor("op_2266"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480835840)))]; + tensor q_39_cast = conv(dilations = var_2266, groups = var_1906, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_2264, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_97_cast)[name = tensor("q_39_cast")]; + tensor var_2270 = const()[name = tensor("op_2270"), val = tensor([1, 1])]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(485554496)))]; + tensor k_39_cast = conv(dilations = var_2272, groups = var_1906, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_2270, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_39_cast")]; + tensor var_2276 = const()[name = tensor("op_2276"), val = tensor([1, 1])]; + tensor var_2278 = const()[name = tensor("op_2278"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489486720)))]; + tensor v_39_cast = conv(dilations = var_2278, groups = var_1906, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_2276, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_39_cast")]; + tensor var_2282 = const()[name = tensor("op_2282"), val = tensor([2, 24, 64, -1])]; + tensor var_2283_cast = reshape(shape = var_2282, x = q_39_cast)[name = tensor("op_2283_cast")]; + tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([2, 24, 64, -1])]; + tensor var_2285_cast = reshape(shape = var_2284, x = k_39_cast)[name = tensor("op_2285_cast")]; + tensor var_2286 = const()[name = tensor("op_2286"), val = tensor([2, 24, 64, -1])]; + tensor var_2287_cast = reshape(shape = var_2286, x = v_39_cast)[name = tensor("op_2287_cast")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_2283_cast, y = var_2285_cast)[name = tensor("attn_weights_77_cast")]; + tensor attn_weights_79_cast = mul(x = attn_weights_77_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_79_cast")]; + tensor var_2291_cast = softmax(axis = var_1890, x = attn_weights_79_cast)[name = tensor("op_2291_cast")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2287_cast, y = var_2291_cast)[name = tensor("attn_39_cast")]; + tensor var_2295 = const()[name = tensor("op_2295"), val = tensor([2, 1536, 1, -1])]; + tensor input_173_cast = reshape(shape = var_2295, x = attn_39_cast)[name = tensor("input_173_cast")]; + tensor var_2300 = const()[name = tensor("op_2300"), val = tensor([1, 1])]; + tensor var_2302 = const()[name = tensor("op_2302"), val = tensor([1, 1])]; + tensor var_2304_pad_type_0 = const()[name = tensor("op_2304_pad_type_0"), val = tensor("custom")]; + tensor var_2304_pad_0 = const()[name = tensor("op_2304_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493418944)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(498137600)))]; + tensor var_2304_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_2302, groups = var_1906, pad = var_2304_pad_0, pad_type = var_2304_pad_type_0, strides = var_2300, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_173_cast)[name = tensor("op_2304_cast")]; + tensor inputs_59_cast = add(x = var_2304_cast, y = inputs_57_cast)[name = tensor("inputs_59_cast")]; + tensor var_2308 = const()[name = tensor("op_2308"), val = tensor([1])]; + tensor channels_mean_59_cast = reduce_mean(axes = var_2308, keep_dims = var_1901, x = inputs_59_cast)[name = tensor("channels_mean_59_cast")]; + tensor zero_mean_59_cast = sub(x = inputs_59_cast, y = channels_mean_59_cast)[name = tensor("zero_mean_59_cast")]; + tensor zero_mean_sq_59_cast = mul(x = zero_mean_59_cast, y = zero_mean_59_cast)[name = tensor("zero_mean_sq_59_cast")]; + tensor var_2312 = const()[name = tensor("op_2312"), val = tensor([1])]; + tensor var_2313_cast = reduce_mean(axes = var_2312, keep_dims = var_1901, x = zero_mean_sq_59_cast)[name = tensor("op_2313_cast")]; + tensor var_2314_to_fp16 = const()[name = tensor("op_2314_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2315_cast = add(x = var_2313_cast, y = var_2314_to_fp16)[name = tensor("op_2315_cast")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_2315_cast)[name = tensor("denom_59_cast")]; + tensor out_59_cast = mul(x = zero_mean_59_cast, y = denom_59_cast)[name = tensor("out_59_cast")]; + tensor var_2319_to_fp16 = const()[name = tensor("op_2319_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(498140736)))]; + tensor var_2320_cast = add(x = out_59_cast, y = var_2319_to_fp16)[name = tensor("op_2320_cast")]; + tensor var_2322_to_fp16 = const()[name = tensor("op_2322_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(498143872)))]; + tensor input_175_cast = mul(x = var_2320_cast, y = var_2322_to_fp16)[name = tensor("input_175_cast")]; + tensor var_2330 = const()[name = tensor("op_2330"), val = tensor([1, 1])]; + tensor var_2332 = const()[name = tensor("op_2332"), val = tensor([1, 1])]; + tensor var_2334_pad_type_0 = const()[name = tensor("op_2334_pad_type_0"), val = tensor("custom")]; + tensor var_2334_pad_0 = const()[name = tensor("op_2334_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(498147008)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(535895808)))]; + tensor var_2334_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_2332, groups = var_1906, pad = var_2334_pad_0, pad_type = var_2334_pad_type_0, strides = var_2330, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_175_cast)[name = tensor("op_2334_cast")]; + tensor var_2335_split_sizes_0 = const()[name = tensor("op_2335_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_2335_axis_0 = const()[name = tensor("op_2335_axis_0"), val = tensor(1)]; + tensor var_2335_cast_0, tensor var_2335_cast_1 = split(axis = var_2335_axis_0, split_sizes = var_2335_split_sizes_0, x = var_2334_cast)[name = tensor("op_2335_cast")]; + tensor var_2337_mode_0 = const()[name = tensor("op_2337_mode_0"), val = tensor("EXACT")]; + tensor var_2337_cast = gelu(mode = var_2337_mode_0, x = var_2335_cast_1)[name = tensor("op_2337_cast")]; + tensor input_177_cast = mul(x = var_2335_cast_0, y = var_2337_cast)[name = tensor("input_177_cast")]; + tensor var_2341 = const()[name = tensor("op_2341"), val = tensor([1, 1])]; + tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, 1])]; + tensor var_2345_pad_type_0 = const()[name = tensor("op_2345_pad_type_0"), val = tensor("custom")]; + tensor var_2345_pad_0 = const()[name = tensor("op_2345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(535920448)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554794880)))]; + tensor var_2345_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_2343, groups = var_1906, pad = var_2345_pad_0, pad_type = var_2345_pad_type_0, strides = var_2341, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_177_cast)[name = tensor("op_2345_cast")]; + tensor inputs_61_cast = add(x = var_2345_cast, y = inputs_59_cast)[name = tensor("inputs_61_cast")]; + tensor var_2355 = const()[name = tensor("op_2355"), val = tensor([1])]; + tensor channels_mean_61_cast = reduce_mean(axes = var_2355, keep_dims = var_1901, x = inputs_61_cast)[name = tensor("channels_mean_61_cast")]; + tensor zero_mean_61_cast = sub(x = inputs_61_cast, y = channels_mean_61_cast)[name = tensor("zero_mean_61_cast")]; + tensor zero_mean_sq_61_cast = mul(x = zero_mean_61_cast, y = zero_mean_61_cast)[name = tensor("zero_mean_sq_61_cast")]; + tensor var_2359 = const()[name = tensor("op_2359"), val = tensor([1])]; + tensor var_2360_cast = reduce_mean(axes = var_2359, keep_dims = var_1901, x = zero_mean_sq_61_cast)[name = tensor("op_2360_cast")]; + tensor var_2361_to_fp16 = const()[name = tensor("op_2361_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2362_cast = add(x = var_2360_cast, y = var_2361_to_fp16)[name = tensor("op_2362_cast")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_2362_cast)[name = tensor("denom_61_cast")]; + tensor out_61_cast = mul(x = zero_mean_61_cast, y = denom_61_cast)[name = tensor("out_61_cast")]; + tensor var_2366_to_fp16 = const()[name = tensor("op_2366_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554798016)))]; + tensor var_2367_cast = add(x = out_61_cast, y = var_2366_to_fp16)[name = tensor("op_2367_cast")]; + tensor var_2369_to_fp16 = const()[name = tensor("op_2369_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554801152)))]; + tensor hidden_states_101_cast = mul(x = var_2367_cast, y = var_2369_to_fp16)[name = tensor("hidden_states_101_cast")]; + tensor var_2376 = const()[name = tensor("op_2376"), val = tensor([1, 1])]; + tensor var_2378 = const()[name = tensor("op_2378"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554804288)))]; + tensor q_41_cast = conv(dilations = var_2378, groups = var_1906, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_2376, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("q_41_cast")]; + tensor var_2382 = const()[name = tensor("op_2382"), val = tensor([1, 1])]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(559522944)))]; + tensor k_41_cast = conv(dilations = var_2384, groups = var_1906, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2382, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("k_41_cast")]; + tensor var_2388 = const()[name = tensor("op_2388"), val = tensor([1, 1])]; + tensor var_2390 = const()[name = tensor("op_2390"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564241600)))]; + tensor v_41_cast = conv(dilations = var_2390, groups = var_1906, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_2388, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("v_41_cast")]; + tensor var_2394 = const()[name = tensor("op_2394"), val = tensor([2, 24, 64, -1])]; + tensor var_2395_cast = reshape(shape = var_2394, x = q_41_cast)[name = tensor("op_2395_cast")]; + tensor var_2396 = const()[name = tensor("op_2396"), val = tensor([2, 24, 64, -1])]; + tensor var_2397_cast = reshape(shape = var_2396, x = k_41_cast)[name = tensor("op_2397_cast")]; + tensor var_2398 = const()[name = tensor("op_2398"), val = tensor([2, 24, 64, -1])]; + tensor var_2399_cast = reshape(shape = var_2398, x = v_41_cast)[name = tensor("op_2399_cast")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2395_cast, y = var_2397_cast)[name = tensor("attn_weights_81_cast")]; + tensor attn_weights_83_cast = mul(x = attn_weights_81_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_83_cast")]; + tensor var_2403_cast = softmax(axis = var_1890, x = attn_weights_83_cast)[name = tensor("op_2403_cast")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2399_cast, y = var_2403_cast)[name = tensor("attn_41_cast")]; + tensor var_2407 = const()[name = tensor("op_2407"), val = tensor([2, 1536, 1, -1])]; + tensor input_179_cast = reshape(shape = var_2407, x = attn_41_cast)[name = tensor("input_179_cast")]; + tensor var_2412 = const()[name = tensor("op_2412"), val = tensor([1, 1])]; + tensor var_2414 = const()[name = tensor("op_2414"), val = tensor([1, 1])]; + tensor var_2416_pad_type_0 = const()[name = tensor("op_2416_pad_type_0"), val = tensor("custom")]; + tensor var_2416_pad_0 = const()[name = tensor("op_2416_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(568960256)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573678912)))]; + tensor var_2416_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_2414, groups = var_1906, pad = var_2416_pad_0, pad_type = var_2416_pad_type_0, strides = var_2412, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_179_cast)[name = tensor("op_2416_cast")]; + tensor inputs_63_cast = add(x = var_2416_cast, y = inputs_61_cast)[name = tensor("inputs_63_cast")]; + tensor var_2420 = const()[name = tensor("op_2420"), val = tensor([1])]; + tensor channels_mean_63_cast = reduce_mean(axes = var_2420, keep_dims = var_1901, x = inputs_63_cast)[name = tensor("channels_mean_63_cast")]; + tensor zero_mean_63_cast = sub(x = inputs_63_cast, y = channels_mean_63_cast)[name = tensor("zero_mean_63_cast")]; + tensor zero_mean_sq_63_cast = mul(x = zero_mean_63_cast, y = zero_mean_63_cast)[name = tensor("zero_mean_sq_63_cast")]; + tensor var_2424 = const()[name = tensor("op_2424"), val = tensor([1])]; + tensor var_2425_cast = reduce_mean(axes = var_2424, keep_dims = var_1901, x = zero_mean_sq_63_cast)[name = tensor("op_2425_cast")]; + tensor var_2426_to_fp16 = const()[name = tensor("op_2426_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2427_cast = add(x = var_2425_cast, y = var_2426_to_fp16)[name = tensor("op_2427_cast")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_2427_cast)[name = tensor("denom_63_cast")]; + tensor out_63_cast = mul(x = zero_mean_63_cast, y = denom_63_cast)[name = tensor("out_63_cast")]; + tensor var_2431_to_fp16 = const()[name = tensor("op_2431_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573682048)))]; + tensor var_2432_cast = add(x = out_63_cast, y = var_2431_to_fp16)[name = tensor("op_2432_cast")]; + tensor var_2434_to_fp16 = const()[name = tensor("op_2434_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573685184)))]; + tensor hidden_states_103_cast = mul(x = var_2432_cast, y = var_2434_to_fp16)[name = tensor("hidden_states_103_cast")]; + tensor var_2441 = const()[name = tensor("op_2441"), val = tensor([1, 1])]; + tensor var_2443 = const()[name = tensor("op_2443"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573688320)))]; + tensor q_43_cast = conv(dilations = var_2443, groups = var_1906, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_2441, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_103_cast)[name = tensor("q_43_cast")]; + tensor var_2447 = const()[name = tensor("op_2447"), val = tensor([1, 1])]; + tensor var_2449 = const()[name = tensor("op_2449"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(578406976)))]; + tensor k_43_cast = conv(dilations = var_2449, groups = var_1906, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_2447, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_43_cast")]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([1, 1])]; + tensor var_2455 = const()[name = tensor("op_2455"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582339200)))]; + tensor v_43_cast = conv(dilations = var_2455, groups = var_1906, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_2453, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_43_cast")]; + tensor var_2459 = const()[name = tensor("op_2459"), val = tensor([2, 24, 64, -1])]; + tensor var_2460_cast = reshape(shape = var_2459, x = q_43_cast)[name = tensor("op_2460_cast")]; + tensor var_2461 = const()[name = tensor("op_2461"), val = tensor([2, 24, 64, -1])]; + tensor var_2462_cast = reshape(shape = var_2461, x = k_43_cast)[name = tensor("op_2462_cast")]; + tensor var_2463 = const()[name = tensor("op_2463"), val = tensor([2, 24, 64, -1])]; + tensor var_2464_cast = reshape(shape = var_2463, x = v_43_cast)[name = tensor("op_2464_cast")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_2460_cast, y = var_2462_cast)[name = tensor("attn_weights_85_cast")]; + tensor attn_weights_87_cast = mul(x = attn_weights_85_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_87_cast")]; + tensor var_2468_cast = softmax(axis = var_1890, x = attn_weights_87_cast)[name = tensor("op_2468_cast")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2464_cast, y = var_2468_cast)[name = tensor("attn_43_cast")]; + tensor var_2472 = const()[name = tensor("op_2472"), val = tensor([2, 1536, 1, -1])]; + tensor input_181_cast = reshape(shape = var_2472, x = attn_43_cast)[name = tensor("input_181_cast")]; + tensor var_2477 = const()[name = tensor("op_2477"), val = tensor([1, 1])]; + tensor var_2479 = const()[name = tensor("op_2479"), val = tensor([1, 1])]; + tensor var_2481_pad_type_0 = const()[name = tensor("op_2481_pad_type_0"), val = tensor("custom")]; + tensor var_2481_pad_0 = const()[name = tensor("op_2481_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586271424)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590990080)))]; + tensor var_2481_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_2479, groups = var_1906, pad = var_2481_pad_0, pad_type = var_2481_pad_type_0, strides = var_2477, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_181_cast)[name = tensor("op_2481_cast")]; + tensor inputs_65_cast = add(x = var_2481_cast, y = inputs_63_cast)[name = tensor("inputs_65_cast")]; + tensor var_2485 = const()[name = tensor("op_2485"), val = tensor([1])]; + tensor channels_mean_65_cast = reduce_mean(axes = var_2485, keep_dims = var_1901, x = inputs_65_cast)[name = tensor("channels_mean_65_cast")]; + tensor zero_mean_65_cast = sub(x = inputs_65_cast, y = channels_mean_65_cast)[name = tensor("zero_mean_65_cast")]; + tensor zero_mean_sq_65_cast = mul(x = zero_mean_65_cast, y = zero_mean_65_cast)[name = tensor("zero_mean_sq_65_cast")]; + tensor var_2489 = const()[name = tensor("op_2489"), val = tensor([1])]; + tensor var_2490_cast = reduce_mean(axes = var_2489, keep_dims = var_1901, x = zero_mean_sq_65_cast)[name = tensor("op_2490_cast")]; + tensor var_2491_to_fp16 = const()[name = tensor("op_2491_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2492_cast = add(x = var_2490_cast, y = var_2491_to_fp16)[name = tensor("op_2492_cast")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_2492_cast)[name = tensor("denom_65_cast")]; + tensor out_65_cast = mul(x = zero_mean_65_cast, y = denom_65_cast)[name = tensor("out_65_cast")]; + tensor var_2496_to_fp16 = const()[name = tensor("op_2496_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590993216)))]; + tensor var_2497_cast = add(x = out_65_cast, y = var_2496_to_fp16)[name = tensor("op_2497_cast")]; + tensor var_2499_to_fp16 = const()[name = tensor("op_2499_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590996352)))]; + tensor input_183_cast = mul(x = var_2497_cast, y = var_2499_to_fp16)[name = tensor("input_183_cast")]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([1, 1])]; + tensor var_2509 = const()[name = tensor("op_2509"), val = tensor([1, 1])]; + tensor var_2511_pad_type_0 = const()[name = tensor("op_2511_pad_type_0"), val = tensor("custom")]; + tensor var_2511_pad_0 = const()[name = tensor("op_2511_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590999488)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628748288)))]; + tensor var_2511_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_2509, groups = var_1906, pad = var_2511_pad_0, pad_type = var_2511_pad_type_0, strides = var_2507, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_183_cast)[name = tensor("op_2511_cast")]; + tensor var_2512_split_sizes_0 = const()[name = tensor("op_2512_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_2512_axis_0 = const()[name = tensor("op_2512_axis_0"), val = tensor(1)]; + tensor var_2512_cast_0, tensor var_2512_cast_1 = split(axis = var_2512_axis_0, split_sizes = var_2512_split_sizes_0, x = var_2511_cast)[name = tensor("op_2512_cast")]; + tensor var_2514_mode_0 = const()[name = tensor("op_2514_mode_0"), val = tensor("EXACT")]; + tensor var_2514_cast = gelu(mode = var_2514_mode_0, x = var_2512_cast_1)[name = tensor("op_2514_cast")]; + tensor input_185_cast = mul(x = var_2512_cast_0, y = var_2514_cast)[name = tensor("input_185_cast")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([1, 1])]; + tensor var_2520 = const()[name = tensor("op_2520"), val = tensor([1, 1])]; + tensor var_2522_pad_type_0 = const()[name = tensor("op_2522_pad_type_0"), val = tensor("custom")]; + tensor var_2522_pad_0 = const()[name = tensor("op_2522_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628772928)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647647360)))]; + tensor var_2522_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_2520, groups = var_1906, pad = var_2522_pad_0, pad_type = var_2522_pad_type_0, strides = var_2518, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_185_cast)[name = tensor("op_2522_cast")]; + tensor inputs_67_cast = add(x = var_2522_cast, y = inputs_65_cast)[name = tensor("inputs_67_cast")]; + tensor var_2532 = const()[name = tensor("op_2532"), val = tensor([1])]; + tensor channels_mean_67_cast = reduce_mean(axes = var_2532, keep_dims = var_1901, x = inputs_67_cast)[name = tensor("channels_mean_67_cast")]; + tensor zero_mean_67_cast = sub(x = inputs_67_cast, y = channels_mean_67_cast)[name = tensor("zero_mean_67_cast")]; + tensor zero_mean_sq_67_cast = mul(x = zero_mean_67_cast, y = zero_mean_67_cast)[name = tensor("zero_mean_sq_67_cast")]; + tensor var_2536 = const()[name = tensor("op_2536"), val = tensor([1])]; + tensor var_2537_cast = reduce_mean(axes = var_2536, keep_dims = var_1901, x = zero_mean_sq_67_cast)[name = tensor("op_2537_cast")]; + tensor var_2538_to_fp16 = const()[name = tensor("op_2538_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2539_cast = add(x = var_2537_cast, y = var_2538_to_fp16)[name = tensor("op_2539_cast")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_2539_cast)[name = tensor("denom_67_cast")]; + tensor out_67_cast = mul(x = zero_mean_67_cast, y = denom_67_cast)[name = tensor("out_67_cast")]; + tensor var_2543_to_fp16 = const()[name = tensor("op_2543_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647650496)))]; + tensor var_2544_cast = add(x = out_67_cast, y = var_2543_to_fp16)[name = tensor("op_2544_cast")]; + tensor var_2546_to_fp16 = const()[name = tensor("op_2546_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647653632)))]; + tensor hidden_states_107_cast = mul(x = var_2544_cast, y = var_2546_to_fp16)[name = tensor("hidden_states_107_cast")]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor var_2555 = const()[name = tensor("op_2555"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647656768)))]; + tensor q_45_cast = conv(dilations = var_2555, groups = var_1906, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_2553, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("q_45_cast")]; + tensor var_2559 = const()[name = tensor("op_2559"), val = tensor([1, 1])]; + tensor var_2561 = const()[name = tensor("op_2561"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652375424)))]; + tensor k_45_cast = conv(dilations = var_2561, groups = var_1906, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_2559, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("k_45_cast")]; + tensor var_2565 = const()[name = tensor("op_2565"), val = tensor([1, 1])]; + tensor var_2567 = const()[name = tensor("op_2567"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(657094080)))]; + tensor v_45_cast = conv(dilations = var_2567, groups = var_1906, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_2565, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("v_45_cast")]; + tensor var_2571 = const()[name = tensor("op_2571"), val = tensor([2, 24, 64, -1])]; + tensor var_2572_cast = reshape(shape = var_2571, x = q_45_cast)[name = tensor("op_2572_cast")]; + tensor var_2573 = const()[name = tensor("op_2573"), val = tensor([2, 24, 64, -1])]; + tensor var_2574_cast = reshape(shape = var_2573, x = k_45_cast)[name = tensor("op_2574_cast")]; + tensor var_2575 = const()[name = tensor("op_2575"), val = tensor([2, 24, 64, -1])]; + tensor var_2576_cast = reshape(shape = var_2575, x = v_45_cast)[name = tensor("op_2576_cast")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2572_cast, y = var_2574_cast)[name = tensor("attn_weights_89_cast")]; + tensor attn_weights_91_cast = mul(x = attn_weights_89_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_91_cast")]; + tensor var_2580_cast = softmax(axis = var_1890, x = attn_weights_91_cast)[name = tensor("op_2580_cast")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2576_cast, y = var_2580_cast)[name = tensor("attn_45_cast")]; + tensor var_2584 = const()[name = tensor("op_2584"), val = tensor([2, 1536, 1, -1])]; + tensor input_187_cast = reshape(shape = var_2584, x = attn_45_cast)[name = tensor("input_187_cast")]; + tensor var_2589 = const()[name = tensor("op_2589"), val = tensor([1, 1])]; + tensor var_2591 = const()[name = tensor("op_2591"), val = tensor([1, 1])]; + tensor var_2593_pad_type_0 = const()[name = tensor("op_2593_pad_type_0"), val = tensor("custom")]; + tensor var_2593_pad_0 = const()[name = tensor("op_2593_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(661812736)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(666531392)))]; + tensor var_2593_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_2591, groups = var_1906, pad = var_2593_pad_0, pad_type = var_2593_pad_type_0, strides = var_2589, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_187_cast)[name = tensor("op_2593_cast")]; + tensor inputs_69_cast = add(x = var_2593_cast, y = inputs_67_cast)[name = tensor("inputs_69_cast")]; + tensor var_2597 = const()[name = tensor("op_2597"), val = tensor([1])]; + tensor channels_mean_69_cast = reduce_mean(axes = var_2597, keep_dims = var_1901, x = inputs_69_cast)[name = tensor("channels_mean_69_cast")]; + tensor zero_mean_69_cast = sub(x = inputs_69_cast, y = channels_mean_69_cast)[name = tensor("zero_mean_69_cast")]; + tensor zero_mean_sq_69_cast = mul(x = zero_mean_69_cast, y = zero_mean_69_cast)[name = tensor("zero_mean_sq_69_cast")]; + tensor var_2601 = const()[name = tensor("op_2601"), val = tensor([1])]; + tensor var_2602_cast = reduce_mean(axes = var_2601, keep_dims = var_1901, x = zero_mean_sq_69_cast)[name = tensor("op_2602_cast")]; + tensor var_2603_to_fp16 = const()[name = tensor("op_2603_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2604_cast = add(x = var_2602_cast, y = var_2603_to_fp16)[name = tensor("op_2604_cast")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_2604_cast)[name = tensor("denom_69_cast")]; + tensor out_69_cast = mul(x = zero_mean_69_cast, y = denom_69_cast)[name = tensor("out_69_cast")]; + tensor var_2608_to_fp16 = const()[name = tensor("op_2608_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(666534528)))]; + tensor var_2609_cast = add(x = out_69_cast, y = var_2608_to_fp16)[name = tensor("op_2609_cast")]; + tensor var_2611_to_fp16 = const()[name = tensor("op_2611_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(666537664)))]; + tensor hidden_states_109_cast = mul(x = var_2609_cast, y = var_2611_to_fp16)[name = tensor("hidden_states_109_cast")]; + tensor var_2618 = const()[name = tensor("op_2618"), val = tensor([1, 1])]; + tensor var_2620 = const()[name = tensor("op_2620"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(666540800)))]; + tensor q_47_cast = conv(dilations = var_2620, groups = var_1906, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_2618, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_109_cast)[name = tensor("q_47_cast")]; + tensor var_2624 = const()[name = tensor("op_2624"), val = tensor([1, 1])]; + tensor var_2626 = const()[name = tensor("op_2626"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671259456)))]; + tensor k_47_cast = conv(dilations = var_2626, groups = var_1906, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_2624, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_47_cast")]; + tensor var_2630 = const()[name = tensor("op_2630"), val = tensor([1, 1])]; + tensor var_2632 = const()[name = tensor("op_2632"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(675191680)))]; + tensor v_47_cast = conv(dilations = var_2632, groups = var_1906, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_2630, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_47_cast")]; + tensor var_2636 = const()[name = tensor("op_2636"), val = tensor([2, 24, 64, -1])]; + tensor var_2637_cast = reshape(shape = var_2636, x = q_47_cast)[name = tensor("op_2637_cast")]; + tensor var_2638 = const()[name = tensor("op_2638"), val = tensor([2, 24, 64, -1])]; + tensor var_2639_cast = reshape(shape = var_2638, x = k_47_cast)[name = tensor("op_2639_cast")]; + tensor var_2640 = const()[name = tensor("op_2640"), val = tensor([2, 24, 64, -1])]; + tensor var_2641_cast = reshape(shape = var_2640, x = v_47_cast)[name = tensor("op_2641_cast")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_2637_cast, y = var_2639_cast)[name = tensor("attn_weights_93_cast")]; + tensor attn_weights_95_cast = mul(x = attn_weights_93_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_95_cast")]; + tensor var_2645_cast = softmax(axis = var_1890, x = attn_weights_95_cast)[name = tensor("op_2645_cast")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2641_cast, y = var_2645_cast)[name = tensor("attn_47_cast")]; + tensor var_2649 = const()[name = tensor("op_2649"), val = tensor([2, 1536, 1, -1])]; + tensor input_189_cast = reshape(shape = var_2649, x = attn_47_cast)[name = tensor("input_189_cast")]; + tensor var_2654 = const()[name = tensor("op_2654"), val = tensor([1, 1])]; + tensor var_2656 = const()[name = tensor("op_2656"), val = tensor([1, 1])]; + tensor var_2658_pad_type_0 = const()[name = tensor("op_2658_pad_type_0"), val = tensor("custom")]; + tensor var_2658_pad_0 = const()[name = tensor("op_2658_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(679123904)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683842560)))]; + tensor var_2658_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_2656, groups = var_1906, pad = var_2658_pad_0, pad_type = var_2658_pad_type_0, strides = var_2654, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_189_cast)[name = tensor("op_2658_cast")]; + tensor inputs_71_cast = add(x = var_2658_cast, y = inputs_69_cast)[name = tensor("inputs_71_cast")]; + tensor var_2662 = const()[name = tensor("op_2662"), val = tensor([1])]; + tensor channels_mean_71_cast = reduce_mean(axes = var_2662, keep_dims = var_1901, x = inputs_71_cast)[name = tensor("channels_mean_71_cast")]; + tensor zero_mean_71_cast = sub(x = inputs_71_cast, y = channels_mean_71_cast)[name = tensor("zero_mean_71_cast")]; + tensor zero_mean_sq_71_cast = mul(x = zero_mean_71_cast, y = zero_mean_71_cast)[name = tensor("zero_mean_sq_71_cast")]; + tensor var_2666 = const()[name = tensor("op_2666"), val = tensor([1])]; + tensor var_2667_cast = reduce_mean(axes = var_2666, keep_dims = var_1901, x = zero_mean_sq_71_cast)[name = tensor("op_2667_cast")]; + tensor var_2668_to_fp16 = const()[name = tensor("op_2668_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2669_cast = add(x = var_2667_cast, y = var_2668_to_fp16)[name = tensor("op_2669_cast")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_2669_cast)[name = tensor("denom_71_cast")]; + tensor out_71_cast = mul(x = zero_mean_71_cast, y = denom_71_cast)[name = tensor("out_71_cast")]; + tensor var_2673_to_fp16 = const()[name = tensor("op_2673_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683845696)))]; + tensor var_2674_cast = add(x = out_71_cast, y = var_2673_to_fp16)[name = tensor("op_2674_cast")]; + tensor var_2676_to_fp16 = const()[name = tensor("op_2676_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683848832)))]; + tensor input_191_cast = mul(x = var_2674_cast, y = var_2676_to_fp16)[name = tensor("input_191_cast")]; + tensor var_2684 = const()[name = tensor("op_2684"), val = tensor([1, 1])]; + tensor var_2686 = const()[name = tensor("op_2686"), val = tensor([1, 1])]; + tensor var_2688_pad_type_0 = const()[name = tensor("op_2688_pad_type_0"), val = tensor("custom")]; + tensor var_2688_pad_0 = const()[name = tensor("op_2688_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683851968)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721600768)))]; + tensor var_2688_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_2686, groups = var_1906, pad = var_2688_pad_0, pad_type = var_2688_pad_type_0, strides = var_2684, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_191_cast)[name = tensor("op_2688_cast")]; + tensor var_2689_split_sizes_0 = const()[name = tensor("op_2689_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_2689_axis_0 = const()[name = tensor("op_2689_axis_0"), val = tensor(1)]; + tensor var_2689_cast_0, tensor var_2689_cast_1 = split(axis = var_2689_axis_0, split_sizes = var_2689_split_sizes_0, x = var_2688_cast)[name = tensor("op_2689_cast")]; + tensor var_2691_mode_0 = const()[name = tensor("op_2691_mode_0"), val = tensor("EXACT")]; + tensor var_2691_cast = gelu(mode = var_2691_mode_0, x = var_2689_cast_1)[name = tensor("op_2691_cast")]; + tensor input_193_cast = mul(x = var_2689_cast_0, y = var_2691_cast)[name = tensor("input_193_cast")]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor var_2697 = const()[name = tensor("op_2697"), val = tensor([1, 1])]; + tensor var_2699_pad_type_0 = const()[name = tensor("op_2699_pad_type_0"), val = tensor("custom")]; + tensor var_2699_pad_0 = const()[name = tensor("op_2699_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721625408)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(740499840)))]; + tensor var_2699_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_2697, groups = var_1906, pad = var_2699_pad_0, pad_type = var_2699_pad_type_0, strides = var_2695, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_193_cast)[name = tensor("op_2699_cast")]; + tensor hidden_states_113_cast = add(x = var_2699_cast, y = inputs_71_cast)[name = tensor("hidden_states_113_cast")]; + tensor var_2701 = const()[name = tensor("op_2701"), val = tensor([2, 1536, 32, 32])]; + tensor input_195_cast = reshape(shape = var_2701, x = hidden_states_113_cast)[name = tensor("input_195_cast")]; + tensor var_2705 = const()[name = tensor("op_2705"), val = tensor([1, 1])]; + tensor var_2707 = const()[name = tensor("op_2707"), val = tensor([1, 1])]; + tensor hidden_states_115_pad_type_0 = const()[name = tensor("hidden_states_115_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_115_pad_0 = const()[name = tensor("hidden_states_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(740502976)))]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(745221632)))]; + tensor hidden_states_115_cast = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_2707, groups = var_1906, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_2705, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_195_cast)[name = tensor("hidden_states_115_cast")]; + tensor input_197_cast = add(x = hidden_states_115_cast, y = hidden_states_85_cast)[name = tensor("input_197_cast")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_197_cast)[name = tensor("reshape_52_cast")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor("reduce_mean_41_cast")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_cast")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(745224768)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(745227904)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_53_cast)[name = tensor("add_27_cast")]; + tensor input_201_cast = silu(x = add_27_cast)[name = tensor("input_201_cast")]; + tensor var_2722 = const()[name = tensor("op_2722"), val = tensor([1, 1])]; + tensor var_2724 = const()[name = tensor("op_2724"), val = tensor([1, 1])]; + tensor hidden_states_117_pad_type_0 = const()[name = tensor("hidden_states_117_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_117_pad_0 = const()[name = tensor("hidden_states_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(745231040)))]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(787698432)))]; + tensor hidden_states_117_cast = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_2724, groups = var_1906, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_2722, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_201_cast)[name = tensor("hidden_states_117_cast")]; + tensor var_2730 = const()[name = tensor("op_2730"), val = tensor([1, 1])]; + tensor var_2732 = const()[name = tensor("op_2732"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(787701568)))]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792420224)))]; + tensor temb_11_cast = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2732, groups = var_1906, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_2730, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_11_cast")]; + tensor input_205_cast = add(x = hidden_states_117_cast, y = temb_11_cast)[name = tensor("input_205_cast")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = input_205_cast)[name = tensor("reshape_56_cast")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor("reduce_mean_44_cast")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792423360)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792426496)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_57_cast)[name = tensor("add_29_cast")]; + tensor input_209_cast = silu(x = add_29_cast)[name = tensor("input_209_cast")]; + tensor var_2742 = const()[name = tensor("op_2742"), val = tensor([1, 1])]; + tensor var_2744 = const()[name = tensor("op_2744"), val = tensor([1, 1])]; + tensor hidden_states_119_pad_type_0 = const()[name = tensor("hidden_states_119_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_119_pad_0 = const()[name = tensor("hidden_states_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792429632)))]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834897024)))]; + tensor hidden_states_119_cast = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_2744, groups = var_1906, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_2742, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_209_cast)[name = tensor("hidden_states_119_cast")]; + tensor hidden_states_121_cast = add(x = input_197_cast, y = hidden_states_119_cast)[name = tensor("hidden_states_121_cast")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = hidden_states_121_cast)[name = tensor("reshape_60_cast")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor("reduce_mean_47_cast")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834900160)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834903296)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_61_cast)[name = tensor("add_31_cast")]; + tensor var_2770 = const()[name = tensor("op_2770"), val = tensor([1, 1])]; + tensor var_2772 = const()[name = tensor("op_2772"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_type_0 = const()[name = tensor("hidden_states_123_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_123_pad_0 = const()[name = tensor("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834906432)))]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(839625088)))]; + tensor hidden_states_123_cast = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_2772, groups = var_1906, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_2770, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_31_cast)[name = tensor("hidden_states_123_cast")]; + tensor var_2777 = const()[name = tensor("op_2777"), val = tensor([2, 1536, 1, 1024])]; + tensor inputs_73_cast = reshape(shape = var_2777, x = hidden_states_123_cast)[name = tensor("inputs_73_cast")]; + tensor var_2787 = const()[name = tensor("op_2787"), val = tensor([1])]; + tensor channels_mean_73_cast = reduce_mean(axes = var_2787, keep_dims = var_1901, x = inputs_73_cast)[name = tensor("channels_mean_73_cast")]; + tensor zero_mean_73_cast = sub(x = inputs_73_cast, y = channels_mean_73_cast)[name = tensor("zero_mean_73_cast")]; + tensor zero_mean_sq_73_cast = mul(x = zero_mean_73_cast, y = zero_mean_73_cast)[name = tensor("zero_mean_sq_73_cast")]; + tensor var_2791 = const()[name = tensor("op_2791"), val = tensor([1])]; + tensor var_2792_cast = reduce_mean(axes = var_2791, keep_dims = var_1901, x = zero_mean_sq_73_cast)[name = tensor("op_2792_cast")]; + tensor var_2793_to_fp16 = const()[name = tensor("op_2793_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2794_cast = add(x = var_2792_cast, y = var_2793_to_fp16)[name = tensor("op_2794_cast")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_2794_cast)[name = tensor("denom_73_cast")]; + tensor out_73_cast = mul(x = zero_mean_73_cast, y = denom_73_cast)[name = tensor("out_73_cast")]; + tensor var_2798_to_fp16 = const()[name = tensor("op_2798_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(839628224)))]; + tensor var_2799_cast = add(x = out_73_cast, y = var_2798_to_fp16)[name = tensor("op_2799_cast")]; + tensor var_2801_to_fp16 = const()[name = tensor("op_2801_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(839631360)))]; + tensor hidden_states_125_cast = mul(x = var_2799_cast, y = var_2801_to_fp16)[name = tensor("hidden_states_125_cast")]; + tensor var_2808 = const()[name = tensor("op_2808"), val = tensor([1, 1])]; + tensor var_2810 = const()[name = tensor("op_2810"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(839634496)))]; + tensor q_49_cast = conv(dilations = var_2810, groups = var_1906, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_2808, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_125_cast)[name = tensor("q_49_cast")]; + tensor var_2814 = const()[name = tensor("op_2814"), val = tensor([1, 1])]; + tensor var_2816 = const()[name = tensor("op_2816"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(844353152)))]; + tensor k_49_cast = conv(dilations = var_2816, groups = var_1906, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_2814, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_125_cast)[name = tensor("k_49_cast")]; + tensor var_2820 = const()[name = tensor("op_2820"), val = tensor([1, 1])]; + tensor var_2822 = const()[name = tensor("op_2822"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(849071808)))]; + tensor v_49_cast = conv(dilations = var_2822, groups = var_1906, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_2820, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_125_cast)[name = tensor("v_49_cast")]; + tensor var_2826 = const()[name = tensor("op_2826"), val = tensor([2, 24, 64, -1])]; + tensor var_2827_cast = reshape(shape = var_2826, x = q_49_cast)[name = tensor("op_2827_cast")]; + tensor var_2828 = const()[name = tensor("op_2828"), val = tensor([2, 24, 64, -1])]; + tensor var_2829_cast = reshape(shape = var_2828, x = k_49_cast)[name = tensor("op_2829_cast")]; + tensor var_2830 = const()[name = tensor("op_2830"), val = tensor([2, 24, 64, -1])]; + tensor var_2831_cast = reshape(shape = var_2830, x = v_49_cast)[name = tensor("op_2831_cast")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_2827_cast, y = var_2829_cast)[name = tensor("attn_weights_97_cast")]; + tensor attn_weights_99_cast = mul(x = attn_weights_97_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_99_cast")]; + tensor var_2835_cast = softmax(axis = var_1890, x = attn_weights_99_cast)[name = tensor("op_2835_cast")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2831_cast, y = var_2835_cast)[name = tensor("attn_49_cast")]; + tensor var_2839 = const()[name = tensor("op_2839"), val = tensor([2, 1536, 1, -1])]; + tensor input_213_cast = reshape(shape = var_2839, x = attn_49_cast)[name = tensor("input_213_cast")]; + tensor var_2844 = const()[name = tensor("op_2844"), val = tensor([1, 1])]; + tensor var_2846 = const()[name = tensor("op_2846"), val = tensor([1, 1])]; + tensor var_2848_pad_type_0 = const()[name = tensor("op_2848_pad_type_0"), val = tensor("custom")]; + tensor var_2848_pad_0 = const()[name = tensor("op_2848_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853790464)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858509120)))]; + tensor var_2848_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2846, groups = var_1906, pad = var_2848_pad_0, pad_type = var_2848_pad_type_0, strides = var_2844, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_213_cast)[name = tensor("op_2848_cast")]; + tensor inputs_75_cast = add(x = var_2848_cast, y = inputs_73_cast)[name = tensor("inputs_75_cast")]; + tensor var_2852 = const()[name = tensor("op_2852"), val = tensor([1])]; + tensor channels_mean_75_cast = reduce_mean(axes = var_2852, keep_dims = var_1901, x = inputs_75_cast)[name = tensor("channels_mean_75_cast")]; + tensor zero_mean_75_cast = sub(x = inputs_75_cast, y = channels_mean_75_cast)[name = tensor("zero_mean_75_cast")]; + tensor zero_mean_sq_75_cast = mul(x = zero_mean_75_cast, y = zero_mean_75_cast)[name = tensor("zero_mean_sq_75_cast")]; + tensor var_2856 = const()[name = tensor("op_2856"), val = tensor([1])]; + tensor var_2857_cast = reduce_mean(axes = var_2856, keep_dims = var_1901, x = zero_mean_sq_75_cast)[name = tensor("op_2857_cast")]; + tensor var_2858_to_fp16 = const()[name = tensor("op_2858_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2859_cast = add(x = var_2857_cast, y = var_2858_to_fp16)[name = tensor("op_2859_cast")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_2859_cast)[name = tensor("denom_75_cast")]; + tensor out_75_cast = mul(x = zero_mean_75_cast, y = denom_75_cast)[name = tensor("out_75_cast")]; + tensor var_2863_to_fp16 = const()[name = tensor("op_2863_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858512256)))]; + tensor var_2864_cast = add(x = out_75_cast, y = var_2863_to_fp16)[name = tensor("op_2864_cast")]; + tensor var_2866_to_fp16 = const()[name = tensor("op_2866_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858515392)))]; + tensor hidden_states_127_cast = mul(x = var_2864_cast, y = var_2866_to_fp16)[name = tensor("hidden_states_127_cast")]; + tensor var_2873 = const()[name = tensor("op_2873"), val = tensor([1, 1])]; + tensor var_2875 = const()[name = tensor("op_2875"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858518528)))]; + tensor q_51_cast = conv(dilations = var_2875, groups = var_1906, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_2873, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_127_cast)[name = tensor("q_51_cast")]; + tensor var_2879 = const()[name = tensor("op_2879"), val = tensor([1, 1])]; + tensor var_2881 = const()[name = tensor("op_2881"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863237184)))]; + tensor k_51_cast = conv(dilations = var_2881, groups = var_1906, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_2879, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_51_cast")]; + tensor var_2885 = const()[name = tensor("op_2885"), val = tensor([1, 1])]; + tensor var_2887 = const()[name = tensor("op_2887"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867169408)))]; + tensor v_51_cast = conv(dilations = var_2887, groups = var_1906, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_2885, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_51_cast")]; + tensor var_2891 = const()[name = tensor("op_2891"), val = tensor([2, 24, 64, -1])]; + tensor var_2892_cast = reshape(shape = var_2891, x = q_51_cast)[name = tensor("op_2892_cast")]; + tensor var_2893 = const()[name = tensor("op_2893"), val = tensor([2, 24, 64, -1])]; + tensor var_2894_cast = reshape(shape = var_2893, x = k_51_cast)[name = tensor("op_2894_cast")]; + tensor var_2895 = const()[name = tensor("op_2895"), val = tensor([2, 24, 64, -1])]; + tensor var_2896_cast = reshape(shape = var_2895, x = v_51_cast)[name = tensor("op_2896_cast")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_2892_cast, y = var_2894_cast)[name = tensor("attn_weights_101_cast")]; + tensor attn_weights_103_cast = mul(x = attn_weights_101_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_103_cast")]; + tensor var_2900_cast = softmax(axis = var_1890, x = attn_weights_103_cast)[name = tensor("op_2900_cast")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2896_cast, y = var_2900_cast)[name = tensor("attn_51_cast")]; + tensor var_2904 = const()[name = tensor("op_2904"), val = tensor([2, 1536, 1, -1])]; + tensor input_215_cast = reshape(shape = var_2904, x = attn_51_cast)[name = tensor("input_215_cast")]; + tensor var_2909 = const()[name = tensor("op_2909"), val = tensor([1, 1])]; + tensor var_2911 = const()[name = tensor("op_2911"), val = tensor([1, 1])]; + tensor var_2913_pad_type_0 = const()[name = tensor("op_2913_pad_type_0"), val = tensor("custom")]; + tensor var_2913_pad_0 = const()[name = tensor("op_2913_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(871101632)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(875820288)))]; + tensor var_2913_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2911, groups = var_1906, pad = var_2913_pad_0, pad_type = var_2913_pad_type_0, strides = var_2909, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_215_cast)[name = tensor("op_2913_cast")]; + tensor inputs_77_cast = add(x = var_2913_cast, y = inputs_75_cast)[name = tensor("inputs_77_cast")]; + tensor var_2917 = const()[name = tensor("op_2917"), val = tensor([1])]; + tensor channels_mean_77_cast = reduce_mean(axes = var_2917, keep_dims = var_1901, x = inputs_77_cast)[name = tensor("channels_mean_77_cast")]; + tensor zero_mean_77_cast = sub(x = inputs_77_cast, y = channels_mean_77_cast)[name = tensor("zero_mean_77_cast")]; + tensor zero_mean_sq_77_cast = mul(x = zero_mean_77_cast, y = zero_mean_77_cast)[name = tensor("zero_mean_sq_77_cast")]; + tensor var_2921 = const()[name = tensor("op_2921"), val = tensor([1])]; + tensor var_2922_cast = reduce_mean(axes = var_2921, keep_dims = var_1901, x = zero_mean_sq_77_cast)[name = tensor("op_2922_cast")]; + tensor var_2923_to_fp16 = const()[name = tensor("op_2923_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2924_cast = add(x = var_2922_cast, y = var_2923_to_fp16)[name = tensor("op_2924_cast")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_2924_cast)[name = tensor("denom_77_cast")]; + tensor out_77_cast = mul(x = zero_mean_77_cast, y = denom_77_cast)[name = tensor("out_77_cast")]; + tensor var_2928_to_fp16 = const()[name = tensor("op_2928_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(875823424)))]; + tensor var_2929_cast = add(x = out_77_cast, y = var_2928_to_fp16)[name = tensor("op_2929_cast")]; + tensor var_2931_to_fp16 = const()[name = tensor("op_2931_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(875826560)))]; + tensor input_217_cast = mul(x = var_2929_cast, y = var_2931_to_fp16)[name = tensor("input_217_cast")]; + tensor var_2939 = const()[name = tensor("op_2939"), val = tensor([1, 1])]; + tensor var_2941 = const()[name = tensor("op_2941"), val = tensor([1, 1])]; + tensor var_2943_pad_type_0 = const()[name = tensor("op_2943_pad_type_0"), val = tensor("custom")]; + tensor var_2943_pad_0 = const()[name = tensor("op_2943_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(875829696)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(913578496)))]; + tensor var_2943_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2941, groups = var_1906, pad = var_2943_pad_0, pad_type = var_2943_pad_type_0, strides = var_2939, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_217_cast)[name = tensor("op_2943_cast")]; + tensor var_2944_split_sizes_0 = const()[name = tensor("op_2944_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_2944_axis_0 = const()[name = tensor("op_2944_axis_0"), val = tensor(1)]; + tensor var_2944_cast_0, tensor var_2944_cast_1 = split(axis = var_2944_axis_0, split_sizes = var_2944_split_sizes_0, x = var_2943_cast)[name = tensor("op_2944_cast")]; + tensor var_2946_mode_0 = const()[name = tensor("op_2946_mode_0"), val = tensor("EXACT")]; + tensor var_2946_cast = gelu(mode = var_2946_mode_0, x = var_2944_cast_1)[name = tensor("op_2946_cast")]; + tensor input_219_cast = mul(x = var_2944_cast_0, y = var_2946_cast)[name = tensor("input_219_cast")]; + tensor var_2950 = const()[name = tensor("op_2950"), val = tensor([1, 1])]; + tensor var_2952 = const()[name = tensor("op_2952"), val = tensor([1, 1])]; + tensor var_2954_pad_type_0 = const()[name = tensor("op_2954_pad_type_0"), val = tensor("custom")]; + tensor var_2954_pad_0 = const()[name = tensor("op_2954_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(913603136)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(932477568)))]; + tensor var_2954_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2952, groups = var_1906, pad = var_2954_pad_0, pad_type = var_2954_pad_type_0, strides = var_2950, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_219_cast)[name = tensor("op_2954_cast")]; + tensor inputs_79_cast = add(x = var_2954_cast, y = inputs_77_cast)[name = tensor("inputs_79_cast")]; + tensor var_2964 = const()[name = tensor("op_2964"), val = tensor([1])]; + tensor channels_mean_79_cast = reduce_mean(axes = var_2964, keep_dims = var_1901, x = inputs_79_cast)[name = tensor("channels_mean_79_cast")]; + tensor zero_mean_79_cast = sub(x = inputs_79_cast, y = channels_mean_79_cast)[name = tensor("zero_mean_79_cast")]; + tensor zero_mean_sq_79_cast = mul(x = zero_mean_79_cast, y = zero_mean_79_cast)[name = tensor("zero_mean_sq_79_cast")]; + tensor var_2968 = const()[name = tensor("op_2968"), val = tensor([1])]; + tensor var_2969_cast = reduce_mean(axes = var_2968, keep_dims = var_1901, x = zero_mean_sq_79_cast)[name = tensor("op_2969_cast")]; + tensor var_2970_to_fp16 = const()[name = tensor("op_2970_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2971_cast = add(x = var_2969_cast, y = var_2970_to_fp16)[name = tensor("op_2971_cast")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_2971_cast)[name = tensor("denom_79_cast")]; + tensor out_79_cast = mul(x = zero_mean_79_cast, y = denom_79_cast)[name = tensor("out_79_cast")]; + tensor var_2975_to_fp16 = const()[name = tensor("op_2975_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(932480704)))]; + tensor var_2976_cast = add(x = out_79_cast, y = var_2975_to_fp16)[name = tensor("op_2976_cast")]; + tensor var_2978_to_fp16 = const()[name = tensor("op_2978_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(932483840)))]; + tensor hidden_states_131_cast = mul(x = var_2976_cast, y = var_2978_to_fp16)[name = tensor("hidden_states_131_cast")]; + tensor var_2985 = const()[name = tensor("op_2985"), val = tensor([1, 1])]; + tensor var_2987 = const()[name = tensor("op_2987"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(932486976)))]; + tensor q_53_cast = conv(dilations = var_2987, groups = var_1906, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_2985, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_131_cast)[name = tensor("q_53_cast")]; + tensor var_2991 = const()[name = tensor("op_2991"), val = tensor([1, 1])]; + tensor var_2993 = const()[name = tensor("op_2993"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(937205632)))]; + tensor k_53_cast = conv(dilations = var_2993, groups = var_1906, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_2991, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_131_cast)[name = tensor("k_53_cast")]; + tensor var_2997 = const()[name = tensor("op_2997"), val = tensor([1, 1])]; + tensor var_2999 = const()[name = tensor("op_2999"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(941924288)))]; + tensor v_53_cast = conv(dilations = var_2999, groups = var_1906, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_2997, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_131_cast)[name = tensor("v_53_cast")]; + tensor var_3003 = const()[name = tensor("op_3003"), val = tensor([2, 24, 64, -1])]; + tensor var_3004_cast = reshape(shape = var_3003, x = q_53_cast)[name = tensor("op_3004_cast")]; + tensor var_3005 = const()[name = tensor("op_3005"), val = tensor([2, 24, 64, -1])]; + tensor var_3006_cast = reshape(shape = var_3005, x = k_53_cast)[name = tensor("op_3006_cast")]; + tensor var_3007 = const()[name = tensor("op_3007"), val = tensor([2, 24, 64, -1])]; + tensor var_3008_cast = reshape(shape = var_3007, x = v_53_cast)[name = tensor("op_3008_cast")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3004_cast, y = var_3006_cast)[name = tensor("attn_weights_105_cast")]; + tensor attn_weights_107_cast = mul(x = attn_weights_105_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_107_cast")]; + tensor var_3012_cast = softmax(axis = var_1890, x = attn_weights_107_cast)[name = tensor("op_3012_cast")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_3008_cast, y = var_3012_cast)[name = tensor("attn_53_cast")]; + tensor var_3016 = const()[name = tensor("op_3016"), val = tensor([2, 1536, 1, -1])]; + tensor input_221_cast = reshape(shape = var_3016, x = attn_53_cast)[name = tensor("input_221_cast")]; + tensor var_3021 = const()[name = tensor("op_3021"), val = tensor([1, 1])]; + tensor var_3023 = const()[name = tensor("op_3023"), val = tensor([1, 1])]; + tensor var_3025_pad_type_0 = const()[name = tensor("op_3025_pad_type_0"), val = tensor("custom")]; + tensor var_3025_pad_0 = const()[name = tensor("op_3025_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(946642944)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(951361600)))]; + tensor var_3025_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_3023, groups = var_1906, pad = var_3025_pad_0, pad_type = var_3025_pad_type_0, strides = var_3021, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_221_cast)[name = tensor("op_3025_cast")]; + tensor inputs_81_cast = add(x = var_3025_cast, y = inputs_79_cast)[name = tensor("inputs_81_cast")]; + tensor var_3029 = const()[name = tensor("op_3029"), val = tensor([1])]; + tensor channels_mean_81_cast = reduce_mean(axes = var_3029, keep_dims = var_1901, x = inputs_81_cast)[name = tensor("channels_mean_81_cast")]; + tensor zero_mean_81_cast = sub(x = inputs_81_cast, y = channels_mean_81_cast)[name = tensor("zero_mean_81_cast")]; + tensor zero_mean_sq_81_cast = mul(x = zero_mean_81_cast, y = zero_mean_81_cast)[name = tensor("zero_mean_sq_81_cast")]; + tensor var_3033 = const()[name = tensor("op_3033"), val = tensor([1])]; + tensor var_3034_cast = reduce_mean(axes = var_3033, keep_dims = var_1901, x = zero_mean_sq_81_cast)[name = tensor("op_3034_cast")]; + tensor var_3035_to_fp16 = const()[name = tensor("op_3035_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3036_cast = add(x = var_3034_cast, y = var_3035_to_fp16)[name = tensor("op_3036_cast")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_3036_cast)[name = tensor("denom_81_cast")]; + tensor out_81_cast = mul(x = zero_mean_81_cast, y = denom_81_cast)[name = tensor("out_81_cast")]; + tensor var_3040_to_fp16 = const()[name = tensor("op_3040_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(951364736)))]; + tensor var_3041_cast = add(x = out_81_cast, y = var_3040_to_fp16)[name = tensor("op_3041_cast")]; + tensor var_3043_to_fp16 = const()[name = tensor("op_3043_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(951367872)))]; + tensor hidden_states_133_cast = mul(x = var_3041_cast, y = var_3043_to_fp16)[name = tensor("hidden_states_133_cast")]; + tensor var_3050 = const()[name = tensor("op_3050"), val = tensor([1, 1])]; + tensor var_3052 = const()[name = tensor("op_3052"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(951371008)))]; + tensor q_55_cast = conv(dilations = var_3052, groups = var_1906, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_3050, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_133_cast)[name = tensor("q_55_cast")]; + tensor var_3056 = const()[name = tensor("op_3056"), val = tensor([1, 1])]; + tensor var_3058 = const()[name = tensor("op_3058"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956089664)))]; + tensor k_55_cast = conv(dilations = var_3058, groups = var_1906, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_3056, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_55_cast")]; + tensor var_3062 = const()[name = tensor("op_3062"), val = tensor([1, 1])]; + tensor var_3064 = const()[name = tensor("op_3064"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(960021888)))]; + tensor v_55_cast = conv(dilations = var_3064, groups = var_1906, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_3062, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_55_cast")]; + tensor var_3068 = const()[name = tensor("op_3068"), val = tensor([2, 24, 64, -1])]; + tensor var_3069_cast = reshape(shape = var_3068, x = q_55_cast)[name = tensor("op_3069_cast")]; + tensor var_3070 = const()[name = tensor("op_3070"), val = tensor([2, 24, 64, -1])]; + tensor var_3071_cast = reshape(shape = var_3070, x = k_55_cast)[name = tensor("op_3071_cast")]; + tensor var_3072 = const()[name = tensor("op_3072"), val = tensor([2, 24, 64, -1])]; + tensor var_3073_cast = reshape(shape = var_3072, x = v_55_cast)[name = tensor("op_3073_cast")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_3069_cast, y = var_3071_cast)[name = tensor("attn_weights_109_cast")]; + tensor attn_weights_111_cast = mul(x = attn_weights_109_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_111_cast")]; + tensor var_3077_cast = softmax(axis = var_1890, x = attn_weights_111_cast)[name = tensor("op_3077_cast")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_3073_cast, y = var_3077_cast)[name = tensor("attn_55_cast")]; + tensor var_3081 = const()[name = tensor("op_3081"), val = tensor([2, 1536, 1, -1])]; + tensor input_223_cast = reshape(shape = var_3081, x = attn_55_cast)[name = tensor("input_223_cast")]; + tensor var_3086 = const()[name = tensor("op_3086"), val = tensor([1, 1])]; + tensor var_3088 = const()[name = tensor("op_3088"), val = tensor([1, 1])]; + tensor var_3090_pad_type_0 = const()[name = tensor("op_3090_pad_type_0"), val = tensor("custom")]; + tensor var_3090_pad_0 = const()[name = tensor("op_3090_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(963954112)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(968672768)))]; + tensor var_3090_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_3088, groups = var_1906, pad = var_3090_pad_0, pad_type = var_3090_pad_type_0, strides = var_3086, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_223_cast)[name = tensor("op_3090_cast")]; + tensor inputs_83_cast = add(x = var_3090_cast, y = inputs_81_cast)[name = tensor("inputs_83_cast")]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1])]; + tensor channels_mean_83_cast = reduce_mean(axes = var_3094, keep_dims = var_1901, x = inputs_83_cast)[name = tensor("channels_mean_83_cast")]; + tensor zero_mean_83_cast = sub(x = inputs_83_cast, y = channels_mean_83_cast)[name = tensor("zero_mean_83_cast")]; + tensor zero_mean_sq_83_cast = mul(x = zero_mean_83_cast, y = zero_mean_83_cast)[name = tensor("zero_mean_sq_83_cast")]; + tensor var_3098 = const()[name = tensor("op_3098"), val = tensor([1])]; + tensor var_3099_cast = reduce_mean(axes = var_3098, keep_dims = var_1901, x = zero_mean_sq_83_cast)[name = tensor("op_3099_cast")]; + tensor var_3100_to_fp16 = const()[name = tensor("op_3100_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3101_cast = add(x = var_3099_cast, y = var_3100_to_fp16)[name = tensor("op_3101_cast")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_3101_cast)[name = tensor("denom_83_cast")]; + tensor out_83_cast = mul(x = zero_mean_83_cast, y = denom_83_cast)[name = tensor("out_83_cast")]; + tensor var_3105_to_fp16 = const()[name = tensor("op_3105_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(968675904)))]; + tensor var_3106_cast = add(x = out_83_cast, y = var_3105_to_fp16)[name = tensor("op_3106_cast")]; + tensor var_3108_to_fp16 = const()[name = tensor("op_3108_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(968679040)))]; + tensor input_225_cast = mul(x = var_3106_cast, y = var_3108_to_fp16)[name = tensor("input_225_cast")]; + tensor var_3116 = const()[name = tensor("op_3116"), val = tensor([1, 1])]; + tensor var_3118 = const()[name = tensor("op_3118"), val = tensor([1, 1])]; + tensor var_3120_pad_type_0 = const()[name = tensor("op_3120_pad_type_0"), val = tensor("custom")]; + tensor var_3120_pad_0 = const()[name = tensor("op_3120_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(968682176)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1006430976)))]; + tensor var_3120_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_3118, groups = var_1906, pad = var_3120_pad_0, pad_type = var_3120_pad_type_0, strides = var_3116, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_225_cast)[name = tensor("op_3120_cast")]; + tensor var_3121_split_sizes_0 = const()[name = tensor("op_3121_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_3121_axis_0 = const()[name = tensor("op_3121_axis_0"), val = tensor(1)]; + tensor var_3121_cast_0, tensor var_3121_cast_1 = split(axis = var_3121_axis_0, split_sizes = var_3121_split_sizes_0, x = var_3120_cast)[name = tensor("op_3121_cast")]; + tensor var_3123_mode_0 = const()[name = tensor("op_3123_mode_0"), val = tensor("EXACT")]; + tensor var_3123_cast = gelu(mode = var_3123_mode_0, x = var_3121_cast_1)[name = tensor("op_3123_cast")]; + tensor input_227_cast = mul(x = var_3121_cast_0, y = var_3123_cast)[name = tensor("input_227_cast")]; + tensor var_3127 = const()[name = tensor("op_3127"), val = tensor([1, 1])]; + tensor var_3129 = const()[name = tensor("op_3129"), val = tensor([1, 1])]; + tensor var_3131_pad_type_0 = const()[name = tensor("op_3131_pad_type_0"), val = tensor("custom")]; + tensor var_3131_pad_0 = const()[name = tensor("op_3131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1006455616)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025330048)))]; + tensor var_3131_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_3129, groups = var_1906, pad = var_3131_pad_0, pad_type = var_3131_pad_type_0, strides = var_3127, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_227_cast)[name = tensor("op_3131_cast")]; + tensor inputs_85_cast = add(x = var_3131_cast, y = inputs_83_cast)[name = tensor("inputs_85_cast")]; + tensor var_3141 = const()[name = tensor("op_3141"), val = tensor([1])]; + tensor channels_mean_85_cast = reduce_mean(axes = var_3141, keep_dims = var_1901, x = inputs_85_cast)[name = tensor("channels_mean_85_cast")]; + tensor zero_mean_85_cast = sub(x = inputs_85_cast, y = channels_mean_85_cast)[name = tensor("zero_mean_85_cast")]; + tensor zero_mean_sq_85_cast = mul(x = zero_mean_85_cast, y = zero_mean_85_cast)[name = tensor("zero_mean_sq_85_cast")]; + tensor var_3145 = const()[name = tensor("op_3145"), val = tensor([1])]; + tensor var_3146_cast = reduce_mean(axes = var_3145, keep_dims = var_1901, x = zero_mean_sq_85_cast)[name = tensor("op_3146_cast")]; + tensor var_3147_to_fp16 = const()[name = tensor("op_3147_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3148_cast = add(x = var_3146_cast, y = var_3147_to_fp16)[name = tensor("op_3148_cast")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_3148_cast)[name = tensor("denom_85_cast")]; + tensor out_85_cast = mul(x = zero_mean_85_cast, y = denom_85_cast)[name = tensor("out_85_cast")]; + tensor var_3152_to_fp16 = const()[name = tensor("op_3152_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025333184)))]; + tensor var_3153_cast = add(x = out_85_cast, y = var_3152_to_fp16)[name = tensor("op_3153_cast")]; + tensor var_3155_to_fp16 = const()[name = tensor("op_3155_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025336320)))]; + tensor hidden_states_137_cast = mul(x = var_3153_cast, y = var_3155_to_fp16)[name = tensor("hidden_states_137_cast")]; + tensor var_3162 = const()[name = tensor("op_3162"), val = tensor([1, 1])]; + tensor var_3164 = const()[name = tensor("op_3164"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025339456)))]; + tensor q_57_cast = conv(dilations = var_3164, groups = var_1906, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_3162, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("q_57_cast")]; + tensor var_3168 = const()[name = tensor("op_3168"), val = tensor([1, 1])]; + tensor var_3170 = const()[name = tensor("op_3170"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1030058112)))]; + tensor k_57_cast = conv(dilations = var_3170, groups = var_1906, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_3168, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("k_57_cast")]; + tensor var_3174 = const()[name = tensor("op_3174"), val = tensor([1, 1])]; + tensor var_3176 = const()[name = tensor("op_3176"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034776768)))]; + tensor v_57_cast = conv(dilations = var_3176, groups = var_1906, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_3174, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("v_57_cast")]; + tensor var_3180 = const()[name = tensor("op_3180"), val = tensor([2, 24, 64, -1])]; + tensor var_3181_cast = reshape(shape = var_3180, x = q_57_cast)[name = tensor("op_3181_cast")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([2, 24, 64, -1])]; + tensor var_3183_cast = reshape(shape = var_3182, x = k_57_cast)[name = tensor("op_3183_cast")]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([2, 24, 64, -1])]; + tensor var_3185_cast = reshape(shape = var_3184, x = v_57_cast)[name = tensor("op_3185_cast")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3181_cast, y = var_3183_cast)[name = tensor("attn_weights_113_cast")]; + tensor attn_weights_115_cast = mul(x = attn_weights_113_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_115_cast")]; + tensor var_3189_cast = softmax(axis = var_1890, x = attn_weights_115_cast)[name = tensor("op_3189_cast")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3185_cast, y = var_3189_cast)[name = tensor("attn_57_cast")]; + tensor var_3193 = const()[name = tensor("op_3193"), val = tensor([2, 1536, 1, -1])]; + tensor input_229_cast = reshape(shape = var_3193, x = attn_57_cast)[name = tensor("input_229_cast")]; + tensor var_3198 = const()[name = tensor("op_3198"), val = tensor([1, 1])]; + tensor var_3200 = const()[name = tensor("op_3200"), val = tensor([1, 1])]; + tensor var_3202_pad_type_0 = const()[name = tensor("op_3202_pad_type_0"), val = tensor("custom")]; + tensor var_3202_pad_0 = const()[name = tensor("op_3202_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1039495424)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044214080)))]; + tensor var_3202_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_3200, groups = var_1906, pad = var_3202_pad_0, pad_type = var_3202_pad_type_0, strides = var_3198, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_229_cast)[name = tensor("op_3202_cast")]; + tensor inputs_87_cast = add(x = var_3202_cast, y = inputs_85_cast)[name = tensor("inputs_87_cast")]; + tensor var_3206 = const()[name = tensor("op_3206"), val = tensor([1])]; + tensor channels_mean_87_cast = reduce_mean(axes = var_3206, keep_dims = var_1901, x = inputs_87_cast)[name = tensor("channels_mean_87_cast")]; + tensor zero_mean_87_cast = sub(x = inputs_87_cast, y = channels_mean_87_cast)[name = tensor("zero_mean_87_cast")]; + tensor zero_mean_sq_87_cast = mul(x = zero_mean_87_cast, y = zero_mean_87_cast)[name = tensor("zero_mean_sq_87_cast")]; + tensor var_3210 = const()[name = tensor("op_3210"), val = tensor([1])]; + tensor var_3211_cast = reduce_mean(axes = var_3210, keep_dims = var_1901, x = zero_mean_sq_87_cast)[name = tensor("op_3211_cast")]; + tensor var_3212_to_fp16 = const()[name = tensor("op_3212_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3213_cast = add(x = var_3211_cast, y = var_3212_to_fp16)[name = tensor("op_3213_cast")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_3213_cast)[name = tensor("denom_87_cast")]; + tensor out_87_cast = mul(x = zero_mean_87_cast, y = denom_87_cast)[name = tensor("out_87_cast")]; + tensor var_3217_to_fp16 = const()[name = tensor("op_3217_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044217216)))]; + tensor var_3218_cast = add(x = out_87_cast, y = var_3217_to_fp16)[name = tensor("op_3218_cast")]; + tensor var_3220_to_fp16 = const()[name = tensor("op_3220_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044220352)))]; + tensor hidden_states_139_cast = mul(x = var_3218_cast, y = var_3220_to_fp16)[name = tensor("hidden_states_139_cast")]; + tensor var_3227 = const()[name = tensor("op_3227"), val = tensor([1, 1])]; + tensor var_3229 = const()[name = tensor("op_3229"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044223488)))]; + tensor q_59_cast = conv(dilations = var_3229, groups = var_1906, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_3227, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_139_cast)[name = tensor("q_59_cast")]; + tensor var_3233 = const()[name = tensor("op_3233"), val = tensor([1, 1])]; + tensor var_3235 = const()[name = tensor("op_3235"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1048942144)))]; + tensor k_59_cast = conv(dilations = var_3235, groups = var_1906, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_3233, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_59_cast")]; + tensor var_3239 = const()[name = tensor("op_3239"), val = tensor([1, 1])]; + tensor var_3241 = const()[name = tensor("op_3241"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1052874368)))]; + tensor v_59_cast = conv(dilations = var_3241, groups = var_1906, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_3239, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_59_cast")]; + tensor var_3245 = const()[name = tensor("op_3245"), val = tensor([2, 24, 64, -1])]; + tensor var_3246_cast = reshape(shape = var_3245, x = q_59_cast)[name = tensor("op_3246_cast")]; + tensor var_3247 = const()[name = tensor("op_3247"), val = tensor([2, 24, 64, -1])]; + tensor var_3248_cast = reshape(shape = var_3247, x = k_59_cast)[name = tensor("op_3248_cast")]; + tensor var_3249 = const()[name = tensor("op_3249"), val = tensor([2, 24, 64, -1])]; + tensor var_3250_cast = reshape(shape = var_3249, x = v_59_cast)[name = tensor("op_3250_cast")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_3246_cast, y = var_3248_cast)[name = tensor("attn_weights_117_cast")]; + tensor attn_weights_119_cast = mul(x = attn_weights_117_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_119_cast")]; + tensor var_3254_cast = softmax(axis = var_1890, x = attn_weights_119_cast)[name = tensor("op_3254_cast")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3250_cast, y = var_3254_cast)[name = tensor("attn_59_cast")]; + tensor var_3258 = const()[name = tensor("op_3258"), val = tensor([2, 1536, 1, -1])]; + tensor input_231_cast = reshape(shape = var_3258, x = attn_59_cast)[name = tensor("input_231_cast")]; + tensor var_3263 = const()[name = tensor("op_3263"), val = tensor([1, 1])]; + tensor var_3265 = const()[name = tensor("op_3265"), val = tensor([1, 1])]; + tensor var_3267_pad_type_0 = const()[name = tensor("op_3267_pad_type_0"), val = tensor("custom")]; + tensor var_3267_pad_0 = const()[name = tensor("op_3267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1056806592)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061525248)))]; + tensor var_3267_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_3265, groups = var_1906, pad = var_3267_pad_0, pad_type = var_3267_pad_type_0, strides = var_3263, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_231_cast)[name = tensor("op_3267_cast")]; + tensor inputs_89_cast = add(x = var_3267_cast, y = inputs_87_cast)[name = tensor("inputs_89_cast")]; + tensor var_3271 = const()[name = tensor("op_3271"), val = tensor([1])]; + tensor channels_mean_89_cast = reduce_mean(axes = var_3271, keep_dims = var_1901, x = inputs_89_cast)[name = tensor("channels_mean_89_cast")]; + tensor zero_mean_89_cast = sub(x = inputs_89_cast, y = channels_mean_89_cast)[name = tensor("zero_mean_89_cast")]; + tensor zero_mean_sq_89_cast = mul(x = zero_mean_89_cast, y = zero_mean_89_cast)[name = tensor("zero_mean_sq_89_cast")]; + tensor var_3275 = const()[name = tensor("op_3275"), val = tensor([1])]; + tensor var_3276_cast = reduce_mean(axes = var_3275, keep_dims = var_1901, x = zero_mean_sq_89_cast)[name = tensor("op_3276_cast")]; + tensor var_3277_to_fp16 = const()[name = tensor("op_3277_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3278_cast = add(x = var_3276_cast, y = var_3277_to_fp16)[name = tensor("op_3278_cast")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_3278_cast)[name = tensor("denom_89_cast")]; + tensor out_89_cast = mul(x = zero_mean_89_cast, y = denom_89_cast)[name = tensor("out_89_cast")]; + tensor var_3282_to_fp16 = const()[name = tensor("op_3282_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061528384)))]; + tensor var_3283_cast = add(x = out_89_cast, y = var_3282_to_fp16)[name = tensor("op_3283_cast")]; + tensor var_3285_to_fp16 = const()[name = tensor("op_3285_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061531520)))]; + tensor input_233_cast = mul(x = var_3283_cast, y = var_3285_to_fp16)[name = tensor("input_233_cast")]; + tensor var_3293 = const()[name = tensor("op_3293"), val = tensor([1, 1])]; + tensor var_3295 = const()[name = tensor("op_3295"), val = tensor([1, 1])]; + tensor var_3297_pad_type_0 = const()[name = tensor("op_3297_pad_type_0"), val = tensor("custom")]; + tensor var_3297_pad_0 = const()[name = tensor("op_3297_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061534656)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1099283456)))]; + tensor var_3297_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_3295, groups = var_1906, pad = var_3297_pad_0, pad_type = var_3297_pad_type_0, strides = var_3293, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_233_cast)[name = tensor("op_3297_cast")]; + tensor var_3298_split_sizes_0 = const()[name = tensor("op_3298_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_3298_axis_0 = const()[name = tensor("op_3298_axis_0"), val = tensor(1)]; + tensor var_3298_cast_0, tensor var_3298_cast_1 = split(axis = var_3298_axis_0, split_sizes = var_3298_split_sizes_0, x = var_3297_cast)[name = tensor("op_3298_cast")]; + tensor var_3300_mode_0 = const()[name = tensor("op_3300_mode_0"), val = tensor("EXACT")]; + tensor var_3300_cast = gelu(mode = var_3300_mode_0, x = var_3298_cast_1)[name = tensor("op_3300_cast")]; + tensor input_235_cast = mul(x = var_3298_cast_0, y = var_3300_cast)[name = tensor("input_235_cast")]; + tensor var_3304 = const()[name = tensor("op_3304"), val = tensor([1, 1])]; + tensor var_3306 = const()[name = tensor("op_3306"), val = tensor([1, 1])]; + tensor var_3308_pad_type_0 = const()[name = tensor("op_3308_pad_type_0"), val = tensor("custom")]; + tensor var_3308_pad_0 = const()[name = tensor("op_3308_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1099308096)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1118182528)))]; + tensor var_3308_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_3306, groups = var_1906, pad = var_3308_pad_0, pad_type = var_3308_pad_type_0, strides = var_3304, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_235_cast)[name = tensor("op_3308_cast")]; + tensor inputs_91_cast = add(x = var_3308_cast, y = inputs_89_cast)[name = tensor("inputs_91_cast")]; + tensor var_3318 = const()[name = tensor("op_3318"), val = tensor([1])]; + tensor channels_mean_91_cast = reduce_mean(axes = var_3318, keep_dims = var_1901, x = inputs_91_cast)[name = tensor("channels_mean_91_cast")]; + tensor zero_mean_91_cast = sub(x = inputs_91_cast, y = channels_mean_91_cast)[name = tensor("zero_mean_91_cast")]; + tensor zero_mean_sq_91_cast = mul(x = zero_mean_91_cast, y = zero_mean_91_cast)[name = tensor("zero_mean_sq_91_cast")]; + tensor var_3322 = const()[name = tensor("op_3322"), val = tensor([1])]; + tensor var_3323_cast = reduce_mean(axes = var_3322, keep_dims = var_1901, x = zero_mean_sq_91_cast)[name = tensor("op_3323_cast")]; + tensor var_3324_to_fp16 = const()[name = tensor("op_3324_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3325_cast = add(x = var_3323_cast, y = var_3324_to_fp16)[name = tensor("op_3325_cast")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_3325_cast)[name = tensor("denom_91_cast")]; + tensor out_91_cast = mul(x = zero_mean_91_cast, y = denom_91_cast)[name = tensor("out_91_cast")]; + tensor var_3329_to_fp16 = const()[name = tensor("op_3329_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1118185664)))]; + tensor var_3330_cast = add(x = out_91_cast, y = var_3329_to_fp16)[name = tensor("op_3330_cast")]; + tensor var_3332_to_fp16 = const()[name = tensor("op_3332_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1118188800)))]; + tensor hidden_states_143_cast = mul(x = var_3330_cast, y = var_3332_to_fp16)[name = tensor("hidden_states_143_cast")]; + tensor var_3339 = const()[name = tensor("op_3339"), val = tensor([1, 1])]; + tensor var_3341 = const()[name = tensor("op_3341"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1118191936)))]; + tensor q_61_cast = conv(dilations = var_3341, groups = var_1906, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_3339, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("q_61_cast")]; + tensor var_3345 = const()[name = tensor("op_3345"), val = tensor([1, 1])]; + tensor var_3347 = const()[name = tensor("op_3347"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122910592)))]; + tensor k_61_cast = conv(dilations = var_3347, groups = var_1906, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_3345, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("k_61_cast")]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1, 1])]; + tensor var_3353 = const()[name = tensor("op_3353"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127629248)))]; + tensor v_61_cast = conv(dilations = var_3353, groups = var_1906, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_3351, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("v_61_cast")]; + tensor var_3357 = const()[name = tensor("op_3357"), val = tensor([2, 24, 64, -1])]; + tensor var_3358_cast = reshape(shape = var_3357, x = q_61_cast)[name = tensor("op_3358_cast")]; + tensor var_3359 = const()[name = tensor("op_3359"), val = tensor([2, 24, 64, -1])]; + tensor var_3360_cast = reshape(shape = var_3359, x = k_61_cast)[name = tensor("op_3360_cast")]; + tensor var_3361 = const()[name = tensor("op_3361"), val = tensor([2, 24, 64, -1])]; + tensor var_3362_cast = reshape(shape = var_3361, x = v_61_cast)[name = tensor("op_3362_cast")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3358_cast, y = var_3360_cast)[name = tensor("attn_weights_121_cast")]; + tensor attn_weights_123_cast = mul(x = attn_weights_121_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_123_cast")]; + tensor var_3366_cast = softmax(axis = var_1890, x = attn_weights_123_cast)[name = tensor("op_3366_cast")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3362_cast, y = var_3366_cast)[name = tensor("attn_61_cast")]; + tensor var_3370 = const()[name = tensor("op_3370"), val = tensor([2, 1536, 1, -1])]; + tensor input_237_cast = reshape(shape = var_3370, x = attn_61_cast)[name = tensor("input_237_cast")]; + tensor var_3375 = const()[name = tensor("op_3375"), val = tensor([1, 1])]; + tensor var_3377 = const()[name = tensor("op_3377"), val = tensor([1, 1])]; + tensor var_3379_pad_type_0 = const()[name = tensor("op_3379_pad_type_0"), val = tensor("custom")]; + tensor var_3379_pad_0 = const()[name = tensor("op_3379_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132347904)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137066560)))]; + tensor var_3379_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_3377, groups = var_1906, pad = var_3379_pad_0, pad_type = var_3379_pad_type_0, strides = var_3375, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_237_cast)[name = tensor("op_3379_cast")]; + tensor inputs_93_cast = add(x = var_3379_cast, y = inputs_91_cast)[name = tensor("inputs_93_cast")]; + tensor var_3383 = const()[name = tensor("op_3383"), val = tensor([1])]; + tensor channels_mean_93_cast = reduce_mean(axes = var_3383, keep_dims = var_1901, x = inputs_93_cast)[name = tensor("channels_mean_93_cast")]; + tensor zero_mean_93_cast = sub(x = inputs_93_cast, y = channels_mean_93_cast)[name = tensor("zero_mean_93_cast")]; + tensor zero_mean_sq_93_cast = mul(x = zero_mean_93_cast, y = zero_mean_93_cast)[name = tensor("zero_mean_sq_93_cast")]; + tensor var_3387 = const()[name = tensor("op_3387"), val = tensor([1])]; + tensor var_3388_cast = reduce_mean(axes = var_3387, keep_dims = var_1901, x = zero_mean_sq_93_cast)[name = tensor("op_3388_cast")]; + tensor var_3389_to_fp16 = const()[name = tensor("op_3389_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3390_cast = add(x = var_3388_cast, y = var_3389_to_fp16)[name = tensor("op_3390_cast")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_3390_cast)[name = tensor("denom_93_cast")]; + tensor out_93_cast = mul(x = zero_mean_93_cast, y = denom_93_cast)[name = tensor("out_93_cast")]; + tensor var_3394_to_fp16 = const()[name = tensor("op_3394_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137069696)))]; + tensor var_3395_cast = add(x = out_93_cast, y = var_3394_to_fp16)[name = tensor("op_3395_cast")]; + tensor var_3397_to_fp16 = const()[name = tensor("op_3397_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137072832)))]; + tensor hidden_states_145_cast = mul(x = var_3395_cast, y = var_3397_to_fp16)[name = tensor("hidden_states_145_cast")]; + tensor var_3404 = const()[name = tensor("op_3404"), val = tensor([1, 1])]; + tensor var_3406 = const()[name = tensor("op_3406"), val = tensor([1, 1])]; + tensor q_63_pad_type_0 = const()[name = tensor("q_63_pad_type_0"), val = tensor("custom")]; + tensor q_63_pad_0 = const()[name = tensor("q_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137075968)))]; + tensor q_63_cast = conv(dilations = var_3406, groups = var_1906, pad = q_63_pad_0, pad_type = q_63_pad_type_0, strides = var_3404, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_145_cast)[name = tensor("q_63_cast")]; + tensor var_3410 = const()[name = tensor("op_3410"), val = tensor([1, 1])]; + tensor var_3412 = const()[name = tensor("op_3412"), val = tensor([1, 1])]; + tensor k_63_pad_type_0 = const()[name = tensor("k_63_pad_type_0"), val = tensor("custom")]; + tensor k_63_pad_0 = const()[name = tensor("k_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1141794624)))]; + tensor k_63_cast = conv(dilations = var_3412, groups = var_1906, pad = k_63_pad_0, pad_type = k_63_pad_type_0, strides = var_3410, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_63_cast")]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor var_3418 = const()[name = tensor("op_3418"), val = tensor([1, 1])]; + tensor v_63_pad_type_0 = const()[name = tensor("v_63_pad_type_0"), val = tensor("custom")]; + tensor v_63_pad_0 = const()[name = tensor("v_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1145726848)))]; + tensor v_63_cast = conv(dilations = var_3418, groups = var_1906, pad = v_63_pad_0, pad_type = v_63_pad_type_0, strides = var_3416, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_63_cast")]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([2, 24, 64, -1])]; + tensor var_3423_cast = reshape(shape = var_3422, x = q_63_cast)[name = tensor("op_3423_cast")]; + tensor var_3424 = const()[name = tensor("op_3424"), val = tensor([2, 24, 64, -1])]; + tensor var_3425_cast = reshape(shape = var_3424, x = k_63_cast)[name = tensor("op_3425_cast")]; + tensor var_3426 = const()[name = tensor("op_3426"), val = tensor([2, 24, 64, -1])]; + tensor var_3427_cast = reshape(shape = var_3426, x = v_63_cast)[name = tensor("op_3427_cast")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_3423_cast, y = var_3425_cast)[name = tensor("attn_weights_125_cast")]; + tensor attn_weights_127_cast = mul(x = attn_weights_125_cast, y = var_1897_to_fp16)[name = tensor("attn_weights_127_cast")]; + tensor var_3431_cast = softmax(axis = var_1890, x = attn_weights_127_cast)[name = tensor("op_3431_cast")]; + tensor attn_63_transpose_x_0 = const()[name = tensor("attn_63_transpose_x_0"), val = tensor(false)]; + tensor attn_63_transpose_y_0 = const()[name = tensor("attn_63_transpose_y_0"), val = tensor(true)]; + tensor attn_63_cast = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3427_cast, y = var_3431_cast)[name = tensor("attn_63_cast")]; + tensor var_3435 = const()[name = tensor("op_3435"), val = tensor([2, 1536, 1, -1])]; + tensor input_239_cast = reshape(shape = var_3435, x = attn_63_cast)[name = tensor("input_239_cast")]; + tensor var_3440 = const()[name = tensor("op_3440"), val = tensor([1, 1])]; + tensor var_3442 = const()[name = tensor("op_3442"), val = tensor([1, 1])]; + tensor var_3444_pad_type_0 = const()[name = tensor("op_3444_pad_type_0"), val = tensor("custom")]; + tensor var_3444_pad_0 = const()[name = tensor("op_3444_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149659072)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154377728)))]; + tensor var_3444_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_3442, groups = var_1906, pad = var_3444_pad_0, pad_type = var_3444_pad_type_0, strides = var_3440, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_239_cast)[name = tensor("op_3444_cast")]; + tensor inputs_95_cast = add(x = var_3444_cast, y = inputs_93_cast)[name = tensor("inputs_95_cast")]; + tensor var_3448 = const()[name = tensor("op_3448"), val = tensor([1])]; + tensor channels_mean_95_cast = reduce_mean(axes = var_3448, keep_dims = var_1901, x = inputs_95_cast)[name = tensor("channels_mean_95_cast")]; + tensor zero_mean_95_cast = sub(x = inputs_95_cast, y = channels_mean_95_cast)[name = tensor("zero_mean_95_cast")]; + tensor zero_mean_sq_95_cast = mul(x = zero_mean_95_cast, y = zero_mean_95_cast)[name = tensor("zero_mean_sq_95_cast")]; + tensor var_3452 = const()[name = tensor("op_3452"), val = tensor([1])]; + tensor var_3453_cast = reduce_mean(axes = var_3452, keep_dims = var_1901, x = zero_mean_sq_95_cast)[name = tensor("op_3453_cast")]; + tensor var_3454_to_fp16 = const()[name = tensor("op_3454_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3455_cast = add(x = var_3453_cast, y = var_3454_to_fp16)[name = tensor("op_3455_cast")]; + tensor denom_95_epsilon_0_to_fp16 = const()[name = tensor("denom_95_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_95_cast = rsqrt(epsilon = denom_95_epsilon_0_to_fp16, x = var_3455_cast)[name = tensor("denom_95_cast")]; + tensor out_95_cast = mul(x = zero_mean_95_cast, y = denom_95_cast)[name = tensor("out_95_cast")]; + tensor var_3459_to_fp16 = const()[name = tensor("op_3459_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154380864)))]; + tensor var_3460_cast = add(x = out_95_cast, y = var_3459_to_fp16)[name = tensor("op_3460_cast")]; + tensor var_3462_to_fp16 = const()[name = tensor("op_3462_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154384000)))]; + tensor input_241_cast = mul(x = var_3460_cast, y = var_3462_to_fp16)[name = tensor("input_241_cast")]; + tensor var_3470 = const()[name = tensor("op_3470"), val = tensor([1, 1])]; + tensor var_3472 = const()[name = tensor("op_3472"), val = tensor([1, 1])]; + tensor var_3474_pad_type_0 = const()[name = tensor("op_3474_pad_type_0"), val = tensor("custom")]; + tensor var_3474_pad_0 = const()[name = tensor("op_3474_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154387136)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1192135936)))]; + tensor var_3474_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_3472, groups = var_1906, pad = var_3474_pad_0, pad_type = var_3474_pad_type_0, strides = var_3470, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_241_cast)[name = tensor("op_3474_cast")]; + tensor var_3475_split_sizes_0 = const()[name = tensor("op_3475_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_3475_axis_0 = const()[name = tensor("op_3475_axis_0"), val = tensor(1)]; + tensor var_3475_cast_0, tensor var_3475_cast_1 = split(axis = var_3475_axis_0, split_sizes = var_3475_split_sizes_0, x = var_3474_cast)[name = tensor("op_3475_cast")]; + tensor var_3477_mode_0 = const()[name = tensor("op_3477_mode_0"), val = tensor("EXACT")]; + tensor var_3477_cast = gelu(mode = var_3477_mode_0, x = var_3475_cast_1)[name = tensor("op_3477_cast")]; + tensor input_243_cast = mul(x = var_3475_cast_0, y = var_3477_cast)[name = tensor("input_243_cast")]; + tensor var_3481 = const()[name = tensor("op_3481"), val = tensor([1, 1])]; + tensor var_3483 = const()[name = tensor("op_3483"), val = tensor([1, 1])]; + tensor var_3485_pad_type_0 = const()[name = tensor("op_3485_pad_type_0"), val = tensor("custom")]; + tensor var_3485_pad_0 = const()[name = tensor("op_3485_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1192160576)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211035008)))]; + tensor var_3485_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_3483, groups = var_1906, pad = var_3485_pad_0, pad_type = var_3485_pad_type_0, strides = var_3481, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_243_cast)[name = tensor("op_3485_cast")]; + tensor hidden_states_149_cast = add(x = var_3485_cast, y = inputs_95_cast)[name = tensor("hidden_states_149_cast")]; + tensor var_3487 = const()[name = tensor("op_3487"), val = tensor([2, 1536, 32, 32])]; + tensor input_245_cast = reshape(shape = var_3487, x = hidden_states_149_cast)[name = tensor("input_245_cast")]; + tensor var_3491 = const()[name = tensor("op_3491"), val = tensor([1, 1])]; + tensor var_3493 = const()[name = tensor("op_3493"), val = tensor([1, 1])]; + tensor hidden_states_151_pad_type_0 = const()[name = tensor("hidden_states_151_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_151_pad_0 = const()[name = tensor("hidden_states_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211038144)))]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1215756800)))]; + tensor hidden_states_151_cast = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3493, groups = var_1906, pad = hidden_states_151_pad_0, pad_type = hidden_states_151_pad_type_0, strides = var_3491, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_245_cast)[name = tensor("hidden_states_151_cast")]; + tensor input_247_cast = add(x = hidden_states_151_cast, y = hidden_states_121_cast)[name = tensor("input_247_cast")]; + tensor var_3500 = const()[name = tensor("op_3500"), val = tensor([2, 2])]; + tensor var_3502 = const()[name = tensor("op_3502"), val = tensor([1, 1])]; + tensor input_249_pad_type_0 = const()[name = tensor("input_249_pad_type_0"), val = tensor("custom")]; + tensor input_249_pad_0 = const()[name = tensor("input_249_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1215759936)))]; + tensor down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1258227328)))]; + tensor input_249_cast = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_3502, groups = var_1906, pad = input_249_pad_0, pad_type = input_249_pad_type_0, strides = var_3500, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = input_247_cast)[name = tensor("input_249_cast")]; + tensor var_3514 = const()[name = tensor("op_3514"), val = tensor(1)]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_249_cast)[name = tensor("reshape_64_cast")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor("reduce_mean_50_cast")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1258230464)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1258233600)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_65_cast)[name = tensor("add_33_cast")]; + tensor input_253_cast = silu(x = add_33_cast)[name = tensor("input_253_cast")]; + tensor var_3530 = const()[name = tensor("op_3530"), val = tensor([1, 1])]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([1, 1])]; + tensor hidden_states_153_pad_type_0 = const()[name = tensor("hidden_states_153_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_153_pad_0 = const()[name = tensor("hidden_states_153_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1258236736)))]; + tensor down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1300704128)))]; + tensor hidden_states_153_cast = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3532, groups = var_3514, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_3530, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_253_cast)[name = tensor("hidden_states_153_cast")]; + tensor var_3538 = const()[name = tensor("op_3538"), val = tensor([1, 1])]; + tensor var_3540 = const()[name = tensor("op_3540"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1300707264)))]; + tensor down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305425920)))]; + tensor temb_13_cast = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3540, groups = var_3514, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_3538, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_13_cast")]; + tensor input_257_cast = add(x = hidden_states_153_cast, y = temb_13_cast)[name = tensor("input_257_cast")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_257_cast)[name = tensor("reshape_68_cast")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor("reduce_mean_53_cast")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305429056)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305432192)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_69_cast)[name = tensor("add_35_cast")]; + tensor input_261_cast = silu(x = add_35_cast)[name = tensor("input_261_cast")]; + tensor var_3550 = const()[name = tensor("op_3550"), val = tensor([1, 1])]; + tensor var_3552 = const()[name = tensor("op_3552"), val = tensor([1, 1])]; + tensor hidden_states_155_pad_type_0 = const()[name = tensor("hidden_states_155_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_155_pad_0 = const()[name = tensor("hidden_states_155_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305435328)))]; + tensor down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1347902720)))]; + tensor hidden_states_155_cast = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3552, groups = var_3514, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_3550, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_261_cast)[name = tensor("hidden_states_155_cast")]; + tensor input_263_cast = add(x = input_249_cast, y = hidden_states_155_cast)[name = tensor("input_263_cast")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = input_263_cast)[name = tensor("reshape_72_cast")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor("reduce_mean_56_cast")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1347905856)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1347908992)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_73_cast)[name = tensor("add_37_cast")]; + tensor input_267_cast = silu(x = add_37_cast)[name = tensor("input_267_cast")]; + tensor var_3567 = const()[name = tensor("op_3567"), val = tensor([1, 1])]; + tensor var_3569 = const()[name = tensor("op_3569"), val = tensor([1, 1])]; + tensor hidden_states_157_pad_type_0 = const()[name = tensor("hidden_states_157_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_157_pad_0 = const()[name = tensor("hidden_states_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1347912128)))]; + tensor down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1390379520)))]; + tensor hidden_states_157_cast = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_3569, groups = var_3514, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_3567, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_267_cast)[name = tensor("hidden_states_157_cast")]; + tensor var_3575 = const()[name = tensor("op_3575"), val = tensor([1, 1])]; + tensor var_3577 = const()[name = tensor("op_3577"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1390382656)))]; + tensor down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395101312)))]; + tensor temb_15_cast = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3577, groups = var_3514, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_3575, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_15_cast")]; + tensor input_271_cast = add(x = hidden_states_157_cast, y = temb_15_cast)[name = tensor("input_271_cast")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_271_cast)[name = tensor("reshape_76_cast")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor("reduce_mean_59_cast")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395104448)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395107584)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_77_cast)[name = tensor("add_39_cast")]; + tensor input_275_cast = silu(x = add_39_cast)[name = tensor("input_275_cast")]; + tensor var_3587 = const()[name = tensor("op_3587"), val = tensor([1, 1])]; + tensor var_3589 = const()[name = tensor("op_3589"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_type_0 = const()[name = tensor("hidden_states_159_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_159_pad_0 = const()[name = tensor("hidden_states_159_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395110720)))]; + tensor down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1437578112)))]; + tensor hidden_states_159_cast = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_3589, groups = var_3514, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_3587, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_275_cast)[name = tensor("hidden_states_159_cast")]; + tensor input_277_cast = add(x = input_263_cast, y = hidden_states_159_cast)[name = tensor("input_277_cast")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor(3)]; + tensor var_3608 = const()[name = tensor("op_3608"), val = tensor(true)]; + tensor var_3613 = const()[name = tensor("op_3613"), val = tensor(1)]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_277_cast)[name = tensor("reshape_80_cast")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor("reduce_mean_62_cast")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1437581248)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1437584384)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_81_cast)[name = tensor("add_41_cast")]; + tensor input_281_cast = silu(x = add_41_cast)[name = tensor("input_281_cast")]; + tensor var_3631 = const()[name = tensor("op_3631"), val = tensor([1, 1])]; + tensor var_3633 = const()[name = tensor("op_3633"), val = tensor([1, 1])]; + tensor hidden_states_161_pad_type_0 = const()[name = tensor("hidden_states_161_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_161_pad_0 = const()[name = tensor("hidden_states_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1437587520)))]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480054912)))]; + tensor hidden_states_161_cast = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_3633, groups = var_3613, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_3631, weight = mid_block_resnets_0_conv1_weight_to_fp16, x = input_281_cast)[name = tensor("hidden_states_161_cast")]; + tensor var_3639 = const()[name = tensor("op_3639"), val = tensor([1, 1])]; + tensor var_3641 = const()[name = tensor("op_3641"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480058048)))]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1484776704)))]; + tensor temb_17_cast = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3641, groups = var_3613, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_3639, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_17_cast")]; + tensor input_285_cast = add(x = hidden_states_161_cast, y = temb_17_cast)[name = tensor("input_285_cast")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_285_cast)[name = tensor("reshape_84_cast")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor("reduce_mean_65_cast")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1484779840)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1484782976)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_85_cast)[name = tensor("add_43_cast")]; + tensor input_289_cast = silu(x = add_43_cast)[name = tensor("input_289_cast")]; + tensor var_3651 = const()[name = tensor("op_3651"), val = tensor([1, 1])]; + tensor var_3653 = const()[name = tensor("op_3653"), val = tensor([1, 1])]; + tensor hidden_states_163_pad_type_0 = const()[name = tensor("hidden_states_163_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_163_pad_0 = const()[name = tensor("hidden_states_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1484786112)))]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527253504)))]; + tensor hidden_states_163_cast = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_3653, groups = var_3613, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = var_3651, weight = mid_block_resnets_0_conv2_weight_to_fp16, x = input_289_cast)[name = tensor("hidden_states_163_cast")]; + tensor hidden_states_165_cast = add(x = input_277_cast, y = hidden_states_163_cast)[name = tensor("hidden_states_165_cast")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_88_cast = reshape(shape = reshape_88_shape_0, x = hidden_states_165_cast)[name = tensor("reshape_88_cast")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast)[name = tensor("reduce_mean_66_cast")]; + tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor("sub_44_cast")]; + tensor square_22_cast = square(x = sub_44_cast)[name = tensor("square_22_cast")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast)[name = tensor("reduce_mean_68_cast")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_44_cast = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast")]; + tensor sqrt_22_cast = sqrt(x = add_44_cast)[name = tensor("sqrt_22_cast")]; + tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast)[name = tensor("real_div_22_cast")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast)[name = tensor("reshape_89_cast")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527256640)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527259776)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_89_cast)[name = tensor("add_45_cast")]; + tensor var_3679 = const()[name = tensor("op_3679"), val = tensor([1, 1])]; + tensor var_3681 = const()[name = tensor("op_3681"), val = tensor([1, 1])]; + tensor hidden_states_167_pad_type_0 = const()[name = tensor("hidden_states_167_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_167_pad_0 = const()[name = tensor("hidden_states_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527262912)))]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531981568)))]; + tensor hidden_states_167_cast = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_3681, groups = var_3613, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = var_3679, weight = mid_block_attentions_0_proj_in_weight_to_fp16, x = add_45_cast)[name = tensor("hidden_states_167_cast")]; + tensor var_3686 = const()[name = tensor("op_3686"), val = tensor([2, 1536, 1, 256])]; + tensor inputs_97_cast = reshape(shape = var_3686, x = hidden_states_167_cast)[name = tensor("inputs_97_cast")]; + tensor var_3696 = const()[name = tensor("op_3696"), val = tensor([1])]; + tensor channels_mean_97_cast = reduce_mean(axes = var_3696, keep_dims = var_3608, x = inputs_97_cast)[name = tensor("channels_mean_97_cast")]; + tensor zero_mean_97_cast = sub(x = inputs_97_cast, y = channels_mean_97_cast)[name = tensor("zero_mean_97_cast")]; + tensor zero_mean_sq_97_cast = mul(x = zero_mean_97_cast, y = zero_mean_97_cast)[name = tensor("zero_mean_sq_97_cast")]; + tensor var_3700 = const()[name = tensor("op_3700"), val = tensor([1])]; + tensor var_3701_cast = reduce_mean(axes = var_3700, keep_dims = var_3608, x = zero_mean_sq_97_cast)[name = tensor("op_3701_cast")]; + tensor var_3702_to_fp16 = const()[name = tensor("op_3702_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3703_cast = add(x = var_3701_cast, y = var_3702_to_fp16)[name = tensor("op_3703_cast")]; + tensor denom_97_epsilon_0_to_fp16 = const()[name = tensor("denom_97_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_97_cast = rsqrt(epsilon = denom_97_epsilon_0_to_fp16, x = var_3703_cast)[name = tensor("denom_97_cast")]; + tensor out_97_cast = mul(x = zero_mean_97_cast, y = denom_97_cast)[name = tensor("out_97_cast")]; + tensor var_3707_to_fp16 = const()[name = tensor("op_3707_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531984704)))]; + tensor var_3708_cast = add(x = out_97_cast, y = var_3707_to_fp16)[name = tensor("op_3708_cast")]; + tensor var_3710_to_fp16 = const()[name = tensor("op_3710_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531987840)))]; + tensor hidden_states_169_cast = mul(x = var_3708_cast, y = var_3710_to_fp16)[name = tensor("hidden_states_169_cast")]; + tensor var_3717 = const()[name = tensor("op_3717"), val = tensor([1, 1])]; + tensor var_3719 = const()[name = tensor("op_3719"), val = tensor([1, 1])]; + tensor q_65_pad_type_0 = const()[name = tensor("q_65_pad_type_0"), val = tensor("custom")]; + tensor q_65_pad_0 = const()[name = tensor("q_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531990976)))]; + tensor q_65_cast = conv(dilations = var_3719, groups = var_3613, pad = q_65_pad_0, pad_type = q_65_pad_type_0, strides = var_3717, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("q_65_cast")]; + tensor var_3723 = const()[name = tensor("op_3723"), val = tensor([1, 1])]; + tensor var_3725 = const()[name = tensor("op_3725"), val = tensor([1, 1])]; + tensor k_65_pad_type_0 = const()[name = tensor("k_65_pad_type_0"), val = tensor("custom")]; + tensor k_65_pad_0 = const()[name = tensor("k_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1536709632)))]; + tensor k_65_cast = conv(dilations = var_3725, groups = var_3613, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_3723, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("k_65_cast")]; + tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([1, 1])]; + tensor var_3731 = const()[name = tensor("op_3731"), val = tensor([1, 1])]; + tensor v_65_pad_type_0 = const()[name = tensor("v_65_pad_type_0"), val = tensor("custom")]; + tensor v_65_pad_0 = const()[name = tensor("v_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1541428288)))]; + tensor v_65_cast = conv(dilations = var_3731, groups = var_3613, pad = v_65_pad_0, pad_type = v_65_pad_type_0, strides = var_3729, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("v_65_cast")]; + tensor var_3735 = const()[name = tensor("op_3735"), val = tensor([2, 24, 64, -1])]; + tensor var_3736_cast = reshape(shape = var_3735, x = q_65_cast)[name = tensor("op_3736_cast")]; + tensor var_3737 = const()[name = tensor("op_3737"), val = tensor([2, 24, 64, -1])]; + tensor var_3738_cast = reshape(shape = var_3737, x = k_65_cast)[name = tensor("op_3738_cast")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([2, 24, 64, -1])]; + tensor var_3740_cast = reshape(shape = var_3739, x = v_65_cast)[name = tensor("op_3740_cast")]; + tensor attn_weights_129_transpose_x_0 = const()[name = tensor("attn_weights_129_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_129_transpose_y_0 = const()[name = tensor("attn_weights_129_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_129_cast = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3736_cast, y = var_3738_cast)[name = tensor("attn_weights_129_cast")]; + tensor var_3604_to_fp16 = const()[name = tensor("op_3604_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_131_cast = mul(x = attn_weights_129_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_131_cast")]; + tensor var_3744_cast = softmax(axis = var_3597, x = attn_weights_131_cast)[name = tensor("op_3744_cast")]; + tensor attn_65_transpose_x_0 = const()[name = tensor("attn_65_transpose_x_0"), val = tensor(false)]; + tensor attn_65_transpose_y_0 = const()[name = tensor("attn_65_transpose_y_0"), val = tensor(true)]; + tensor attn_65_cast = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3740_cast, y = var_3744_cast)[name = tensor("attn_65_cast")]; + tensor var_3748 = const()[name = tensor("op_3748"), val = tensor([2, 1536, 1, -1])]; + tensor input_293_cast = reshape(shape = var_3748, x = attn_65_cast)[name = tensor("input_293_cast")]; + tensor var_3753 = const()[name = tensor("op_3753"), val = tensor([1, 1])]; + tensor var_3755 = const()[name = tensor("op_3755"), val = tensor([1, 1])]; + tensor var_3757_pad_type_0 = const()[name = tensor("op_3757_pad_type_0"), val = tensor("custom")]; + tensor var_3757_pad_0 = const()[name = tensor("op_3757_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1546146944)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550865600)))]; + tensor var_3757_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3755, groups = var_3613, pad = var_3757_pad_0, pad_type = var_3757_pad_type_0, strides = var_3753, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_293_cast)[name = tensor("op_3757_cast")]; + tensor inputs_99_cast = add(x = var_3757_cast, y = inputs_97_cast)[name = tensor("inputs_99_cast")]; + tensor var_3761 = const()[name = tensor("op_3761"), val = tensor([1])]; + tensor channels_mean_99_cast = reduce_mean(axes = var_3761, keep_dims = var_3608, x = inputs_99_cast)[name = tensor("channels_mean_99_cast")]; + tensor zero_mean_99_cast = sub(x = inputs_99_cast, y = channels_mean_99_cast)[name = tensor("zero_mean_99_cast")]; + tensor zero_mean_sq_99_cast = mul(x = zero_mean_99_cast, y = zero_mean_99_cast)[name = tensor("zero_mean_sq_99_cast")]; + tensor var_3765 = const()[name = tensor("op_3765"), val = tensor([1])]; + tensor var_3766_cast = reduce_mean(axes = var_3765, keep_dims = var_3608, x = zero_mean_sq_99_cast)[name = tensor("op_3766_cast")]; + tensor var_3767_to_fp16 = const()[name = tensor("op_3767_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3768_cast = add(x = var_3766_cast, y = var_3767_to_fp16)[name = tensor("op_3768_cast")]; + tensor denom_99_epsilon_0_to_fp16 = const()[name = tensor("denom_99_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_99_cast = rsqrt(epsilon = denom_99_epsilon_0_to_fp16, x = var_3768_cast)[name = tensor("denom_99_cast")]; + tensor out_99_cast = mul(x = zero_mean_99_cast, y = denom_99_cast)[name = tensor("out_99_cast")]; + tensor var_3772_to_fp16 = const()[name = tensor("op_3772_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550868736)))]; + tensor var_3773_cast = add(x = out_99_cast, y = var_3772_to_fp16)[name = tensor("op_3773_cast")]; + tensor var_3775_to_fp16 = const()[name = tensor("op_3775_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550871872)))]; + tensor hidden_states_171_cast = mul(x = var_3773_cast, y = var_3775_to_fp16)[name = tensor("hidden_states_171_cast")]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([1, 1])]; + tensor var_3784 = const()[name = tensor("op_3784"), val = tensor([1, 1])]; + tensor q_67_pad_type_0 = const()[name = tensor("q_67_pad_type_0"), val = tensor("custom")]; + tensor q_67_pad_0 = const()[name = tensor("q_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550875008)))]; + tensor q_67_cast = conv(dilations = var_3784, groups = var_3613, pad = q_67_pad_0, pad_type = q_67_pad_type_0, strides = var_3782, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_171_cast)[name = tensor("q_67_cast")]; + tensor var_3788 = const()[name = tensor("op_3788"), val = tensor([1, 1])]; + tensor var_3790 = const()[name = tensor("op_3790"), val = tensor([1, 1])]; + tensor k_67_pad_type_0 = const()[name = tensor("k_67_pad_type_0"), val = tensor("custom")]; + tensor k_67_pad_0 = const()[name = tensor("k_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1555593664)))]; + tensor k_67_cast = conv(dilations = var_3790, groups = var_3613, pad = k_67_pad_0, pad_type = k_67_pad_type_0, strides = var_3788, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_67_cast")]; + tensor var_3794 = const()[name = tensor("op_3794"), val = tensor([1, 1])]; + tensor var_3796 = const()[name = tensor("op_3796"), val = tensor([1, 1])]; + tensor v_67_pad_type_0 = const()[name = tensor("v_67_pad_type_0"), val = tensor("custom")]; + tensor v_67_pad_0 = const()[name = tensor("v_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1559525888)))]; + tensor v_67_cast = conv(dilations = var_3796, groups = var_3613, pad = v_67_pad_0, pad_type = v_67_pad_type_0, strides = var_3794, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_67_cast")]; + tensor var_3800 = const()[name = tensor("op_3800"), val = tensor([2, 24, 64, -1])]; + tensor var_3801_cast = reshape(shape = var_3800, x = q_67_cast)[name = tensor("op_3801_cast")]; + tensor var_3802 = const()[name = tensor("op_3802"), val = tensor([2, 24, 64, -1])]; + tensor var_3803_cast = reshape(shape = var_3802, x = k_67_cast)[name = tensor("op_3803_cast")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([2, 24, 64, -1])]; + tensor var_3805_cast = reshape(shape = var_3804, x = v_67_cast)[name = tensor("op_3805_cast")]; + tensor attn_weights_133_transpose_x_0 = const()[name = tensor("attn_weights_133_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_133_transpose_y_0 = const()[name = tensor("attn_weights_133_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = var_3801_cast, y = var_3803_cast)[name = tensor("attn_weights_133_cast")]; + tensor attn_weights_135_cast = mul(x = attn_weights_133_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_135_cast")]; + tensor var_3809_cast = softmax(axis = var_3597, x = attn_weights_135_cast)[name = tensor("op_3809_cast")]; + tensor attn_67_transpose_x_0 = const()[name = tensor("attn_67_transpose_x_0"), val = tensor(false)]; + tensor attn_67_transpose_y_0 = const()[name = tensor("attn_67_transpose_y_0"), val = tensor(true)]; + tensor attn_67_cast = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3805_cast, y = var_3809_cast)[name = tensor("attn_67_cast")]; + tensor var_3813 = const()[name = tensor("op_3813"), val = tensor([2, 1536, 1, -1])]; + tensor input_295_cast = reshape(shape = var_3813, x = attn_67_cast)[name = tensor("input_295_cast")]; + tensor var_3818 = const()[name = tensor("op_3818"), val = tensor([1, 1])]; + tensor var_3820 = const()[name = tensor("op_3820"), val = tensor([1, 1])]; + tensor var_3822_pad_type_0 = const()[name = tensor("op_3822_pad_type_0"), val = tensor("custom")]; + tensor var_3822_pad_0 = const()[name = tensor("op_3822_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1563458112)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1568176768)))]; + tensor var_3822_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3820, groups = var_3613, pad = var_3822_pad_0, pad_type = var_3822_pad_type_0, strides = var_3818, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_295_cast)[name = tensor("op_3822_cast")]; + tensor inputs_101_cast = add(x = var_3822_cast, y = inputs_99_cast)[name = tensor("inputs_101_cast")]; + tensor var_3826 = const()[name = tensor("op_3826"), val = tensor([1])]; + tensor channels_mean_101_cast = reduce_mean(axes = var_3826, keep_dims = var_3608, x = inputs_101_cast)[name = tensor("channels_mean_101_cast")]; + tensor zero_mean_101_cast = sub(x = inputs_101_cast, y = channels_mean_101_cast)[name = tensor("zero_mean_101_cast")]; + tensor zero_mean_sq_101_cast = mul(x = zero_mean_101_cast, y = zero_mean_101_cast)[name = tensor("zero_mean_sq_101_cast")]; + tensor var_3830 = const()[name = tensor("op_3830"), val = tensor([1])]; + tensor var_3831_cast = reduce_mean(axes = var_3830, keep_dims = var_3608, x = zero_mean_sq_101_cast)[name = tensor("op_3831_cast")]; + tensor var_3832_to_fp16 = const()[name = tensor("op_3832_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3833_cast = add(x = var_3831_cast, y = var_3832_to_fp16)[name = tensor("op_3833_cast")]; + tensor denom_101_epsilon_0_to_fp16 = const()[name = tensor("denom_101_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_101_cast = rsqrt(epsilon = denom_101_epsilon_0_to_fp16, x = var_3833_cast)[name = tensor("denom_101_cast")]; + tensor out_101_cast = mul(x = zero_mean_101_cast, y = denom_101_cast)[name = tensor("out_101_cast")]; + tensor var_3837_to_fp16 = const()[name = tensor("op_3837_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1568179904)))]; + tensor var_3838_cast = add(x = out_101_cast, y = var_3837_to_fp16)[name = tensor("op_3838_cast")]; + tensor var_3840_to_fp16 = const()[name = tensor("op_3840_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1568183040)))]; + tensor input_297_cast = mul(x = var_3838_cast, y = var_3840_to_fp16)[name = tensor("input_297_cast")]; + tensor var_3848 = const()[name = tensor("op_3848"), val = tensor([1, 1])]; + tensor var_3850 = const()[name = tensor("op_3850"), val = tensor([1, 1])]; + tensor var_3852_pad_type_0 = const()[name = tensor("op_3852_pad_type_0"), val = tensor("custom")]; + tensor var_3852_pad_0 = const()[name = tensor("op_3852_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1568186176)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1605934976)))]; + tensor var_3852_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3850, groups = var_3613, pad = var_3852_pad_0, pad_type = var_3852_pad_type_0, strides = var_3848, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_297_cast)[name = tensor("op_3852_cast")]; + tensor var_3853_split_sizes_0 = const()[name = tensor("op_3853_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_3853_axis_0 = const()[name = tensor("op_3853_axis_0"), val = tensor(1)]; + tensor var_3853_cast_0, tensor var_3853_cast_1 = split(axis = var_3853_axis_0, split_sizes = var_3853_split_sizes_0, x = var_3852_cast)[name = tensor("op_3853_cast")]; + tensor var_3855_mode_0 = const()[name = tensor("op_3855_mode_0"), val = tensor("EXACT")]; + tensor var_3855_cast = gelu(mode = var_3855_mode_0, x = var_3853_cast_1)[name = tensor("op_3855_cast")]; + tensor input_299_cast = mul(x = var_3853_cast_0, y = var_3855_cast)[name = tensor("input_299_cast")]; + tensor var_3859 = const()[name = tensor("op_3859"), val = tensor([1, 1])]; + tensor var_3861 = const()[name = tensor("op_3861"), val = tensor([1, 1])]; + tensor var_3863_pad_type_0 = const()[name = tensor("op_3863_pad_type_0"), val = tensor("custom")]; + tensor var_3863_pad_0 = const()[name = tensor("op_3863_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1605959616)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1624834048)))]; + tensor var_3863_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3861, groups = var_3613, pad = var_3863_pad_0, pad_type = var_3863_pad_type_0, strides = var_3859, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_299_cast)[name = tensor("op_3863_cast")]; + tensor inputs_103_cast = add(x = var_3863_cast, y = inputs_101_cast)[name = tensor("inputs_103_cast")]; + tensor var_3873 = const()[name = tensor("op_3873"), val = tensor([1])]; + tensor channels_mean_103_cast = reduce_mean(axes = var_3873, keep_dims = var_3608, x = inputs_103_cast)[name = tensor("channels_mean_103_cast")]; + tensor zero_mean_103_cast = sub(x = inputs_103_cast, y = channels_mean_103_cast)[name = tensor("zero_mean_103_cast")]; + tensor zero_mean_sq_103_cast = mul(x = zero_mean_103_cast, y = zero_mean_103_cast)[name = tensor("zero_mean_sq_103_cast")]; + tensor var_3877 = const()[name = tensor("op_3877"), val = tensor([1])]; + tensor var_3878_cast = reduce_mean(axes = var_3877, keep_dims = var_3608, x = zero_mean_sq_103_cast)[name = tensor("op_3878_cast")]; + tensor var_3879_to_fp16 = const()[name = tensor("op_3879_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3880_cast = add(x = var_3878_cast, y = var_3879_to_fp16)[name = tensor("op_3880_cast")]; + tensor denom_103_epsilon_0_to_fp16 = const()[name = tensor("denom_103_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_103_cast = rsqrt(epsilon = denom_103_epsilon_0_to_fp16, x = var_3880_cast)[name = tensor("denom_103_cast")]; + tensor out_103_cast = mul(x = zero_mean_103_cast, y = denom_103_cast)[name = tensor("out_103_cast")]; + tensor var_3884_to_fp16 = const()[name = tensor("op_3884_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1624837184)))]; + tensor var_3885_cast = add(x = out_103_cast, y = var_3884_to_fp16)[name = tensor("op_3885_cast")]; + tensor var_3887_to_fp16 = const()[name = tensor("op_3887_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1624840320)))]; + tensor hidden_states_175_cast = mul(x = var_3885_cast, y = var_3887_to_fp16)[name = tensor("hidden_states_175_cast")]; + tensor var_3894 = const()[name = tensor("op_3894"), val = tensor([1, 1])]; + tensor var_3896 = const()[name = tensor("op_3896"), val = tensor([1, 1])]; + tensor q_69_pad_type_0 = const()[name = tensor("q_69_pad_type_0"), val = tensor("custom")]; + tensor q_69_pad_0 = const()[name = tensor("q_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1624843456)))]; + tensor q_69_cast = conv(dilations = var_3896, groups = var_3613, pad = q_69_pad_0, pad_type = q_69_pad_type_0, strides = var_3894, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("q_69_cast")]; + tensor var_3900 = const()[name = tensor("op_3900"), val = tensor([1, 1])]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor k_69_pad_type_0 = const()[name = tensor("k_69_pad_type_0"), val = tensor("custom")]; + tensor k_69_pad_0 = const()[name = tensor("k_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629562112)))]; + tensor k_69_cast = conv(dilations = var_3902, groups = var_3613, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_3900, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("k_69_cast")]; + tensor var_3906 = const()[name = tensor("op_3906"), val = tensor([1, 1])]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([1, 1])]; + tensor v_69_pad_type_0 = const()[name = tensor("v_69_pad_type_0"), val = tensor("custom")]; + tensor v_69_pad_0 = const()[name = tensor("v_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1634280768)))]; + tensor v_69_cast = conv(dilations = var_3908, groups = var_3613, pad = v_69_pad_0, pad_type = v_69_pad_type_0, strides = var_3906, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("v_69_cast")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([2, 24, 64, -1])]; + tensor var_3913_cast = reshape(shape = var_3912, x = q_69_cast)[name = tensor("op_3913_cast")]; + tensor var_3914 = const()[name = tensor("op_3914"), val = tensor([2, 24, 64, -1])]; + tensor var_3915_cast = reshape(shape = var_3914, x = k_69_cast)[name = tensor("op_3915_cast")]; + tensor var_3916 = const()[name = tensor("op_3916"), val = tensor([2, 24, 64, -1])]; + tensor var_3917_cast = reshape(shape = var_3916, x = v_69_cast)[name = tensor("op_3917_cast")]; + tensor attn_weights_137_transpose_x_0 = const()[name = tensor("attn_weights_137_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_137_transpose_y_0 = const()[name = tensor("attn_weights_137_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_137_cast = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3913_cast, y = var_3915_cast)[name = tensor("attn_weights_137_cast")]; + tensor attn_weights_139_cast = mul(x = attn_weights_137_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_139_cast")]; + tensor var_3921_cast = softmax(axis = var_3597, x = attn_weights_139_cast)[name = tensor("op_3921_cast")]; + tensor attn_69_transpose_x_0 = const()[name = tensor("attn_69_transpose_x_0"), val = tensor(false)]; + tensor attn_69_transpose_y_0 = const()[name = tensor("attn_69_transpose_y_0"), val = tensor(true)]; + tensor attn_69_cast = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3917_cast, y = var_3921_cast)[name = tensor("attn_69_cast")]; + tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([2, 1536, 1, -1])]; + tensor input_301_cast = reshape(shape = var_3925, x = attn_69_cast)[name = tensor("input_301_cast")]; + tensor var_3930 = const()[name = tensor("op_3930"), val = tensor([1, 1])]; + tensor var_3932 = const()[name = tensor("op_3932"), val = tensor([1, 1])]; + tensor var_3934_pad_type_0 = const()[name = tensor("op_3934_pad_type_0"), val = tensor("custom")]; + tensor var_3934_pad_0 = const()[name = tensor("op_3934_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638999424)))]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643718080)))]; + tensor var_3934_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_3932, groups = var_3613, pad = var_3934_pad_0, pad_type = var_3934_pad_type_0, strides = var_3930, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_301_cast)[name = tensor("op_3934_cast")]; + tensor inputs_105_cast = add(x = var_3934_cast, y = inputs_103_cast)[name = tensor("inputs_105_cast")]; + tensor var_3938 = const()[name = tensor("op_3938"), val = tensor([1])]; + tensor channels_mean_105_cast = reduce_mean(axes = var_3938, keep_dims = var_3608, x = inputs_105_cast)[name = tensor("channels_mean_105_cast")]; + tensor zero_mean_105_cast = sub(x = inputs_105_cast, y = channels_mean_105_cast)[name = tensor("zero_mean_105_cast")]; + tensor zero_mean_sq_105_cast = mul(x = zero_mean_105_cast, y = zero_mean_105_cast)[name = tensor("zero_mean_sq_105_cast")]; + tensor var_3942 = const()[name = tensor("op_3942"), val = tensor([1])]; + tensor var_3943_cast = reduce_mean(axes = var_3942, keep_dims = var_3608, x = zero_mean_sq_105_cast)[name = tensor("op_3943_cast")]; + tensor var_3944_to_fp16 = const()[name = tensor("op_3944_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3945_cast = add(x = var_3943_cast, y = var_3944_to_fp16)[name = tensor("op_3945_cast")]; + tensor denom_105_epsilon_0_to_fp16 = const()[name = tensor("denom_105_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_105_cast = rsqrt(epsilon = denom_105_epsilon_0_to_fp16, x = var_3945_cast)[name = tensor("denom_105_cast")]; + tensor out_105_cast = mul(x = zero_mean_105_cast, y = denom_105_cast)[name = tensor("out_105_cast")]; + tensor var_3949_to_fp16 = const()[name = tensor("op_3949_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643721216)))]; + tensor var_3950_cast = add(x = out_105_cast, y = var_3949_to_fp16)[name = tensor("op_3950_cast")]; + tensor var_3952_to_fp16 = const()[name = tensor("op_3952_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643724352)))]; + tensor hidden_states_177_cast = mul(x = var_3950_cast, y = var_3952_to_fp16)[name = tensor("hidden_states_177_cast")]; + tensor var_3959 = const()[name = tensor("op_3959"), val = tensor([1, 1])]; + tensor var_3961 = const()[name = tensor("op_3961"), val = tensor([1, 1])]; + tensor q_71_pad_type_0 = const()[name = tensor("q_71_pad_type_0"), val = tensor("custom")]; + tensor q_71_pad_0 = const()[name = tensor("q_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643727488)))]; + tensor q_71_cast = conv(dilations = var_3961, groups = var_3613, pad = q_71_pad_0, pad_type = q_71_pad_type_0, strides = var_3959, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_177_cast)[name = tensor("q_71_cast")]; + tensor var_3965 = const()[name = tensor("op_3965"), val = tensor([1, 1])]; + tensor var_3967 = const()[name = tensor("op_3967"), val = tensor([1, 1])]; + tensor k_71_pad_type_0 = const()[name = tensor("k_71_pad_type_0"), val = tensor("custom")]; + tensor k_71_pad_0 = const()[name = tensor("k_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648446144)))]; + tensor k_71_cast = conv(dilations = var_3967, groups = var_3613, pad = k_71_pad_0, pad_type = k_71_pad_type_0, strides = var_3965, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_71_cast")]; + tensor var_3971 = const()[name = tensor("op_3971"), val = tensor([1, 1])]; + tensor var_3973 = const()[name = tensor("op_3973"), val = tensor([1, 1])]; + tensor v_71_pad_type_0 = const()[name = tensor("v_71_pad_type_0"), val = tensor("custom")]; + tensor v_71_pad_0 = const()[name = tensor("v_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1652378368)))]; + tensor v_71_cast = conv(dilations = var_3973, groups = var_3613, pad = v_71_pad_0, pad_type = v_71_pad_type_0, strides = var_3971, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_71_cast")]; + tensor var_3977 = const()[name = tensor("op_3977"), val = tensor([2, 24, 64, -1])]; + tensor var_3978_cast = reshape(shape = var_3977, x = q_71_cast)[name = tensor("op_3978_cast")]; + tensor var_3979 = const()[name = tensor("op_3979"), val = tensor([2, 24, 64, -1])]; + tensor var_3980_cast = reshape(shape = var_3979, x = k_71_cast)[name = tensor("op_3980_cast")]; + tensor var_3981 = const()[name = tensor("op_3981"), val = tensor([2, 24, 64, -1])]; + tensor var_3982_cast = reshape(shape = var_3981, x = v_71_cast)[name = tensor("op_3982_cast")]; + tensor attn_weights_141_transpose_x_0 = const()[name = tensor("attn_weights_141_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_141_transpose_y_0 = const()[name = tensor("attn_weights_141_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_141_cast = matmul(transpose_x = attn_weights_141_transpose_x_0, transpose_y = attn_weights_141_transpose_y_0, x = var_3978_cast, y = var_3980_cast)[name = tensor("attn_weights_141_cast")]; + tensor attn_weights_143_cast = mul(x = attn_weights_141_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_143_cast")]; + tensor var_3986_cast = softmax(axis = var_3597, x = attn_weights_143_cast)[name = tensor("op_3986_cast")]; + tensor attn_71_transpose_x_0 = const()[name = tensor("attn_71_transpose_x_0"), val = tensor(false)]; + tensor attn_71_transpose_y_0 = const()[name = tensor("attn_71_transpose_y_0"), val = tensor(true)]; + tensor attn_71_cast = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3982_cast, y = var_3986_cast)[name = tensor("attn_71_cast")]; + tensor var_3990 = const()[name = tensor("op_3990"), val = tensor([2, 1536, 1, -1])]; + tensor input_303_cast = reshape(shape = var_3990, x = attn_71_cast)[name = tensor("input_303_cast")]; + tensor var_3995 = const()[name = tensor("op_3995"), val = tensor([1, 1])]; + tensor var_3997 = const()[name = tensor("op_3997"), val = tensor([1, 1])]; + tensor var_3999_pad_type_0 = const()[name = tensor("op_3999_pad_type_0"), val = tensor("custom")]; + tensor var_3999_pad_0 = const()[name = tensor("op_3999_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1656310592)))]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1661029248)))]; + tensor var_3999_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_3997, groups = var_3613, pad = var_3999_pad_0, pad_type = var_3999_pad_type_0, strides = var_3995, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_303_cast)[name = tensor("op_3999_cast")]; + tensor inputs_107_cast = add(x = var_3999_cast, y = inputs_105_cast)[name = tensor("inputs_107_cast")]; + tensor var_4003 = const()[name = tensor("op_4003"), val = tensor([1])]; + tensor channels_mean_107_cast = reduce_mean(axes = var_4003, keep_dims = var_3608, x = inputs_107_cast)[name = tensor("channels_mean_107_cast")]; + tensor zero_mean_107_cast = sub(x = inputs_107_cast, y = channels_mean_107_cast)[name = tensor("zero_mean_107_cast")]; + tensor zero_mean_sq_107_cast = mul(x = zero_mean_107_cast, y = zero_mean_107_cast)[name = tensor("zero_mean_sq_107_cast")]; + tensor var_4007 = const()[name = tensor("op_4007"), val = tensor([1])]; + tensor var_4008_cast = reduce_mean(axes = var_4007, keep_dims = var_3608, x = zero_mean_sq_107_cast)[name = tensor("op_4008_cast")]; + tensor var_4009_to_fp16 = const()[name = tensor("op_4009_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4010_cast = add(x = var_4008_cast, y = var_4009_to_fp16)[name = tensor("op_4010_cast")]; + tensor denom_107_epsilon_0_to_fp16 = const()[name = tensor("denom_107_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_107_cast = rsqrt(epsilon = denom_107_epsilon_0_to_fp16, x = var_4010_cast)[name = tensor("denom_107_cast")]; + tensor out_107_cast = mul(x = zero_mean_107_cast, y = denom_107_cast)[name = tensor("out_107_cast")]; + tensor var_4014_to_fp16 = const()[name = tensor("op_4014_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1661032384)))]; + tensor var_4015_cast = add(x = out_107_cast, y = var_4014_to_fp16)[name = tensor("op_4015_cast")]; + tensor var_4017_to_fp16 = const()[name = tensor("op_4017_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1661035520)))]; + tensor input_305_cast = mul(x = var_4015_cast, y = var_4017_to_fp16)[name = tensor("input_305_cast")]; + tensor var_4025 = const()[name = tensor("op_4025"), val = tensor([1, 1])]; + tensor var_4027 = const()[name = tensor("op_4027"), val = tensor([1, 1])]; + tensor var_4029_pad_type_0 = const()[name = tensor("op_4029_pad_type_0"), val = tensor("custom")]; + tensor var_4029_pad_0 = const()[name = tensor("op_4029_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1661038656)))]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698787456)))]; + tensor var_4029_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_4027, groups = var_3613, pad = var_4029_pad_0, pad_type = var_4029_pad_type_0, strides = var_4025, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_305_cast)[name = tensor("op_4029_cast")]; + tensor var_4030_split_sizes_0 = const()[name = tensor("op_4030_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_4030_axis_0 = const()[name = tensor("op_4030_axis_0"), val = tensor(1)]; + tensor var_4030_cast_0, tensor var_4030_cast_1 = split(axis = var_4030_axis_0, split_sizes = var_4030_split_sizes_0, x = var_4029_cast)[name = tensor("op_4030_cast")]; + tensor var_4032_mode_0 = const()[name = tensor("op_4032_mode_0"), val = tensor("EXACT")]; + tensor var_4032_cast = gelu(mode = var_4032_mode_0, x = var_4030_cast_1)[name = tensor("op_4032_cast")]; + tensor input_307_cast = mul(x = var_4030_cast_0, y = var_4032_cast)[name = tensor("input_307_cast")]; + tensor var_4036 = const()[name = tensor("op_4036"), val = tensor([1, 1])]; + tensor var_4038 = const()[name = tensor("op_4038"), val = tensor([1, 1])]; + tensor var_4040_pad_type_0 = const()[name = tensor("op_4040_pad_type_0"), val = tensor("custom")]; + tensor var_4040_pad_0 = const()[name = tensor("op_4040_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698812096)))]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717686528)))]; + tensor var_4040_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_4038, groups = var_3613, pad = var_4040_pad_0, pad_type = var_4040_pad_type_0, strides = var_4036, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_307_cast)[name = tensor("op_4040_cast")]; + tensor inputs_109_cast = add(x = var_4040_cast, y = inputs_107_cast)[name = tensor("inputs_109_cast")]; + tensor var_4050 = const()[name = tensor("op_4050"), val = tensor([1])]; + tensor channels_mean_109_cast = reduce_mean(axes = var_4050, keep_dims = var_3608, x = inputs_109_cast)[name = tensor("channels_mean_109_cast")]; + tensor zero_mean_109_cast = sub(x = inputs_109_cast, y = channels_mean_109_cast)[name = tensor("zero_mean_109_cast")]; + tensor zero_mean_sq_109_cast = mul(x = zero_mean_109_cast, y = zero_mean_109_cast)[name = tensor("zero_mean_sq_109_cast")]; + tensor var_4054 = const()[name = tensor("op_4054"), val = tensor([1])]; + tensor var_4055_cast = reduce_mean(axes = var_4054, keep_dims = var_3608, x = zero_mean_sq_109_cast)[name = tensor("op_4055_cast")]; + tensor var_4056_to_fp16 = const()[name = tensor("op_4056_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4057_cast = add(x = var_4055_cast, y = var_4056_to_fp16)[name = tensor("op_4057_cast")]; + tensor denom_109_epsilon_0_to_fp16 = const()[name = tensor("denom_109_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_109_cast = rsqrt(epsilon = denom_109_epsilon_0_to_fp16, x = var_4057_cast)[name = tensor("denom_109_cast")]; + tensor out_109_cast = mul(x = zero_mean_109_cast, y = denom_109_cast)[name = tensor("out_109_cast")]; + tensor var_4061_to_fp16 = const()[name = tensor("op_4061_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717689664)))]; + tensor var_4062_cast = add(x = out_109_cast, y = var_4061_to_fp16)[name = tensor("op_4062_cast")]; + tensor var_4064_to_fp16 = const()[name = tensor("op_4064_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717692800)))]; + tensor hidden_states_181_cast = mul(x = var_4062_cast, y = var_4064_to_fp16)[name = tensor("hidden_states_181_cast")]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor q_73_pad_type_0 = const()[name = tensor("q_73_pad_type_0"), val = tensor("custom")]; + tensor q_73_pad_0 = const()[name = tensor("q_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717695936)))]; + tensor q_73_cast = conv(dilations = var_4073, groups = var_3613, pad = q_73_pad_0, pad_type = q_73_pad_type_0, strides = var_4071, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("q_73_cast")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([1, 1])]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([1, 1])]; + tensor k_73_pad_type_0 = const()[name = tensor("k_73_pad_type_0"), val = tensor("custom")]; + tensor k_73_pad_0 = const()[name = tensor("k_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722414592)))]; + tensor k_73_cast = conv(dilations = var_4079, groups = var_3613, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_4077, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("k_73_cast")]; + tensor var_4083 = const()[name = tensor("op_4083"), val = tensor([1, 1])]; + tensor var_4085 = const()[name = tensor("op_4085"), val = tensor([1, 1])]; + tensor v_73_pad_type_0 = const()[name = tensor("v_73_pad_type_0"), val = tensor("custom")]; + tensor v_73_pad_0 = const()[name = tensor("v_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727133248)))]; + tensor v_73_cast = conv(dilations = var_4085, groups = var_3613, pad = v_73_pad_0, pad_type = v_73_pad_type_0, strides = var_4083, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("v_73_cast")]; + tensor var_4089 = const()[name = tensor("op_4089"), val = tensor([2, 24, 64, -1])]; + tensor var_4090_cast = reshape(shape = var_4089, x = q_73_cast)[name = tensor("op_4090_cast")]; + tensor var_4091 = const()[name = tensor("op_4091"), val = tensor([2, 24, 64, -1])]; + tensor var_4092_cast = reshape(shape = var_4091, x = k_73_cast)[name = tensor("op_4092_cast")]; + tensor var_4093 = const()[name = tensor("op_4093"), val = tensor([2, 24, 64, -1])]; + tensor var_4094_cast = reshape(shape = var_4093, x = v_73_cast)[name = tensor("op_4094_cast")]; + tensor attn_weights_145_transpose_x_0 = const()[name = tensor("attn_weights_145_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_145_transpose_y_0 = const()[name = tensor("attn_weights_145_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_145_cast = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_4090_cast, y = var_4092_cast)[name = tensor("attn_weights_145_cast")]; + tensor attn_weights_147_cast = mul(x = attn_weights_145_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_147_cast")]; + tensor var_4098_cast = softmax(axis = var_3597, x = attn_weights_147_cast)[name = tensor("op_4098_cast")]; + tensor attn_73_transpose_x_0 = const()[name = tensor("attn_73_transpose_x_0"), val = tensor(false)]; + tensor attn_73_transpose_y_0 = const()[name = tensor("attn_73_transpose_y_0"), val = tensor(true)]; + tensor attn_73_cast = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_4094_cast, y = var_4098_cast)[name = tensor("attn_73_cast")]; + tensor var_4102 = const()[name = tensor("op_4102"), val = tensor([2, 1536, 1, -1])]; + tensor input_309_cast = reshape(shape = var_4102, x = attn_73_cast)[name = tensor("input_309_cast")]; + tensor var_4107 = const()[name = tensor("op_4107"), val = tensor([1, 1])]; + tensor var_4109 = const()[name = tensor("op_4109"), val = tensor([1, 1])]; + tensor var_4111_pad_type_0 = const()[name = tensor("op_4111_pad_type_0"), val = tensor("custom")]; + tensor var_4111_pad_0 = const()[name = tensor("op_4111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1731851904)))]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1736570560)))]; + tensor var_4111_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_4109, groups = var_3613, pad = var_4111_pad_0, pad_type = var_4111_pad_type_0, strides = var_4107, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_309_cast)[name = tensor("op_4111_cast")]; + tensor inputs_111_cast = add(x = var_4111_cast, y = inputs_109_cast)[name = tensor("inputs_111_cast")]; + tensor var_4115 = const()[name = tensor("op_4115"), val = tensor([1])]; + tensor channels_mean_111_cast = reduce_mean(axes = var_4115, keep_dims = var_3608, x = inputs_111_cast)[name = tensor("channels_mean_111_cast")]; + tensor zero_mean_111_cast = sub(x = inputs_111_cast, y = channels_mean_111_cast)[name = tensor("zero_mean_111_cast")]; + tensor zero_mean_sq_111_cast = mul(x = zero_mean_111_cast, y = zero_mean_111_cast)[name = tensor("zero_mean_sq_111_cast")]; + tensor var_4119 = const()[name = tensor("op_4119"), val = tensor([1])]; + tensor var_4120_cast = reduce_mean(axes = var_4119, keep_dims = var_3608, x = zero_mean_sq_111_cast)[name = tensor("op_4120_cast")]; + tensor var_4121_to_fp16 = const()[name = tensor("op_4121_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4122_cast = add(x = var_4120_cast, y = var_4121_to_fp16)[name = tensor("op_4122_cast")]; + tensor denom_111_epsilon_0_to_fp16 = const()[name = tensor("denom_111_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_111_cast = rsqrt(epsilon = denom_111_epsilon_0_to_fp16, x = var_4122_cast)[name = tensor("denom_111_cast")]; + tensor out_111_cast = mul(x = zero_mean_111_cast, y = denom_111_cast)[name = tensor("out_111_cast")]; + tensor var_4126_to_fp16 = const()[name = tensor("op_4126_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1736573696)))]; + tensor var_4127_cast = add(x = out_111_cast, y = var_4126_to_fp16)[name = tensor("op_4127_cast")]; + tensor var_4129_to_fp16 = const()[name = tensor("op_4129_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1736576832)))]; + tensor hidden_states_183_cast = mul(x = var_4127_cast, y = var_4129_to_fp16)[name = tensor("hidden_states_183_cast")]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor var_4138 = const()[name = tensor("op_4138"), val = tensor([1, 1])]; + tensor q_75_pad_type_0 = const()[name = tensor("q_75_pad_type_0"), val = tensor("custom")]; + tensor q_75_pad_0 = const()[name = tensor("q_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1736579968)))]; + tensor q_75_cast = conv(dilations = var_4138, groups = var_3613, pad = q_75_pad_0, pad_type = q_75_pad_type_0, strides = var_4136, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_183_cast)[name = tensor("q_75_cast")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([1, 1])]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([1, 1])]; + tensor k_75_pad_type_0 = const()[name = tensor("k_75_pad_type_0"), val = tensor("custom")]; + tensor k_75_pad_0 = const()[name = tensor("k_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1741298624)))]; + tensor k_75_cast = conv(dilations = var_4144, groups = var_3613, pad = k_75_pad_0, pad_type = k_75_pad_type_0, strides = var_4142, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_75_cast")]; + tensor var_4148 = const()[name = tensor("op_4148"), val = tensor([1, 1])]; + tensor var_4150 = const()[name = tensor("op_4150"), val = tensor([1, 1])]; + tensor v_75_pad_type_0 = const()[name = tensor("v_75_pad_type_0"), val = tensor("custom")]; + tensor v_75_pad_0 = const()[name = tensor("v_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1745230848)))]; + tensor v_75_cast = conv(dilations = var_4150, groups = var_3613, pad = v_75_pad_0, pad_type = v_75_pad_type_0, strides = var_4148, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_75_cast")]; + tensor var_4154 = const()[name = tensor("op_4154"), val = tensor([2, 24, 64, -1])]; + tensor var_4155_cast = reshape(shape = var_4154, x = q_75_cast)[name = tensor("op_4155_cast")]; + tensor var_4156 = const()[name = tensor("op_4156"), val = tensor([2, 24, 64, -1])]; + tensor var_4157_cast = reshape(shape = var_4156, x = k_75_cast)[name = tensor("op_4157_cast")]; + tensor var_4158 = const()[name = tensor("op_4158"), val = tensor([2, 24, 64, -1])]; + tensor var_4159_cast = reshape(shape = var_4158, x = v_75_cast)[name = tensor("op_4159_cast")]; + tensor attn_weights_149_transpose_x_0 = const()[name = tensor("attn_weights_149_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_149_transpose_y_0 = const()[name = tensor("attn_weights_149_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_149_cast = matmul(transpose_x = attn_weights_149_transpose_x_0, transpose_y = attn_weights_149_transpose_y_0, x = var_4155_cast, y = var_4157_cast)[name = tensor("attn_weights_149_cast")]; + tensor attn_weights_151_cast = mul(x = attn_weights_149_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_151_cast")]; + tensor var_4163_cast = softmax(axis = var_3597, x = attn_weights_151_cast)[name = tensor("op_4163_cast")]; + tensor attn_75_transpose_x_0 = const()[name = tensor("attn_75_transpose_x_0"), val = tensor(false)]; + tensor attn_75_transpose_y_0 = const()[name = tensor("attn_75_transpose_y_0"), val = tensor(true)]; + tensor attn_75_cast = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_4159_cast, y = var_4163_cast)[name = tensor("attn_75_cast")]; + tensor var_4167 = const()[name = tensor("op_4167"), val = tensor([2, 1536, 1, -1])]; + tensor input_311_cast = reshape(shape = var_4167, x = attn_75_cast)[name = tensor("input_311_cast")]; + tensor var_4172 = const()[name = tensor("op_4172"), val = tensor([1, 1])]; + tensor var_4174 = const()[name = tensor("op_4174"), val = tensor([1, 1])]; + tensor var_4176_pad_type_0 = const()[name = tensor("op_4176_pad_type_0"), val = tensor("custom")]; + tensor var_4176_pad_0 = const()[name = tensor("op_4176_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1749163072)))]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753881728)))]; + tensor var_4176_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_4174, groups = var_3613, pad = var_4176_pad_0, pad_type = var_4176_pad_type_0, strides = var_4172, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_311_cast)[name = tensor("op_4176_cast")]; + tensor inputs_113_cast = add(x = var_4176_cast, y = inputs_111_cast)[name = tensor("inputs_113_cast")]; + tensor var_4180 = const()[name = tensor("op_4180"), val = tensor([1])]; + tensor channels_mean_113_cast = reduce_mean(axes = var_4180, keep_dims = var_3608, x = inputs_113_cast)[name = tensor("channels_mean_113_cast")]; + tensor zero_mean_113_cast = sub(x = inputs_113_cast, y = channels_mean_113_cast)[name = tensor("zero_mean_113_cast")]; + tensor zero_mean_sq_113_cast = mul(x = zero_mean_113_cast, y = zero_mean_113_cast)[name = tensor("zero_mean_sq_113_cast")]; + tensor var_4184 = const()[name = tensor("op_4184"), val = tensor([1])]; + tensor var_4185_cast = reduce_mean(axes = var_4184, keep_dims = var_3608, x = zero_mean_sq_113_cast)[name = tensor("op_4185_cast")]; + tensor var_4186_to_fp16 = const()[name = tensor("op_4186_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4187_cast = add(x = var_4185_cast, y = var_4186_to_fp16)[name = tensor("op_4187_cast")]; + tensor denom_113_epsilon_0_to_fp16 = const()[name = tensor("denom_113_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_113_cast = rsqrt(epsilon = denom_113_epsilon_0_to_fp16, x = var_4187_cast)[name = tensor("denom_113_cast")]; + tensor out_113_cast = mul(x = zero_mean_113_cast, y = denom_113_cast)[name = tensor("out_113_cast")]; + tensor var_4191_to_fp16 = const()[name = tensor("op_4191_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753884864)))]; + tensor var_4192_cast = add(x = out_113_cast, y = var_4191_to_fp16)[name = tensor("op_4192_cast")]; + tensor var_4194_to_fp16 = const()[name = tensor("op_4194_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753888000)))]; + tensor input_313_cast = mul(x = var_4192_cast, y = var_4194_to_fp16)[name = tensor("input_313_cast")]; + tensor var_4202 = const()[name = tensor("op_4202"), val = tensor([1, 1])]; + tensor var_4204 = const()[name = tensor("op_4204"), val = tensor([1, 1])]; + tensor var_4206_pad_type_0 = const()[name = tensor("op_4206_pad_type_0"), val = tensor("custom")]; + tensor var_4206_pad_0 = const()[name = tensor("op_4206_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753891136)))]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1791639936)))]; + tensor var_4206_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_4204, groups = var_3613, pad = var_4206_pad_0, pad_type = var_4206_pad_type_0, strides = var_4202, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_313_cast)[name = tensor("op_4206_cast")]; + tensor var_4207_split_sizes_0 = const()[name = tensor("op_4207_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_4207_axis_0 = const()[name = tensor("op_4207_axis_0"), val = tensor(1)]; + tensor var_4207_cast_0, tensor var_4207_cast_1 = split(axis = var_4207_axis_0, split_sizes = var_4207_split_sizes_0, x = var_4206_cast)[name = tensor("op_4207_cast")]; + tensor var_4209_mode_0 = const()[name = tensor("op_4209_mode_0"), val = tensor("EXACT")]; + tensor var_4209_cast = gelu(mode = var_4209_mode_0, x = var_4207_cast_1)[name = tensor("op_4209_cast")]; + tensor input_315_cast = mul(x = var_4207_cast_0, y = var_4209_cast)[name = tensor("input_315_cast")]; + tensor var_4213 = const()[name = tensor("op_4213"), val = tensor([1, 1])]; + tensor var_4215 = const()[name = tensor("op_4215"), val = tensor([1, 1])]; + tensor var_4217_pad_type_0 = const()[name = tensor("op_4217_pad_type_0"), val = tensor("custom")]; + tensor var_4217_pad_0 = const()[name = tensor("op_4217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1791664576)))]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810539008)))]; + tensor var_4217_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_4215, groups = var_3613, pad = var_4217_pad_0, pad_type = var_4217_pad_type_0, strides = var_4213, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_315_cast)[name = tensor("op_4217_cast")]; + tensor inputs_115_cast = add(x = var_4217_cast, y = inputs_113_cast)[name = tensor("inputs_115_cast")]; + tensor var_4227 = const()[name = tensor("op_4227"), val = tensor([1])]; + tensor channels_mean_115_cast = reduce_mean(axes = var_4227, keep_dims = var_3608, x = inputs_115_cast)[name = tensor("channels_mean_115_cast")]; + tensor zero_mean_115_cast = sub(x = inputs_115_cast, y = channels_mean_115_cast)[name = tensor("zero_mean_115_cast")]; + tensor zero_mean_sq_115_cast = mul(x = zero_mean_115_cast, y = zero_mean_115_cast)[name = tensor("zero_mean_sq_115_cast")]; + tensor var_4231 = const()[name = tensor("op_4231"), val = tensor([1])]; + tensor var_4232_cast = reduce_mean(axes = var_4231, keep_dims = var_3608, x = zero_mean_sq_115_cast)[name = tensor("op_4232_cast")]; + tensor var_4233_to_fp16 = const()[name = tensor("op_4233_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4234_cast = add(x = var_4232_cast, y = var_4233_to_fp16)[name = tensor("op_4234_cast")]; + tensor denom_115_epsilon_0_to_fp16 = const()[name = tensor("denom_115_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_115_cast = rsqrt(epsilon = denom_115_epsilon_0_to_fp16, x = var_4234_cast)[name = tensor("denom_115_cast")]; + tensor out_115_cast = mul(x = zero_mean_115_cast, y = denom_115_cast)[name = tensor("out_115_cast")]; + tensor var_4238_to_fp16 = const()[name = tensor("op_4238_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810542144)))]; + tensor var_4239_cast = add(x = out_115_cast, y = var_4238_to_fp16)[name = tensor("op_4239_cast")]; + tensor var_4241_to_fp16 = const()[name = tensor("op_4241_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810545280)))]; + tensor hidden_states_187_cast = mul(x = var_4239_cast, y = var_4241_to_fp16)[name = tensor("hidden_states_187_cast")]; + tensor var_4248 = const()[name = tensor("op_4248"), val = tensor([1, 1])]; + tensor var_4250 = const()[name = tensor("op_4250"), val = tensor([1, 1])]; + tensor q_77_pad_type_0 = const()[name = tensor("q_77_pad_type_0"), val = tensor("custom")]; + tensor q_77_pad_0 = const()[name = tensor("q_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810548416)))]; + tensor q_77_cast = conv(dilations = var_4250, groups = var_3613, pad = q_77_pad_0, pad_type = q_77_pad_type_0, strides = var_4248, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("q_77_cast")]; + tensor var_4254 = const()[name = tensor("op_4254"), val = tensor([1, 1])]; + tensor var_4256 = const()[name = tensor("op_4256"), val = tensor([1, 1])]; + tensor k_77_pad_type_0 = const()[name = tensor("k_77_pad_type_0"), val = tensor("custom")]; + tensor k_77_pad_0 = const()[name = tensor("k_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1815267072)))]; + tensor k_77_cast = conv(dilations = var_4256, groups = var_3613, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_4254, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("k_77_cast")]; + tensor var_4260 = const()[name = tensor("op_4260"), val = tensor([1, 1])]; + tensor var_4262 = const()[name = tensor("op_4262"), val = tensor([1, 1])]; + tensor v_77_pad_type_0 = const()[name = tensor("v_77_pad_type_0"), val = tensor("custom")]; + tensor v_77_pad_0 = const()[name = tensor("v_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1819985728)))]; + tensor v_77_cast = conv(dilations = var_4262, groups = var_3613, pad = v_77_pad_0, pad_type = v_77_pad_type_0, strides = var_4260, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("v_77_cast")]; + tensor var_4266 = const()[name = tensor("op_4266"), val = tensor([2, 24, 64, -1])]; + tensor var_4267_cast = reshape(shape = var_4266, x = q_77_cast)[name = tensor("op_4267_cast")]; + tensor var_4268 = const()[name = tensor("op_4268"), val = tensor([2, 24, 64, -1])]; + tensor var_4269_cast = reshape(shape = var_4268, x = k_77_cast)[name = tensor("op_4269_cast")]; + tensor var_4270 = const()[name = tensor("op_4270"), val = tensor([2, 24, 64, -1])]; + tensor var_4271_cast = reshape(shape = var_4270, x = v_77_cast)[name = tensor("op_4271_cast")]; + tensor attn_weights_153_transpose_x_0 = const()[name = tensor("attn_weights_153_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_153_transpose_y_0 = const()[name = tensor("attn_weights_153_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_153_cast = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_4267_cast, y = var_4269_cast)[name = tensor("attn_weights_153_cast")]; + tensor attn_weights_155_cast = mul(x = attn_weights_153_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_155_cast")]; + tensor var_4275_cast = softmax(axis = var_3597, x = attn_weights_155_cast)[name = tensor("op_4275_cast")]; + tensor attn_77_transpose_x_0 = const()[name = tensor("attn_77_transpose_x_0"), val = tensor(false)]; + tensor attn_77_transpose_y_0 = const()[name = tensor("attn_77_transpose_y_0"), val = tensor(true)]; + tensor attn_77_cast = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4271_cast, y = var_4275_cast)[name = tensor("attn_77_cast")]; + tensor var_4279 = const()[name = tensor("op_4279"), val = tensor([2, 1536, 1, -1])]; + tensor input_317_cast = reshape(shape = var_4279, x = attn_77_cast)[name = tensor("input_317_cast")]; + tensor var_4284 = const()[name = tensor("op_4284"), val = tensor([1, 1])]; + tensor var_4286 = const()[name = tensor("op_4286"), val = tensor([1, 1])]; + tensor var_4288_pad_type_0 = const()[name = tensor("op_4288_pad_type_0"), val = tensor("custom")]; + tensor var_4288_pad_0 = const()[name = tensor("op_4288_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1824704384)))]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1829423040)))]; + tensor var_4288_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_4286, groups = var_3613, pad = var_4288_pad_0, pad_type = var_4288_pad_type_0, strides = var_4284, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_317_cast)[name = tensor("op_4288_cast")]; + tensor inputs_117_cast = add(x = var_4288_cast, y = inputs_115_cast)[name = tensor("inputs_117_cast")]; + tensor var_4292 = const()[name = tensor("op_4292"), val = tensor([1])]; + tensor channels_mean_117_cast = reduce_mean(axes = var_4292, keep_dims = var_3608, x = inputs_117_cast)[name = tensor("channels_mean_117_cast")]; + tensor zero_mean_117_cast = sub(x = inputs_117_cast, y = channels_mean_117_cast)[name = tensor("zero_mean_117_cast")]; + tensor zero_mean_sq_117_cast = mul(x = zero_mean_117_cast, y = zero_mean_117_cast)[name = tensor("zero_mean_sq_117_cast")]; + tensor var_4296 = const()[name = tensor("op_4296"), val = tensor([1])]; + tensor var_4297_cast = reduce_mean(axes = var_4296, keep_dims = var_3608, x = zero_mean_sq_117_cast)[name = tensor("op_4297_cast")]; + tensor var_4298_to_fp16 = const()[name = tensor("op_4298_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4299_cast = add(x = var_4297_cast, y = var_4298_to_fp16)[name = tensor("op_4299_cast")]; + tensor denom_117_epsilon_0_to_fp16 = const()[name = tensor("denom_117_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_117_cast = rsqrt(epsilon = denom_117_epsilon_0_to_fp16, x = var_4299_cast)[name = tensor("denom_117_cast")]; + tensor out_117_cast = mul(x = zero_mean_117_cast, y = denom_117_cast)[name = tensor("out_117_cast")]; + tensor var_4303_to_fp16 = const()[name = tensor("op_4303_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1829426176)))]; + tensor var_4304_cast = add(x = out_117_cast, y = var_4303_to_fp16)[name = tensor("op_4304_cast")]; + tensor var_4306_to_fp16 = const()[name = tensor("op_4306_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1829429312)))]; + tensor hidden_states_189_cast = mul(x = var_4304_cast, y = var_4306_to_fp16)[name = tensor("hidden_states_189_cast")]; + tensor var_4313 = const()[name = tensor("op_4313"), val = tensor([1, 1])]; + tensor var_4315 = const()[name = tensor("op_4315"), val = tensor([1, 1])]; + tensor q_79_pad_type_0 = const()[name = tensor("q_79_pad_type_0"), val = tensor("custom")]; + tensor q_79_pad_0 = const()[name = tensor("q_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1829432448)))]; + tensor q_79_cast = conv(dilations = var_4315, groups = var_3613, pad = q_79_pad_0, pad_type = q_79_pad_type_0, strides = var_4313, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_189_cast)[name = tensor("q_79_cast")]; + tensor var_4319 = const()[name = tensor("op_4319"), val = tensor([1, 1])]; + tensor var_4321 = const()[name = tensor("op_4321"), val = tensor([1, 1])]; + tensor k_79_pad_type_0 = const()[name = tensor("k_79_pad_type_0"), val = tensor("custom")]; + tensor k_79_pad_0 = const()[name = tensor("k_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1834151104)))]; + tensor k_79_cast = conv(dilations = var_4321, groups = var_3613, pad = k_79_pad_0, pad_type = k_79_pad_type_0, strides = var_4319, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_79_cast")]; + tensor var_4325 = const()[name = tensor("op_4325"), val = tensor([1, 1])]; + tensor var_4327 = const()[name = tensor("op_4327"), val = tensor([1, 1])]; + tensor v_79_pad_type_0 = const()[name = tensor("v_79_pad_type_0"), val = tensor("custom")]; + tensor v_79_pad_0 = const()[name = tensor("v_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1838083328)))]; + tensor v_79_cast = conv(dilations = var_4327, groups = var_3613, pad = v_79_pad_0, pad_type = v_79_pad_type_0, strides = var_4325, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_79_cast")]; + tensor var_4331 = const()[name = tensor("op_4331"), val = tensor([2, 24, 64, -1])]; + tensor var_4332_cast = reshape(shape = var_4331, x = q_79_cast)[name = tensor("op_4332_cast")]; + tensor var_4333 = const()[name = tensor("op_4333"), val = tensor([2, 24, 64, -1])]; + tensor var_4334_cast = reshape(shape = var_4333, x = k_79_cast)[name = tensor("op_4334_cast")]; + tensor var_4335 = const()[name = tensor("op_4335"), val = tensor([2, 24, 64, -1])]; + tensor var_4336_cast = reshape(shape = var_4335, x = v_79_cast)[name = tensor("op_4336_cast")]; + tensor attn_weights_157_transpose_x_0 = const()[name = tensor("attn_weights_157_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_157_transpose_y_0 = const()[name = tensor("attn_weights_157_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_157_cast = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = var_4332_cast, y = var_4334_cast)[name = tensor("attn_weights_157_cast")]; + tensor attn_weights_159_cast = mul(x = attn_weights_157_cast, y = var_3604_to_fp16)[name = tensor("attn_weights_159_cast")]; + tensor var_4340_cast = softmax(axis = var_3597, x = attn_weights_159_cast)[name = tensor("op_4340_cast")]; + tensor attn_79_transpose_x_0 = const()[name = tensor("attn_79_transpose_x_0"), val = tensor(false)]; + tensor attn_79_transpose_y_0 = const()[name = tensor("attn_79_transpose_y_0"), val = tensor(true)]; + tensor attn_79_cast = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4336_cast, y = var_4340_cast)[name = tensor("attn_79_cast")]; + tensor var_4344 = const()[name = tensor("op_4344"), val = tensor([2, 1536, 1, -1])]; + tensor input_319_cast = reshape(shape = var_4344, x = attn_79_cast)[name = tensor("input_319_cast")]; + tensor var_4349 = const()[name = tensor("op_4349"), val = tensor([1, 1])]; + tensor var_4351 = const()[name = tensor("op_4351"), val = tensor([1, 1])]; + tensor var_4353_pad_type_0 = const()[name = tensor("op_4353_pad_type_0"), val = tensor("custom")]; + tensor var_4353_pad_0 = const()[name = tensor("op_4353_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1842015552)))]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1846734208)))]; + tensor var_4353_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_4351, groups = var_3613, pad = var_4353_pad_0, pad_type = var_4353_pad_type_0, strides = var_4349, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_319_cast)[name = tensor("op_4353_cast")]; + tensor inputs_119_cast = add(x = var_4353_cast, y = inputs_117_cast)[name = tensor("inputs_119_cast")]; + tensor var_4357 = const()[name = tensor("op_4357"), val = tensor([1])]; + tensor channels_mean_119_cast = reduce_mean(axes = var_4357, keep_dims = var_3608, x = inputs_119_cast)[name = tensor("channels_mean_119_cast")]; + tensor zero_mean_119_cast = sub(x = inputs_119_cast, y = channels_mean_119_cast)[name = tensor("zero_mean_119_cast")]; + tensor zero_mean_sq_119_cast = mul(x = zero_mean_119_cast, y = zero_mean_119_cast)[name = tensor("zero_mean_sq_119_cast")]; + tensor var_4361 = const()[name = tensor("op_4361"), val = tensor([1])]; + tensor var_4362_cast = reduce_mean(axes = var_4361, keep_dims = var_3608, x = zero_mean_sq_119_cast)[name = tensor("op_4362_cast")]; + tensor var_4363_to_fp16 = const()[name = tensor("op_4363_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4364_cast = add(x = var_4362_cast, y = var_4363_to_fp16)[name = tensor("op_4364_cast")]; + tensor denom_119_epsilon_0_to_fp16 = const()[name = tensor("denom_119_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_119_cast = rsqrt(epsilon = denom_119_epsilon_0_to_fp16, x = var_4364_cast)[name = tensor("denom_119_cast")]; + tensor out_119_cast = mul(x = zero_mean_119_cast, y = denom_119_cast)[name = tensor("out_119_cast")]; + tensor var_4368_to_fp16 = const()[name = tensor("op_4368_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1846737344)))]; + tensor var_4369_cast = add(x = out_119_cast, y = var_4368_to_fp16)[name = tensor("op_4369_cast")]; + tensor var_4371_to_fp16 = const()[name = tensor("op_4371_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1846740480)))]; + tensor input_321_cast = mul(x = var_4369_cast, y = var_4371_to_fp16)[name = tensor("input_321_cast")]; + tensor var_4379 = const()[name = tensor("op_4379"), val = tensor([1, 1])]; + tensor var_4381 = const()[name = tensor("op_4381"), val = tensor([1, 1])]; + tensor var_4383_pad_type_0 = const()[name = tensor("op_4383_pad_type_0"), val = tensor("custom")]; + tensor var_4383_pad_0 = const()[name = tensor("op_4383_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1846743616)))]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884492416)))]; + tensor var_4383_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_4381, groups = var_3613, pad = var_4383_pad_0, pad_type = var_4383_pad_type_0, strides = var_4379, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_321_cast)[name = tensor("op_4383_cast")]; + tensor var_4384_split_sizes_0 = const()[name = tensor("op_4384_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_4384_axis_0 = const()[name = tensor("op_4384_axis_0"), val = tensor(1)]; + tensor var_4384_cast_0, tensor var_4384_cast_1 = split(axis = var_4384_axis_0, split_sizes = var_4384_split_sizes_0, x = var_4383_cast)[name = tensor("op_4384_cast")]; + tensor var_4386_mode_0 = const()[name = tensor("op_4386_mode_0"), val = tensor("EXACT")]; + tensor var_4386_cast = gelu(mode = var_4386_mode_0, x = var_4384_cast_1)[name = tensor("op_4386_cast")]; + tensor input_323_cast = mul(x = var_4384_cast_0, y = var_4386_cast)[name = tensor("input_323_cast")]; + tensor var_4390 = const()[name = tensor("op_4390"), val = tensor([1, 1])]; + tensor var_4392 = const()[name = tensor("op_4392"), val = tensor([1, 1])]; + tensor var_4394_pad_type_0 = const()[name = tensor("op_4394_pad_type_0"), val = tensor("custom")]; + tensor var_4394_pad_0 = const()[name = tensor("op_4394_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884517056)))]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1903391488)))]; + tensor var_4394_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_4392, groups = var_3613, pad = var_4394_pad_0, pad_type = var_4394_pad_type_0, strides = var_4390, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_323_cast)[name = tensor("op_4394_cast")]; + tensor hidden_states_193_cast = add(x = var_4394_cast, y = inputs_119_cast)[name = tensor("hidden_states_193_cast")]; + tensor var_4396 = const()[name = tensor("op_4396"), val = tensor([2, 1536, 16, 16])]; + tensor input_325_cast = reshape(shape = var_4396, x = hidden_states_193_cast)[name = tensor("input_325_cast")]; + tensor var_4400 = const()[name = tensor("op_4400"), val = tensor([1, 1])]; + tensor var_4402 = const()[name = tensor("op_4402"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_type_0 = const()[name = tensor("hidden_states_195_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_195_pad_0 = const()[name = tensor("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1903394624)))]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908113280)))]; + tensor hidden_states_195_cast = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_4402, groups = var_3613, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_4400, weight = mid_block_attentions_0_proj_out_weight_to_fp16, x = input_325_cast)[name = tensor("hidden_states_195_cast")]; + tensor input_327_cast = add(x = hidden_states_195_cast, y = hidden_states_165_cast)[name = tensor("input_327_cast")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_92_cast = reshape(shape = reshape_92_shape_0, x = input_327_cast)[name = tensor("reshape_92_cast")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast)[name = tensor("reduce_mean_69_cast")]; + tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor("sub_46_cast")]; + tensor square_23_cast = square(x = sub_46_cast)[name = tensor("square_23_cast")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast)[name = tensor("reduce_mean_71_cast")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_46_cast = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast")]; + tensor sqrt_23_cast = sqrt(x = add_46_cast)[name = tensor("sqrt_23_cast")]; + tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast)[name = tensor("real_div_23_cast")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast)[name = tensor("reshape_93_cast")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908116416)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908119552)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_93_cast)[name = tensor("add_47_cast")]; + tensor input_331_cast = silu(x = add_47_cast)[name = tensor("input_331_cast")]; + tensor var_4417 = const()[name = tensor("op_4417"), val = tensor([1, 1])]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor hidden_states_197_pad_type_0 = const()[name = tensor("hidden_states_197_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_197_pad_0 = const()[name = tensor("hidden_states_197_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908122688)))]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1950590080)))]; + tensor hidden_states_197_cast = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_4419, groups = var_3613, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = var_4417, weight = mid_block_resnets_1_conv1_weight_to_fp16, x = input_331_cast)[name = tensor("hidden_states_197_cast")]; + tensor var_4425 = const()[name = tensor("op_4425"), val = tensor([1, 1])]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1950593216)))]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1955311872)))]; + tensor temb_19_cast = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4427, groups = var_3613, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_4425, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_19_cast")]; + tensor input_335_cast = add(x = hidden_states_197_cast, y = temb_19_cast)[name = tensor("input_335_cast")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_96_cast = reshape(shape = reshape_96_shape_0, x = input_335_cast)[name = tensor("reshape_96_cast")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast)[name = tensor("reduce_mean_72_cast")]; + tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor("sub_48_cast")]; + tensor square_24_cast = square(x = sub_48_cast)[name = tensor("square_24_cast")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast)[name = tensor("reduce_mean_74_cast")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_48_cast = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast")]; + tensor sqrt_24_cast = sqrt(x = add_48_cast)[name = tensor("sqrt_24_cast")]; + tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast)[name = tensor("real_div_24_cast")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor("reshape_97_cast")]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1955315008)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1955318144)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_97_cast)[name = tensor("add_49_cast")]; + tensor input_339_cast = silu(x = add_49_cast)[name = tensor("input_339_cast")]; + tensor var_4437 = const()[name = tensor("op_4437"), val = tensor([1, 1])]; + tensor var_4439 = const()[name = tensor("op_4439"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1955321280)))]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1997788672)))]; + tensor hidden_states_199_cast = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_4439, groups = var_3613, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_4437, weight = mid_block_resnets_1_conv2_weight_to_fp16, x = input_339_cast)[name = tensor("hidden_states_199_cast")]; + tensor hidden_states_201_cast = add(x = input_327_cast, y = hidden_states_199_cast)[name = tensor("hidden_states_201_cast")]; + tensor var_4450 = const()[name = tensor("op_4450"), val = tensor(1)]; + tensor input_341_interleave_0 = const()[name = tensor("input_341_interleave_0"), val = tensor(false)]; + tensor input_341_cast = concat(axis = var_4450, interleave = input_341_interleave_0, values = (hidden_states_201_cast, input_277_cast))[name = tensor("input_341_cast")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 96, 16, 16])]; + tensor reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_341_cast)[name = tensor("reshape_100_cast")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast)[name = tensor("reduce_mean_75_cast")]; + tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor("sub_50_cast")]; + tensor square_25_cast = square(x = sub_50_cast)[name = tensor("square_25_cast")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast)[name = tensor("reduce_mean_77_cast")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast")]; + tensor sqrt_25_cast = sqrt(x = add_50_cast)[name = tensor("sqrt_25_cast")]; + tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast)[name = tensor("real_div_25_cast")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 3072, 16, 16])]; + tensor reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast)[name = tensor("reshape_101_cast")]; + tensor add_51_mean_0_to_fp16 = const()[name = tensor("add_51_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1997791808)))]; + tensor add_51_variance_0_to_fp16 = const()[name = tensor("add_51_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1997798016)))]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1997804224)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1997810432)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_51_mean_0_to_fp16, variance = add_51_variance_0_to_fp16, x = reshape_101_cast)[name = tensor("add_51_cast")]; + tensor input_345_cast = silu(x = add_51_cast)[name = tensor("input_345_cast")]; + tensor var_4473 = const()[name = tensor("op_4473"), val = tensor([1, 1])]; + tensor var_4475 = const()[name = tensor("op_4475"), val = tensor([1, 1])]; + tensor hidden_states_203_pad_type_0 = const()[name = tensor("hidden_states_203_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_203_pad_0 = const()[name = tensor("hidden_states_203_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1997816640)))]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2082751360)))]; + tensor hidden_states_203_cast = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_4475, groups = var_4450, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = var_4473, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_345_cast)[name = tensor("hidden_states_203_cast")]; + tensor var_4481 = const()[name = tensor("op_4481"), val = tensor([1, 1])]; + tensor var_4483 = const()[name = tensor("op_4483"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2082754496)))]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2087473152)))]; + tensor temb_21_cast = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4483, groups = var_4450, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_4481, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_21_cast")]; + tensor input_349_cast = add(x = hidden_states_203_cast, y = temb_21_cast)[name = tensor("input_349_cast")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_104_cast = reshape(shape = reshape_104_shape_0, x = input_349_cast)[name = tensor("reshape_104_cast")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast)[name = tensor("reduce_mean_78_cast")]; + tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor("sub_52_cast")]; + tensor square_26_cast = square(x = sub_52_cast)[name = tensor("square_26_cast")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast)[name = tensor("reduce_mean_80_cast")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_52_cast = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast")]; + tensor sqrt_26_cast = sqrt(x = add_52_cast)[name = tensor("sqrt_26_cast")]; + tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast)[name = tensor("real_div_26_cast")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast)[name = tensor("reshape_105_cast")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2087476288)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2087479424)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_105_cast)[name = tensor("add_53_cast")]; + tensor input_353_cast = silu(x = add_53_cast)[name = tensor("input_353_cast")]; + tensor var_4493 = const()[name = tensor("op_4493"), val = tensor([1, 1])]; + tensor var_4495 = const()[name = tensor("op_4495"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_type_0 = const()[name = tensor("hidden_states_205_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_205_pad_0 = const()[name = tensor("hidden_states_205_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2087482560)))]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2129949952)))]; + tensor hidden_states_205_cast = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_4495, groups = var_4450, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_4493, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_353_cast)[name = tensor("hidden_states_205_cast")]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor var_4502 = const()[name = tensor("op_4502"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2129953088)))]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2139390336)))]; + tensor x_5_cast = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4502, groups = var_4450, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_4500, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16, x = input_341_cast)[name = tensor("x_5_cast")]; + tensor hidden_states_207_cast = add(x = x_5_cast, y = hidden_states_205_cast)[name = tensor("hidden_states_207_cast")]; + tensor input_355_interleave_0 = const()[name = tensor("input_355_interleave_0"), val = tensor(false)]; + tensor input_355_cast = concat(axis = var_4450, interleave = input_355_interleave_0, values = (hidden_states_207_cast, input_263_cast))[name = tensor("input_355_cast")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 96, 16, 16])]; + tensor reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_355_cast)[name = tensor("reshape_108_cast")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast)[name = tensor("reduce_mean_81_cast")]; + tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor("sub_54_cast")]; + tensor square_27_cast = square(x = sub_54_cast)[name = tensor("square_27_cast")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast)[name = tensor("reduce_mean_83_cast")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast")]; + tensor sqrt_27_cast = sqrt(x = add_54_cast)[name = tensor("sqrt_27_cast")]; + tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast)[name = tensor("real_div_27_cast")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 3072, 16, 16])]; + tensor reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor("reshape_109_cast")]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2139393472)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2139399680)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_51_mean_0_to_fp16, variance = add_51_variance_0_to_fp16, x = reshape_109_cast)[name = tensor("add_55_cast")]; + tensor input_359_cast = silu(x = add_55_cast)[name = tensor("input_359_cast")]; + tensor var_4520 = const()[name = tensor("op_4520"), val = tensor([1, 1])]; + tensor var_4522 = const()[name = tensor("op_4522"), val = tensor([1, 1])]; + tensor hidden_states_209_pad_type_0 = const()[name = tensor("hidden_states_209_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_209_pad_0 = const()[name = tensor("hidden_states_209_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2139405888)))]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2224340608)))]; + tensor hidden_states_209_cast = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_4522, groups = var_4450, pad = hidden_states_209_pad_0, pad_type = hidden_states_209_pad_type_0, strides = var_4520, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_359_cast)[name = tensor("hidden_states_209_cast")]; + tensor var_4528 = const()[name = tensor("op_4528"), val = tensor([1, 1])]; + tensor var_4530 = const()[name = tensor("op_4530"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2224343744)))]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2229062400)))]; + tensor temb_23_cast = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4530, groups = var_4450, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_4528, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_23_cast")]; + tensor input_363_cast = add(x = hidden_states_209_cast, y = temb_23_cast)[name = tensor("input_363_cast")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_363_cast)[name = tensor("reshape_112_cast")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast)[name = tensor("reduce_mean_84_cast")]; + tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor("sub_56_cast")]; + tensor square_28_cast = square(x = sub_56_cast)[name = tensor("square_28_cast")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast)[name = tensor("reduce_mean_86_cast")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast")]; + tensor sqrt_28_cast = sqrt(x = add_56_cast)[name = tensor("sqrt_28_cast")]; + tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast)[name = tensor("real_div_28_cast")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast)[name = tensor("reshape_113_cast")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2229065536)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2229068672)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_113_cast)[name = tensor("add_57_cast")]; + tensor input_367_cast = silu(x = add_57_cast)[name = tensor("input_367_cast")]; + tensor var_4540 = const()[name = tensor("op_4540"), val = tensor([1, 1])]; + tensor var_4542 = const()[name = tensor("op_4542"), val = tensor([1, 1])]; + tensor hidden_states_211_pad_type_0 = const()[name = tensor("hidden_states_211_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_211_pad_0 = const()[name = tensor("hidden_states_211_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2229071808)))]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2271539200)))]; + tensor hidden_states_211_cast = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_4542, groups = var_4450, pad = hidden_states_211_pad_0, pad_type = hidden_states_211_pad_type_0, strides = var_4540, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_367_cast)[name = tensor("hidden_states_211_cast")]; + tensor var_4547 = const()[name = tensor("op_4547"), val = tensor([1, 1])]; + tensor var_4549 = const()[name = tensor("op_4549"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2271542336)))]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2280979584)))]; + tensor x_7_cast = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4549, groups = var_4450, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_4547, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16, x = input_355_cast)[name = tensor("x_7_cast")]; + tensor hidden_states_213_cast = add(x = x_7_cast, y = hidden_states_211_cast)[name = tensor("hidden_states_213_cast")]; + tensor input_369_interleave_0 = const()[name = tensor("input_369_interleave_0"), val = tensor(false)]; + tensor input_369_cast = concat(axis = var_4450, interleave = input_369_interleave_0, values = (hidden_states_213_cast, input_249_cast))[name = tensor("input_369_cast")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 96, 16, 16])]; + tensor reshape_116_cast = reshape(shape = reshape_116_shape_0, x = input_369_cast)[name = tensor("reshape_116_cast")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast)[name = tensor("reduce_mean_87_cast")]; + tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor("sub_58_cast")]; + tensor square_29_cast = square(x = sub_58_cast)[name = tensor("square_29_cast")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast)[name = tensor("reduce_mean_89_cast")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_58_cast = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast")]; + tensor sqrt_29_cast = sqrt(x = add_58_cast)[name = tensor("sqrt_29_cast")]; + tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast)[name = tensor("real_div_29_cast")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 3072, 16, 16])]; + tensor reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor("reshape_117_cast")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2280982720)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2280988928)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_51_mean_0_to_fp16, variance = add_51_variance_0_to_fp16, x = reshape_117_cast)[name = tensor("add_59_cast")]; + tensor input_373_cast = silu(x = add_59_cast)[name = tensor("input_373_cast")]; + tensor var_4567 = const()[name = tensor("op_4567"), val = tensor([1, 1])]; + tensor var_4569 = const()[name = tensor("op_4569"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_type_0 = const()[name = tensor("hidden_states_215_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_215_pad_0 = const()[name = tensor("hidden_states_215_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2280995136)))]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2365929856)))]; + tensor hidden_states_215_cast = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_4569, groups = var_4450, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_4567, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_373_cast)[name = tensor("hidden_states_215_cast")]; + tensor var_4575 = const()[name = tensor("op_4575"), val = tensor([1, 1])]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2365932992)))]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2370651648)))]; + tensor temb_25_cast = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4577, groups = var_4450, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_4575, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_25_cast")]; + tensor input_377_cast = add(x = hidden_states_215_cast, y = temb_25_cast)[name = tensor("input_377_cast")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 48, 16, 16])]; + tensor reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_377_cast)[name = tensor("reshape_120_cast")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_90_cast = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast)[name = tensor("reduce_mean_90_cast")]; + tensor sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast)[name = tensor("sub_60_cast")]; + tensor square_30_cast = square(x = sub_60_cast)[name = tensor("square_30_cast")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_92_cast = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast)[name = tensor("reduce_mean_92_cast")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast = add(x = reduce_mean_92_cast, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast")]; + tensor sqrt_30_cast = sqrt(x = add_60_cast)[name = tensor("sqrt_30_cast")]; + tensor real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast)[name = tensor("real_div_30_cast")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1536, 16, 16])]; + tensor reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast)[name = tensor("reshape_121_cast")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2370654784)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2370657920)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_121_cast)[name = tensor("add_61_cast")]; + tensor input_381_cast = silu(x = add_61_cast)[name = tensor("input_381_cast")]; + tensor var_4587 = const()[name = tensor("op_4587"), val = tensor([1, 1])]; + tensor var_4589 = const()[name = tensor("op_4589"), val = tensor([1, 1])]; + tensor hidden_states_217_pad_type_0 = const()[name = tensor("hidden_states_217_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_217_pad_0 = const()[name = tensor("hidden_states_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2370661056)))]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2413128448)))]; + tensor hidden_states_217_cast = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_4589, groups = var_4450, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_4587, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_381_cast)[name = tensor("hidden_states_217_cast")]; + tensor var_4594 = const()[name = tensor("op_4594"), val = tensor([1, 1])]; + tensor var_4596 = const()[name = tensor("op_4596"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2413131584)))]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2422568832)))]; + tensor x_9_cast = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4596, groups = var_4450, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_4594, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16, x = input_369_cast)[name = tensor("x_9_cast")]; + tensor input_383_cast = add(x = x_9_cast, y = hidden_states_217_cast)[name = tensor("input_383_cast")]; + tensor input_385_scale_factor_height_0 = const()[name = tensor("input_385_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_385_scale_factor_width_0 = const()[name = tensor("input_385_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_385_cast = upsample_nearest_neighbor(scale_factor_height = input_385_scale_factor_height_0, scale_factor_width = input_385_scale_factor_width_0, x = input_383_cast)[name = tensor("input_385_cast")]; + tensor var_4605 = const()[name = tensor("op_4605"), val = tensor([1, 1])]; + tensor var_4607 = const()[name = tensor("op_4607"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_type_0 = const()[name = tensor("hidden_states_219_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_219_pad_0 = const()[name = tensor("hidden_states_219_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2422571968)))]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2465039360)))]; + tensor hidden_states_219_cast = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_4607, groups = var_4450, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_4605, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_385_cast)[name = tensor("hidden_states_219_cast")]; + tensor var_4612 = const()[name = tensor("op_4612"), val = tensor(3)]; + tensor var_4623 = const()[name = tensor("op_4623"), val = tensor(true)]; + tensor var_4628 = const()[name = tensor("op_4628"), val = tensor(1)]; + tensor input_387_interleave_0 = const()[name = tensor("input_387_interleave_0"), val = tensor(false)]; + tensor input_387_cast = concat(axis = var_4628, interleave = input_387_interleave_0, values = (hidden_states_219_cast, input_247_cast))[name = tensor("input_387_cast")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 96, 32, 32])]; + tensor reshape_124_cast = reshape(shape = reshape_124_shape_0, x = input_387_cast)[name = tensor("reshape_124_cast")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast)[name = tensor("reduce_mean_93_cast")]; + tensor sub_62_cast = sub(x = reshape_124_cast, y = reduce_mean_93_cast)[name = tensor("sub_62_cast")]; + tensor square_31_cast = square(x = sub_62_cast)[name = tensor("square_31_cast")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast)[name = tensor("reduce_mean_95_cast")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast = add(x = reduce_mean_95_cast, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast")]; + tensor sqrt_31_cast = sqrt(x = add_62_cast)[name = tensor("sqrt_31_cast")]; + tensor real_div_31_cast = real_div(x = sub_62_cast, y = sqrt_31_cast)[name = tensor("real_div_31_cast")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 3072, 32, 32])]; + tensor reshape_125_cast = reshape(shape = reshape_125_shape_0, x = real_div_31_cast)[name = tensor("reshape_125_cast")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2465042496)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2465048704)))]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_51_mean_0_to_fp16, variance = add_51_variance_0_to_fp16, x = reshape_125_cast)[name = tensor("add_63_cast")]; + tensor input_391_cast = silu(x = add_63_cast)[name = tensor("input_391_cast")]; + tensor var_4657 = const()[name = tensor("op_4657"), val = tensor([1, 1])]; + tensor var_4659 = const()[name = tensor("op_4659"), val = tensor([1, 1])]; + tensor hidden_states_221_pad_type_0 = const()[name = tensor("hidden_states_221_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_221_pad_0 = const()[name = tensor("hidden_states_221_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2465054912)))]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549989632)))]; + tensor hidden_states_221_cast = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_4659, groups = var_4628, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_4657, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_391_cast)[name = tensor("hidden_states_221_cast")]; + tensor var_4665 = const()[name = tensor("op_4665"), val = tensor([1, 1])]; + tensor var_4667 = const()[name = tensor("op_4667"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549992768)))]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554711424)))]; + tensor temb_27_cast = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4667, groups = var_4628, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_4665, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_27_cast")]; + tensor input_395_cast = add(x = hidden_states_221_cast, y = temb_27_cast)[name = tensor("input_395_cast")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_128_cast = reshape(shape = reshape_128_shape_0, x = input_395_cast)[name = tensor("reshape_128_cast")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast)[name = tensor("reduce_mean_96_cast")]; + tensor sub_64_cast = sub(x = reshape_128_cast, y = reduce_mean_96_cast)[name = tensor("sub_64_cast")]; + tensor square_32_cast = square(x = sub_64_cast)[name = tensor("square_32_cast")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast)[name = tensor("reduce_mean_98_cast")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_64_cast = add(x = reduce_mean_98_cast, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast")]; + tensor sqrt_32_cast = sqrt(x = add_64_cast)[name = tensor("sqrt_32_cast")]; + tensor real_div_32_cast = real_div(x = sub_64_cast, y = sqrt_32_cast)[name = tensor("real_div_32_cast")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_129_cast = reshape(shape = reshape_129_shape_0, x = real_div_32_cast)[name = tensor("reshape_129_cast")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554714560)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554717696)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_129_cast)[name = tensor("add_65_cast")]; + tensor input_399_cast = silu(x = add_65_cast)[name = tensor("input_399_cast")]; + tensor var_4677 = const()[name = tensor("op_4677"), val = tensor([1, 1])]; + tensor var_4679 = const()[name = tensor("op_4679"), val = tensor([1, 1])]; + tensor hidden_states_223_pad_type_0 = const()[name = tensor("hidden_states_223_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_223_pad_0 = const()[name = tensor("hidden_states_223_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554720832)))]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2597188224)))]; + tensor hidden_states_223_cast = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_4679, groups = var_4628, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = var_4677, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_399_cast)[name = tensor("hidden_states_223_cast")]; + tensor var_4684 = const()[name = tensor("op_4684"), val = tensor([1, 1])]; + tensor var_4686 = const()[name = tensor("op_4686"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2597191360)))]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2606628608)))]; + tensor x_11_cast = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4686, groups = var_4628, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_4684, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_387_cast)[name = tensor("x_11_cast")]; + tensor hidden_states_225_cast = add(x = x_11_cast, y = hidden_states_223_cast)[name = tensor("hidden_states_225_cast")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_132_cast = reshape(shape = reshape_132_shape_0, x = hidden_states_225_cast)[name = tensor("reshape_132_cast")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast)[name = tensor("reduce_mean_99_cast")]; + tensor sub_66_cast = sub(x = reshape_132_cast, y = reduce_mean_99_cast)[name = tensor("sub_66_cast")]; + tensor square_33_cast = square(x = sub_66_cast)[name = tensor("square_33_cast")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast)[name = tensor("reduce_mean_101_cast")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_66_cast = add(x = reduce_mean_101_cast, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast")]; + tensor sqrt_33_cast = sqrt(x = add_66_cast)[name = tensor("sqrt_33_cast")]; + tensor real_div_33_cast = real_div(x = sub_66_cast, y = sqrt_33_cast)[name = tensor("real_div_33_cast")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_133_cast = reshape(shape = reshape_133_shape_0, x = real_div_33_cast)[name = tensor("reshape_133_cast")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2606631744)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2606634880)))]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_133_cast)[name = tensor("add_67_cast")]; + tensor var_4712 = const()[name = tensor("op_4712"), val = tensor([1, 1])]; + tensor var_4714 = const()[name = tensor("op_4714"), val = tensor([1, 1])]; + tensor hidden_states_227_pad_type_0 = const()[name = tensor("hidden_states_227_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_227_pad_0 = const()[name = tensor("hidden_states_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2606638016)))]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2611356672)))]; + tensor hidden_states_227_cast = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_4714, groups = var_4628, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = var_4712, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_67_cast)[name = tensor("hidden_states_227_cast")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([2, 1536, 1, 1024])]; + tensor inputs_121_cast = reshape(shape = var_4719, x = hidden_states_227_cast)[name = tensor("inputs_121_cast")]; + tensor var_4729 = const()[name = tensor("op_4729"), val = tensor([1])]; + tensor channels_mean_121_cast = reduce_mean(axes = var_4729, keep_dims = var_4623, x = inputs_121_cast)[name = tensor("channels_mean_121_cast")]; + tensor zero_mean_121_cast = sub(x = inputs_121_cast, y = channels_mean_121_cast)[name = tensor("zero_mean_121_cast")]; + tensor zero_mean_sq_121_cast = mul(x = zero_mean_121_cast, y = zero_mean_121_cast)[name = tensor("zero_mean_sq_121_cast")]; + tensor var_4733 = const()[name = tensor("op_4733"), val = tensor([1])]; + tensor var_4734_cast = reduce_mean(axes = var_4733, keep_dims = var_4623, x = zero_mean_sq_121_cast)[name = tensor("op_4734_cast")]; + tensor var_4735_to_fp16 = const()[name = tensor("op_4735_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4736_cast = add(x = var_4734_cast, y = var_4735_to_fp16)[name = tensor("op_4736_cast")]; + tensor denom_121_epsilon_0_to_fp16 = const()[name = tensor("denom_121_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_121_cast = rsqrt(epsilon = denom_121_epsilon_0_to_fp16, x = var_4736_cast)[name = tensor("denom_121_cast")]; + tensor out_121_cast = mul(x = zero_mean_121_cast, y = denom_121_cast)[name = tensor("out_121_cast")]; + tensor var_4740_to_fp16 = const()[name = tensor("op_4740_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2611359808)))]; + tensor var_4741_cast = add(x = out_121_cast, y = var_4740_to_fp16)[name = tensor("op_4741_cast")]; + tensor var_4743_to_fp16 = const()[name = tensor("op_4743_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2611362944)))]; + tensor hidden_states_229_cast = mul(x = var_4741_cast, y = var_4743_to_fp16)[name = tensor("hidden_states_229_cast")]; + tensor var_4750 = const()[name = tensor("op_4750"), val = tensor([1, 1])]; + tensor var_4752 = const()[name = tensor("op_4752"), val = tensor([1, 1])]; + tensor q_81_pad_type_0 = const()[name = tensor("q_81_pad_type_0"), val = tensor("custom")]; + tensor q_81_pad_0 = const()[name = tensor("q_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2611366080)))]; + tensor q_81_cast = conv(dilations = var_4752, groups = var_4628, pad = q_81_pad_0, pad_type = q_81_pad_type_0, strides = var_4750, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_229_cast)[name = tensor("q_81_cast")]; + tensor var_4756 = const()[name = tensor("op_4756"), val = tensor([1, 1])]; + tensor var_4758 = const()[name = tensor("op_4758"), val = tensor([1, 1])]; + tensor k_81_pad_type_0 = const()[name = tensor("k_81_pad_type_0"), val = tensor("custom")]; + tensor k_81_pad_0 = const()[name = tensor("k_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2616084736)))]; + tensor k_81_cast = conv(dilations = var_4758, groups = var_4628, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_4756, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_229_cast)[name = tensor("k_81_cast")]; + tensor var_4762 = const()[name = tensor("op_4762"), val = tensor([1, 1])]; + tensor var_4764 = const()[name = tensor("op_4764"), val = tensor([1, 1])]; + tensor v_81_pad_type_0 = const()[name = tensor("v_81_pad_type_0"), val = tensor("custom")]; + tensor v_81_pad_0 = const()[name = tensor("v_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2620803392)))]; + tensor v_81_cast = conv(dilations = var_4764, groups = var_4628, pad = v_81_pad_0, pad_type = v_81_pad_type_0, strides = var_4762, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_229_cast)[name = tensor("v_81_cast")]; + tensor var_4768 = const()[name = tensor("op_4768"), val = tensor([2, 24, 64, -1])]; + tensor var_4769_cast = reshape(shape = var_4768, x = q_81_cast)[name = tensor("op_4769_cast")]; + tensor var_4770 = const()[name = tensor("op_4770"), val = tensor([2, 24, 64, -1])]; + tensor var_4771_cast = reshape(shape = var_4770, x = k_81_cast)[name = tensor("op_4771_cast")]; + tensor var_4772 = const()[name = tensor("op_4772"), val = tensor([2, 24, 64, -1])]; + tensor var_4773_cast = reshape(shape = var_4772, x = v_81_cast)[name = tensor("op_4773_cast")]; + tensor attn_weights_161_transpose_x_0 = const()[name = tensor("attn_weights_161_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_161_transpose_y_0 = const()[name = tensor("attn_weights_161_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_161_cast = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4769_cast, y = var_4771_cast)[name = tensor("attn_weights_161_cast")]; + tensor var_4619_to_fp16 = const()[name = tensor("op_4619_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_163_cast = mul(x = attn_weights_161_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_163_cast")]; + tensor var_4777_cast = softmax(axis = var_4612, x = attn_weights_163_cast)[name = tensor("op_4777_cast")]; + tensor attn_81_transpose_x_0 = const()[name = tensor("attn_81_transpose_x_0"), val = tensor(false)]; + tensor attn_81_transpose_y_0 = const()[name = tensor("attn_81_transpose_y_0"), val = tensor(true)]; + tensor attn_81_cast = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4773_cast, y = var_4777_cast)[name = tensor("attn_81_cast")]; + tensor var_4781 = const()[name = tensor("op_4781"), val = tensor([2, 1536, 1, -1])]; + tensor input_403_cast = reshape(shape = var_4781, x = attn_81_cast)[name = tensor("input_403_cast")]; + tensor var_4786 = const()[name = tensor("op_4786"), val = tensor([1, 1])]; + tensor var_4788 = const()[name = tensor("op_4788"), val = tensor([1, 1])]; + tensor var_4790_pad_type_0 = const()[name = tensor("op_4790_pad_type_0"), val = tensor("custom")]; + tensor var_4790_pad_0 = const()[name = tensor("op_4790_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2625522048)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2630240704)))]; + tensor var_4790_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4788, groups = var_4628, pad = var_4790_pad_0, pad_type = var_4790_pad_type_0, strides = var_4786, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_403_cast)[name = tensor("op_4790_cast")]; + tensor inputs_123_cast = add(x = var_4790_cast, y = inputs_121_cast)[name = tensor("inputs_123_cast")]; + tensor var_4794 = const()[name = tensor("op_4794"), val = tensor([1])]; + tensor channels_mean_123_cast = reduce_mean(axes = var_4794, keep_dims = var_4623, x = inputs_123_cast)[name = tensor("channels_mean_123_cast")]; + tensor zero_mean_123_cast = sub(x = inputs_123_cast, y = channels_mean_123_cast)[name = tensor("zero_mean_123_cast")]; + tensor zero_mean_sq_123_cast = mul(x = zero_mean_123_cast, y = zero_mean_123_cast)[name = tensor("zero_mean_sq_123_cast")]; + tensor var_4798 = const()[name = tensor("op_4798"), val = tensor([1])]; + tensor var_4799_cast = reduce_mean(axes = var_4798, keep_dims = var_4623, x = zero_mean_sq_123_cast)[name = tensor("op_4799_cast")]; + tensor var_4800_to_fp16 = const()[name = tensor("op_4800_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4801_cast = add(x = var_4799_cast, y = var_4800_to_fp16)[name = tensor("op_4801_cast")]; + tensor denom_123_epsilon_0_to_fp16 = const()[name = tensor("denom_123_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_123_cast = rsqrt(epsilon = denom_123_epsilon_0_to_fp16, x = var_4801_cast)[name = tensor("denom_123_cast")]; + tensor out_123_cast = mul(x = zero_mean_123_cast, y = denom_123_cast)[name = tensor("out_123_cast")]; + tensor var_4805_to_fp16 = const()[name = tensor("op_4805_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2630243840)))]; + tensor var_4806_cast = add(x = out_123_cast, y = var_4805_to_fp16)[name = tensor("op_4806_cast")]; + tensor var_4808_to_fp16 = const()[name = tensor("op_4808_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2630246976)))]; + tensor hidden_states_231_cast = mul(x = var_4806_cast, y = var_4808_to_fp16)[name = tensor("hidden_states_231_cast")]; + tensor var_4815 = const()[name = tensor("op_4815"), val = tensor([1, 1])]; + tensor var_4817 = const()[name = tensor("op_4817"), val = tensor([1, 1])]; + tensor q_83_pad_type_0 = const()[name = tensor("q_83_pad_type_0"), val = tensor("custom")]; + tensor q_83_pad_0 = const()[name = tensor("q_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2630250112)))]; + tensor q_83_cast = conv(dilations = var_4817, groups = var_4628, pad = q_83_pad_0, pad_type = q_83_pad_type_0, strides = var_4815, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_231_cast)[name = tensor("q_83_cast")]; + tensor var_4821 = const()[name = tensor("op_4821"), val = tensor([1, 1])]; + tensor var_4823 = const()[name = tensor("op_4823"), val = tensor([1, 1])]; + tensor k_83_pad_type_0 = const()[name = tensor("k_83_pad_type_0"), val = tensor("custom")]; + tensor k_83_pad_0 = const()[name = tensor("k_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2634968768)))]; + tensor k_83_cast = conv(dilations = var_4823, groups = var_4628, pad = k_83_pad_0, pad_type = k_83_pad_type_0, strides = var_4821, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_83_cast")]; + tensor var_4827 = const()[name = tensor("op_4827"), val = tensor([1, 1])]; + tensor var_4829 = const()[name = tensor("op_4829"), val = tensor([1, 1])]; + tensor v_83_pad_type_0 = const()[name = tensor("v_83_pad_type_0"), val = tensor("custom")]; + tensor v_83_pad_0 = const()[name = tensor("v_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2638900992)))]; + tensor v_83_cast = conv(dilations = var_4829, groups = var_4628, pad = v_83_pad_0, pad_type = v_83_pad_type_0, strides = var_4827, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_83_cast")]; + tensor var_4833 = const()[name = tensor("op_4833"), val = tensor([2, 24, 64, -1])]; + tensor var_4834_cast = reshape(shape = var_4833, x = q_83_cast)[name = tensor("op_4834_cast")]; + tensor var_4835 = const()[name = tensor("op_4835"), val = tensor([2, 24, 64, -1])]; + tensor var_4836_cast = reshape(shape = var_4835, x = k_83_cast)[name = tensor("op_4836_cast")]; + tensor var_4837 = const()[name = tensor("op_4837"), val = tensor([2, 24, 64, -1])]; + tensor var_4838_cast = reshape(shape = var_4837, x = v_83_cast)[name = tensor("op_4838_cast")]; + tensor attn_weights_165_transpose_x_0 = const()[name = tensor("attn_weights_165_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_165_transpose_y_0 = const()[name = tensor("attn_weights_165_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_165_cast = matmul(transpose_x = attn_weights_165_transpose_x_0, transpose_y = attn_weights_165_transpose_y_0, x = var_4834_cast, y = var_4836_cast)[name = tensor("attn_weights_165_cast")]; + tensor attn_weights_167_cast = mul(x = attn_weights_165_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_167_cast")]; + tensor var_4842_cast = softmax(axis = var_4612, x = attn_weights_167_cast)[name = tensor("op_4842_cast")]; + tensor attn_83_transpose_x_0 = const()[name = tensor("attn_83_transpose_x_0"), val = tensor(false)]; + tensor attn_83_transpose_y_0 = const()[name = tensor("attn_83_transpose_y_0"), val = tensor(true)]; + tensor attn_83_cast = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4838_cast, y = var_4842_cast)[name = tensor("attn_83_cast")]; + tensor var_4846 = const()[name = tensor("op_4846"), val = tensor([2, 1536, 1, -1])]; + tensor input_405_cast = reshape(shape = var_4846, x = attn_83_cast)[name = tensor("input_405_cast")]; + tensor var_4851 = const()[name = tensor("op_4851"), val = tensor([1, 1])]; + tensor var_4853 = const()[name = tensor("op_4853"), val = tensor([1, 1])]; + tensor var_4855_pad_type_0 = const()[name = tensor("op_4855_pad_type_0"), val = tensor("custom")]; + tensor var_4855_pad_0 = const()[name = tensor("op_4855_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2642833216)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2647551872)))]; + tensor var_4855_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4853, groups = var_4628, pad = var_4855_pad_0, pad_type = var_4855_pad_type_0, strides = var_4851, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_405_cast)[name = tensor("op_4855_cast")]; + tensor inputs_125_cast = add(x = var_4855_cast, y = inputs_123_cast)[name = tensor("inputs_125_cast")]; + tensor var_4859 = const()[name = tensor("op_4859"), val = tensor([1])]; + tensor channels_mean_125_cast = reduce_mean(axes = var_4859, keep_dims = var_4623, x = inputs_125_cast)[name = tensor("channels_mean_125_cast")]; + tensor zero_mean_125_cast = sub(x = inputs_125_cast, y = channels_mean_125_cast)[name = tensor("zero_mean_125_cast")]; + tensor zero_mean_sq_125_cast = mul(x = zero_mean_125_cast, y = zero_mean_125_cast)[name = tensor("zero_mean_sq_125_cast")]; + tensor var_4863 = const()[name = tensor("op_4863"), val = tensor([1])]; + tensor var_4864_cast = reduce_mean(axes = var_4863, keep_dims = var_4623, x = zero_mean_sq_125_cast)[name = tensor("op_4864_cast")]; + tensor var_4865_to_fp16 = const()[name = tensor("op_4865_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4866_cast = add(x = var_4864_cast, y = var_4865_to_fp16)[name = tensor("op_4866_cast")]; + tensor denom_125_epsilon_0_to_fp16 = const()[name = tensor("denom_125_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_125_cast = rsqrt(epsilon = denom_125_epsilon_0_to_fp16, x = var_4866_cast)[name = tensor("denom_125_cast")]; + tensor out_125_cast = mul(x = zero_mean_125_cast, y = denom_125_cast)[name = tensor("out_125_cast")]; + tensor var_4870_to_fp16 = const()[name = tensor("op_4870_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2647555008)))]; + tensor var_4871_cast = add(x = out_125_cast, y = var_4870_to_fp16)[name = tensor("op_4871_cast")]; + tensor var_4873_to_fp16 = const()[name = tensor("op_4873_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2647558144)))]; + tensor input_407_cast = mul(x = var_4871_cast, y = var_4873_to_fp16)[name = tensor("input_407_cast")]; + tensor var_4881 = const()[name = tensor("op_4881"), val = tensor([1, 1])]; + tensor var_4883 = const()[name = tensor("op_4883"), val = tensor([1, 1])]; + tensor var_4885_pad_type_0 = const()[name = tensor("op_4885_pad_type_0"), val = tensor("custom")]; + tensor var_4885_pad_0 = const()[name = tensor("op_4885_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2647561280)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2685310080)))]; + tensor var_4885_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4883, groups = var_4628, pad = var_4885_pad_0, pad_type = var_4885_pad_type_0, strides = var_4881, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_407_cast)[name = tensor("op_4885_cast")]; + tensor var_4886_split_sizes_0 = const()[name = tensor("op_4886_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_4886_axis_0 = const()[name = tensor("op_4886_axis_0"), val = tensor(1)]; + tensor var_4886_cast_0, tensor var_4886_cast_1 = split(axis = var_4886_axis_0, split_sizes = var_4886_split_sizes_0, x = var_4885_cast)[name = tensor("op_4886_cast")]; + tensor var_4888_mode_0 = const()[name = tensor("op_4888_mode_0"), val = tensor("EXACT")]; + tensor var_4888_cast = gelu(mode = var_4888_mode_0, x = var_4886_cast_1)[name = tensor("op_4888_cast")]; + tensor input_409_cast = mul(x = var_4886_cast_0, y = var_4888_cast)[name = tensor("input_409_cast")]; + tensor var_4892 = const()[name = tensor("op_4892"), val = tensor([1, 1])]; + tensor var_4894 = const()[name = tensor("op_4894"), val = tensor([1, 1])]; + tensor var_4896_pad_type_0 = const()[name = tensor("op_4896_pad_type_0"), val = tensor("custom")]; + tensor var_4896_pad_0 = const()[name = tensor("op_4896_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2685334720)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2704209152)))]; + tensor var_4896_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4894, groups = var_4628, pad = var_4896_pad_0, pad_type = var_4896_pad_type_0, strides = var_4892, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_409_cast)[name = tensor("op_4896_cast")]; + tensor inputs_127_cast = add(x = var_4896_cast, y = inputs_125_cast)[name = tensor("inputs_127_cast")]; + tensor var_4906 = const()[name = tensor("op_4906"), val = tensor([1])]; + tensor channels_mean_127_cast = reduce_mean(axes = var_4906, keep_dims = var_4623, x = inputs_127_cast)[name = tensor("channels_mean_127_cast")]; + tensor zero_mean_127_cast = sub(x = inputs_127_cast, y = channels_mean_127_cast)[name = tensor("zero_mean_127_cast")]; + tensor zero_mean_sq_127_cast = mul(x = zero_mean_127_cast, y = zero_mean_127_cast)[name = tensor("zero_mean_sq_127_cast")]; + tensor var_4910 = const()[name = tensor("op_4910"), val = tensor([1])]; + tensor var_4911_cast = reduce_mean(axes = var_4910, keep_dims = var_4623, x = zero_mean_sq_127_cast)[name = tensor("op_4911_cast")]; + tensor var_4912_to_fp16 = const()[name = tensor("op_4912_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4913_cast = add(x = var_4911_cast, y = var_4912_to_fp16)[name = tensor("op_4913_cast")]; + tensor denom_127_epsilon_0_to_fp16 = const()[name = tensor("denom_127_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_127_cast = rsqrt(epsilon = denom_127_epsilon_0_to_fp16, x = var_4913_cast)[name = tensor("denom_127_cast")]; + tensor out_127_cast = mul(x = zero_mean_127_cast, y = denom_127_cast)[name = tensor("out_127_cast")]; + tensor var_4917_to_fp16 = const()[name = tensor("op_4917_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2704212288)))]; + tensor var_4918_cast = add(x = out_127_cast, y = var_4917_to_fp16)[name = tensor("op_4918_cast")]; + tensor var_4920_to_fp16 = const()[name = tensor("op_4920_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2704215424)))]; + tensor hidden_states_235_cast = mul(x = var_4918_cast, y = var_4920_to_fp16)[name = tensor("hidden_states_235_cast")]; + tensor var_4927 = const()[name = tensor("op_4927"), val = tensor([1, 1])]; + tensor var_4929 = const()[name = tensor("op_4929"), val = tensor([1, 1])]; + tensor q_85_pad_type_0 = const()[name = tensor("q_85_pad_type_0"), val = tensor("custom")]; + tensor q_85_pad_0 = const()[name = tensor("q_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2704218560)))]; + tensor q_85_cast = conv(dilations = var_4929, groups = var_4628, pad = q_85_pad_0, pad_type = q_85_pad_type_0, strides = var_4927, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_235_cast)[name = tensor("q_85_cast")]; + tensor var_4933 = const()[name = tensor("op_4933"), val = tensor([1, 1])]; + tensor var_4935 = const()[name = tensor("op_4935"), val = tensor([1, 1])]; + tensor k_85_pad_type_0 = const()[name = tensor("k_85_pad_type_0"), val = tensor("custom")]; + tensor k_85_pad_0 = const()[name = tensor("k_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2708937216)))]; + tensor k_85_cast = conv(dilations = var_4935, groups = var_4628, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_4933, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_235_cast)[name = tensor("k_85_cast")]; + tensor var_4939 = const()[name = tensor("op_4939"), val = tensor([1, 1])]; + tensor var_4941 = const()[name = tensor("op_4941"), val = tensor([1, 1])]; + tensor v_85_pad_type_0 = const()[name = tensor("v_85_pad_type_0"), val = tensor("custom")]; + tensor v_85_pad_0 = const()[name = tensor("v_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2713655872)))]; + tensor v_85_cast = conv(dilations = var_4941, groups = var_4628, pad = v_85_pad_0, pad_type = v_85_pad_type_0, strides = var_4939, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_235_cast)[name = tensor("v_85_cast")]; + tensor var_4945 = const()[name = tensor("op_4945"), val = tensor([2, 24, 64, -1])]; + tensor var_4946_cast = reshape(shape = var_4945, x = q_85_cast)[name = tensor("op_4946_cast")]; + tensor var_4947 = const()[name = tensor("op_4947"), val = tensor([2, 24, 64, -1])]; + tensor var_4948_cast = reshape(shape = var_4947, x = k_85_cast)[name = tensor("op_4948_cast")]; + tensor var_4949 = const()[name = tensor("op_4949"), val = tensor([2, 24, 64, -1])]; + tensor var_4950_cast = reshape(shape = var_4949, x = v_85_cast)[name = tensor("op_4950_cast")]; + tensor attn_weights_169_transpose_x_0 = const()[name = tensor("attn_weights_169_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_169_transpose_y_0 = const()[name = tensor("attn_weights_169_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_169_cast = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4946_cast, y = var_4948_cast)[name = tensor("attn_weights_169_cast")]; + tensor attn_weights_171_cast = mul(x = attn_weights_169_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_171_cast")]; + tensor var_4954_cast = softmax(axis = var_4612, x = attn_weights_171_cast)[name = tensor("op_4954_cast")]; + tensor attn_85_transpose_x_0 = const()[name = tensor("attn_85_transpose_x_0"), val = tensor(false)]; + tensor attn_85_transpose_y_0 = const()[name = tensor("attn_85_transpose_y_0"), val = tensor(true)]; + tensor attn_85_cast = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4950_cast, y = var_4954_cast)[name = tensor("attn_85_cast")]; + tensor var_4958 = const()[name = tensor("op_4958"), val = tensor([2, 1536, 1, -1])]; + tensor input_411_cast = reshape(shape = var_4958, x = attn_85_cast)[name = tensor("input_411_cast")]; + tensor var_4963 = const()[name = tensor("op_4963"), val = tensor([1, 1])]; + tensor var_4965 = const()[name = tensor("op_4965"), val = tensor([1, 1])]; + tensor var_4967_pad_type_0 = const()[name = tensor("op_4967_pad_type_0"), val = tensor("custom")]; + tensor var_4967_pad_0 = const()[name = tensor("op_4967_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2718374528)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2723093184)))]; + tensor var_4967_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_4965, groups = var_4628, pad = var_4967_pad_0, pad_type = var_4967_pad_type_0, strides = var_4963, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_411_cast)[name = tensor("op_4967_cast")]; + tensor inputs_129_cast = add(x = var_4967_cast, y = inputs_127_cast)[name = tensor("inputs_129_cast")]; + tensor var_4971 = const()[name = tensor("op_4971"), val = tensor([1])]; + tensor channels_mean_129_cast = reduce_mean(axes = var_4971, keep_dims = var_4623, x = inputs_129_cast)[name = tensor("channels_mean_129_cast")]; + tensor zero_mean_129_cast = sub(x = inputs_129_cast, y = channels_mean_129_cast)[name = tensor("zero_mean_129_cast")]; + tensor zero_mean_sq_129_cast = mul(x = zero_mean_129_cast, y = zero_mean_129_cast)[name = tensor("zero_mean_sq_129_cast")]; + tensor var_4975 = const()[name = tensor("op_4975"), val = tensor([1])]; + tensor var_4976_cast = reduce_mean(axes = var_4975, keep_dims = var_4623, x = zero_mean_sq_129_cast)[name = tensor("op_4976_cast")]; + tensor var_4977_to_fp16 = const()[name = tensor("op_4977_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4978_cast = add(x = var_4976_cast, y = var_4977_to_fp16)[name = tensor("op_4978_cast")]; + tensor denom_129_epsilon_0_to_fp16 = const()[name = tensor("denom_129_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_129_cast = rsqrt(epsilon = denom_129_epsilon_0_to_fp16, x = var_4978_cast)[name = tensor("denom_129_cast")]; + tensor out_129_cast = mul(x = zero_mean_129_cast, y = denom_129_cast)[name = tensor("out_129_cast")]; + tensor var_4982_to_fp16 = const()[name = tensor("op_4982_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2723096320)))]; + tensor var_4983_cast = add(x = out_129_cast, y = var_4982_to_fp16)[name = tensor("op_4983_cast")]; + tensor var_4985_to_fp16 = const()[name = tensor("op_4985_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2723099456)))]; + tensor hidden_states_237_cast = mul(x = var_4983_cast, y = var_4985_to_fp16)[name = tensor("hidden_states_237_cast")]; + tensor var_4992 = const()[name = tensor("op_4992"), val = tensor([1, 1])]; + tensor var_4994 = const()[name = tensor("op_4994"), val = tensor([1, 1])]; + tensor q_87_pad_type_0 = const()[name = tensor("q_87_pad_type_0"), val = tensor("custom")]; + tensor q_87_pad_0 = const()[name = tensor("q_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2723102592)))]; + tensor q_87_cast = conv(dilations = var_4994, groups = var_4628, pad = q_87_pad_0, pad_type = q_87_pad_type_0, strides = var_4992, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_237_cast)[name = tensor("q_87_cast")]; + tensor var_4998 = const()[name = tensor("op_4998"), val = tensor([1, 1])]; + tensor var_5000 = const()[name = tensor("op_5000"), val = tensor([1, 1])]; + tensor k_87_pad_type_0 = const()[name = tensor("k_87_pad_type_0"), val = tensor("custom")]; + tensor k_87_pad_0 = const()[name = tensor("k_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727821248)))]; + tensor k_87_cast = conv(dilations = var_5000, groups = var_4628, pad = k_87_pad_0, pad_type = k_87_pad_type_0, strides = var_4998, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_87_cast")]; + tensor var_5004 = const()[name = tensor("op_5004"), val = tensor([1, 1])]; + tensor var_5006 = const()[name = tensor("op_5006"), val = tensor([1, 1])]; + tensor v_87_pad_type_0 = const()[name = tensor("v_87_pad_type_0"), val = tensor("custom")]; + tensor v_87_pad_0 = const()[name = tensor("v_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2731753472)))]; + tensor v_87_cast = conv(dilations = var_5006, groups = var_4628, pad = v_87_pad_0, pad_type = v_87_pad_type_0, strides = var_5004, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_87_cast")]; + tensor var_5010 = const()[name = tensor("op_5010"), val = tensor([2, 24, 64, -1])]; + tensor var_5011_cast = reshape(shape = var_5010, x = q_87_cast)[name = tensor("op_5011_cast")]; + tensor var_5012 = const()[name = tensor("op_5012"), val = tensor([2, 24, 64, -1])]; + tensor var_5013_cast = reshape(shape = var_5012, x = k_87_cast)[name = tensor("op_5013_cast")]; + tensor var_5014 = const()[name = tensor("op_5014"), val = tensor([2, 24, 64, -1])]; + tensor var_5015_cast = reshape(shape = var_5014, x = v_87_cast)[name = tensor("op_5015_cast")]; + tensor attn_weights_173_transpose_x_0 = const()[name = tensor("attn_weights_173_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_173_transpose_y_0 = const()[name = tensor("attn_weights_173_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_173_cast = matmul(transpose_x = attn_weights_173_transpose_x_0, transpose_y = attn_weights_173_transpose_y_0, x = var_5011_cast, y = var_5013_cast)[name = tensor("attn_weights_173_cast")]; + tensor attn_weights_175_cast = mul(x = attn_weights_173_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_175_cast")]; + tensor var_5019_cast = softmax(axis = var_4612, x = attn_weights_175_cast)[name = tensor("op_5019_cast")]; + tensor attn_87_transpose_x_0 = const()[name = tensor("attn_87_transpose_x_0"), val = tensor(false)]; + tensor attn_87_transpose_y_0 = const()[name = tensor("attn_87_transpose_y_0"), val = tensor(true)]; + tensor attn_87_cast = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_5015_cast, y = var_5019_cast)[name = tensor("attn_87_cast")]; + tensor var_5023 = const()[name = tensor("op_5023"), val = tensor([2, 1536, 1, -1])]; + tensor input_413_cast = reshape(shape = var_5023, x = attn_87_cast)[name = tensor("input_413_cast")]; + tensor var_5028 = const()[name = tensor("op_5028"), val = tensor([1, 1])]; + tensor var_5030 = const()[name = tensor("op_5030"), val = tensor([1, 1])]; + tensor var_5032_pad_type_0 = const()[name = tensor("op_5032_pad_type_0"), val = tensor("custom")]; + tensor var_5032_pad_0 = const()[name = tensor("op_5032_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2735685696)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740404352)))]; + tensor var_5032_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_5030, groups = var_4628, pad = var_5032_pad_0, pad_type = var_5032_pad_type_0, strides = var_5028, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_413_cast)[name = tensor("op_5032_cast")]; + tensor inputs_131_cast = add(x = var_5032_cast, y = inputs_129_cast)[name = tensor("inputs_131_cast")]; + tensor var_5036 = const()[name = tensor("op_5036"), val = tensor([1])]; + tensor channels_mean_131_cast = reduce_mean(axes = var_5036, keep_dims = var_4623, x = inputs_131_cast)[name = tensor("channels_mean_131_cast")]; + tensor zero_mean_131_cast = sub(x = inputs_131_cast, y = channels_mean_131_cast)[name = tensor("zero_mean_131_cast")]; + tensor zero_mean_sq_131_cast = mul(x = zero_mean_131_cast, y = zero_mean_131_cast)[name = tensor("zero_mean_sq_131_cast")]; + tensor var_5040 = const()[name = tensor("op_5040"), val = tensor([1])]; + tensor var_5041_cast = reduce_mean(axes = var_5040, keep_dims = var_4623, x = zero_mean_sq_131_cast)[name = tensor("op_5041_cast")]; + tensor var_5042_to_fp16 = const()[name = tensor("op_5042_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5043_cast = add(x = var_5041_cast, y = var_5042_to_fp16)[name = tensor("op_5043_cast")]; + tensor denom_131_epsilon_0_to_fp16 = const()[name = tensor("denom_131_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_131_cast = rsqrt(epsilon = denom_131_epsilon_0_to_fp16, x = var_5043_cast)[name = tensor("denom_131_cast")]; + tensor out_131_cast = mul(x = zero_mean_131_cast, y = denom_131_cast)[name = tensor("out_131_cast")]; + tensor var_5047_to_fp16 = const()[name = tensor("op_5047_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740407488)))]; + tensor var_5048_cast = add(x = out_131_cast, y = var_5047_to_fp16)[name = tensor("op_5048_cast")]; + tensor var_5050_to_fp16 = const()[name = tensor("op_5050_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740410624)))]; + tensor input_415_cast = mul(x = var_5048_cast, y = var_5050_to_fp16)[name = tensor("input_415_cast")]; + tensor var_5058 = const()[name = tensor("op_5058"), val = tensor([1, 1])]; + tensor var_5060 = const()[name = tensor("op_5060"), val = tensor([1, 1])]; + tensor var_5062_pad_type_0 = const()[name = tensor("op_5062_pad_type_0"), val = tensor("custom")]; + tensor var_5062_pad_0 = const()[name = tensor("op_5062_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740413760)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2778162560)))]; + tensor var_5062_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_5060, groups = var_4628, pad = var_5062_pad_0, pad_type = var_5062_pad_type_0, strides = var_5058, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_415_cast)[name = tensor("op_5062_cast")]; + tensor var_5063_split_sizes_0 = const()[name = tensor("op_5063_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_5063_axis_0 = const()[name = tensor("op_5063_axis_0"), val = tensor(1)]; + tensor var_5063_cast_0, tensor var_5063_cast_1 = split(axis = var_5063_axis_0, split_sizes = var_5063_split_sizes_0, x = var_5062_cast)[name = tensor("op_5063_cast")]; + tensor var_5065_mode_0 = const()[name = tensor("op_5065_mode_0"), val = tensor("EXACT")]; + tensor var_5065_cast = gelu(mode = var_5065_mode_0, x = var_5063_cast_1)[name = tensor("op_5065_cast")]; + tensor input_417_cast = mul(x = var_5063_cast_0, y = var_5065_cast)[name = tensor("input_417_cast")]; + tensor var_5069 = const()[name = tensor("op_5069"), val = tensor([1, 1])]; + tensor var_5071 = const()[name = tensor("op_5071"), val = tensor([1, 1])]; + tensor var_5073_pad_type_0 = const()[name = tensor("op_5073_pad_type_0"), val = tensor("custom")]; + tensor var_5073_pad_0 = const()[name = tensor("op_5073_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2778187200)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2797061632)))]; + tensor var_5073_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_5071, groups = var_4628, pad = var_5073_pad_0, pad_type = var_5073_pad_type_0, strides = var_5069, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_417_cast)[name = tensor("op_5073_cast")]; + tensor inputs_133_cast = add(x = var_5073_cast, y = inputs_131_cast)[name = tensor("inputs_133_cast")]; + tensor var_5083 = const()[name = tensor("op_5083"), val = tensor([1])]; + tensor channels_mean_133_cast = reduce_mean(axes = var_5083, keep_dims = var_4623, x = inputs_133_cast)[name = tensor("channels_mean_133_cast")]; + tensor zero_mean_133_cast = sub(x = inputs_133_cast, y = channels_mean_133_cast)[name = tensor("zero_mean_133_cast")]; + tensor zero_mean_sq_133_cast = mul(x = zero_mean_133_cast, y = zero_mean_133_cast)[name = tensor("zero_mean_sq_133_cast")]; + tensor var_5087 = const()[name = tensor("op_5087"), val = tensor([1])]; + tensor var_5088_cast = reduce_mean(axes = var_5087, keep_dims = var_4623, x = zero_mean_sq_133_cast)[name = tensor("op_5088_cast")]; + tensor var_5089_to_fp16 = const()[name = tensor("op_5089_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5090_cast = add(x = var_5088_cast, y = var_5089_to_fp16)[name = tensor("op_5090_cast")]; + tensor denom_133_epsilon_0_to_fp16 = const()[name = tensor("denom_133_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_133_cast = rsqrt(epsilon = denom_133_epsilon_0_to_fp16, x = var_5090_cast)[name = tensor("denom_133_cast")]; + tensor out_133_cast = mul(x = zero_mean_133_cast, y = denom_133_cast)[name = tensor("out_133_cast")]; + tensor var_5094_to_fp16 = const()[name = tensor("op_5094_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2797064768)))]; + tensor var_5095_cast = add(x = out_133_cast, y = var_5094_to_fp16)[name = tensor("op_5095_cast")]; + tensor var_5097_to_fp16 = const()[name = tensor("op_5097_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2797067904)))]; + tensor hidden_states_241_cast = mul(x = var_5095_cast, y = var_5097_to_fp16)[name = tensor("hidden_states_241_cast")]; + tensor var_5104 = const()[name = tensor("op_5104"), val = tensor([1, 1])]; + tensor var_5106 = const()[name = tensor("op_5106"), val = tensor([1, 1])]; + tensor q_89_pad_type_0 = const()[name = tensor("q_89_pad_type_0"), val = tensor("custom")]; + tensor q_89_pad_0 = const()[name = tensor("q_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2797071040)))]; + tensor q_89_cast = conv(dilations = var_5106, groups = var_4628, pad = q_89_pad_0, pad_type = q_89_pad_type_0, strides = var_5104, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_241_cast)[name = tensor("q_89_cast")]; + tensor var_5110 = const()[name = tensor("op_5110"), val = tensor([1, 1])]; + tensor var_5112 = const()[name = tensor("op_5112"), val = tensor([1, 1])]; + tensor k_89_pad_type_0 = const()[name = tensor("k_89_pad_type_0"), val = tensor("custom")]; + tensor k_89_pad_0 = const()[name = tensor("k_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2801789696)))]; + tensor k_89_cast = conv(dilations = var_5112, groups = var_4628, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_5110, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_241_cast)[name = tensor("k_89_cast")]; + tensor var_5116 = const()[name = tensor("op_5116"), val = tensor([1, 1])]; + tensor var_5118 = const()[name = tensor("op_5118"), val = tensor([1, 1])]; + tensor v_89_pad_type_0 = const()[name = tensor("v_89_pad_type_0"), val = tensor("custom")]; + tensor v_89_pad_0 = const()[name = tensor("v_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2806508352)))]; + tensor v_89_cast = conv(dilations = var_5118, groups = var_4628, pad = v_89_pad_0, pad_type = v_89_pad_type_0, strides = var_5116, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_241_cast)[name = tensor("v_89_cast")]; + tensor var_5122 = const()[name = tensor("op_5122"), val = tensor([2, 24, 64, -1])]; + tensor var_5123_cast = reshape(shape = var_5122, x = q_89_cast)[name = tensor("op_5123_cast")]; + tensor var_5124 = const()[name = tensor("op_5124"), val = tensor([2, 24, 64, -1])]; + tensor var_5125_cast = reshape(shape = var_5124, x = k_89_cast)[name = tensor("op_5125_cast")]; + tensor var_5126 = const()[name = tensor("op_5126"), val = tensor([2, 24, 64, -1])]; + tensor var_5127_cast = reshape(shape = var_5126, x = v_89_cast)[name = tensor("op_5127_cast")]; + tensor attn_weights_177_transpose_x_0 = const()[name = tensor("attn_weights_177_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_177_transpose_y_0 = const()[name = tensor("attn_weights_177_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_177_cast = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_5123_cast, y = var_5125_cast)[name = tensor("attn_weights_177_cast")]; + tensor attn_weights_179_cast = mul(x = attn_weights_177_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_179_cast")]; + tensor var_5131_cast = softmax(axis = var_4612, x = attn_weights_179_cast)[name = tensor("op_5131_cast")]; + tensor attn_89_transpose_x_0 = const()[name = tensor("attn_89_transpose_x_0"), val = tensor(false)]; + tensor attn_89_transpose_y_0 = const()[name = tensor("attn_89_transpose_y_0"), val = tensor(true)]; + tensor attn_89_cast = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_5127_cast, y = var_5131_cast)[name = tensor("attn_89_cast")]; + tensor var_5135 = const()[name = tensor("op_5135"), val = tensor([2, 1536, 1, -1])]; + tensor input_419_cast = reshape(shape = var_5135, x = attn_89_cast)[name = tensor("input_419_cast")]; + tensor var_5140 = const()[name = tensor("op_5140"), val = tensor([1, 1])]; + tensor var_5142 = const()[name = tensor("op_5142"), val = tensor([1, 1])]; + tensor var_5144_pad_type_0 = const()[name = tensor("op_5144_pad_type_0"), val = tensor("custom")]; + tensor var_5144_pad_0 = const()[name = tensor("op_5144_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2811227008)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2815945664)))]; + tensor var_5144_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_5142, groups = var_4628, pad = var_5144_pad_0, pad_type = var_5144_pad_type_0, strides = var_5140, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_419_cast)[name = tensor("op_5144_cast")]; + tensor inputs_135_cast = add(x = var_5144_cast, y = inputs_133_cast)[name = tensor("inputs_135_cast")]; + tensor var_5148 = const()[name = tensor("op_5148"), val = tensor([1])]; + tensor channels_mean_135_cast = reduce_mean(axes = var_5148, keep_dims = var_4623, x = inputs_135_cast)[name = tensor("channels_mean_135_cast")]; + tensor zero_mean_135_cast = sub(x = inputs_135_cast, y = channels_mean_135_cast)[name = tensor("zero_mean_135_cast")]; + tensor zero_mean_sq_135_cast = mul(x = zero_mean_135_cast, y = zero_mean_135_cast)[name = tensor("zero_mean_sq_135_cast")]; + tensor var_5152 = const()[name = tensor("op_5152"), val = tensor([1])]; + tensor var_5153_cast = reduce_mean(axes = var_5152, keep_dims = var_4623, x = zero_mean_sq_135_cast)[name = tensor("op_5153_cast")]; + tensor var_5154_to_fp16 = const()[name = tensor("op_5154_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5155_cast = add(x = var_5153_cast, y = var_5154_to_fp16)[name = tensor("op_5155_cast")]; + tensor denom_135_epsilon_0_to_fp16 = const()[name = tensor("denom_135_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_135_cast = rsqrt(epsilon = denom_135_epsilon_0_to_fp16, x = var_5155_cast)[name = tensor("denom_135_cast")]; + tensor out_135_cast = mul(x = zero_mean_135_cast, y = denom_135_cast)[name = tensor("out_135_cast")]; + tensor var_5159_to_fp16 = const()[name = tensor("op_5159_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2815948800)))]; + tensor var_5160_cast = add(x = out_135_cast, y = var_5159_to_fp16)[name = tensor("op_5160_cast")]; + tensor var_5162_to_fp16 = const()[name = tensor("op_5162_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2815951936)))]; + tensor hidden_states_243_cast = mul(x = var_5160_cast, y = var_5162_to_fp16)[name = tensor("hidden_states_243_cast")]; + tensor var_5169 = const()[name = tensor("op_5169"), val = tensor([1, 1])]; + tensor var_5171 = const()[name = tensor("op_5171"), val = tensor([1, 1])]; + tensor q_91_pad_type_0 = const()[name = tensor("q_91_pad_type_0"), val = tensor("custom")]; + tensor q_91_pad_0 = const()[name = tensor("q_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2815955072)))]; + tensor q_91_cast = conv(dilations = var_5171, groups = var_4628, pad = q_91_pad_0, pad_type = q_91_pad_type_0, strides = var_5169, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_243_cast)[name = tensor("q_91_cast")]; + tensor var_5175 = const()[name = tensor("op_5175"), val = tensor([1, 1])]; + tensor var_5177 = const()[name = tensor("op_5177"), val = tensor([1, 1])]; + tensor k_91_pad_type_0 = const()[name = tensor("k_91_pad_type_0"), val = tensor("custom")]; + tensor k_91_pad_0 = const()[name = tensor("k_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2820673728)))]; + tensor k_91_cast = conv(dilations = var_5177, groups = var_4628, pad = k_91_pad_0, pad_type = k_91_pad_type_0, strides = var_5175, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_91_cast")]; + tensor var_5181 = const()[name = tensor("op_5181"), val = tensor([1, 1])]; + tensor var_5183 = const()[name = tensor("op_5183"), val = tensor([1, 1])]; + tensor v_91_pad_type_0 = const()[name = tensor("v_91_pad_type_0"), val = tensor("custom")]; + tensor v_91_pad_0 = const()[name = tensor("v_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2824605952)))]; + tensor v_91_cast = conv(dilations = var_5183, groups = var_4628, pad = v_91_pad_0, pad_type = v_91_pad_type_0, strides = var_5181, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_91_cast")]; + tensor var_5187 = const()[name = tensor("op_5187"), val = tensor([2, 24, 64, -1])]; + tensor var_5188_cast = reshape(shape = var_5187, x = q_91_cast)[name = tensor("op_5188_cast")]; + tensor var_5189 = const()[name = tensor("op_5189"), val = tensor([2, 24, 64, -1])]; + tensor var_5190_cast = reshape(shape = var_5189, x = k_91_cast)[name = tensor("op_5190_cast")]; + tensor var_5191 = const()[name = tensor("op_5191"), val = tensor([2, 24, 64, -1])]; + tensor var_5192_cast = reshape(shape = var_5191, x = v_91_cast)[name = tensor("op_5192_cast")]; + tensor attn_weights_181_transpose_x_0 = const()[name = tensor("attn_weights_181_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_181_transpose_y_0 = const()[name = tensor("attn_weights_181_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_181_cast = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = var_5188_cast, y = var_5190_cast)[name = tensor("attn_weights_181_cast")]; + tensor attn_weights_183_cast = mul(x = attn_weights_181_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_183_cast")]; + tensor var_5196_cast = softmax(axis = var_4612, x = attn_weights_183_cast)[name = tensor("op_5196_cast")]; + tensor attn_91_transpose_x_0 = const()[name = tensor("attn_91_transpose_x_0"), val = tensor(false)]; + tensor attn_91_transpose_y_0 = const()[name = tensor("attn_91_transpose_y_0"), val = tensor(true)]; + tensor attn_91_cast = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_5192_cast, y = var_5196_cast)[name = tensor("attn_91_cast")]; + tensor var_5200 = const()[name = tensor("op_5200"), val = tensor([2, 1536, 1, -1])]; + tensor input_421_cast = reshape(shape = var_5200, x = attn_91_cast)[name = tensor("input_421_cast")]; + tensor var_5205 = const()[name = tensor("op_5205"), val = tensor([1, 1])]; + tensor var_5207 = const()[name = tensor("op_5207"), val = tensor([1, 1])]; + tensor var_5209_pad_type_0 = const()[name = tensor("op_5209_pad_type_0"), val = tensor("custom")]; + tensor var_5209_pad_0 = const()[name = tensor("op_5209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2828538176)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2833256832)))]; + tensor var_5209_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_5207, groups = var_4628, pad = var_5209_pad_0, pad_type = var_5209_pad_type_0, strides = var_5205, weight = up_blocks_1_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_421_cast)[name = tensor("op_5209_cast")]; + tensor inputs_137_cast = add(x = var_5209_cast, y = inputs_135_cast)[name = tensor("inputs_137_cast")]; + tensor var_5213 = const()[name = tensor("op_5213"), val = tensor([1])]; + tensor channels_mean_137_cast = reduce_mean(axes = var_5213, keep_dims = var_4623, x = inputs_137_cast)[name = tensor("channels_mean_137_cast")]; + tensor zero_mean_137_cast = sub(x = inputs_137_cast, y = channels_mean_137_cast)[name = tensor("zero_mean_137_cast")]; + tensor zero_mean_sq_137_cast = mul(x = zero_mean_137_cast, y = zero_mean_137_cast)[name = tensor("zero_mean_sq_137_cast")]; + tensor var_5217 = const()[name = tensor("op_5217"), val = tensor([1])]; + tensor var_5218_cast = reduce_mean(axes = var_5217, keep_dims = var_4623, x = zero_mean_sq_137_cast)[name = tensor("op_5218_cast")]; + tensor var_5219_to_fp16 = const()[name = tensor("op_5219_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5220_cast = add(x = var_5218_cast, y = var_5219_to_fp16)[name = tensor("op_5220_cast")]; + tensor denom_137_epsilon_0_to_fp16 = const()[name = tensor("denom_137_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_137_cast = rsqrt(epsilon = denom_137_epsilon_0_to_fp16, x = var_5220_cast)[name = tensor("denom_137_cast")]; + tensor out_137_cast = mul(x = zero_mean_137_cast, y = denom_137_cast)[name = tensor("out_137_cast")]; + tensor var_5224_to_fp16 = const()[name = tensor("op_5224_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2833259968)))]; + tensor var_5225_cast = add(x = out_137_cast, y = var_5224_to_fp16)[name = tensor("op_5225_cast")]; + tensor var_5227_to_fp16 = const()[name = tensor("op_5227_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2833263104)))]; + tensor input_423_cast = mul(x = var_5225_cast, y = var_5227_to_fp16)[name = tensor("input_423_cast")]; + tensor var_5235 = const()[name = tensor("op_5235"), val = tensor([1, 1])]; + tensor var_5237 = const()[name = tensor("op_5237"), val = tensor([1, 1])]; + tensor var_5239_pad_type_0 = const()[name = tensor("op_5239_pad_type_0"), val = tensor("custom")]; + tensor var_5239_pad_0 = const()[name = tensor("op_5239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2833266240)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2871015040)))]; + tensor var_5239_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_5237, groups = var_4628, pad = var_5239_pad_0, pad_type = var_5239_pad_type_0, strides = var_5235, weight = up_blocks_1_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_423_cast)[name = tensor("op_5239_cast")]; + tensor var_5240_split_sizes_0 = const()[name = tensor("op_5240_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_5240_axis_0 = const()[name = tensor("op_5240_axis_0"), val = tensor(1)]; + tensor var_5240_cast_0, tensor var_5240_cast_1 = split(axis = var_5240_axis_0, split_sizes = var_5240_split_sizes_0, x = var_5239_cast)[name = tensor("op_5240_cast")]; + tensor var_5242_mode_0 = const()[name = tensor("op_5242_mode_0"), val = tensor("EXACT")]; + tensor var_5242_cast = gelu(mode = var_5242_mode_0, x = var_5240_cast_1)[name = tensor("op_5242_cast")]; + tensor input_425_cast = mul(x = var_5240_cast_0, y = var_5242_cast)[name = tensor("input_425_cast")]; + tensor var_5246 = const()[name = tensor("op_5246"), val = tensor([1, 1])]; + tensor var_5248 = const()[name = tensor("op_5248"), val = tensor([1, 1])]; + tensor var_5250_pad_type_0 = const()[name = tensor("op_5250_pad_type_0"), val = tensor("custom")]; + tensor var_5250_pad_0 = const()[name = tensor("op_5250_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2871039680)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2889914112)))]; + tensor var_5250_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_5248, groups = var_4628, pad = var_5250_pad_0, pad_type = var_5250_pad_type_0, strides = var_5246, weight = up_blocks_1_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_425_cast)[name = tensor("op_5250_cast")]; + tensor inputs_139_cast = add(x = var_5250_cast, y = inputs_137_cast)[name = tensor("inputs_139_cast")]; + tensor var_5260 = const()[name = tensor("op_5260"), val = tensor([1])]; + tensor channels_mean_139_cast = reduce_mean(axes = var_5260, keep_dims = var_4623, x = inputs_139_cast)[name = tensor("channels_mean_139_cast")]; + tensor zero_mean_139_cast = sub(x = inputs_139_cast, y = channels_mean_139_cast)[name = tensor("zero_mean_139_cast")]; + tensor zero_mean_sq_139_cast = mul(x = zero_mean_139_cast, y = zero_mean_139_cast)[name = tensor("zero_mean_sq_139_cast")]; + tensor var_5264 = const()[name = tensor("op_5264"), val = tensor([1])]; + tensor var_5265_cast = reduce_mean(axes = var_5264, keep_dims = var_4623, x = zero_mean_sq_139_cast)[name = tensor("op_5265_cast")]; + tensor var_5266_to_fp16 = const()[name = tensor("op_5266_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5267_cast = add(x = var_5265_cast, y = var_5266_to_fp16)[name = tensor("op_5267_cast")]; + tensor denom_139_epsilon_0_to_fp16 = const()[name = tensor("denom_139_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_139_cast = rsqrt(epsilon = denom_139_epsilon_0_to_fp16, x = var_5267_cast)[name = tensor("denom_139_cast")]; + tensor out_139_cast = mul(x = zero_mean_139_cast, y = denom_139_cast)[name = tensor("out_139_cast")]; + tensor var_5271_to_fp16 = const()[name = tensor("op_5271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2889917248)))]; + tensor var_5272_cast = add(x = out_139_cast, y = var_5271_to_fp16)[name = tensor("op_5272_cast")]; + tensor var_5274_to_fp16 = const()[name = tensor("op_5274_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2889920384)))]; + tensor hidden_states_247_cast = mul(x = var_5272_cast, y = var_5274_to_fp16)[name = tensor("hidden_states_247_cast")]; + tensor var_5281 = const()[name = tensor("op_5281"), val = tensor([1, 1])]; + tensor var_5283 = const()[name = tensor("op_5283"), val = tensor([1, 1])]; + tensor q_93_pad_type_0 = const()[name = tensor("q_93_pad_type_0"), val = tensor("custom")]; + tensor q_93_pad_0 = const()[name = tensor("q_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2889923520)))]; + tensor q_93_cast = conv(dilations = var_5283, groups = var_4628, pad = q_93_pad_0, pad_type = q_93_pad_type_0, strides = var_5281, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_247_cast)[name = tensor("q_93_cast")]; + tensor var_5287 = const()[name = tensor("op_5287"), val = tensor([1, 1])]; + tensor var_5289 = const()[name = tensor("op_5289"), val = tensor([1, 1])]; + tensor k_93_pad_type_0 = const()[name = tensor("k_93_pad_type_0"), val = tensor("custom")]; + tensor k_93_pad_0 = const()[name = tensor("k_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2894642176)))]; + tensor k_93_cast = conv(dilations = var_5289, groups = var_4628, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_5287, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_247_cast)[name = tensor("k_93_cast")]; + tensor var_5293 = const()[name = tensor("op_5293"), val = tensor([1, 1])]; + tensor var_5295 = const()[name = tensor("op_5295"), val = tensor([1, 1])]; + tensor v_93_pad_type_0 = const()[name = tensor("v_93_pad_type_0"), val = tensor("custom")]; + tensor v_93_pad_0 = const()[name = tensor("v_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2899360832)))]; + tensor v_93_cast = conv(dilations = var_5295, groups = var_4628, pad = v_93_pad_0, pad_type = v_93_pad_type_0, strides = var_5293, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_247_cast)[name = tensor("v_93_cast")]; + tensor var_5299 = const()[name = tensor("op_5299"), val = tensor([2, 24, 64, -1])]; + tensor var_5300_cast = reshape(shape = var_5299, x = q_93_cast)[name = tensor("op_5300_cast")]; + tensor var_5301 = const()[name = tensor("op_5301"), val = tensor([2, 24, 64, -1])]; + tensor var_5302_cast = reshape(shape = var_5301, x = k_93_cast)[name = tensor("op_5302_cast")]; + tensor var_5303 = const()[name = tensor("op_5303"), val = tensor([2, 24, 64, -1])]; + tensor var_5304_cast = reshape(shape = var_5303, x = v_93_cast)[name = tensor("op_5304_cast")]; + tensor attn_weights_185_transpose_x_0 = const()[name = tensor("attn_weights_185_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_185_transpose_y_0 = const()[name = tensor("attn_weights_185_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_185_cast = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_5300_cast, y = var_5302_cast)[name = tensor("attn_weights_185_cast")]; + tensor attn_weights_187_cast = mul(x = attn_weights_185_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_187_cast")]; + tensor var_5308_cast = softmax(axis = var_4612, x = attn_weights_187_cast)[name = tensor("op_5308_cast")]; + tensor attn_93_transpose_x_0 = const()[name = tensor("attn_93_transpose_x_0"), val = tensor(false)]; + tensor attn_93_transpose_y_0 = const()[name = tensor("attn_93_transpose_y_0"), val = tensor(true)]; + tensor attn_93_cast = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_5304_cast, y = var_5308_cast)[name = tensor("attn_93_cast")]; + tensor var_5312 = const()[name = tensor("op_5312"), val = tensor([2, 1536, 1, -1])]; + tensor input_427_cast = reshape(shape = var_5312, x = attn_93_cast)[name = tensor("input_427_cast")]; + tensor var_5317 = const()[name = tensor("op_5317"), val = tensor([1, 1])]; + tensor var_5319 = const()[name = tensor("op_5319"), val = tensor([1, 1])]; + tensor var_5321_pad_type_0 = const()[name = tensor("op_5321_pad_type_0"), val = tensor("custom")]; + tensor var_5321_pad_0 = const()[name = tensor("op_5321_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2904079488)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2908798144)))]; + tensor var_5321_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_5319, groups = var_4628, pad = var_5321_pad_0, pad_type = var_5321_pad_type_0, strides = var_5317, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_427_cast)[name = tensor("op_5321_cast")]; + tensor inputs_141_cast = add(x = var_5321_cast, y = inputs_139_cast)[name = tensor("inputs_141_cast")]; + tensor var_5325 = const()[name = tensor("op_5325"), val = tensor([1])]; + tensor channels_mean_141_cast = reduce_mean(axes = var_5325, keep_dims = var_4623, x = inputs_141_cast)[name = tensor("channels_mean_141_cast")]; + tensor zero_mean_141_cast = sub(x = inputs_141_cast, y = channels_mean_141_cast)[name = tensor("zero_mean_141_cast")]; + tensor zero_mean_sq_141_cast = mul(x = zero_mean_141_cast, y = zero_mean_141_cast)[name = tensor("zero_mean_sq_141_cast")]; + tensor var_5329 = const()[name = tensor("op_5329"), val = tensor([1])]; + tensor var_5330_cast = reduce_mean(axes = var_5329, keep_dims = var_4623, x = zero_mean_sq_141_cast)[name = tensor("op_5330_cast")]; + tensor var_5331_to_fp16 = const()[name = tensor("op_5331_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5332_cast = add(x = var_5330_cast, y = var_5331_to_fp16)[name = tensor("op_5332_cast")]; + tensor denom_141_epsilon_0_to_fp16 = const()[name = tensor("denom_141_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_141_cast = rsqrt(epsilon = denom_141_epsilon_0_to_fp16, x = var_5332_cast)[name = tensor("denom_141_cast")]; + tensor out_141_cast = mul(x = zero_mean_141_cast, y = denom_141_cast)[name = tensor("out_141_cast")]; + tensor var_5336_to_fp16 = const()[name = tensor("op_5336_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2908801280)))]; + tensor var_5337_cast = add(x = out_141_cast, y = var_5336_to_fp16)[name = tensor("op_5337_cast")]; + tensor var_5339_to_fp16 = const()[name = tensor("op_5339_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2908804416)))]; + tensor hidden_states_249_cast = mul(x = var_5337_cast, y = var_5339_to_fp16)[name = tensor("hidden_states_249_cast")]; + tensor var_5346 = const()[name = tensor("op_5346"), val = tensor([1, 1])]; + tensor var_5348 = const()[name = tensor("op_5348"), val = tensor([1, 1])]; + tensor q_95_pad_type_0 = const()[name = tensor("q_95_pad_type_0"), val = tensor("custom")]; + tensor q_95_pad_0 = const()[name = tensor("q_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2908807552)))]; + tensor q_95_cast = conv(dilations = var_5348, groups = var_4628, pad = q_95_pad_0, pad_type = q_95_pad_type_0, strides = var_5346, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_249_cast)[name = tensor("q_95_cast")]; + tensor var_5352 = const()[name = tensor("op_5352"), val = tensor([1, 1])]; + tensor var_5354 = const()[name = tensor("op_5354"), val = tensor([1, 1])]; + tensor k_95_pad_type_0 = const()[name = tensor("k_95_pad_type_0"), val = tensor("custom")]; + tensor k_95_pad_0 = const()[name = tensor("k_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2913526208)))]; + tensor k_95_cast = conv(dilations = var_5354, groups = var_4628, pad = k_95_pad_0, pad_type = k_95_pad_type_0, strides = var_5352, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_95_cast")]; + tensor var_5358 = const()[name = tensor("op_5358"), val = tensor([1, 1])]; + tensor var_5360 = const()[name = tensor("op_5360"), val = tensor([1, 1])]; + tensor v_95_pad_type_0 = const()[name = tensor("v_95_pad_type_0"), val = tensor("custom")]; + tensor v_95_pad_0 = const()[name = tensor("v_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2917458432)))]; + tensor v_95_cast = conv(dilations = var_5360, groups = var_4628, pad = v_95_pad_0, pad_type = v_95_pad_type_0, strides = var_5358, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_95_cast")]; + tensor var_5364 = const()[name = tensor("op_5364"), val = tensor([2, 24, 64, -1])]; + tensor var_5365_cast = reshape(shape = var_5364, x = q_95_cast)[name = tensor("op_5365_cast")]; + tensor var_5366 = const()[name = tensor("op_5366"), val = tensor([2, 24, 64, -1])]; + tensor var_5367_cast = reshape(shape = var_5366, x = k_95_cast)[name = tensor("op_5367_cast")]; + tensor var_5368 = const()[name = tensor("op_5368"), val = tensor([2, 24, 64, -1])]; + tensor var_5369_cast = reshape(shape = var_5368, x = v_95_cast)[name = tensor("op_5369_cast")]; + tensor attn_weights_189_transpose_x_0 = const()[name = tensor("attn_weights_189_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_189_transpose_y_0 = const()[name = tensor("attn_weights_189_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_189_cast = matmul(transpose_x = attn_weights_189_transpose_x_0, transpose_y = attn_weights_189_transpose_y_0, x = var_5365_cast, y = var_5367_cast)[name = tensor("attn_weights_189_cast")]; + tensor attn_weights_191_cast = mul(x = attn_weights_189_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_191_cast")]; + tensor var_5373_cast = softmax(axis = var_4612, x = attn_weights_191_cast)[name = tensor("op_5373_cast")]; + tensor attn_95_transpose_x_0 = const()[name = tensor("attn_95_transpose_x_0"), val = tensor(false)]; + tensor attn_95_transpose_y_0 = const()[name = tensor("attn_95_transpose_y_0"), val = tensor(true)]; + tensor attn_95_cast = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_5369_cast, y = var_5373_cast)[name = tensor("attn_95_cast")]; + tensor var_5377 = const()[name = tensor("op_5377"), val = tensor([2, 1536, 1, -1])]; + tensor input_429_cast = reshape(shape = var_5377, x = attn_95_cast)[name = tensor("input_429_cast")]; + tensor var_5382 = const()[name = tensor("op_5382"), val = tensor([1, 1])]; + tensor var_5384 = const()[name = tensor("op_5384"), val = tensor([1, 1])]; + tensor var_5386_pad_type_0 = const()[name = tensor("op_5386_pad_type_0"), val = tensor("custom")]; + tensor var_5386_pad_0 = const()[name = tensor("op_5386_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2921390656)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2926109312)))]; + tensor var_5386_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_5384, groups = var_4628, pad = var_5386_pad_0, pad_type = var_5386_pad_type_0, strides = var_5382, weight = up_blocks_1_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_429_cast)[name = tensor("op_5386_cast")]; + tensor inputs_143_cast = add(x = var_5386_cast, y = inputs_141_cast)[name = tensor("inputs_143_cast")]; + tensor var_5390 = const()[name = tensor("op_5390"), val = tensor([1])]; + tensor channels_mean_143_cast = reduce_mean(axes = var_5390, keep_dims = var_4623, x = inputs_143_cast)[name = tensor("channels_mean_143_cast")]; + tensor zero_mean_143_cast = sub(x = inputs_143_cast, y = channels_mean_143_cast)[name = tensor("zero_mean_143_cast")]; + tensor zero_mean_sq_143_cast = mul(x = zero_mean_143_cast, y = zero_mean_143_cast)[name = tensor("zero_mean_sq_143_cast")]; + tensor var_5394 = const()[name = tensor("op_5394"), val = tensor([1])]; + tensor var_5395_cast = reduce_mean(axes = var_5394, keep_dims = var_4623, x = zero_mean_sq_143_cast)[name = tensor("op_5395_cast")]; + tensor var_5396_to_fp16 = const()[name = tensor("op_5396_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5397_cast = add(x = var_5395_cast, y = var_5396_to_fp16)[name = tensor("op_5397_cast")]; + tensor denom_143_epsilon_0_to_fp16 = const()[name = tensor("denom_143_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_143_cast = rsqrt(epsilon = denom_143_epsilon_0_to_fp16, x = var_5397_cast)[name = tensor("denom_143_cast")]; + tensor out_143_cast = mul(x = zero_mean_143_cast, y = denom_143_cast)[name = tensor("out_143_cast")]; + tensor var_5401_to_fp16 = const()[name = tensor("op_5401_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2926112448)))]; + tensor var_5402_cast = add(x = out_143_cast, y = var_5401_to_fp16)[name = tensor("op_5402_cast")]; + tensor var_5404_to_fp16 = const()[name = tensor("op_5404_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2926115584)))]; + tensor input_431_cast = mul(x = var_5402_cast, y = var_5404_to_fp16)[name = tensor("input_431_cast")]; + tensor var_5412 = const()[name = tensor("op_5412"), val = tensor([1, 1])]; + tensor var_5414 = const()[name = tensor("op_5414"), val = tensor([1, 1])]; + tensor var_5416_pad_type_0 = const()[name = tensor("op_5416_pad_type_0"), val = tensor("custom")]; + tensor var_5416_pad_0 = const()[name = tensor("op_5416_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2926118720)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2963867520)))]; + tensor var_5416_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_5414, groups = var_4628, pad = var_5416_pad_0, pad_type = var_5416_pad_type_0, strides = var_5412, weight = up_blocks_1_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_431_cast)[name = tensor("op_5416_cast")]; + tensor var_5417_split_sizes_0 = const()[name = tensor("op_5417_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_5417_axis_0 = const()[name = tensor("op_5417_axis_0"), val = tensor(1)]; + tensor var_5417_cast_0, tensor var_5417_cast_1 = split(axis = var_5417_axis_0, split_sizes = var_5417_split_sizes_0, x = var_5416_cast)[name = tensor("op_5417_cast")]; + tensor var_5419_mode_0 = const()[name = tensor("op_5419_mode_0"), val = tensor("EXACT")]; + tensor var_5419_cast = gelu(mode = var_5419_mode_0, x = var_5417_cast_1)[name = tensor("op_5419_cast")]; + tensor input_433_cast = mul(x = var_5417_cast_0, y = var_5419_cast)[name = tensor("input_433_cast")]; + tensor var_5423 = const()[name = tensor("op_5423"), val = tensor([1, 1])]; + tensor var_5425 = const()[name = tensor("op_5425"), val = tensor([1, 1])]; + tensor var_5427_pad_type_0 = const()[name = tensor("op_5427_pad_type_0"), val = tensor("custom")]; + tensor var_5427_pad_0 = const()[name = tensor("op_5427_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2963892160)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2982766592)))]; + tensor var_5427_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_5425, groups = var_4628, pad = var_5427_pad_0, pad_type = var_5427_pad_type_0, strides = var_5423, weight = up_blocks_1_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_433_cast)[name = tensor("op_5427_cast")]; + tensor hidden_states_253_cast = add(x = var_5427_cast, y = inputs_143_cast)[name = tensor("hidden_states_253_cast")]; + tensor var_5429 = const()[name = tensor("op_5429"), val = tensor([2, 1536, 32, 32])]; + tensor input_435_cast = reshape(shape = var_5429, x = hidden_states_253_cast)[name = tensor("input_435_cast")]; + tensor var_5433 = const()[name = tensor("op_5433"), val = tensor([1, 1])]; + tensor var_5435 = const()[name = tensor("op_5435"), val = tensor([1, 1])]; + tensor hidden_states_255_pad_type_0 = const()[name = tensor("hidden_states_255_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_255_pad_0 = const()[name = tensor("hidden_states_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2982769728)))]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2987488384)))]; + tensor hidden_states_255_cast = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_5435, groups = var_4628, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_5433, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_435_cast)[name = tensor("hidden_states_255_cast")]; + tensor hidden_states_257_cast = add(x = hidden_states_255_cast, y = hidden_states_225_cast)[name = tensor("hidden_states_257_cast")]; + tensor input_437_interleave_0 = const()[name = tensor("input_437_interleave_0"), val = tensor(false)]; + tensor input_437_cast = concat(axis = var_4628, interleave = input_437_interleave_0, values = (hidden_states_257_cast, input_197_cast))[name = tensor("input_437_cast")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 96, 32, 32])]; + tensor reshape_136_cast = reshape(shape = reshape_136_shape_0, x = input_437_cast)[name = tensor("reshape_136_cast")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast)[name = tensor("reduce_mean_102_cast")]; + tensor sub_68_cast = sub(x = reshape_136_cast, y = reduce_mean_102_cast)[name = tensor("sub_68_cast")]; + tensor square_34_cast = square(x = sub_68_cast)[name = tensor("square_34_cast")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast)[name = tensor("reduce_mean_104_cast")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast = add(x = reduce_mean_104_cast, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast")]; + tensor sqrt_34_cast = sqrt(x = add_68_cast)[name = tensor("sqrt_34_cast")]; + tensor real_div_34_cast = real_div(x = sub_68_cast, y = sqrt_34_cast)[name = tensor("real_div_34_cast")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 3072, 32, 32])]; + tensor reshape_137_cast = reshape(shape = reshape_137_shape_0, x = real_div_34_cast)[name = tensor("reshape_137_cast")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2987491520)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2987497728)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_51_mean_0_to_fp16, variance = add_51_variance_0_to_fp16, x = reshape_137_cast)[name = tensor("add_69_cast")]; + tensor input_441_cast = silu(x = add_69_cast)[name = tensor("input_441_cast")]; + tensor var_5453 = const()[name = tensor("op_5453"), val = tensor([1, 1])]; + tensor var_5455 = const()[name = tensor("op_5455"), val = tensor([1, 1])]; + tensor hidden_states_259_pad_type_0 = const()[name = tensor("hidden_states_259_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_259_pad_0 = const()[name = tensor("hidden_states_259_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2987503936)))]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3072438656)))]; + tensor hidden_states_259_cast = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_5455, groups = var_4628, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_5453, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_441_cast)[name = tensor("hidden_states_259_cast")]; + tensor var_5461 = const()[name = tensor("op_5461"), val = tensor([1, 1])]; + tensor var_5463 = const()[name = tensor("op_5463"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3072441792)))]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3077160448)))]; + tensor temb_29_cast = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_5463, groups = var_4628, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_5461, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_29_cast")]; + tensor input_445_cast = add(x = hidden_states_259_cast, y = temb_29_cast)[name = tensor("input_445_cast")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_140_cast = reshape(shape = reshape_140_shape_0, x = input_445_cast)[name = tensor("reshape_140_cast")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast)[name = tensor("reduce_mean_105_cast")]; + tensor sub_70_cast = sub(x = reshape_140_cast, y = reduce_mean_105_cast)[name = tensor("sub_70_cast")]; + tensor square_35_cast = square(x = sub_70_cast)[name = tensor("square_35_cast")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast)[name = tensor("reduce_mean_107_cast")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_70_cast = add(x = reduce_mean_107_cast, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast")]; + tensor sqrt_35_cast = sqrt(x = add_70_cast)[name = tensor("sqrt_35_cast")]; + tensor real_div_35_cast = real_div(x = sub_70_cast, y = sqrt_35_cast)[name = tensor("real_div_35_cast")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_141_cast = reshape(shape = reshape_141_shape_0, x = real_div_35_cast)[name = tensor("reshape_141_cast")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3077163584)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3077166720)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_141_cast)[name = tensor("add_71_cast")]; + tensor input_449_cast = silu(x = add_71_cast)[name = tensor("input_449_cast")]; + tensor var_5473 = const()[name = tensor("op_5473"), val = tensor([1, 1])]; + tensor var_5475 = const()[name = tensor("op_5475"), val = tensor([1, 1])]; + tensor hidden_states_261_pad_type_0 = const()[name = tensor("hidden_states_261_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_261_pad_0 = const()[name = tensor("hidden_states_261_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3077169856)))]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3119637248)))]; + tensor hidden_states_261_cast = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_5475, groups = var_4628, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_5473, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_449_cast)[name = tensor("hidden_states_261_cast")]; + tensor var_5480 = const()[name = tensor("op_5480"), val = tensor([1, 1])]; + tensor var_5482 = const()[name = tensor("op_5482"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3119640384)))]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3129077632)))]; + tensor x_13_cast = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_5482, groups = var_4628, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_5480, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16, x = input_437_cast)[name = tensor("x_13_cast")]; + tensor hidden_states_263_cast = add(x = x_13_cast, y = hidden_states_261_cast)[name = tensor("hidden_states_263_cast")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_144_cast = reshape(shape = reshape_144_shape_0, x = hidden_states_263_cast)[name = tensor("reshape_144_cast")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast)[name = tensor("reduce_mean_108_cast")]; + tensor sub_72_cast = sub(x = reshape_144_cast, y = reduce_mean_108_cast)[name = tensor("sub_72_cast")]; + tensor square_36_cast = square(x = sub_72_cast)[name = tensor("square_36_cast")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast)[name = tensor("reduce_mean_110_cast")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_72_cast = add(x = reduce_mean_110_cast, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast")]; + tensor sqrt_36_cast = sqrt(x = add_72_cast)[name = tensor("sqrt_36_cast")]; + tensor real_div_36_cast = real_div(x = sub_72_cast, y = sqrt_36_cast)[name = tensor("real_div_36_cast")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_145_cast = reshape(shape = reshape_145_shape_0, x = real_div_36_cast)[name = tensor("reshape_145_cast")]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3129080768)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3129083904)))]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_145_cast)[name = tensor("add_73_cast")]; + tensor var_5508 = const()[name = tensor("op_5508"), val = tensor([1, 1])]; + tensor var_5510 = const()[name = tensor("op_5510"), val = tensor([1, 1])]; + tensor hidden_states_265_pad_type_0 = const()[name = tensor("hidden_states_265_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_265_pad_0 = const()[name = tensor("hidden_states_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3129087040)))]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3133805696)))]; + tensor hidden_states_265_cast = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_5510, groups = var_4628, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_5508, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_73_cast)[name = tensor("hidden_states_265_cast")]; + tensor var_5515 = const()[name = tensor("op_5515"), val = tensor([2, 1536, 1, 1024])]; + tensor inputs_145_cast = reshape(shape = var_5515, x = hidden_states_265_cast)[name = tensor("inputs_145_cast")]; + tensor var_5525 = const()[name = tensor("op_5525"), val = tensor([1])]; + tensor channels_mean_145_cast = reduce_mean(axes = var_5525, keep_dims = var_4623, x = inputs_145_cast)[name = tensor("channels_mean_145_cast")]; + tensor zero_mean_145_cast = sub(x = inputs_145_cast, y = channels_mean_145_cast)[name = tensor("zero_mean_145_cast")]; + tensor zero_mean_sq_145_cast = mul(x = zero_mean_145_cast, y = zero_mean_145_cast)[name = tensor("zero_mean_sq_145_cast")]; + tensor var_5529 = const()[name = tensor("op_5529"), val = tensor([1])]; + tensor var_5530_cast = reduce_mean(axes = var_5529, keep_dims = var_4623, x = zero_mean_sq_145_cast)[name = tensor("op_5530_cast")]; + tensor var_5531_to_fp16 = const()[name = tensor("op_5531_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5532_cast = add(x = var_5530_cast, y = var_5531_to_fp16)[name = tensor("op_5532_cast")]; + tensor denom_145_epsilon_0_to_fp16 = const()[name = tensor("denom_145_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_145_cast = rsqrt(epsilon = denom_145_epsilon_0_to_fp16, x = var_5532_cast)[name = tensor("denom_145_cast")]; + tensor out_145_cast = mul(x = zero_mean_145_cast, y = denom_145_cast)[name = tensor("out_145_cast")]; + tensor var_5536_to_fp16 = const()[name = tensor("op_5536_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3133808832)))]; + tensor var_5537_cast = add(x = out_145_cast, y = var_5536_to_fp16)[name = tensor("op_5537_cast")]; + tensor var_5539_to_fp16 = const()[name = tensor("op_5539_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3133811968)))]; + tensor hidden_states_267_cast = mul(x = var_5537_cast, y = var_5539_to_fp16)[name = tensor("hidden_states_267_cast")]; + tensor var_5546 = const()[name = tensor("op_5546"), val = tensor([1, 1])]; + tensor var_5548 = const()[name = tensor("op_5548"), val = tensor([1, 1])]; + tensor q_97_pad_type_0 = const()[name = tensor("q_97_pad_type_0"), val = tensor("custom")]; + tensor q_97_pad_0 = const()[name = tensor("q_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3133815104)))]; + tensor q_97_cast = conv(dilations = var_5548, groups = var_4628, pad = q_97_pad_0, pad_type = q_97_pad_type_0, strides = var_5546, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_267_cast)[name = tensor("q_97_cast")]; + tensor var_5552 = const()[name = tensor("op_5552"), val = tensor([1, 1])]; + tensor var_5554 = const()[name = tensor("op_5554"), val = tensor([1, 1])]; + tensor k_97_pad_type_0 = const()[name = tensor("k_97_pad_type_0"), val = tensor("custom")]; + tensor k_97_pad_0 = const()[name = tensor("k_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3138533760)))]; + tensor k_97_cast = conv(dilations = var_5554, groups = var_4628, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_5552, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_267_cast)[name = tensor("k_97_cast")]; + tensor var_5558 = const()[name = tensor("op_5558"), val = tensor([1, 1])]; + tensor var_5560 = const()[name = tensor("op_5560"), val = tensor([1, 1])]; + tensor v_97_pad_type_0 = const()[name = tensor("v_97_pad_type_0"), val = tensor("custom")]; + tensor v_97_pad_0 = const()[name = tensor("v_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3143252416)))]; + tensor v_97_cast = conv(dilations = var_5560, groups = var_4628, pad = v_97_pad_0, pad_type = v_97_pad_type_0, strides = var_5558, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_267_cast)[name = tensor("v_97_cast")]; + tensor var_5564 = const()[name = tensor("op_5564"), val = tensor([2, 24, 64, -1])]; + tensor var_5565_cast = reshape(shape = var_5564, x = q_97_cast)[name = tensor("op_5565_cast")]; + tensor var_5566 = const()[name = tensor("op_5566"), val = tensor([2, 24, 64, -1])]; + tensor var_5567_cast = reshape(shape = var_5566, x = k_97_cast)[name = tensor("op_5567_cast")]; + tensor var_5568 = const()[name = tensor("op_5568"), val = tensor([2, 24, 64, -1])]; + tensor var_5569_cast = reshape(shape = var_5568, x = v_97_cast)[name = tensor("op_5569_cast")]; + tensor attn_weights_193_transpose_x_0 = const()[name = tensor("attn_weights_193_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_193_transpose_y_0 = const()[name = tensor("attn_weights_193_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_193_cast = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_5565_cast, y = var_5567_cast)[name = tensor("attn_weights_193_cast")]; + tensor attn_weights_195_cast = mul(x = attn_weights_193_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_195_cast")]; + tensor var_5573_cast = softmax(axis = var_4612, x = attn_weights_195_cast)[name = tensor("op_5573_cast")]; + tensor attn_97_transpose_x_0 = const()[name = tensor("attn_97_transpose_x_0"), val = tensor(false)]; + tensor attn_97_transpose_y_0 = const()[name = tensor("attn_97_transpose_y_0"), val = tensor(true)]; + tensor attn_97_cast = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5569_cast, y = var_5573_cast)[name = tensor("attn_97_cast")]; + tensor var_5577 = const()[name = tensor("op_5577"), val = tensor([2, 1536, 1, -1])]; + tensor input_453_cast = reshape(shape = var_5577, x = attn_97_cast)[name = tensor("input_453_cast")]; + tensor var_5582 = const()[name = tensor("op_5582"), val = tensor([1, 1])]; + tensor var_5584 = const()[name = tensor("op_5584"), val = tensor([1, 1])]; + tensor var_5586_pad_type_0 = const()[name = tensor("op_5586_pad_type_0"), val = tensor("custom")]; + tensor var_5586_pad_0 = const()[name = tensor("op_5586_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3147971072)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3152689728)))]; + tensor var_5586_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5584, groups = var_4628, pad = var_5586_pad_0, pad_type = var_5586_pad_type_0, strides = var_5582, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_453_cast)[name = tensor("op_5586_cast")]; + tensor inputs_147_cast = add(x = var_5586_cast, y = inputs_145_cast)[name = tensor("inputs_147_cast")]; + tensor var_5590 = const()[name = tensor("op_5590"), val = tensor([1])]; + tensor channels_mean_147_cast = reduce_mean(axes = var_5590, keep_dims = var_4623, x = inputs_147_cast)[name = tensor("channels_mean_147_cast")]; + tensor zero_mean_147_cast = sub(x = inputs_147_cast, y = channels_mean_147_cast)[name = tensor("zero_mean_147_cast")]; + tensor zero_mean_sq_147_cast = mul(x = zero_mean_147_cast, y = zero_mean_147_cast)[name = tensor("zero_mean_sq_147_cast")]; + tensor var_5594 = const()[name = tensor("op_5594"), val = tensor([1])]; + tensor var_5595_cast = reduce_mean(axes = var_5594, keep_dims = var_4623, x = zero_mean_sq_147_cast)[name = tensor("op_5595_cast")]; + tensor var_5596_to_fp16 = const()[name = tensor("op_5596_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5597_cast = add(x = var_5595_cast, y = var_5596_to_fp16)[name = tensor("op_5597_cast")]; + tensor denom_147_epsilon_0_to_fp16 = const()[name = tensor("denom_147_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_147_cast = rsqrt(epsilon = denom_147_epsilon_0_to_fp16, x = var_5597_cast)[name = tensor("denom_147_cast")]; + tensor out_147_cast = mul(x = zero_mean_147_cast, y = denom_147_cast)[name = tensor("out_147_cast")]; + tensor var_5601_to_fp16 = const()[name = tensor("op_5601_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3152692864)))]; + tensor var_5602_cast = add(x = out_147_cast, y = var_5601_to_fp16)[name = tensor("op_5602_cast")]; + tensor var_5604_to_fp16 = const()[name = tensor("op_5604_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3152696000)))]; + tensor hidden_states_269_cast = mul(x = var_5602_cast, y = var_5604_to_fp16)[name = tensor("hidden_states_269_cast")]; + tensor var_5611 = const()[name = tensor("op_5611"), val = tensor([1, 1])]; + tensor var_5613 = const()[name = tensor("op_5613"), val = tensor([1, 1])]; + tensor q_99_pad_type_0 = const()[name = tensor("q_99_pad_type_0"), val = tensor("custom")]; + tensor q_99_pad_0 = const()[name = tensor("q_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3152699136)))]; + tensor q_99_cast = conv(dilations = var_5613, groups = var_4628, pad = q_99_pad_0, pad_type = q_99_pad_type_0, strides = var_5611, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_269_cast)[name = tensor("q_99_cast")]; + tensor var_5617 = const()[name = tensor("op_5617"), val = tensor([1, 1])]; + tensor var_5619 = const()[name = tensor("op_5619"), val = tensor([1, 1])]; + tensor k_99_pad_type_0 = const()[name = tensor("k_99_pad_type_0"), val = tensor("custom")]; + tensor k_99_pad_0 = const()[name = tensor("k_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157417792)))]; + tensor k_99_cast = conv(dilations = var_5619, groups = var_4628, pad = k_99_pad_0, pad_type = k_99_pad_type_0, strides = var_5617, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_99_cast")]; + tensor var_5623 = const()[name = tensor("op_5623"), val = tensor([1, 1])]; + tensor var_5625 = const()[name = tensor("op_5625"), val = tensor([1, 1])]; + tensor v_99_pad_type_0 = const()[name = tensor("v_99_pad_type_0"), val = tensor("custom")]; + tensor v_99_pad_0 = const()[name = tensor("v_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3161350016)))]; + tensor v_99_cast = conv(dilations = var_5625, groups = var_4628, pad = v_99_pad_0, pad_type = v_99_pad_type_0, strides = var_5623, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_99_cast")]; + tensor var_5629 = const()[name = tensor("op_5629"), val = tensor([2, 24, 64, -1])]; + tensor var_5630_cast = reshape(shape = var_5629, x = q_99_cast)[name = tensor("op_5630_cast")]; + tensor var_5631 = const()[name = tensor("op_5631"), val = tensor([2, 24, 64, -1])]; + tensor var_5632_cast = reshape(shape = var_5631, x = k_99_cast)[name = tensor("op_5632_cast")]; + tensor var_5633 = const()[name = tensor("op_5633"), val = tensor([2, 24, 64, -1])]; + tensor var_5634_cast = reshape(shape = var_5633, x = v_99_cast)[name = tensor("op_5634_cast")]; + tensor attn_weights_197_transpose_x_0 = const()[name = tensor("attn_weights_197_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_197_transpose_y_0 = const()[name = tensor("attn_weights_197_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_197_cast = matmul(transpose_x = attn_weights_197_transpose_x_0, transpose_y = attn_weights_197_transpose_y_0, x = var_5630_cast, y = var_5632_cast)[name = tensor("attn_weights_197_cast")]; + tensor attn_weights_199_cast = mul(x = attn_weights_197_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_199_cast")]; + tensor var_5638_cast = softmax(axis = var_4612, x = attn_weights_199_cast)[name = tensor("op_5638_cast")]; + tensor attn_99_transpose_x_0 = const()[name = tensor("attn_99_transpose_x_0"), val = tensor(false)]; + tensor attn_99_transpose_y_0 = const()[name = tensor("attn_99_transpose_y_0"), val = tensor(true)]; + tensor attn_99_cast = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5634_cast, y = var_5638_cast)[name = tensor("attn_99_cast")]; + tensor var_5642 = const()[name = tensor("op_5642"), val = tensor([2, 1536, 1, -1])]; + tensor input_455_cast = reshape(shape = var_5642, x = attn_99_cast)[name = tensor("input_455_cast")]; + tensor var_5647 = const()[name = tensor("op_5647"), val = tensor([1, 1])]; + tensor var_5649 = const()[name = tensor("op_5649"), val = tensor([1, 1])]; + tensor var_5651_pad_type_0 = const()[name = tensor("op_5651_pad_type_0"), val = tensor("custom")]; + tensor var_5651_pad_0 = const()[name = tensor("op_5651_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3165282240)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3170000896)))]; + tensor var_5651_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5649, groups = var_4628, pad = var_5651_pad_0, pad_type = var_5651_pad_type_0, strides = var_5647, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_455_cast)[name = tensor("op_5651_cast")]; + tensor inputs_149_cast = add(x = var_5651_cast, y = inputs_147_cast)[name = tensor("inputs_149_cast")]; + tensor var_5655 = const()[name = tensor("op_5655"), val = tensor([1])]; + tensor channels_mean_149_cast = reduce_mean(axes = var_5655, keep_dims = var_4623, x = inputs_149_cast)[name = tensor("channels_mean_149_cast")]; + tensor zero_mean_149_cast = sub(x = inputs_149_cast, y = channels_mean_149_cast)[name = tensor("zero_mean_149_cast")]; + tensor zero_mean_sq_149_cast = mul(x = zero_mean_149_cast, y = zero_mean_149_cast)[name = tensor("zero_mean_sq_149_cast")]; + tensor var_5659 = const()[name = tensor("op_5659"), val = tensor([1])]; + tensor var_5660_cast = reduce_mean(axes = var_5659, keep_dims = var_4623, x = zero_mean_sq_149_cast)[name = tensor("op_5660_cast")]; + tensor var_5661_to_fp16 = const()[name = tensor("op_5661_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5662_cast = add(x = var_5660_cast, y = var_5661_to_fp16)[name = tensor("op_5662_cast")]; + tensor denom_149_epsilon_0_to_fp16 = const()[name = tensor("denom_149_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_149_cast = rsqrt(epsilon = denom_149_epsilon_0_to_fp16, x = var_5662_cast)[name = tensor("denom_149_cast")]; + tensor out_149_cast = mul(x = zero_mean_149_cast, y = denom_149_cast)[name = tensor("out_149_cast")]; + tensor var_5666_to_fp16 = const()[name = tensor("op_5666_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3170004032)))]; + tensor var_5667_cast = add(x = out_149_cast, y = var_5666_to_fp16)[name = tensor("op_5667_cast")]; + tensor var_5669_to_fp16 = const()[name = tensor("op_5669_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3170007168)))]; + tensor input_457_cast = mul(x = var_5667_cast, y = var_5669_to_fp16)[name = tensor("input_457_cast")]; + tensor var_5677 = const()[name = tensor("op_5677"), val = tensor([1, 1])]; + tensor var_5679 = const()[name = tensor("op_5679"), val = tensor([1, 1])]; + tensor var_5681_pad_type_0 = const()[name = tensor("op_5681_pad_type_0"), val = tensor("custom")]; + tensor var_5681_pad_0 = const()[name = tensor("op_5681_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3170010304)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3207759104)))]; + tensor var_5681_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_5679, groups = var_4628, pad = var_5681_pad_0, pad_type = var_5681_pad_type_0, strides = var_5677, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_457_cast)[name = tensor("op_5681_cast")]; + tensor var_5682_split_sizes_0 = const()[name = tensor("op_5682_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_5682_axis_0 = const()[name = tensor("op_5682_axis_0"), val = tensor(1)]; + tensor var_5682_cast_0, tensor var_5682_cast_1 = split(axis = var_5682_axis_0, split_sizes = var_5682_split_sizes_0, x = var_5681_cast)[name = tensor("op_5682_cast")]; + tensor var_5684_mode_0 = const()[name = tensor("op_5684_mode_0"), val = tensor("EXACT")]; + tensor var_5684_cast = gelu(mode = var_5684_mode_0, x = var_5682_cast_1)[name = tensor("op_5684_cast")]; + tensor input_459_cast = mul(x = var_5682_cast_0, y = var_5684_cast)[name = tensor("input_459_cast")]; + tensor var_5688 = const()[name = tensor("op_5688"), val = tensor([1, 1])]; + tensor var_5690 = const()[name = tensor("op_5690"), val = tensor([1, 1])]; + tensor var_5692_pad_type_0 = const()[name = tensor("op_5692_pad_type_0"), val = tensor("custom")]; + tensor var_5692_pad_0 = const()[name = tensor("op_5692_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3207783744)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3226658176)))]; + tensor var_5692_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5690, groups = var_4628, pad = var_5692_pad_0, pad_type = var_5692_pad_type_0, strides = var_5688, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_459_cast)[name = tensor("op_5692_cast")]; + tensor inputs_151_cast = add(x = var_5692_cast, y = inputs_149_cast)[name = tensor("inputs_151_cast")]; + tensor var_5702 = const()[name = tensor("op_5702"), val = tensor([1])]; + tensor channels_mean_151_cast = reduce_mean(axes = var_5702, keep_dims = var_4623, x = inputs_151_cast)[name = tensor("channels_mean_151_cast")]; + tensor zero_mean_151_cast = sub(x = inputs_151_cast, y = channels_mean_151_cast)[name = tensor("zero_mean_151_cast")]; + tensor zero_mean_sq_151_cast = mul(x = zero_mean_151_cast, y = zero_mean_151_cast)[name = tensor("zero_mean_sq_151_cast")]; + tensor var_5706 = const()[name = tensor("op_5706"), val = tensor([1])]; + tensor var_5707_cast = reduce_mean(axes = var_5706, keep_dims = var_4623, x = zero_mean_sq_151_cast)[name = tensor("op_5707_cast")]; + tensor var_5708_to_fp16 = const()[name = tensor("op_5708_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5709_cast = add(x = var_5707_cast, y = var_5708_to_fp16)[name = tensor("op_5709_cast")]; + tensor denom_151_epsilon_0_to_fp16 = const()[name = tensor("denom_151_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_151_cast = rsqrt(epsilon = denom_151_epsilon_0_to_fp16, x = var_5709_cast)[name = tensor("denom_151_cast")]; + tensor out_151_cast = mul(x = zero_mean_151_cast, y = denom_151_cast)[name = tensor("out_151_cast")]; + tensor var_5713_to_fp16 = const()[name = tensor("op_5713_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3226661312)))]; + tensor var_5714_cast = add(x = out_151_cast, y = var_5713_to_fp16)[name = tensor("op_5714_cast")]; + tensor var_5716_to_fp16 = const()[name = tensor("op_5716_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3226664448)))]; + tensor hidden_states_273_cast = mul(x = var_5714_cast, y = var_5716_to_fp16)[name = tensor("hidden_states_273_cast")]; + tensor var_5723 = const()[name = tensor("op_5723"), val = tensor([1, 1])]; + tensor var_5725 = const()[name = tensor("op_5725"), val = tensor([1, 1])]; + tensor q_101_pad_type_0 = const()[name = tensor("q_101_pad_type_0"), val = tensor("custom")]; + tensor q_101_pad_0 = const()[name = tensor("q_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3226667584)))]; + tensor q_101_cast = conv(dilations = var_5725, groups = var_4628, pad = q_101_pad_0, pad_type = q_101_pad_type_0, strides = var_5723, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_273_cast)[name = tensor("q_101_cast")]; + tensor var_5729 = const()[name = tensor("op_5729"), val = tensor([1, 1])]; + tensor var_5731 = const()[name = tensor("op_5731"), val = tensor([1, 1])]; + tensor k_101_pad_type_0 = const()[name = tensor("k_101_pad_type_0"), val = tensor("custom")]; + tensor k_101_pad_0 = const()[name = tensor("k_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3231386240)))]; + tensor k_101_cast = conv(dilations = var_5731, groups = var_4628, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_5729, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_273_cast)[name = tensor("k_101_cast")]; + tensor var_5735 = const()[name = tensor("op_5735"), val = tensor([1, 1])]; + tensor var_5737 = const()[name = tensor("op_5737"), val = tensor([1, 1])]; + tensor v_101_pad_type_0 = const()[name = tensor("v_101_pad_type_0"), val = tensor("custom")]; + tensor v_101_pad_0 = const()[name = tensor("v_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3236104896)))]; + tensor v_101_cast = conv(dilations = var_5737, groups = var_4628, pad = v_101_pad_0, pad_type = v_101_pad_type_0, strides = var_5735, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_273_cast)[name = tensor("v_101_cast")]; + tensor var_5741 = const()[name = tensor("op_5741"), val = tensor([2, 24, 64, -1])]; + tensor var_5742_cast = reshape(shape = var_5741, x = q_101_cast)[name = tensor("op_5742_cast")]; + tensor var_5743 = const()[name = tensor("op_5743"), val = tensor([2, 24, 64, -1])]; + tensor var_5744_cast = reshape(shape = var_5743, x = k_101_cast)[name = tensor("op_5744_cast")]; + tensor var_5745 = const()[name = tensor("op_5745"), val = tensor([2, 24, 64, -1])]; + tensor var_5746_cast = reshape(shape = var_5745, x = v_101_cast)[name = tensor("op_5746_cast")]; + tensor attn_weights_201_transpose_x_0 = const()[name = tensor("attn_weights_201_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_201_transpose_y_0 = const()[name = tensor("attn_weights_201_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_201_cast = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_5742_cast, y = var_5744_cast)[name = tensor("attn_weights_201_cast")]; + tensor attn_weights_203_cast = mul(x = attn_weights_201_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_203_cast")]; + tensor var_5750_cast = softmax(axis = var_4612, x = attn_weights_203_cast)[name = tensor("op_5750_cast")]; + tensor attn_101_transpose_x_0 = const()[name = tensor("attn_101_transpose_x_0"), val = tensor(false)]; + tensor attn_101_transpose_y_0 = const()[name = tensor("attn_101_transpose_y_0"), val = tensor(true)]; + tensor attn_101_cast = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5746_cast, y = var_5750_cast)[name = tensor("attn_101_cast")]; + tensor var_5754 = const()[name = tensor("op_5754"), val = tensor([2, 1536, 1, -1])]; + tensor input_461_cast = reshape(shape = var_5754, x = attn_101_cast)[name = tensor("input_461_cast")]; + tensor var_5759 = const()[name = tensor("op_5759"), val = tensor([1, 1])]; + tensor var_5761 = const()[name = tensor("op_5761"), val = tensor([1, 1])]; + tensor var_5763_pad_type_0 = const()[name = tensor("op_5763_pad_type_0"), val = tensor("custom")]; + tensor var_5763_pad_0 = const()[name = tensor("op_5763_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3240823552)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3245542208)))]; + tensor var_5763_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_5761, groups = var_4628, pad = var_5763_pad_0, pad_type = var_5763_pad_type_0, strides = var_5759, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_461_cast)[name = tensor("op_5763_cast")]; + tensor inputs_153_cast = add(x = var_5763_cast, y = inputs_151_cast)[name = tensor("inputs_153_cast")]; + tensor var_5767 = const()[name = tensor("op_5767"), val = tensor([1])]; + tensor channels_mean_153_cast = reduce_mean(axes = var_5767, keep_dims = var_4623, x = inputs_153_cast)[name = tensor("channels_mean_153_cast")]; + tensor zero_mean_153_cast = sub(x = inputs_153_cast, y = channels_mean_153_cast)[name = tensor("zero_mean_153_cast")]; + tensor zero_mean_sq_153_cast = mul(x = zero_mean_153_cast, y = zero_mean_153_cast)[name = tensor("zero_mean_sq_153_cast")]; + tensor var_5771 = const()[name = tensor("op_5771"), val = tensor([1])]; + tensor var_5772_cast = reduce_mean(axes = var_5771, keep_dims = var_4623, x = zero_mean_sq_153_cast)[name = tensor("op_5772_cast")]; + tensor var_5773_to_fp16 = const()[name = tensor("op_5773_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5774_cast = add(x = var_5772_cast, y = var_5773_to_fp16)[name = tensor("op_5774_cast")]; + tensor denom_153_epsilon_0_to_fp16 = const()[name = tensor("denom_153_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_153_cast = rsqrt(epsilon = denom_153_epsilon_0_to_fp16, x = var_5774_cast)[name = tensor("denom_153_cast")]; + tensor out_153_cast = mul(x = zero_mean_153_cast, y = denom_153_cast)[name = tensor("out_153_cast")]; + tensor var_5778_to_fp16 = const()[name = tensor("op_5778_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3245545344)))]; + tensor var_5779_cast = add(x = out_153_cast, y = var_5778_to_fp16)[name = tensor("op_5779_cast")]; + tensor var_5781_to_fp16 = const()[name = tensor("op_5781_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3245548480)))]; + tensor hidden_states_275_cast = mul(x = var_5779_cast, y = var_5781_to_fp16)[name = tensor("hidden_states_275_cast")]; + tensor var_5788 = const()[name = tensor("op_5788"), val = tensor([1, 1])]; + tensor var_5790 = const()[name = tensor("op_5790"), val = tensor([1, 1])]; + tensor q_103_pad_type_0 = const()[name = tensor("q_103_pad_type_0"), val = tensor("custom")]; + tensor q_103_pad_0 = const()[name = tensor("q_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3245551616)))]; + tensor q_103_cast = conv(dilations = var_5790, groups = var_4628, pad = q_103_pad_0, pad_type = q_103_pad_type_0, strides = var_5788, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_275_cast)[name = tensor("q_103_cast")]; + tensor var_5794 = const()[name = tensor("op_5794"), val = tensor([1, 1])]; + tensor var_5796 = const()[name = tensor("op_5796"), val = tensor([1, 1])]; + tensor k_103_pad_type_0 = const()[name = tensor("k_103_pad_type_0"), val = tensor("custom")]; + tensor k_103_pad_0 = const()[name = tensor("k_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3250270272)))]; + tensor k_103_cast = conv(dilations = var_5796, groups = var_4628, pad = k_103_pad_0, pad_type = k_103_pad_type_0, strides = var_5794, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_103_cast")]; + tensor var_5800 = const()[name = tensor("op_5800"), val = tensor([1, 1])]; + tensor var_5802 = const()[name = tensor("op_5802"), val = tensor([1, 1])]; + tensor v_103_pad_type_0 = const()[name = tensor("v_103_pad_type_0"), val = tensor("custom")]; + tensor v_103_pad_0 = const()[name = tensor("v_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3254202496)))]; + tensor v_103_cast = conv(dilations = var_5802, groups = var_4628, pad = v_103_pad_0, pad_type = v_103_pad_type_0, strides = var_5800, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_103_cast")]; + tensor var_5806 = const()[name = tensor("op_5806"), val = tensor([2, 24, 64, -1])]; + tensor var_5807_cast = reshape(shape = var_5806, x = q_103_cast)[name = tensor("op_5807_cast")]; + tensor var_5808 = const()[name = tensor("op_5808"), val = tensor([2, 24, 64, -1])]; + tensor var_5809_cast = reshape(shape = var_5808, x = k_103_cast)[name = tensor("op_5809_cast")]; + tensor var_5810 = const()[name = tensor("op_5810"), val = tensor([2, 24, 64, -1])]; + tensor var_5811_cast = reshape(shape = var_5810, x = v_103_cast)[name = tensor("op_5811_cast")]; + tensor attn_weights_205_transpose_x_0 = const()[name = tensor("attn_weights_205_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_205_transpose_y_0 = const()[name = tensor("attn_weights_205_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_205_cast = matmul(transpose_x = attn_weights_205_transpose_x_0, transpose_y = attn_weights_205_transpose_y_0, x = var_5807_cast, y = var_5809_cast)[name = tensor("attn_weights_205_cast")]; + tensor attn_weights_207_cast = mul(x = attn_weights_205_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_207_cast")]; + tensor var_5815_cast = softmax(axis = var_4612, x = attn_weights_207_cast)[name = tensor("op_5815_cast")]; + tensor attn_103_transpose_x_0 = const()[name = tensor("attn_103_transpose_x_0"), val = tensor(false)]; + tensor attn_103_transpose_y_0 = const()[name = tensor("attn_103_transpose_y_0"), val = tensor(true)]; + tensor attn_103_cast = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5811_cast, y = var_5815_cast)[name = tensor("attn_103_cast")]; + tensor var_5819 = const()[name = tensor("op_5819"), val = tensor([2, 1536, 1, -1])]; + tensor input_463_cast = reshape(shape = var_5819, x = attn_103_cast)[name = tensor("input_463_cast")]; + tensor var_5824 = const()[name = tensor("op_5824"), val = tensor([1, 1])]; + tensor var_5826 = const()[name = tensor("op_5826"), val = tensor([1, 1])]; + tensor var_5828_pad_type_0 = const()[name = tensor("op_5828_pad_type_0"), val = tensor("custom")]; + tensor var_5828_pad_0 = const()[name = tensor("op_5828_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3258134720)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3262853376)))]; + tensor var_5828_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_5826, groups = var_4628, pad = var_5828_pad_0, pad_type = var_5828_pad_type_0, strides = var_5824, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_463_cast)[name = tensor("op_5828_cast")]; + tensor inputs_155_cast = add(x = var_5828_cast, y = inputs_153_cast)[name = tensor("inputs_155_cast")]; + tensor var_5832 = const()[name = tensor("op_5832"), val = tensor([1])]; + tensor channels_mean_155_cast = reduce_mean(axes = var_5832, keep_dims = var_4623, x = inputs_155_cast)[name = tensor("channels_mean_155_cast")]; + tensor zero_mean_155_cast = sub(x = inputs_155_cast, y = channels_mean_155_cast)[name = tensor("zero_mean_155_cast")]; + tensor zero_mean_sq_155_cast = mul(x = zero_mean_155_cast, y = zero_mean_155_cast)[name = tensor("zero_mean_sq_155_cast")]; + tensor var_5836 = const()[name = tensor("op_5836"), val = tensor([1])]; + tensor var_5837_cast = reduce_mean(axes = var_5836, keep_dims = var_4623, x = zero_mean_sq_155_cast)[name = tensor("op_5837_cast")]; + tensor var_5838_to_fp16 = const()[name = tensor("op_5838_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5839_cast = add(x = var_5837_cast, y = var_5838_to_fp16)[name = tensor("op_5839_cast")]; + tensor denom_155_epsilon_0_to_fp16 = const()[name = tensor("denom_155_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_155_cast = rsqrt(epsilon = denom_155_epsilon_0_to_fp16, x = var_5839_cast)[name = tensor("denom_155_cast")]; + tensor out_155_cast = mul(x = zero_mean_155_cast, y = denom_155_cast)[name = tensor("out_155_cast")]; + tensor var_5843_to_fp16 = const()[name = tensor("op_5843_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3262856512)))]; + tensor var_5844_cast = add(x = out_155_cast, y = var_5843_to_fp16)[name = tensor("op_5844_cast")]; + tensor var_5846_to_fp16 = const()[name = tensor("op_5846_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3262859648)))]; + tensor input_465_cast = mul(x = var_5844_cast, y = var_5846_to_fp16)[name = tensor("input_465_cast")]; + tensor var_5854 = const()[name = tensor("op_5854"), val = tensor([1, 1])]; + tensor var_5856 = const()[name = tensor("op_5856"), val = tensor([1, 1])]; + tensor var_5858_pad_type_0 = const()[name = tensor("op_5858_pad_type_0"), val = tensor("custom")]; + tensor var_5858_pad_0 = const()[name = tensor("op_5858_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3262862784)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3300611584)))]; + tensor var_5858_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_5856, groups = var_4628, pad = var_5858_pad_0, pad_type = var_5858_pad_type_0, strides = var_5854, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_465_cast)[name = tensor("op_5858_cast")]; + tensor var_5859_split_sizes_0 = const()[name = tensor("op_5859_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_5859_axis_0 = const()[name = tensor("op_5859_axis_0"), val = tensor(1)]; + tensor var_5859_cast_0, tensor var_5859_cast_1 = split(axis = var_5859_axis_0, split_sizes = var_5859_split_sizes_0, x = var_5858_cast)[name = tensor("op_5859_cast")]; + tensor var_5861_mode_0 = const()[name = tensor("op_5861_mode_0"), val = tensor("EXACT")]; + tensor var_5861_cast = gelu(mode = var_5861_mode_0, x = var_5859_cast_1)[name = tensor("op_5861_cast")]; + tensor input_467_cast = mul(x = var_5859_cast_0, y = var_5861_cast)[name = tensor("input_467_cast")]; + tensor var_5865 = const()[name = tensor("op_5865"), val = tensor([1, 1])]; + tensor var_5867 = const()[name = tensor("op_5867"), val = tensor([1, 1])]; + tensor var_5869_pad_type_0 = const()[name = tensor("op_5869_pad_type_0"), val = tensor("custom")]; + tensor var_5869_pad_0 = const()[name = tensor("op_5869_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3300636224)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3319510656)))]; + tensor var_5869_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_5867, groups = var_4628, pad = var_5869_pad_0, pad_type = var_5869_pad_type_0, strides = var_5865, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_467_cast)[name = tensor("op_5869_cast")]; + tensor inputs_157_cast = add(x = var_5869_cast, y = inputs_155_cast)[name = tensor("inputs_157_cast")]; + tensor var_5879 = const()[name = tensor("op_5879"), val = tensor([1])]; + tensor channels_mean_157_cast = reduce_mean(axes = var_5879, keep_dims = var_4623, x = inputs_157_cast)[name = tensor("channels_mean_157_cast")]; + tensor zero_mean_157_cast = sub(x = inputs_157_cast, y = channels_mean_157_cast)[name = tensor("zero_mean_157_cast")]; + tensor zero_mean_sq_157_cast = mul(x = zero_mean_157_cast, y = zero_mean_157_cast)[name = tensor("zero_mean_sq_157_cast")]; + tensor var_5883 = const()[name = tensor("op_5883"), val = tensor([1])]; + tensor var_5884_cast = reduce_mean(axes = var_5883, keep_dims = var_4623, x = zero_mean_sq_157_cast)[name = tensor("op_5884_cast")]; + tensor var_5885_to_fp16 = const()[name = tensor("op_5885_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5886_cast = add(x = var_5884_cast, y = var_5885_to_fp16)[name = tensor("op_5886_cast")]; + tensor denom_157_epsilon_0_to_fp16 = const()[name = tensor("denom_157_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_157_cast = rsqrt(epsilon = denom_157_epsilon_0_to_fp16, x = var_5886_cast)[name = tensor("denom_157_cast")]; + tensor out_157_cast = mul(x = zero_mean_157_cast, y = denom_157_cast)[name = tensor("out_157_cast")]; + tensor var_5890_to_fp16 = const()[name = tensor("op_5890_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3319513792)))]; + tensor var_5891_cast = add(x = out_157_cast, y = var_5890_to_fp16)[name = tensor("op_5891_cast")]; + tensor var_5893_to_fp16 = const()[name = tensor("op_5893_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3319516928)))]; + tensor hidden_states_279_cast = mul(x = var_5891_cast, y = var_5893_to_fp16)[name = tensor("hidden_states_279_cast")]; + tensor var_5900 = const()[name = tensor("op_5900"), val = tensor([1, 1])]; + tensor var_5902 = const()[name = tensor("op_5902"), val = tensor([1, 1])]; + tensor q_105_pad_type_0 = const()[name = tensor("q_105_pad_type_0"), val = tensor("custom")]; + tensor q_105_pad_0 = const()[name = tensor("q_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3319520064)))]; + tensor q_105_cast = conv(dilations = var_5902, groups = var_4628, pad = q_105_pad_0, pad_type = q_105_pad_type_0, strides = var_5900, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_279_cast)[name = tensor("q_105_cast")]; + tensor var_5906 = const()[name = tensor("op_5906"), val = tensor([1, 1])]; + tensor var_5908 = const()[name = tensor("op_5908"), val = tensor([1, 1])]; + tensor k_105_pad_type_0 = const()[name = tensor("k_105_pad_type_0"), val = tensor("custom")]; + tensor k_105_pad_0 = const()[name = tensor("k_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3324238720)))]; + tensor k_105_cast = conv(dilations = var_5908, groups = var_4628, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_5906, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_279_cast)[name = tensor("k_105_cast")]; + tensor var_5912 = const()[name = tensor("op_5912"), val = tensor([1, 1])]; + tensor var_5914 = const()[name = tensor("op_5914"), val = tensor([1, 1])]; + tensor v_105_pad_type_0 = const()[name = tensor("v_105_pad_type_0"), val = tensor("custom")]; + tensor v_105_pad_0 = const()[name = tensor("v_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3328957376)))]; + tensor v_105_cast = conv(dilations = var_5914, groups = var_4628, pad = v_105_pad_0, pad_type = v_105_pad_type_0, strides = var_5912, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_279_cast)[name = tensor("v_105_cast")]; + tensor var_5918 = const()[name = tensor("op_5918"), val = tensor([2, 24, 64, -1])]; + tensor var_5919_cast = reshape(shape = var_5918, x = q_105_cast)[name = tensor("op_5919_cast")]; + tensor var_5920 = const()[name = tensor("op_5920"), val = tensor([2, 24, 64, -1])]; + tensor var_5921_cast = reshape(shape = var_5920, x = k_105_cast)[name = tensor("op_5921_cast")]; + tensor var_5922 = const()[name = tensor("op_5922"), val = tensor([2, 24, 64, -1])]; + tensor var_5923_cast = reshape(shape = var_5922, x = v_105_cast)[name = tensor("op_5923_cast")]; + tensor attn_weights_209_transpose_x_0 = const()[name = tensor("attn_weights_209_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_209_transpose_y_0 = const()[name = tensor("attn_weights_209_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_209_cast = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5919_cast, y = var_5921_cast)[name = tensor("attn_weights_209_cast")]; + tensor attn_weights_211_cast = mul(x = attn_weights_209_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_211_cast")]; + tensor var_5927_cast = softmax(axis = var_4612, x = attn_weights_211_cast)[name = tensor("op_5927_cast")]; + tensor attn_105_transpose_x_0 = const()[name = tensor("attn_105_transpose_x_0"), val = tensor(false)]; + tensor attn_105_transpose_y_0 = const()[name = tensor("attn_105_transpose_y_0"), val = tensor(true)]; + tensor attn_105_cast = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5923_cast, y = var_5927_cast)[name = tensor("attn_105_cast")]; + tensor var_5931 = const()[name = tensor("op_5931"), val = tensor([2, 1536, 1, -1])]; + tensor input_469_cast = reshape(shape = var_5931, x = attn_105_cast)[name = tensor("input_469_cast")]; + tensor var_5936 = const()[name = tensor("op_5936"), val = tensor([1, 1])]; + tensor var_5938 = const()[name = tensor("op_5938"), val = tensor([1, 1])]; + tensor var_5940_pad_type_0 = const()[name = tensor("op_5940_pad_type_0"), val = tensor("custom")]; + tensor var_5940_pad_0 = const()[name = tensor("op_5940_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3333676032)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3338394688)))]; + tensor var_5940_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_5938, groups = var_4628, pad = var_5940_pad_0, pad_type = var_5940_pad_type_0, strides = var_5936, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_469_cast)[name = tensor("op_5940_cast")]; + tensor inputs_159_cast = add(x = var_5940_cast, y = inputs_157_cast)[name = tensor("inputs_159_cast")]; + tensor var_5944 = const()[name = tensor("op_5944"), val = tensor([1])]; + tensor channels_mean_159_cast = reduce_mean(axes = var_5944, keep_dims = var_4623, x = inputs_159_cast)[name = tensor("channels_mean_159_cast")]; + tensor zero_mean_159_cast = sub(x = inputs_159_cast, y = channels_mean_159_cast)[name = tensor("zero_mean_159_cast")]; + tensor zero_mean_sq_159_cast = mul(x = zero_mean_159_cast, y = zero_mean_159_cast)[name = tensor("zero_mean_sq_159_cast")]; + tensor var_5948 = const()[name = tensor("op_5948"), val = tensor([1])]; + tensor var_5949_cast = reduce_mean(axes = var_5948, keep_dims = var_4623, x = zero_mean_sq_159_cast)[name = tensor("op_5949_cast")]; + tensor var_5950_to_fp16 = const()[name = tensor("op_5950_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5951_cast = add(x = var_5949_cast, y = var_5950_to_fp16)[name = tensor("op_5951_cast")]; + tensor denom_159_epsilon_0_to_fp16 = const()[name = tensor("denom_159_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_159_cast = rsqrt(epsilon = denom_159_epsilon_0_to_fp16, x = var_5951_cast)[name = tensor("denom_159_cast")]; + tensor out_159_cast = mul(x = zero_mean_159_cast, y = denom_159_cast)[name = tensor("out_159_cast")]; + tensor var_5955_to_fp16 = const()[name = tensor("op_5955_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3338397824)))]; + tensor var_5956_cast = add(x = out_159_cast, y = var_5955_to_fp16)[name = tensor("op_5956_cast")]; + tensor var_5958_to_fp16 = const()[name = tensor("op_5958_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3338400960)))]; + tensor hidden_states_281_cast = mul(x = var_5956_cast, y = var_5958_to_fp16)[name = tensor("hidden_states_281_cast")]; + tensor var_5965 = const()[name = tensor("op_5965"), val = tensor([1, 1])]; + tensor var_5967 = const()[name = tensor("op_5967"), val = tensor([1, 1])]; + tensor q_107_pad_type_0 = const()[name = tensor("q_107_pad_type_0"), val = tensor("custom")]; + tensor q_107_pad_0 = const()[name = tensor("q_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3338404096)))]; + tensor q_107_cast = conv(dilations = var_5967, groups = var_4628, pad = q_107_pad_0, pad_type = q_107_pad_type_0, strides = var_5965, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_281_cast)[name = tensor("q_107_cast")]; + tensor var_5971 = const()[name = tensor("op_5971"), val = tensor([1, 1])]; + tensor var_5973 = const()[name = tensor("op_5973"), val = tensor([1, 1])]; + tensor k_107_pad_type_0 = const()[name = tensor("k_107_pad_type_0"), val = tensor("custom")]; + tensor k_107_pad_0 = const()[name = tensor("k_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3343122752)))]; + tensor k_107_cast = conv(dilations = var_5973, groups = var_4628, pad = k_107_pad_0, pad_type = k_107_pad_type_0, strides = var_5971, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_107_cast")]; + tensor var_5977 = const()[name = tensor("op_5977"), val = tensor([1, 1])]; + tensor var_5979 = const()[name = tensor("op_5979"), val = tensor([1, 1])]; + tensor v_107_pad_type_0 = const()[name = tensor("v_107_pad_type_0"), val = tensor("custom")]; + tensor v_107_pad_0 = const()[name = tensor("v_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3347054976)))]; + tensor v_107_cast = conv(dilations = var_5979, groups = var_4628, pad = v_107_pad_0, pad_type = v_107_pad_type_0, strides = var_5977, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_107_cast")]; + tensor var_5983 = const()[name = tensor("op_5983"), val = tensor([2, 24, 64, -1])]; + tensor var_5984_cast = reshape(shape = var_5983, x = q_107_cast)[name = tensor("op_5984_cast")]; + tensor var_5985 = const()[name = tensor("op_5985"), val = tensor([2, 24, 64, -1])]; + tensor var_5986_cast = reshape(shape = var_5985, x = k_107_cast)[name = tensor("op_5986_cast")]; + tensor var_5987 = const()[name = tensor("op_5987"), val = tensor([2, 24, 64, -1])]; + tensor var_5988_cast = reshape(shape = var_5987, x = v_107_cast)[name = tensor("op_5988_cast")]; + tensor attn_weights_213_transpose_x_0 = const()[name = tensor("attn_weights_213_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_213_transpose_y_0 = const()[name = tensor("attn_weights_213_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_213_cast = matmul(transpose_x = attn_weights_213_transpose_x_0, transpose_y = attn_weights_213_transpose_y_0, x = var_5984_cast, y = var_5986_cast)[name = tensor("attn_weights_213_cast")]; + tensor attn_weights_215_cast = mul(x = attn_weights_213_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_215_cast")]; + tensor var_5992_cast = softmax(axis = var_4612, x = attn_weights_215_cast)[name = tensor("op_5992_cast")]; + tensor attn_107_transpose_x_0 = const()[name = tensor("attn_107_transpose_x_0"), val = tensor(false)]; + tensor attn_107_transpose_y_0 = const()[name = tensor("attn_107_transpose_y_0"), val = tensor(true)]; + tensor attn_107_cast = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5988_cast, y = var_5992_cast)[name = tensor("attn_107_cast")]; + tensor var_5996 = const()[name = tensor("op_5996"), val = tensor([2, 1536, 1, -1])]; + tensor input_471_cast = reshape(shape = var_5996, x = attn_107_cast)[name = tensor("input_471_cast")]; + tensor var_6001 = const()[name = tensor("op_6001"), val = tensor([1, 1])]; + tensor var_6003 = const()[name = tensor("op_6003"), val = tensor([1, 1])]; + tensor var_6005_pad_type_0 = const()[name = tensor("op_6005_pad_type_0"), val = tensor("custom")]; + tensor var_6005_pad_0 = const()[name = tensor("op_6005_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3350987200)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3355705856)))]; + tensor var_6005_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_6003, groups = var_4628, pad = var_6005_pad_0, pad_type = var_6005_pad_type_0, strides = var_6001, weight = up_blocks_1_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_471_cast)[name = tensor("op_6005_cast")]; + tensor inputs_161_cast = add(x = var_6005_cast, y = inputs_159_cast)[name = tensor("inputs_161_cast")]; + tensor var_6009 = const()[name = tensor("op_6009"), val = tensor([1])]; + tensor channels_mean_161_cast = reduce_mean(axes = var_6009, keep_dims = var_4623, x = inputs_161_cast)[name = tensor("channels_mean_161_cast")]; + tensor zero_mean_161_cast = sub(x = inputs_161_cast, y = channels_mean_161_cast)[name = tensor("zero_mean_161_cast")]; + tensor zero_mean_sq_161_cast = mul(x = zero_mean_161_cast, y = zero_mean_161_cast)[name = tensor("zero_mean_sq_161_cast")]; + tensor var_6013 = const()[name = tensor("op_6013"), val = tensor([1])]; + tensor var_6014_cast = reduce_mean(axes = var_6013, keep_dims = var_4623, x = zero_mean_sq_161_cast)[name = tensor("op_6014_cast")]; + tensor var_6015_to_fp16 = const()[name = tensor("op_6015_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6016_cast = add(x = var_6014_cast, y = var_6015_to_fp16)[name = tensor("op_6016_cast")]; + tensor denom_161_epsilon_0_to_fp16 = const()[name = tensor("denom_161_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_161_cast = rsqrt(epsilon = denom_161_epsilon_0_to_fp16, x = var_6016_cast)[name = tensor("denom_161_cast")]; + tensor out_161_cast = mul(x = zero_mean_161_cast, y = denom_161_cast)[name = tensor("out_161_cast")]; + tensor var_6020_to_fp16 = const()[name = tensor("op_6020_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3355708992)))]; + tensor var_6021_cast = add(x = out_161_cast, y = var_6020_to_fp16)[name = tensor("op_6021_cast")]; + tensor var_6023_to_fp16 = const()[name = tensor("op_6023_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3355712128)))]; + tensor input_473_cast = mul(x = var_6021_cast, y = var_6023_to_fp16)[name = tensor("input_473_cast")]; + tensor var_6031 = const()[name = tensor("op_6031"), val = tensor([1, 1])]; + tensor var_6033 = const()[name = tensor("op_6033"), val = tensor([1, 1])]; + tensor var_6035_pad_type_0 = const()[name = tensor("op_6035_pad_type_0"), val = tensor("custom")]; + tensor var_6035_pad_0 = const()[name = tensor("op_6035_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3355715264)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3393464064)))]; + tensor var_6035_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_6033, groups = var_4628, pad = var_6035_pad_0, pad_type = var_6035_pad_type_0, strides = var_6031, weight = up_blocks_1_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_473_cast)[name = tensor("op_6035_cast")]; + tensor var_6036_split_sizes_0 = const()[name = tensor("op_6036_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_6036_axis_0 = const()[name = tensor("op_6036_axis_0"), val = tensor(1)]; + tensor var_6036_cast_0, tensor var_6036_cast_1 = split(axis = var_6036_axis_0, split_sizes = var_6036_split_sizes_0, x = var_6035_cast)[name = tensor("op_6036_cast")]; + tensor var_6038_mode_0 = const()[name = tensor("op_6038_mode_0"), val = tensor("EXACT")]; + tensor var_6038_cast = gelu(mode = var_6038_mode_0, x = var_6036_cast_1)[name = tensor("op_6038_cast")]; + tensor input_475_cast = mul(x = var_6036_cast_0, y = var_6038_cast)[name = tensor("input_475_cast")]; + tensor var_6042 = const()[name = tensor("op_6042"), val = tensor([1, 1])]; + tensor var_6044 = const()[name = tensor("op_6044"), val = tensor([1, 1])]; + tensor var_6046_pad_type_0 = const()[name = tensor("op_6046_pad_type_0"), val = tensor("custom")]; + tensor var_6046_pad_0 = const()[name = tensor("op_6046_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3393488704)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3412363136)))]; + tensor var_6046_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_6044, groups = var_4628, pad = var_6046_pad_0, pad_type = var_6046_pad_type_0, strides = var_6042, weight = up_blocks_1_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_475_cast)[name = tensor("op_6046_cast")]; + tensor inputs_163_cast = add(x = var_6046_cast, y = inputs_161_cast)[name = tensor("inputs_163_cast")]; + tensor var_6056 = const()[name = tensor("op_6056"), val = tensor([1])]; + tensor channels_mean_163_cast = reduce_mean(axes = var_6056, keep_dims = var_4623, x = inputs_163_cast)[name = tensor("channels_mean_163_cast")]; + tensor zero_mean_163_cast = sub(x = inputs_163_cast, y = channels_mean_163_cast)[name = tensor("zero_mean_163_cast")]; + tensor zero_mean_sq_163_cast = mul(x = zero_mean_163_cast, y = zero_mean_163_cast)[name = tensor("zero_mean_sq_163_cast")]; + tensor var_6060 = const()[name = tensor("op_6060"), val = tensor([1])]; + tensor var_6061_cast = reduce_mean(axes = var_6060, keep_dims = var_4623, x = zero_mean_sq_163_cast)[name = tensor("op_6061_cast")]; + tensor var_6062_to_fp16 = const()[name = tensor("op_6062_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6063_cast = add(x = var_6061_cast, y = var_6062_to_fp16)[name = tensor("op_6063_cast")]; + tensor denom_163_epsilon_0_to_fp16 = const()[name = tensor("denom_163_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_163_cast = rsqrt(epsilon = denom_163_epsilon_0_to_fp16, x = var_6063_cast)[name = tensor("denom_163_cast")]; + tensor out_163_cast = mul(x = zero_mean_163_cast, y = denom_163_cast)[name = tensor("out_163_cast")]; + tensor var_6067_to_fp16 = const()[name = tensor("op_6067_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3412366272)))]; + tensor var_6068_cast = add(x = out_163_cast, y = var_6067_to_fp16)[name = tensor("op_6068_cast")]; + tensor var_6070_to_fp16 = const()[name = tensor("op_6070_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3412369408)))]; + tensor hidden_states_285_cast = mul(x = var_6068_cast, y = var_6070_to_fp16)[name = tensor("hidden_states_285_cast")]; + tensor var_6077 = const()[name = tensor("op_6077"), val = tensor([1, 1])]; + tensor var_6079 = const()[name = tensor("op_6079"), val = tensor([1, 1])]; + tensor q_109_pad_type_0 = const()[name = tensor("q_109_pad_type_0"), val = tensor("custom")]; + tensor q_109_pad_0 = const()[name = tensor("q_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3412372544)))]; + tensor q_109_cast = conv(dilations = var_6079, groups = var_4628, pad = q_109_pad_0, pad_type = q_109_pad_type_0, strides = var_6077, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_285_cast)[name = tensor("q_109_cast")]; + tensor var_6083 = const()[name = tensor("op_6083"), val = tensor([1, 1])]; + tensor var_6085 = const()[name = tensor("op_6085"), val = tensor([1, 1])]; + tensor k_109_pad_type_0 = const()[name = tensor("k_109_pad_type_0"), val = tensor("custom")]; + tensor k_109_pad_0 = const()[name = tensor("k_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3417091200)))]; + tensor k_109_cast = conv(dilations = var_6085, groups = var_4628, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_6083, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_285_cast)[name = tensor("k_109_cast")]; + tensor var_6089 = const()[name = tensor("op_6089"), val = tensor([1, 1])]; + tensor var_6091 = const()[name = tensor("op_6091"), val = tensor([1, 1])]; + tensor v_109_pad_type_0 = const()[name = tensor("v_109_pad_type_0"), val = tensor("custom")]; + tensor v_109_pad_0 = const()[name = tensor("v_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3421809856)))]; + tensor v_109_cast = conv(dilations = var_6091, groups = var_4628, pad = v_109_pad_0, pad_type = v_109_pad_type_0, strides = var_6089, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_285_cast)[name = tensor("v_109_cast")]; + tensor var_6095 = const()[name = tensor("op_6095"), val = tensor([2, 24, 64, -1])]; + tensor var_6096_cast = reshape(shape = var_6095, x = q_109_cast)[name = tensor("op_6096_cast")]; + tensor var_6097 = const()[name = tensor("op_6097"), val = tensor([2, 24, 64, -1])]; + tensor var_6098_cast = reshape(shape = var_6097, x = k_109_cast)[name = tensor("op_6098_cast")]; + tensor var_6099 = const()[name = tensor("op_6099"), val = tensor([2, 24, 64, -1])]; + tensor var_6100_cast = reshape(shape = var_6099, x = v_109_cast)[name = tensor("op_6100_cast")]; + tensor attn_weights_217_transpose_x_0 = const()[name = tensor("attn_weights_217_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_217_transpose_y_0 = const()[name = tensor("attn_weights_217_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_217_cast = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_6096_cast, y = var_6098_cast)[name = tensor("attn_weights_217_cast")]; + tensor attn_weights_219_cast = mul(x = attn_weights_217_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_219_cast")]; + tensor var_6104_cast = softmax(axis = var_4612, x = attn_weights_219_cast)[name = tensor("op_6104_cast")]; + tensor attn_109_transpose_x_0 = const()[name = tensor("attn_109_transpose_x_0"), val = tensor(false)]; + tensor attn_109_transpose_y_0 = const()[name = tensor("attn_109_transpose_y_0"), val = tensor(true)]; + tensor attn_109_cast = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_6100_cast, y = var_6104_cast)[name = tensor("attn_109_cast")]; + tensor var_6108 = const()[name = tensor("op_6108"), val = tensor([2, 1536, 1, -1])]; + tensor input_477_cast = reshape(shape = var_6108, x = attn_109_cast)[name = tensor("input_477_cast")]; + tensor var_6113 = const()[name = tensor("op_6113"), val = tensor([1, 1])]; + tensor var_6115 = const()[name = tensor("op_6115"), val = tensor([1, 1])]; + tensor var_6117_pad_type_0 = const()[name = tensor("op_6117_pad_type_0"), val = tensor("custom")]; + tensor var_6117_pad_0 = const()[name = tensor("op_6117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3426528512)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3431247168)))]; + tensor var_6117_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_6115, groups = var_4628, pad = var_6117_pad_0, pad_type = var_6117_pad_type_0, strides = var_6113, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_477_cast)[name = tensor("op_6117_cast")]; + tensor inputs_165_cast = add(x = var_6117_cast, y = inputs_163_cast)[name = tensor("inputs_165_cast")]; + tensor var_6121 = const()[name = tensor("op_6121"), val = tensor([1])]; + tensor channels_mean_165_cast = reduce_mean(axes = var_6121, keep_dims = var_4623, x = inputs_165_cast)[name = tensor("channels_mean_165_cast")]; + tensor zero_mean_165_cast = sub(x = inputs_165_cast, y = channels_mean_165_cast)[name = tensor("zero_mean_165_cast")]; + tensor zero_mean_sq_165_cast = mul(x = zero_mean_165_cast, y = zero_mean_165_cast)[name = tensor("zero_mean_sq_165_cast")]; + tensor var_6125 = const()[name = tensor("op_6125"), val = tensor([1])]; + tensor var_6126_cast = reduce_mean(axes = var_6125, keep_dims = var_4623, x = zero_mean_sq_165_cast)[name = tensor("op_6126_cast")]; + tensor var_6127_to_fp16 = const()[name = tensor("op_6127_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6128_cast = add(x = var_6126_cast, y = var_6127_to_fp16)[name = tensor("op_6128_cast")]; + tensor denom_165_epsilon_0_to_fp16 = const()[name = tensor("denom_165_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_165_cast = rsqrt(epsilon = denom_165_epsilon_0_to_fp16, x = var_6128_cast)[name = tensor("denom_165_cast")]; + tensor out_165_cast = mul(x = zero_mean_165_cast, y = denom_165_cast)[name = tensor("out_165_cast")]; + tensor var_6132_to_fp16 = const()[name = tensor("op_6132_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3431250304)))]; + tensor var_6133_cast = add(x = out_165_cast, y = var_6132_to_fp16)[name = tensor("op_6133_cast")]; + tensor var_6135_to_fp16 = const()[name = tensor("op_6135_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3431253440)))]; + tensor hidden_states_287_cast = mul(x = var_6133_cast, y = var_6135_to_fp16)[name = tensor("hidden_states_287_cast")]; + tensor var_6142 = const()[name = tensor("op_6142"), val = tensor([1, 1])]; + tensor var_6144 = const()[name = tensor("op_6144"), val = tensor([1, 1])]; + tensor q_111_pad_type_0 = const()[name = tensor("q_111_pad_type_0"), val = tensor("custom")]; + tensor q_111_pad_0 = const()[name = tensor("q_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3431256576)))]; + tensor q_111_cast = conv(dilations = var_6144, groups = var_4628, pad = q_111_pad_0, pad_type = q_111_pad_type_0, strides = var_6142, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_287_cast)[name = tensor("q_111_cast")]; + tensor var_6148 = const()[name = tensor("op_6148"), val = tensor([1, 1])]; + tensor var_6150 = const()[name = tensor("op_6150"), val = tensor([1, 1])]; + tensor k_111_pad_type_0 = const()[name = tensor("k_111_pad_type_0"), val = tensor("custom")]; + tensor k_111_pad_0 = const()[name = tensor("k_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3435975232)))]; + tensor k_111_cast = conv(dilations = var_6150, groups = var_4628, pad = k_111_pad_0, pad_type = k_111_pad_type_0, strides = var_6148, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_111_cast")]; + tensor var_6154 = const()[name = tensor("op_6154"), val = tensor([1, 1])]; + tensor var_6156 = const()[name = tensor("op_6156"), val = tensor([1, 1])]; + tensor v_111_pad_type_0 = const()[name = tensor("v_111_pad_type_0"), val = tensor("custom")]; + tensor v_111_pad_0 = const()[name = tensor("v_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3439907456)))]; + tensor v_111_cast = conv(dilations = var_6156, groups = var_4628, pad = v_111_pad_0, pad_type = v_111_pad_type_0, strides = var_6154, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_111_cast")]; + tensor var_6160 = const()[name = tensor("op_6160"), val = tensor([2, 24, 64, -1])]; + tensor var_6161_cast = reshape(shape = var_6160, x = q_111_cast)[name = tensor("op_6161_cast")]; + tensor var_6162 = const()[name = tensor("op_6162"), val = tensor([2, 24, 64, -1])]; + tensor var_6163_cast = reshape(shape = var_6162, x = k_111_cast)[name = tensor("op_6163_cast")]; + tensor var_6164 = const()[name = tensor("op_6164"), val = tensor([2, 24, 64, -1])]; + tensor var_6165_cast = reshape(shape = var_6164, x = v_111_cast)[name = tensor("op_6165_cast")]; + tensor attn_weights_221_transpose_x_0 = const()[name = tensor("attn_weights_221_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_221_transpose_y_0 = const()[name = tensor("attn_weights_221_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_221_cast = matmul(transpose_x = attn_weights_221_transpose_x_0, transpose_y = attn_weights_221_transpose_y_0, x = var_6161_cast, y = var_6163_cast)[name = tensor("attn_weights_221_cast")]; + tensor attn_weights_223_cast = mul(x = attn_weights_221_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_223_cast")]; + tensor var_6169_cast = softmax(axis = var_4612, x = attn_weights_223_cast)[name = tensor("op_6169_cast")]; + tensor attn_111_transpose_x_0 = const()[name = tensor("attn_111_transpose_x_0"), val = tensor(false)]; + tensor attn_111_transpose_y_0 = const()[name = tensor("attn_111_transpose_y_0"), val = tensor(true)]; + tensor attn_111_cast = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_6165_cast, y = var_6169_cast)[name = tensor("attn_111_cast")]; + tensor var_6173 = const()[name = tensor("op_6173"), val = tensor([2, 1536, 1, -1])]; + tensor input_479_cast = reshape(shape = var_6173, x = attn_111_cast)[name = tensor("input_479_cast")]; + tensor var_6178 = const()[name = tensor("op_6178"), val = tensor([1, 1])]; + tensor var_6180 = const()[name = tensor("op_6180"), val = tensor([1, 1])]; + tensor var_6182_pad_type_0 = const()[name = tensor("op_6182_pad_type_0"), val = tensor("custom")]; + tensor var_6182_pad_0 = const()[name = tensor("op_6182_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3443839680)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3448558336)))]; + tensor var_6182_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_6180, groups = var_4628, pad = var_6182_pad_0, pad_type = var_6182_pad_type_0, strides = var_6178, weight = up_blocks_1_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_479_cast)[name = tensor("op_6182_cast")]; + tensor inputs_167_cast = add(x = var_6182_cast, y = inputs_165_cast)[name = tensor("inputs_167_cast")]; + tensor var_6186 = const()[name = tensor("op_6186"), val = tensor([1])]; + tensor channels_mean_167_cast = reduce_mean(axes = var_6186, keep_dims = var_4623, x = inputs_167_cast)[name = tensor("channels_mean_167_cast")]; + tensor zero_mean_167_cast = sub(x = inputs_167_cast, y = channels_mean_167_cast)[name = tensor("zero_mean_167_cast")]; + tensor zero_mean_sq_167_cast = mul(x = zero_mean_167_cast, y = zero_mean_167_cast)[name = tensor("zero_mean_sq_167_cast")]; + tensor var_6190 = const()[name = tensor("op_6190"), val = tensor([1])]; + tensor var_6191_cast = reduce_mean(axes = var_6190, keep_dims = var_4623, x = zero_mean_sq_167_cast)[name = tensor("op_6191_cast")]; + tensor var_6192_to_fp16 = const()[name = tensor("op_6192_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6193_cast = add(x = var_6191_cast, y = var_6192_to_fp16)[name = tensor("op_6193_cast")]; + tensor denom_167_epsilon_0_to_fp16 = const()[name = tensor("denom_167_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_167_cast = rsqrt(epsilon = denom_167_epsilon_0_to_fp16, x = var_6193_cast)[name = tensor("denom_167_cast")]; + tensor out_167_cast = mul(x = zero_mean_167_cast, y = denom_167_cast)[name = tensor("out_167_cast")]; + tensor var_6197_to_fp16 = const()[name = tensor("op_6197_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3448561472)))]; + tensor var_6198_cast = add(x = out_167_cast, y = var_6197_to_fp16)[name = tensor("op_6198_cast")]; + tensor var_6200_to_fp16 = const()[name = tensor("op_6200_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3448564608)))]; + tensor input_481_cast = mul(x = var_6198_cast, y = var_6200_to_fp16)[name = tensor("input_481_cast")]; + tensor var_6208 = const()[name = tensor("op_6208"), val = tensor([1, 1])]; + tensor var_6210 = const()[name = tensor("op_6210"), val = tensor([1, 1])]; + tensor var_6212_pad_type_0 = const()[name = tensor("op_6212_pad_type_0"), val = tensor("custom")]; + tensor var_6212_pad_0 = const()[name = tensor("op_6212_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3448567744)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3486316544)))]; + tensor var_6212_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_6210, groups = var_4628, pad = var_6212_pad_0, pad_type = var_6212_pad_type_0, strides = var_6208, weight = up_blocks_1_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_481_cast)[name = tensor("op_6212_cast")]; + tensor var_6213_split_sizes_0 = const()[name = tensor("op_6213_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_6213_axis_0 = const()[name = tensor("op_6213_axis_0"), val = tensor(1)]; + tensor var_6213_cast_0, tensor var_6213_cast_1 = split(axis = var_6213_axis_0, split_sizes = var_6213_split_sizes_0, x = var_6212_cast)[name = tensor("op_6213_cast")]; + tensor var_6215_mode_0 = const()[name = tensor("op_6215_mode_0"), val = tensor("EXACT")]; + tensor var_6215_cast = gelu(mode = var_6215_mode_0, x = var_6213_cast_1)[name = tensor("op_6215_cast")]; + tensor input_483_cast = mul(x = var_6213_cast_0, y = var_6215_cast)[name = tensor("input_483_cast")]; + tensor var_6219 = const()[name = tensor("op_6219"), val = tensor([1, 1])]; + tensor var_6221 = const()[name = tensor("op_6221"), val = tensor([1, 1])]; + tensor var_6223_pad_type_0 = const()[name = tensor("op_6223_pad_type_0"), val = tensor("custom")]; + tensor var_6223_pad_0 = const()[name = tensor("op_6223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3486341184)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3505215616)))]; + tensor var_6223_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_6221, groups = var_4628, pad = var_6223_pad_0, pad_type = var_6223_pad_type_0, strides = var_6219, weight = up_blocks_1_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_483_cast)[name = tensor("op_6223_cast")]; + tensor hidden_states_291_cast = add(x = var_6223_cast, y = inputs_167_cast)[name = tensor("hidden_states_291_cast")]; + tensor var_6225 = const()[name = tensor("op_6225"), val = tensor([2, 1536, 32, 32])]; + tensor input_485_cast = reshape(shape = var_6225, x = hidden_states_291_cast)[name = tensor("input_485_cast")]; + tensor var_6229 = const()[name = tensor("op_6229"), val = tensor([1, 1])]; + tensor var_6231 = const()[name = tensor("op_6231"), val = tensor([1, 1])]; + tensor hidden_states_293_pad_type_0 = const()[name = tensor("hidden_states_293_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_293_pad_0 = const()[name = tensor("hidden_states_293_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3505218752)))]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3509937408)))]; + tensor hidden_states_293_cast = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_6231, groups = var_4628, pad = hidden_states_293_pad_0, pad_type = hidden_states_293_pad_type_0, strides = var_6229, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_485_cast)[name = tensor("hidden_states_293_cast")]; + tensor hidden_states_295_cast = add(x = hidden_states_293_cast, y = hidden_states_263_cast)[name = tensor("hidden_states_295_cast")]; + tensor input_487_interleave_0 = const()[name = tensor("input_487_interleave_0"), val = tensor(false)]; + tensor input_487_cast = concat(axis = var_4628, interleave = input_487_interleave_0, values = (hidden_states_295_cast, input_147_cast))[name = tensor("input_487_cast")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 72, 32, 32])]; + tensor reshape_148_cast = reshape(shape = reshape_148_shape_0, x = input_487_cast)[name = tensor("reshape_148_cast")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast)[name = tensor("reduce_mean_111_cast")]; + tensor sub_74_cast = sub(x = reshape_148_cast, y = reduce_mean_111_cast)[name = tensor("sub_74_cast")]; + tensor square_37_cast = square(x = sub_74_cast)[name = tensor("square_37_cast")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast)[name = tensor("reduce_mean_113_cast")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast = add(x = reduce_mean_113_cast, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast")]; + tensor sqrt_37_cast = sqrt(x = add_74_cast)[name = tensor("sqrt_37_cast")]; + tensor real_div_37_cast = real_div(x = sub_74_cast, y = sqrt_37_cast)[name = tensor("real_div_37_cast")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 2304, 32, 32])]; + tensor reshape_149_cast = reshape(shape = reshape_149_shape_0, x = real_div_37_cast)[name = tensor("reshape_149_cast")]; + tensor add_75_mean_0_to_fp16 = const()[name = tensor("add_75_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3509940544)))]; + tensor add_75_variance_0_to_fp16 = const()[name = tensor("add_75_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3509945216)))]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3509949888)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3509954560)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_75_mean_0_to_fp16, variance = add_75_variance_0_to_fp16, x = reshape_149_cast)[name = tensor("add_75_cast")]; + tensor input_491_cast = silu(x = add_75_cast)[name = tensor("input_491_cast")]; + tensor var_6249 = const()[name = tensor("op_6249"), val = tensor([1, 1])]; + tensor var_6251 = const()[name = tensor("op_6251"), val = tensor([1, 1])]; + tensor hidden_states_297_pad_type_0 = const()[name = tensor("hidden_states_297_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_297_pad_0 = const()[name = tensor("hidden_states_297_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3509959232)))]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3573660288)))]; + tensor hidden_states_297_cast = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_6251, groups = var_4628, pad = hidden_states_297_pad_0, pad_type = hidden_states_297_pad_type_0, strides = var_6249, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_491_cast)[name = tensor("hidden_states_297_cast")]; + tensor var_6257 = const()[name = tensor("op_6257"), val = tensor([1, 1])]; + tensor var_6259 = const()[name = tensor("op_6259"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3573663424)))]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3578382080)))]; + tensor temb_31_cast = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_6259, groups = var_4628, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_6257, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_31_cast")]; + tensor input_495_cast = add(x = hidden_states_297_cast, y = temb_31_cast)[name = tensor("input_495_cast")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_152_cast = reshape(shape = reshape_152_shape_0, x = input_495_cast)[name = tensor("reshape_152_cast")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast)[name = tensor("reduce_mean_114_cast")]; + tensor sub_76_cast = sub(x = reshape_152_cast, y = reduce_mean_114_cast)[name = tensor("sub_76_cast")]; + tensor square_38_cast = square(x = sub_76_cast)[name = tensor("square_38_cast")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast)[name = tensor("reduce_mean_116_cast")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_76_cast = add(x = reduce_mean_116_cast, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast")]; + tensor sqrt_38_cast = sqrt(x = add_76_cast)[name = tensor("sqrt_38_cast")]; + tensor real_div_38_cast = real_div(x = sub_76_cast, y = sqrt_38_cast)[name = tensor("real_div_38_cast")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_153_cast = reshape(shape = reshape_153_shape_0, x = real_div_38_cast)[name = tensor("reshape_153_cast")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3578385216)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3578388352)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_153_cast)[name = tensor("add_77_cast")]; + tensor input_499_cast = silu(x = add_77_cast)[name = tensor("input_499_cast")]; + tensor var_6269 = const()[name = tensor("op_6269"), val = tensor([1, 1])]; + tensor var_6271 = const()[name = tensor("op_6271"), val = tensor([1, 1])]; + tensor hidden_states_299_pad_type_0 = const()[name = tensor("hidden_states_299_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_299_pad_0 = const()[name = tensor("hidden_states_299_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3578391488)))]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3620858880)))]; + tensor hidden_states_299_cast = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_6271, groups = var_4628, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_6269, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_499_cast)[name = tensor("hidden_states_299_cast")]; + tensor var_6276 = const()[name = tensor("op_6276"), val = tensor([1, 1])]; + tensor var_6278 = const()[name = tensor("op_6278"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3620862016)))]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627939968)))]; + tensor x_15_cast = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_6278, groups = var_4628, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_6276, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16, x = input_487_cast)[name = tensor("x_15_cast")]; + tensor hidden_states_301_cast = add(x = x_15_cast, y = hidden_states_299_cast)[name = tensor("hidden_states_301_cast")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 48, 32, 32])]; + tensor reshape_156_cast = reshape(shape = reshape_156_shape_0, x = hidden_states_301_cast)[name = tensor("reshape_156_cast")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast)[name = tensor("reduce_mean_117_cast")]; + tensor sub_78_cast = sub(x = reshape_156_cast, y = reduce_mean_117_cast)[name = tensor("sub_78_cast")]; + tensor square_39_cast = square(x = sub_78_cast)[name = tensor("square_39_cast")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast)[name = tensor("reduce_mean_119_cast")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_78_cast = add(x = reduce_mean_119_cast, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast")]; + tensor sqrt_39_cast = sqrt(x = add_78_cast)[name = tensor("sqrt_39_cast")]; + tensor real_div_39_cast = real_div(x = sub_78_cast, y = sqrt_39_cast)[name = tensor("real_div_39_cast")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 1536, 32, 32])]; + tensor reshape_157_cast = reshape(shape = reshape_157_shape_0, x = real_div_39_cast)[name = tensor("reshape_157_cast")]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627943104)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627946240)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_157_cast)[name = tensor("add_79_cast")]; + tensor var_6304 = const()[name = tensor("op_6304"), val = tensor([1, 1])]; + tensor var_6306 = const()[name = tensor("op_6306"), val = tensor([1, 1])]; + tensor hidden_states_303_pad_type_0 = const()[name = tensor("hidden_states_303_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_303_pad_0 = const()[name = tensor("hidden_states_303_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627949376)))]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3632668032)))]; + tensor hidden_states_303_cast = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_6306, groups = var_4628, pad = hidden_states_303_pad_0, pad_type = hidden_states_303_pad_type_0, strides = var_6304, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16, x = add_79_cast)[name = tensor("hidden_states_303_cast")]; + tensor var_6311 = const()[name = tensor("op_6311"), val = tensor([2, 1536, 1, 1024])]; + tensor inputs_169_cast = reshape(shape = var_6311, x = hidden_states_303_cast)[name = tensor("inputs_169_cast")]; + tensor var_6321 = const()[name = tensor("op_6321"), val = tensor([1])]; + tensor channels_mean_169_cast = reduce_mean(axes = var_6321, keep_dims = var_4623, x = inputs_169_cast)[name = tensor("channels_mean_169_cast")]; + tensor zero_mean_169_cast = sub(x = inputs_169_cast, y = channels_mean_169_cast)[name = tensor("zero_mean_169_cast")]; + tensor zero_mean_sq_169_cast = mul(x = zero_mean_169_cast, y = zero_mean_169_cast)[name = tensor("zero_mean_sq_169_cast")]; + tensor var_6325 = const()[name = tensor("op_6325"), val = tensor([1])]; + tensor var_6326_cast = reduce_mean(axes = var_6325, keep_dims = var_4623, x = zero_mean_sq_169_cast)[name = tensor("op_6326_cast")]; + tensor var_6327_to_fp16 = const()[name = tensor("op_6327_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6328_cast = add(x = var_6326_cast, y = var_6327_to_fp16)[name = tensor("op_6328_cast")]; + tensor denom_169_epsilon_0_to_fp16 = const()[name = tensor("denom_169_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_169_cast = rsqrt(epsilon = denom_169_epsilon_0_to_fp16, x = var_6328_cast)[name = tensor("denom_169_cast")]; + tensor out_169_cast = mul(x = zero_mean_169_cast, y = denom_169_cast)[name = tensor("out_169_cast")]; + tensor var_6332_to_fp16 = const()[name = tensor("op_6332_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3632671168)))]; + tensor var_6333_cast = add(x = out_169_cast, y = var_6332_to_fp16)[name = tensor("op_6333_cast")]; + tensor var_6335_to_fp16 = const()[name = tensor("op_6335_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3632674304)))]; + tensor hidden_states_305_cast = mul(x = var_6333_cast, y = var_6335_to_fp16)[name = tensor("hidden_states_305_cast")]; + tensor var_6342 = const()[name = tensor("op_6342"), val = tensor([1, 1])]; + tensor var_6344 = const()[name = tensor("op_6344"), val = tensor([1, 1])]; + tensor q_113_pad_type_0 = const()[name = tensor("q_113_pad_type_0"), val = tensor("custom")]; + tensor q_113_pad_0 = const()[name = tensor("q_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3632677440)))]; + tensor q_113_cast = conv(dilations = var_6344, groups = var_4628, pad = q_113_pad_0, pad_type = q_113_pad_type_0, strides = var_6342, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("q_113_cast")]; + tensor var_6348 = const()[name = tensor("op_6348"), val = tensor([1, 1])]; + tensor var_6350 = const()[name = tensor("op_6350"), val = tensor([1, 1])]; + tensor k_113_pad_type_0 = const()[name = tensor("k_113_pad_type_0"), val = tensor("custom")]; + tensor k_113_pad_0 = const()[name = tensor("k_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3637396096)))]; + tensor k_113_cast = conv(dilations = var_6350, groups = var_4628, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_6348, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("k_113_cast")]; + tensor var_6354 = const()[name = tensor("op_6354"), val = tensor([1, 1])]; + tensor var_6356 = const()[name = tensor("op_6356"), val = tensor([1, 1])]; + tensor v_113_pad_type_0 = const()[name = tensor("v_113_pad_type_0"), val = tensor("custom")]; + tensor v_113_pad_0 = const()[name = tensor("v_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3642114752)))]; + tensor v_113_cast = conv(dilations = var_6356, groups = var_4628, pad = v_113_pad_0, pad_type = v_113_pad_type_0, strides = var_6354, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("v_113_cast")]; + tensor var_6360 = const()[name = tensor("op_6360"), val = tensor([2, 24, 64, -1])]; + tensor var_6361_cast = reshape(shape = var_6360, x = q_113_cast)[name = tensor("op_6361_cast")]; + tensor var_6362 = const()[name = tensor("op_6362"), val = tensor([2, 24, 64, -1])]; + tensor var_6363_cast = reshape(shape = var_6362, x = k_113_cast)[name = tensor("op_6363_cast")]; + tensor var_6364 = const()[name = tensor("op_6364"), val = tensor([2, 24, 64, -1])]; + tensor var_6365_cast = reshape(shape = var_6364, x = v_113_cast)[name = tensor("op_6365_cast")]; + tensor attn_weights_225_transpose_x_0 = const()[name = tensor("attn_weights_225_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_225_transpose_y_0 = const()[name = tensor("attn_weights_225_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_225_cast = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_6361_cast, y = var_6363_cast)[name = tensor("attn_weights_225_cast")]; + tensor attn_weights_227_cast = mul(x = attn_weights_225_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_227_cast")]; + tensor var_6369_cast = softmax(axis = var_4612, x = attn_weights_227_cast)[name = tensor("op_6369_cast")]; + tensor attn_113_transpose_x_0 = const()[name = tensor("attn_113_transpose_x_0"), val = tensor(false)]; + tensor attn_113_transpose_y_0 = const()[name = tensor("attn_113_transpose_y_0"), val = tensor(true)]; + tensor attn_113_cast = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_6365_cast, y = var_6369_cast)[name = tensor("attn_113_cast")]; + tensor var_6373 = const()[name = tensor("op_6373"), val = tensor([2, 1536, 1, -1])]; + tensor input_503_cast = reshape(shape = var_6373, x = attn_113_cast)[name = tensor("input_503_cast")]; + tensor var_6378 = const()[name = tensor("op_6378"), val = tensor([1, 1])]; + tensor var_6380 = const()[name = tensor("op_6380"), val = tensor([1, 1])]; + tensor var_6382_pad_type_0 = const()[name = tensor("op_6382_pad_type_0"), val = tensor("custom")]; + tensor var_6382_pad_0 = const()[name = tensor("op_6382_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3646833408)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3651552064)))]; + tensor var_6382_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_6380, groups = var_4628, pad = var_6382_pad_0, pad_type = var_6382_pad_type_0, strides = var_6378, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_503_cast)[name = tensor("op_6382_cast")]; + tensor inputs_171_cast = add(x = var_6382_cast, y = inputs_169_cast)[name = tensor("inputs_171_cast")]; + tensor var_6386 = const()[name = tensor("op_6386"), val = tensor([1])]; + tensor channels_mean_171_cast = reduce_mean(axes = var_6386, keep_dims = var_4623, x = inputs_171_cast)[name = tensor("channels_mean_171_cast")]; + tensor zero_mean_171_cast = sub(x = inputs_171_cast, y = channels_mean_171_cast)[name = tensor("zero_mean_171_cast")]; + tensor zero_mean_sq_171_cast = mul(x = zero_mean_171_cast, y = zero_mean_171_cast)[name = tensor("zero_mean_sq_171_cast")]; + tensor var_6390 = const()[name = tensor("op_6390"), val = tensor([1])]; + tensor var_6391_cast = reduce_mean(axes = var_6390, keep_dims = var_4623, x = zero_mean_sq_171_cast)[name = tensor("op_6391_cast")]; + tensor var_6392_to_fp16 = const()[name = tensor("op_6392_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6393_cast = add(x = var_6391_cast, y = var_6392_to_fp16)[name = tensor("op_6393_cast")]; + tensor denom_171_epsilon_0_to_fp16 = const()[name = tensor("denom_171_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_171_cast = rsqrt(epsilon = denom_171_epsilon_0_to_fp16, x = var_6393_cast)[name = tensor("denom_171_cast")]; + tensor out_171_cast = mul(x = zero_mean_171_cast, y = denom_171_cast)[name = tensor("out_171_cast")]; + tensor var_6397_to_fp16 = const()[name = tensor("op_6397_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3651555200)))]; + tensor var_6398_cast = add(x = out_171_cast, y = var_6397_to_fp16)[name = tensor("op_6398_cast")]; + tensor var_6400_to_fp16 = const()[name = tensor("op_6400_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3651558336)))]; + tensor hidden_states_307_cast = mul(x = var_6398_cast, y = var_6400_to_fp16)[name = tensor("hidden_states_307_cast")]; + tensor var_6407 = const()[name = tensor("op_6407"), val = tensor([1, 1])]; + tensor var_6409 = const()[name = tensor("op_6409"), val = tensor([1, 1])]; + tensor q_115_pad_type_0 = const()[name = tensor("q_115_pad_type_0"), val = tensor("custom")]; + tensor q_115_pad_0 = const()[name = tensor("q_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3651561472)))]; + tensor q_115_cast = conv(dilations = var_6409, groups = var_4628, pad = q_115_pad_0, pad_type = q_115_pad_type_0, strides = var_6407, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_307_cast)[name = tensor("q_115_cast")]; + tensor var_6413 = const()[name = tensor("op_6413"), val = tensor([1, 1])]; + tensor var_6415 = const()[name = tensor("op_6415"), val = tensor([1, 1])]; + tensor k_115_pad_type_0 = const()[name = tensor("k_115_pad_type_0"), val = tensor("custom")]; + tensor k_115_pad_0 = const()[name = tensor("k_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3656280128)))]; + tensor k_115_cast = conv(dilations = var_6415, groups = var_4628, pad = k_115_pad_0, pad_type = k_115_pad_type_0, strides = var_6413, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_115_cast")]; + tensor var_6419 = const()[name = tensor("op_6419"), val = tensor([1, 1])]; + tensor var_6421 = const()[name = tensor("op_6421"), val = tensor([1, 1])]; + tensor v_115_pad_type_0 = const()[name = tensor("v_115_pad_type_0"), val = tensor("custom")]; + tensor v_115_pad_0 = const()[name = tensor("v_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3660212352)))]; + tensor v_115_cast = conv(dilations = var_6421, groups = var_4628, pad = v_115_pad_0, pad_type = v_115_pad_type_0, strides = var_6419, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_115_cast")]; + tensor var_6425 = const()[name = tensor("op_6425"), val = tensor([2, 24, 64, -1])]; + tensor var_6426_cast = reshape(shape = var_6425, x = q_115_cast)[name = tensor("op_6426_cast")]; + tensor var_6427 = const()[name = tensor("op_6427"), val = tensor([2, 24, 64, -1])]; + tensor var_6428_cast = reshape(shape = var_6427, x = k_115_cast)[name = tensor("op_6428_cast")]; + tensor var_6429 = const()[name = tensor("op_6429"), val = tensor([2, 24, 64, -1])]; + tensor var_6430_cast = reshape(shape = var_6429, x = v_115_cast)[name = tensor("op_6430_cast")]; + tensor attn_weights_229_transpose_x_0 = const()[name = tensor("attn_weights_229_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_229_transpose_y_0 = const()[name = tensor("attn_weights_229_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_229_cast = matmul(transpose_x = attn_weights_229_transpose_x_0, transpose_y = attn_weights_229_transpose_y_0, x = var_6426_cast, y = var_6428_cast)[name = tensor("attn_weights_229_cast")]; + tensor attn_weights_231_cast = mul(x = attn_weights_229_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_231_cast")]; + tensor var_6434_cast = softmax(axis = var_4612, x = attn_weights_231_cast)[name = tensor("op_6434_cast")]; + tensor attn_115_transpose_x_0 = const()[name = tensor("attn_115_transpose_x_0"), val = tensor(false)]; + tensor attn_115_transpose_y_0 = const()[name = tensor("attn_115_transpose_y_0"), val = tensor(true)]; + tensor attn_115_cast = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_6430_cast, y = var_6434_cast)[name = tensor("attn_115_cast")]; + tensor var_6438 = const()[name = tensor("op_6438"), val = tensor([2, 1536, 1, -1])]; + tensor input_505_cast = reshape(shape = var_6438, x = attn_115_cast)[name = tensor("input_505_cast")]; + tensor var_6443 = const()[name = tensor("op_6443"), val = tensor([1, 1])]; + tensor var_6445 = const()[name = tensor("op_6445"), val = tensor([1, 1])]; + tensor var_6447_pad_type_0 = const()[name = tensor("op_6447_pad_type_0"), val = tensor("custom")]; + tensor var_6447_pad_0 = const()[name = tensor("op_6447_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3664144576)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3668863232)))]; + tensor var_6447_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_6445, groups = var_4628, pad = var_6447_pad_0, pad_type = var_6447_pad_type_0, strides = var_6443, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_505_cast)[name = tensor("op_6447_cast")]; + tensor inputs_173_cast = add(x = var_6447_cast, y = inputs_171_cast)[name = tensor("inputs_173_cast")]; + tensor var_6451 = const()[name = tensor("op_6451"), val = tensor([1])]; + tensor channels_mean_173_cast = reduce_mean(axes = var_6451, keep_dims = var_4623, x = inputs_173_cast)[name = tensor("channels_mean_173_cast")]; + tensor zero_mean_173_cast = sub(x = inputs_173_cast, y = channels_mean_173_cast)[name = tensor("zero_mean_173_cast")]; + tensor zero_mean_sq_173_cast = mul(x = zero_mean_173_cast, y = zero_mean_173_cast)[name = tensor("zero_mean_sq_173_cast")]; + tensor var_6455 = const()[name = tensor("op_6455"), val = tensor([1])]; + tensor var_6456_cast = reduce_mean(axes = var_6455, keep_dims = var_4623, x = zero_mean_sq_173_cast)[name = tensor("op_6456_cast")]; + tensor var_6457_to_fp16 = const()[name = tensor("op_6457_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6458_cast = add(x = var_6456_cast, y = var_6457_to_fp16)[name = tensor("op_6458_cast")]; + tensor denom_173_epsilon_0_to_fp16 = const()[name = tensor("denom_173_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_173_cast = rsqrt(epsilon = denom_173_epsilon_0_to_fp16, x = var_6458_cast)[name = tensor("denom_173_cast")]; + tensor out_173_cast = mul(x = zero_mean_173_cast, y = denom_173_cast)[name = tensor("out_173_cast")]; + tensor var_6462_to_fp16 = const()[name = tensor("op_6462_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3668866368)))]; + tensor var_6463_cast = add(x = out_173_cast, y = var_6462_to_fp16)[name = tensor("op_6463_cast")]; + tensor var_6465_to_fp16 = const()[name = tensor("op_6465_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3668869504)))]; + tensor input_507_cast = mul(x = var_6463_cast, y = var_6465_to_fp16)[name = tensor("input_507_cast")]; + tensor var_6473 = const()[name = tensor("op_6473"), val = tensor([1, 1])]; + tensor var_6475 = const()[name = tensor("op_6475"), val = tensor([1, 1])]; + tensor var_6477_pad_type_0 = const()[name = tensor("op_6477_pad_type_0"), val = tensor("custom")]; + tensor var_6477_pad_0 = const()[name = tensor("op_6477_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3668872640)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3706621440)))]; + tensor var_6477_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_6475, groups = var_4628, pad = var_6477_pad_0, pad_type = var_6477_pad_type_0, strides = var_6473, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_507_cast)[name = tensor("op_6477_cast")]; + tensor var_6478_split_sizes_0 = const()[name = tensor("op_6478_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_6478_axis_0 = const()[name = tensor("op_6478_axis_0"), val = tensor(1)]; + tensor var_6478_cast_0, tensor var_6478_cast_1 = split(axis = var_6478_axis_0, split_sizes = var_6478_split_sizes_0, x = var_6477_cast)[name = tensor("op_6478_cast")]; + tensor var_6480_mode_0 = const()[name = tensor("op_6480_mode_0"), val = tensor("EXACT")]; + tensor var_6480_cast = gelu(mode = var_6480_mode_0, x = var_6478_cast_1)[name = tensor("op_6480_cast")]; + tensor input_509_cast = mul(x = var_6478_cast_0, y = var_6480_cast)[name = tensor("input_509_cast")]; + tensor var_6484 = const()[name = tensor("op_6484"), val = tensor([1, 1])]; + tensor var_6486 = const()[name = tensor("op_6486"), val = tensor([1, 1])]; + tensor var_6488_pad_type_0 = const()[name = tensor("op_6488_pad_type_0"), val = tensor("custom")]; + tensor var_6488_pad_0 = const()[name = tensor("op_6488_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3706646080)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3725520512)))]; + tensor var_6488_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_6486, groups = var_4628, pad = var_6488_pad_0, pad_type = var_6488_pad_type_0, strides = var_6484, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_509_cast)[name = tensor("op_6488_cast")]; + tensor inputs_175_cast = add(x = var_6488_cast, y = inputs_173_cast)[name = tensor("inputs_175_cast")]; + tensor var_6498 = const()[name = tensor("op_6498"), val = tensor([1])]; + tensor channels_mean_175_cast = reduce_mean(axes = var_6498, keep_dims = var_4623, x = inputs_175_cast)[name = tensor("channels_mean_175_cast")]; + tensor zero_mean_175_cast = sub(x = inputs_175_cast, y = channels_mean_175_cast)[name = tensor("zero_mean_175_cast")]; + tensor zero_mean_sq_175_cast = mul(x = zero_mean_175_cast, y = zero_mean_175_cast)[name = tensor("zero_mean_sq_175_cast")]; + tensor var_6502 = const()[name = tensor("op_6502"), val = tensor([1])]; + tensor var_6503_cast = reduce_mean(axes = var_6502, keep_dims = var_4623, x = zero_mean_sq_175_cast)[name = tensor("op_6503_cast")]; + tensor var_6504_to_fp16 = const()[name = tensor("op_6504_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6505_cast = add(x = var_6503_cast, y = var_6504_to_fp16)[name = tensor("op_6505_cast")]; + tensor denom_175_epsilon_0_to_fp16 = const()[name = tensor("denom_175_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_175_cast = rsqrt(epsilon = denom_175_epsilon_0_to_fp16, x = var_6505_cast)[name = tensor("denom_175_cast")]; + tensor out_175_cast = mul(x = zero_mean_175_cast, y = denom_175_cast)[name = tensor("out_175_cast")]; + tensor var_6509_to_fp16 = const()[name = tensor("op_6509_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3725523648)))]; + tensor var_6510_cast = add(x = out_175_cast, y = var_6509_to_fp16)[name = tensor("op_6510_cast")]; + tensor var_6512_to_fp16 = const()[name = tensor("op_6512_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3725526784)))]; + tensor hidden_states_311_cast = mul(x = var_6510_cast, y = var_6512_to_fp16)[name = tensor("hidden_states_311_cast")]; + tensor var_6519 = const()[name = tensor("op_6519"), val = tensor([1, 1])]; + tensor var_6521 = const()[name = tensor("op_6521"), val = tensor([1, 1])]; + tensor q_117_pad_type_0 = const()[name = tensor("q_117_pad_type_0"), val = tensor("custom")]; + tensor q_117_pad_0 = const()[name = tensor("q_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3725529920)))]; + tensor q_117_cast = conv(dilations = var_6521, groups = var_4628, pad = q_117_pad_0, pad_type = q_117_pad_type_0, strides = var_6519, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("q_117_cast")]; + tensor var_6525 = const()[name = tensor("op_6525"), val = tensor([1, 1])]; + tensor var_6527 = const()[name = tensor("op_6527"), val = tensor([1, 1])]; + tensor k_117_pad_type_0 = const()[name = tensor("k_117_pad_type_0"), val = tensor("custom")]; + tensor k_117_pad_0 = const()[name = tensor("k_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3730248576)))]; + tensor k_117_cast = conv(dilations = var_6527, groups = var_4628, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_6525, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("k_117_cast")]; + tensor var_6531 = const()[name = tensor("op_6531"), val = tensor([1, 1])]; + tensor var_6533 = const()[name = tensor("op_6533"), val = tensor([1, 1])]; + tensor v_117_pad_type_0 = const()[name = tensor("v_117_pad_type_0"), val = tensor("custom")]; + tensor v_117_pad_0 = const()[name = tensor("v_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3734967232)))]; + tensor v_117_cast = conv(dilations = var_6533, groups = var_4628, pad = v_117_pad_0, pad_type = v_117_pad_type_0, strides = var_6531, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("v_117_cast")]; + tensor var_6537 = const()[name = tensor("op_6537"), val = tensor([2, 24, 64, -1])]; + tensor var_6538_cast = reshape(shape = var_6537, x = q_117_cast)[name = tensor("op_6538_cast")]; + tensor var_6539 = const()[name = tensor("op_6539"), val = tensor([2, 24, 64, -1])]; + tensor var_6540_cast = reshape(shape = var_6539, x = k_117_cast)[name = tensor("op_6540_cast")]; + tensor var_6541 = const()[name = tensor("op_6541"), val = tensor([2, 24, 64, -1])]; + tensor var_6542_cast = reshape(shape = var_6541, x = v_117_cast)[name = tensor("op_6542_cast")]; + tensor attn_weights_233_transpose_x_0 = const()[name = tensor("attn_weights_233_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_233_transpose_y_0 = const()[name = tensor("attn_weights_233_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_233_cast = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_6538_cast, y = var_6540_cast)[name = tensor("attn_weights_233_cast")]; + tensor attn_weights_235_cast = mul(x = attn_weights_233_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_235_cast")]; + tensor var_6546_cast = softmax(axis = var_4612, x = attn_weights_235_cast)[name = tensor("op_6546_cast")]; + tensor attn_117_transpose_x_0 = const()[name = tensor("attn_117_transpose_x_0"), val = tensor(false)]; + tensor attn_117_transpose_y_0 = const()[name = tensor("attn_117_transpose_y_0"), val = tensor(true)]; + tensor attn_117_cast = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_6542_cast, y = var_6546_cast)[name = tensor("attn_117_cast")]; + tensor var_6550 = const()[name = tensor("op_6550"), val = tensor([2, 1536, 1, -1])]; + tensor input_511_cast = reshape(shape = var_6550, x = attn_117_cast)[name = tensor("input_511_cast")]; + tensor var_6555 = const()[name = tensor("op_6555"), val = tensor([1, 1])]; + tensor var_6557 = const()[name = tensor("op_6557"), val = tensor([1, 1])]; + tensor var_6559_pad_type_0 = const()[name = tensor("op_6559_pad_type_0"), val = tensor("custom")]; + tensor var_6559_pad_0 = const()[name = tensor("op_6559_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3739685888)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3744404544)))]; + tensor var_6559_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_6557, groups = var_4628, pad = var_6559_pad_0, pad_type = var_6559_pad_type_0, strides = var_6555, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_511_cast)[name = tensor("op_6559_cast")]; + tensor inputs_177_cast = add(x = var_6559_cast, y = inputs_175_cast)[name = tensor("inputs_177_cast")]; + tensor var_6563 = const()[name = tensor("op_6563"), val = tensor([1])]; + tensor channels_mean_177_cast = reduce_mean(axes = var_6563, keep_dims = var_4623, x = inputs_177_cast)[name = tensor("channels_mean_177_cast")]; + tensor zero_mean_177_cast = sub(x = inputs_177_cast, y = channels_mean_177_cast)[name = tensor("zero_mean_177_cast")]; + tensor zero_mean_sq_177_cast = mul(x = zero_mean_177_cast, y = zero_mean_177_cast)[name = tensor("zero_mean_sq_177_cast")]; + tensor var_6567 = const()[name = tensor("op_6567"), val = tensor([1])]; + tensor var_6568_cast = reduce_mean(axes = var_6567, keep_dims = var_4623, x = zero_mean_sq_177_cast)[name = tensor("op_6568_cast")]; + tensor var_6569_to_fp16 = const()[name = tensor("op_6569_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6570_cast = add(x = var_6568_cast, y = var_6569_to_fp16)[name = tensor("op_6570_cast")]; + tensor denom_177_epsilon_0_to_fp16 = const()[name = tensor("denom_177_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_177_cast = rsqrt(epsilon = denom_177_epsilon_0_to_fp16, x = var_6570_cast)[name = tensor("denom_177_cast")]; + tensor out_177_cast = mul(x = zero_mean_177_cast, y = denom_177_cast)[name = tensor("out_177_cast")]; + tensor var_6574_to_fp16 = const()[name = tensor("op_6574_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3744407680)))]; + tensor var_6575_cast = add(x = out_177_cast, y = var_6574_to_fp16)[name = tensor("op_6575_cast")]; + tensor var_6577_to_fp16 = const()[name = tensor("op_6577_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3744410816)))]; + tensor hidden_states_313_cast = mul(x = var_6575_cast, y = var_6577_to_fp16)[name = tensor("hidden_states_313_cast")]; + tensor var_6584 = const()[name = tensor("op_6584"), val = tensor([1, 1])]; + tensor var_6586 = const()[name = tensor("op_6586"), val = tensor([1, 1])]; + tensor q_119_pad_type_0 = const()[name = tensor("q_119_pad_type_0"), val = tensor("custom")]; + tensor q_119_pad_0 = const()[name = tensor("q_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3744413952)))]; + tensor q_119_cast = conv(dilations = var_6586, groups = var_4628, pad = q_119_pad_0, pad_type = q_119_pad_type_0, strides = var_6584, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_313_cast)[name = tensor("q_119_cast")]; + tensor var_6590 = const()[name = tensor("op_6590"), val = tensor([1, 1])]; + tensor var_6592 = const()[name = tensor("op_6592"), val = tensor([1, 1])]; + tensor k_119_pad_type_0 = const()[name = tensor("k_119_pad_type_0"), val = tensor("custom")]; + tensor k_119_pad_0 = const()[name = tensor("k_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749132608)))]; + tensor k_119_cast = conv(dilations = var_6592, groups = var_4628, pad = k_119_pad_0, pad_type = k_119_pad_type_0, strides = var_6590, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_119_cast")]; + tensor var_6596 = const()[name = tensor("op_6596"), val = tensor([1, 1])]; + tensor var_6598 = const()[name = tensor("op_6598"), val = tensor([1, 1])]; + tensor v_119_pad_type_0 = const()[name = tensor("v_119_pad_type_0"), val = tensor("custom")]; + tensor v_119_pad_0 = const()[name = tensor("v_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3753064832)))]; + tensor v_119_cast = conv(dilations = var_6598, groups = var_4628, pad = v_119_pad_0, pad_type = v_119_pad_type_0, strides = var_6596, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_119_cast")]; + tensor var_6602 = const()[name = tensor("op_6602"), val = tensor([2, 24, 64, -1])]; + tensor var_6603_cast = reshape(shape = var_6602, x = q_119_cast)[name = tensor("op_6603_cast")]; + tensor var_6604 = const()[name = tensor("op_6604"), val = tensor([2, 24, 64, -1])]; + tensor var_6605_cast = reshape(shape = var_6604, x = k_119_cast)[name = tensor("op_6605_cast")]; + tensor var_6606 = const()[name = tensor("op_6606"), val = tensor([2, 24, 64, -1])]; + tensor var_6607_cast = reshape(shape = var_6606, x = v_119_cast)[name = tensor("op_6607_cast")]; + tensor attn_weights_237_transpose_x_0 = const()[name = tensor("attn_weights_237_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_237_transpose_y_0 = const()[name = tensor("attn_weights_237_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_237_cast = matmul(transpose_x = attn_weights_237_transpose_x_0, transpose_y = attn_weights_237_transpose_y_0, x = var_6603_cast, y = var_6605_cast)[name = tensor("attn_weights_237_cast")]; + tensor attn_weights_239_cast = mul(x = attn_weights_237_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_239_cast")]; + tensor var_6611_cast = softmax(axis = var_4612, x = attn_weights_239_cast)[name = tensor("op_6611_cast")]; + tensor attn_119_transpose_x_0 = const()[name = tensor("attn_119_transpose_x_0"), val = tensor(false)]; + tensor attn_119_transpose_y_0 = const()[name = tensor("attn_119_transpose_y_0"), val = tensor(true)]; + tensor attn_119_cast = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_6607_cast, y = var_6611_cast)[name = tensor("attn_119_cast")]; + tensor var_6615 = const()[name = tensor("op_6615"), val = tensor([2, 1536, 1, -1])]; + tensor input_513_cast = reshape(shape = var_6615, x = attn_119_cast)[name = tensor("input_513_cast")]; + tensor var_6620 = const()[name = tensor("op_6620"), val = tensor([1, 1])]; + tensor var_6622 = const()[name = tensor("op_6622"), val = tensor([1, 1])]; + tensor var_6624_pad_type_0 = const()[name = tensor("op_6624_pad_type_0"), val = tensor("custom")]; + tensor var_6624_pad_0 = const()[name = tensor("op_6624_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3756997056)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3761715712)))]; + tensor var_6624_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_6622, groups = var_4628, pad = var_6624_pad_0, pad_type = var_6624_pad_type_0, strides = var_6620, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_513_cast)[name = tensor("op_6624_cast")]; + tensor inputs_179_cast = add(x = var_6624_cast, y = inputs_177_cast)[name = tensor("inputs_179_cast")]; + tensor var_6628 = const()[name = tensor("op_6628"), val = tensor([1])]; + tensor channels_mean_179_cast = reduce_mean(axes = var_6628, keep_dims = var_4623, x = inputs_179_cast)[name = tensor("channels_mean_179_cast")]; + tensor zero_mean_179_cast = sub(x = inputs_179_cast, y = channels_mean_179_cast)[name = tensor("zero_mean_179_cast")]; + tensor zero_mean_sq_179_cast = mul(x = zero_mean_179_cast, y = zero_mean_179_cast)[name = tensor("zero_mean_sq_179_cast")]; + tensor var_6632 = const()[name = tensor("op_6632"), val = tensor([1])]; + tensor var_6633_cast = reduce_mean(axes = var_6632, keep_dims = var_4623, x = zero_mean_sq_179_cast)[name = tensor("op_6633_cast")]; + tensor var_6634_to_fp16 = const()[name = tensor("op_6634_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6635_cast = add(x = var_6633_cast, y = var_6634_to_fp16)[name = tensor("op_6635_cast")]; + tensor denom_179_epsilon_0_to_fp16 = const()[name = tensor("denom_179_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_179_cast = rsqrt(epsilon = denom_179_epsilon_0_to_fp16, x = var_6635_cast)[name = tensor("denom_179_cast")]; + tensor out_179_cast = mul(x = zero_mean_179_cast, y = denom_179_cast)[name = tensor("out_179_cast")]; + tensor var_6639_to_fp16 = const()[name = tensor("op_6639_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3761718848)))]; + tensor var_6640_cast = add(x = out_179_cast, y = var_6639_to_fp16)[name = tensor("op_6640_cast")]; + tensor var_6642_to_fp16 = const()[name = tensor("op_6642_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3761721984)))]; + tensor input_515_cast = mul(x = var_6640_cast, y = var_6642_to_fp16)[name = tensor("input_515_cast")]; + tensor var_6650 = const()[name = tensor("op_6650"), val = tensor([1, 1])]; + tensor var_6652 = const()[name = tensor("op_6652"), val = tensor([1, 1])]; + tensor var_6654_pad_type_0 = const()[name = tensor("op_6654_pad_type_0"), val = tensor("custom")]; + tensor var_6654_pad_0 = const()[name = tensor("op_6654_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3761725120)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3799473920)))]; + tensor var_6654_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_6652, groups = var_4628, pad = var_6654_pad_0, pad_type = var_6654_pad_type_0, strides = var_6650, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_515_cast)[name = tensor("op_6654_cast")]; + tensor var_6655_split_sizes_0 = const()[name = tensor("op_6655_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_6655_axis_0 = const()[name = tensor("op_6655_axis_0"), val = tensor(1)]; + tensor var_6655_cast_0, tensor var_6655_cast_1 = split(axis = var_6655_axis_0, split_sizes = var_6655_split_sizes_0, x = var_6654_cast)[name = tensor("op_6655_cast")]; + tensor var_6657_mode_0 = const()[name = tensor("op_6657_mode_0"), val = tensor("EXACT")]; + tensor var_6657_cast = gelu(mode = var_6657_mode_0, x = var_6655_cast_1)[name = tensor("op_6657_cast")]; + tensor input_517_cast = mul(x = var_6655_cast_0, y = var_6657_cast)[name = tensor("input_517_cast")]; + tensor var_6661 = const()[name = tensor("op_6661"), val = tensor([1, 1])]; + tensor var_6663 = const()[name = tensor("op_6663"), val = tensor([1, 1])]; + tensor var_6665_pad_type_0 = const()[name = tensor("op_6665_pad_type_0"), val = tensor("custom")]; + tensor var_6665_pad_0 = const()[name = tensor("op_6665_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3799498560)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818372992)))]; + tensor var_6665_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_6663, groups = var_4628, pad = var_6665_pad_0, pad_type = var_6665_pad_type_0, strides = var_6661, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_517_cast)[name = tensor("op_6665_cast")]; + tensor inputs_181_cast = add(x = var_6665_cast, y = inputs_179_cast)[name = tensor("inputs_181_cast")]; + tensor var_6675 = const()[name = tensor("op_6675"), val = tensor([1])]; + tensor channels_mean_181_cast = reduce_mean(axes = var_6675, keep_dims = var_4623, x = inputs_181_cast)[name = tensor("channels_mean_181_cast")]; + tensor zero_mean_181_cast = sub(x = inputs_181_cast, y = channels_mean_181_cast)[name = tensor("zero_mean_181_cast")]; + tensor zero_mean_sq_181_cast = mul(x = zero_mean_181_cast, y = zero_mean_181_cast)[name = tensor("zero_mean_sq_181_cast")]; + tensor var_6679 = const()[name = tensor("op_6679"), val = tensor([1])]; + tensor var_6680_cast = reduce_mean(axes = var_6679, keep_dims = var_4623, x = zero_mean_sq_181_cast)[name = tensor("op_6680_cast")]; + tensor var_6681_to_fp16 = const()[name = tensor("op_6681_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6682_cast = add(x = var_6680_cast, y = var_6681_to_fp16)[name = tensor("op_6682_cast")]; + tensor denom_181_epsilon_0_to_fp16 = const()[name = tensor("denom_181_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_181_cast = rsqrt(epsilon = denom_181_epsilon_0_to_fp16, x = var_6682_cast)[name = tensor("denom_181_cast")]; + tensor out_181_cast = mul(x = zero_mean_181_cast, y = denom_181_cast)[name = tensor("out_181_cast")]; + tensor var_6686_to_fp16 = const()[name = tensor("op_6686_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818376128)))]; + tensor var_6687_cast = add(x = out_181_cast, y = var_6686_to_fp16)[name = tensor("op_6687_cast")]; + tensor var_6689_to_fp16 = const()[name = tensor("op_6689_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818379264)))]; + tensor hidden_states_317_cast = mul(x = var_6687_cast, y = var_6689_to_fp16)[name = tensor("hidden_states_317_cast")]; + tensor var_6696 = const()[name = tensor("op_6696"), val = tensor([1, 1])]; + tensor var_6698 = const()[name = tensor("op_6698"), val = tensor([1, 1])]; + tensor q_121_pad_type_0 = const()[name = tensor("q_121_pad_type_0"), val = tensor("custom")]; + tensor q_121_pad_0 = const()[name = tensor("q_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818382400)))]; + tensor q_121_cast = conv(dilations = var_6698, groups = var_4628, pad = q_121_pad_0, pad_type = q_121_pad_type_0, strides = var_6696, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("q_121_cast")]; + tensor var_6702 = const()[name = tensor("op_6702"), val = tensor([1, 1])]; + tensor var_6704 = const()[name = tensor("op_6704"), val = tensor([1, 1])]; + tensor k_121_pad_type_0 = const()[name = tensor("k_121_pad_type_0"), val = tensor("custom")]; + tensor k_121_pad_0 = const()[name = tensor("k_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3823101056)))]; + tensor k_121_cast = conv(dilations = var_6704, groups = var_4628, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_6702, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("k_121_cast")]; + tensor var_6708 = const()[name = tensor("op_6708"), val = tensor([1, 1])]; + tensor var_6710 = const()[name = tensor("op_6710"), val = tensor([1, 1])]; + tensor v_121_pad_type_0 = const()[name = tensor("v_121_pad_type_0"), val = tensor("custom")]; + tensor v_121_pad_0 = const()[name = tensor("v_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3827819712)))]; + tensor v_121_cast = conv(dilations = var_6710, groups = var_4628, pad = v_121_pad_0, pad_type = v_121_pad_type_0, strides = var_6708, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("v_121_cast")]; + tensor var_6714 = const()[name = tensor("op_6714"), val = tensor([2, 24, 64, -1])]; + tensor var_6715_cast = reshape(shape = var_6714, x = q_121_cast)[name = tensor("op_6715_cast")]; + tensor var_6716 = const()[name = tensor("op_6716"), val = tensor([2, 24, 64, -1])]; + tensor var_6717_cast = reshape(shape = var_6716, x = k_121_cast)[name = tensor("op_6717_cast")]; + tensor var_6718 = const()[name = tensor("op_6718"), val = tensor([2, 24, 64, -1])]; + tensor var_6719_cast = reshape(shape = var_6718, x = v_121_cast)[name = tensor("op_6719_cast")]; + tensor attn_weights_241_transpose_x_0 = const()[name = tensor("attn_weights_241_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_241_transpose_y_0 = const()[name = tensor("attn_weights_241_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_241_cast = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_6715_cast, y = var_6717_cast)[name = tensor("attn_weights_241_cast")]; + tensor attn_weights_243_cast = mul(x = attn_weights_241_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_243_cast")]; + tensor var_6723_cast = softmax(axis = var_4612, x = attn_weights_243_cast)[name = tensor("op_6723_cast")]; + tensor attn_121_transpose_x_0 = const()[name = tensor("attn_121_transpose_x_0"), val = tensor(false)]; + tensor attn_121_transpose_y_0 = const()[name = tensor("attn_121_transpose_y_0"), val = tensor(true)]; + tensor attn_121_cast = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6719_cast, y = var_6723_cast)[name = tensor("attn_121_cast")]; + tensor var_6727 = const()[name = tensor("op_6727"), val = tensor([2, 1536, 1, -1])]; + tensor input_519_cast = reshape(shape = var_6727, x = attn_121_cast)[name = tensor("input_519_cast")]; + tensor var_6732 = const()[name = tensor("op_6732"), val = tensor([1, 1])]; + tensor var_6734 = const()[name = tensor("op_6734"), val = tensor([1, 1])]; + tensor var_6736_pad_type_0 = const()[name = tensor("op_6736_pad_type_0"), val = tensor("custom")]; + tensor var_6736_pad_0 = const()[name = tensor("op_6736_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3832538368)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3837257024)))]; + tensor var_6736_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_6734, groups = var_4628, pad = var_6736_pad_0, pad_type = var_6736_pad_type_0, strides = var_6732, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_519_cast)[name = tensor("op_6736_cast")]; + tensor inputs_183_cast = add(x = var_6736_cast, y = inputs_181_cast)[name = tensor("inputs_183_cast")]; + tensor var_6740 = const()[name = tensor("op_6740"), val = tensor([1])]; + tensor channels_mean_183_cast = reduce_mean(axes = var_6740, keep_dims = var_4623, x = inputs_183_cast)[name = tensor("channels_mean_183_cast")]; + tensor zero_mean_183_cast = sub(x = inputs_183_cast, y = channels_mean_183_cast)[name = tensor("zero_mean_183_cast")]; + tensor zero_mean_sq_183_cast = mul(x = zero_mean_183_cast, y = zero_mean_183_cast)[name = tensor("zero_mean_sq_183_cast")]; + tensor var_6744 = const()[name = tensor("op_6744"), val = tensor([1])]; + tensor var_6745_cast = reduce_mean(axes = var_6744, keep_dims = var_4623, x = zero_mean_sq_183_cast)[name = tensor("op_6745_cast")]; + tensor var_6746_to_fp16 = const()[name = tensor("op_6746_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6747_cast = add(x = var_6745_cast, y = var_6746_to_fp16)[name = tensor("op_6747_cast")]; + tensor denom_183_epsilon_0_to_fp16 = const()[name = tensor("denom_183_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_183_cast = rsqrt(epsilon = denom_183_epsilon_0_to_fp16, x = var_6747_cast)[name = tensor("denom_183_cast")]; + tensor out_183_cast = mul(x = zero_mean_183_cast, y = denom_183_cast)[name = tensor("out_183_cast")]; + tensor var_6751_to_fp16 = const()[name = tensor("op_6751_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3837260160)))]; + tensor var_6752_cast = add(x = out_183_cast, y = var_6751_to_fp16)[name = tensor("op_6752_cast")]; + tensor var_6754_to_fp16 = const()[name = tensor("op_6754_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3837263296)))]; + tensor hidden_states_319_cast = mul(x = var_6752_cast, y = var_6754_to_fp16)[name = tensor("hidden_states_319_cast")]; + tensor var_6761 = const()[name = tensor("op_6761"), val = tensor([1, 1])]; + tensor var_6763 = const()[name = tensor("op_6763"), val = tensor([1, 1])]; + tensor q_123_pad_type_0 = const()[name = tensor("q_123_pad_type_0"), val = tensor("custom")]; + tensor q_123_pad_0 = const()[name = tensor("q_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3837266432)))]; + tensor q_123_cast = conv(dilations = var_6763, groups = var_4628, pad = q_123_pad_0, pad_type = q_123_pad_type_0, strides = var_6761, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_319_cast)[name = tensor("q_123_cast")]; + tensor var_6767 = const()[name = tensor("op_6767"), val = tensor([1, 1])]; + tensor var_6769 = const()[name = tensor("op_6769"), val = tensor([1, 1])]; + tensor k_123_pad_type_0 = const()[name = tensor("k_123_pad_type_0"), val = tensor("custom")]; + tensor k_123_pad_0 = const()[name = tensor("k_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3841985088)))]; + tensor k_123_cast = conv(dilations = var_6769, groups = var_4628, pad = k_123_pad_0, pad_type = k_123_pad_type_0, strides = var_6767, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_123_cast")]; + tensor var_6773 = const()[name = tensor("op_6773"), val = tensor([1, 1])]; + tensor var_6775 = const()[name = tensor("op_6775"), val = tensor([1, 1])]; + tensor v_123_pad_type_0 = const()[name = tensor("v_123_pad_type_0"), val = tensor("custom")]; + tensor v_123_pad_0 = const()[name = tensor("v_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3845917312)))]; + tensor v_123_cast = conv(dilations = var_6775, groups = var_4628, pad = v_123_pad_0, pad_type = v_123_pad_type_0, strides = var_6773, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_123_cast")]; + tensor var_6779 = const()[name = tensor("op_6779"), val = tensor([2, 24, 64, -1])]; + tensor var_6780_cast = reshape(shape = var_6779, x = q_123_cast)[name = tensor("op_6780_cast")]; + tensor var_6781 = const()[name = tensor("op_6781"), val = tensor([2, 24, 64, -1])]; + tensor var_6782_cast = reshape(shape = var_6781, x = k_123_cast)[name = tensor("op_6782_cast")]; + tensor var_6783 = const()[name = tensor("op_6783"), val = tensor([2, 24, 64, -1])]; + tensor var_6784_cast = reshape(shape = var_6783, x = v_123_cast)[name = tensor("op_6784_cast")]; + tensor attn_weights_245_transpose_x_0 = const()[name = tensor("attn_weights_245_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_245_transpose_y_0 = const()[name = tensor("attn_weights_245_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_245_cast = matmul(transpose_x = attn_weights_245_transpose_x_0, transpose_y = attn_weights_245_transpose_y_0, x = var_6780_cast, y = var_6782_cast)[name = tensor("attn_weights_245_cast")]; + tensor attn_weights_247_cast = mul(x = attn_weights_245_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_247_cast")]; + tensor var_6788_cast = softmax(axis = var_4612, x = attn_weights_247_cast)[name = tensor("op_6788_cast")]; + tensor attn_123_transpose_x_0 = const()[name = tensor("attn_123_transpose_x_0"), val = tensor(false)]; + tensor attn_123_transpose_y_0 = const()[name = tensor("attn_123_transpose_y_0"), val = tensor(true)]; + tensor attn_123_cast = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6784_cast, y = var_6788_cast)[name = tensor("attn_123_cast")]; + tensor var_6792 = const()[name = tensor("op_6792"), val = tensor([2, 1536, 1, -1])]; + tensor input_521_cast = reshape(shape = var_6792, x = attn_123_cast)[name = tensor("input_521_cast")]; + tensor var_6797 = const()[name = tensor("op_6797"), val = tensor([1, 1])]; + tensor var_6799 = const()[name = tensor("op_6799"), val = tensor([1, 1])]; + tensor var_6801_pad_type_0 = const()[name = tensor("op_6801_pad_type_0"), val = tensor("custom")]; + tensor var_6801_pad_0 = const()[name = tensor("op_6801_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3849849536)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3854568192)))]; + tensor var_6801_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_6799, groups = var_4628, pad = var_6801_pad_0, pad_type = var_6801_pad_type_0, strides = var_6797, weight = up_blocks_1_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_521_cast)[name = tensor("op_6801_cast")]; + tensor inputs_185_cast = add(x = var_6801_cast, y = inputs_183_cast)[name = tensor("inputs_185_cast")]; + tensor var_6805 = const()[name = tensor("op_6805"), val = tensor([1])]; + tensor channels_mean_185_cast = reduce_mean(axes = var_6805, keep_dims = var_4623, x = inputs_185_cast)[name = tensor("channels_mean_185_cast")]; + tensor zero_mean_185_cast = sub(x = inputs_185_cast, y = channels_mean_185_cast)[name = tensor("zero_mean_185_cast")]; + tensor zero_mean_sq_185_cast = mul(x = zero_mean_185_cast, y = zero_mean_185_cast)[name = tensor("zero_mean_sq_185_cast")]; + tensor var_6809 = const()[name = tensor("op_6809"), val = tensor([1])]; + tensor var_6810_cast = reduce_mean(axes = var_6809, keep_dims = var_4623, x = zero_mean_sq_185_cast)[name = tensor("op_6810_cast")]; + tensor var_6811_to_fp16 = const()[name = tensor("op_6811_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6812_cast = add(x = var_6810_cast, y = var_6811_to_fp16)[name = tensor("op_6812_cast")]; + tensor denom_185_epsilon_0_to_fp16 = const()[name = tensor("denom_185_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_185_cast = rsqrt(epsilon = denom_185_epsilon_0_to_fp16, x = var_6812_cast)[name = tensor("denom_185_cast")]; + tensor out_185_cast = mul(x = zero_mean_185_cast, y = denom_185_cast)[name = tensor("out_185_cast")]; + tensor var_6816_to_fp16 = const()[name = tensor("op_6816_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3854571328)))]; + tensor var_6817_cast = add(x = out_185_cast, y = var_6816_to_fp16)[name = tensor("op_6817_cast")]; + tensor var_6819_to_fp16 = const()[name = tensor("op_6819_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3854574464)))]; + tensor input_523_cast = mul(x = var_6817_cast, y = var_6819_to_fp16)[name = tensor("input_523_cast")]; + tensor var_6827 = const()[name = tensor("op_6827"), val = tensor([1, 1])]; + tensor var_6829 = const()[name = tensor("op_6829"), val = tensor([1, 1])]; + tensor var_6831_pad_type_0 = const()[name = tensor("op_6831_pad_type_0"), val = tensor("custom")]; + tensor var_6831_pad_0 = const()[name = tensor("op_6831_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3854577600)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3892326400)))]; + tensor var_6831_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_6829, groups = var_4628, pad = var_6831_pad_0, pad_type = var_6831_pad_type_0, strides = var_6827, weight = up_blocks_1_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_523_cast)[name = tensor("op_6831_cast")]; + tensor var_6832_split_sizes_0 = const()[name = tensor("op_6832_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_6832_axis_0 = const()[name = tensor("op_6832_axis_0"), val = tensor(1)]; + tensor var_6832_cast_0, tensor var_6832_cast_1 = split(axis = var_6832_axis_0, split_sizes = var_6832_split_sizes_0, x = var_6831_cast)[name = tensor("op_6832_cast")]; + tensor var_6834_mode_0 = const()[name = tensor("op_6834_mode_0"), val = tensor("EXACT")]; + tensor var_6834_cast = gelu(mode = var_6834_mode_0, x = var_6832_cast_1)[name = tensor("op_6834_cast")]; + tensor input_525_cast = mul(x = var_6832_cast_0, y = var_6834_cast)[name = tensor("input_525_cast")]; + tensor var_6838 = const()[name = tensor("op_6838"), val = tensor([1, 1])]; + tensor var_6840 = const()[name = tensor("op_6840"), val = tensor([1, 1])]; + tensor var_6842_pad_type_0 = const()[name = tensor("op_6842_pad_type_0"), val = tensor("custom")]; + tensor var_6842_pad_0 = const()[name = tensor("op_6842_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3892351040)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3911225472)))]; + tensor var_6842_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_6840, groups = var_4628, pad = var_6842_pad_0, pad_type = var_6842_pad_type_0, strides = var_6838, weight = up_blocks_1_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_525_cast)[name = tensor("op_6842_cast")]; + tensor inputs_187_cast = add(x = var_6842_cast, y = inputs_185_cast)[name = tensor("inputs_187_cast")]; + tensor var_6852 = const()[name = tensor("op_6852"), val = tensor([1])]; + tensor channels_mean_187_cast = reduce_mean(axes = var_6852, keep_dims = var_4623, x = inputs_187_cast)[name = tensor("channels_mean_187_cast")]; + tensor zero_mean_187_cast = sub(x = inputs_187_cast, y = channels_mean_187_cast)[name = tensor("zero_mean_187_cast")]; + tensor zero_mean_sq_187_cast = mul(x = zero_mean_187_cast, y = zero_mean_187_cast)[name = tensor("zero_mean_sq_187_cast")]; + tensor var_6856 = const()[name = tensor("op_6856"), val = tensor([1])]; + tensor var_6857_cast = reduce_mean(axes = var_6856, keep_dims = var_4623, x = zero_mean_sq_187_cast)[name = tensor("op_6857_cast")]; + tensor var_6858_to_fp16 = const()[name = tensor("op_6858_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6859_cast = add(x = var_6857_cast, y = var_6858_to_fp16)[name = tensor("op_6859_cast")]; + tensor denom_187_epsilon_0_to_fp16 = const()[name = tensor("denom_187_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_187_cast = rsqrt(epsilon = denom_187_epsilon_0_to_fp16, x = var_6859_cast)[name = tensor("denom_187_cast")]; + tensor out_187_cast = mul(x = zero_mean_187_cast, y = denom_187_cast)[name = tensor("out_187_cast")]; + tensor var_6863_to_fp16 = const()[name = tensor("op_6863_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3911228608)))]; + tensor var_6864_cast = add(x = out_187_cast, y = var_6863_to_fp16)[name = tensor("op_6864_cast")]; + tensor var_6866_to_fp16 = const()[name = tensor("op_6866_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3911231744)))]; + tensor hidden_states_323_cast = mul(x = var_6864_cast, y = var_6866_to_fp16)[name = tensor("hidden_states_323_cast")]; + tensor var_6873 = const()[name = tensor("op_6873"), val = tensor([1, 1])]; + tensor var_6875 = const()[name = tensor("op_6875"), val = tensor([1, 1])]; + tensor q_125_pad_type_0 = const()[name = tensor("q_125_pad_type_0"), val = tensor("custom")]; + tensor q_125_pad_0 = const()[name = tensor("q_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3911234880)))]; + tensor q_125_cast = conv(dilations = var_6875, groups = var_4628, pad = q_125_pad_0, pad_type = q_125_pad_type_0, strides = var_6873, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("q_125_cast")]; + tensor var_6879 = const()[name = tensor("op_6879"), val = tensor([1, 1])]; + tensor var_6881 = const()[name = tensor("op_6881"), val = tensor([1, 1])]; + tensor k_125_pad_type_0 = const()[name = tensor("k_125_pad_type_0"), val = tensor("custom")]; + tensor k_125_pad_0 = const()[name = tensor("k_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3915953536)))]; + tensor k_125_cast = conv(dilations = var_6881, groups = var_4628, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_6879, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("k_125_cast")]; + tensor var_6885 = const()[name = tensor("op_6885"), val = tensor([1, 1])]; + tensor var_6887 = const()[name = tensor("op_6887"), val = tensor([1, 1])]; + tensor v_125_pad_type_0 = const()[name = tensor("v_125_pad_type_0"), val = tensor("custom")]; + tensor v_125_pad_0 = const()[name = tensor("v_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3920672192)))]; + tensor v_125_cast = conv(dilations = var_6887, groups = var_4628, pad = v_125_pad_0, pad_type = v_125_pad_type_0, strides = var_6885, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("v_125_cast")]; + tensor var_6891 = const()[name = tensor("op_6891"), val = tensor([2, 24, 64, -1])]; + tensor var_6892_cast = reshape(shape = var_6891, x = q_125_cast)[name = tensor("op_6892_cast")]; + tensor var_6893 = const()[name = tensor("op_6893"), val = tensor([2, 24, 64, -1])]; + tensor var_6894_cast = reshape(shape = var_6893, x = k_125_cast)[name = tensor("op_6894_cast")]; + tensor var_6895 = const()[name = tensor("op_6895"), val = tensor([2, 24, 64, -1])]; + tensor var_6896_cast = reshape(shape = var_6895, x = v_125_cast)[name = tensor("op_6896_cast")]; + tensor attn_weights_249_transpose_x_0 = const()[name = tensor("attn_weights_249_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_249_transpose_y_0 = const()[name = tensor("attn_weights_249_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_249_cast = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_6892_cast, y = var_6894_cast)[name = tensor("attn_weights_249_cast")]; + tensor attn_weights_251_cast = mul(x = attn_weights_249_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_251_cast")]; + tensor var_6900_cast = softmax(axis = var_4612, x = attn_weights_251_cast)[name = tensor("op_6900_cast")]; + tensor attn_125_transpose_x_0 = const()[name = tensor("attn_125_transpose_x_0"), val = tensor(false)]; + tensor attn_125_transpose_y_0 = const()[name = tensor("attn_125_transpose_y_0"), val = tensor(true)]; + tensor attn_125_cast = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6896_cast, y = var_6900_cast)[name = tensor("attn_125_cast")]; + tensor var_6904 = const()[name = tensor("op_6904"), val = tensor([2, 1536, 1, -1])]; + tensor input_527_cast = reshape(shape = var_6904, x = attn_125_cast)[name = tensor("input_527_cast")]; + tensor var_6909 = const()[name = tensor("op_6909"), val = tensor([1, 1])]; + tensor var_6911 = const()[name = tensor("op_6911"), val = tensor([1, 1])]; + tensor var_6913_pad_type_0 = const()[name = tensor("op_6913_pad_type_0"), val = tensor("custom")]; + tensor var_6913_pad_0 = const()[name = tensor("op_6913_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3925390848)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3930109504)))]; + tensor var_6913_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_6911, groups = var_4628, pad = var_6913_pad_0, pad_type = var_6913_pad_type_0, strides = var_6909, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_527_cast)[name = tensor("op_6913_cast")]; + tensor inputs_189_cast = add(x = var_6913_cast, y = inputs_187_cast)[name = tensor("inputs_189_cast")]; + tensor var_6917 = const()[name = tensor("op_6917"), val = tensor([1])]; + tensor channels_mean_189_cast = reduce_mean(axes = var_6917, keep_dims = var_4623, x = inputs_189_cast)[name = tensor("channels_mean_189_cast")]; + tensor zero_mean_189_cast = sub(x = inputs_189_cast, y = channels_mean_189_cast)[name = tensor("zero_mean_189_cast")]; + tensor zero_mean_sq_189_cast = mul(x = zero_mean_189_cast, y = zero_mean_189_cast)[name = tensor("zero_mean_sq_189_cast")]; + tensor var_6921 = const()[name = tensor("op_6921"), val = tensor([1])]; + tensor var_6922_cast = reduce_mean(axes = var_6921, keep_dims = var_4623, x = zero_mean_sq_189_cast)[name = tensor("op_6922_cast")]; + tensor var_6923_to_fp16 = const()[name = tensor("op_6923_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6924_cast = add(x = var_6922_cast, y = var_6923_to_fp16)[name = tensor("op_6924_cast")]; + tensor denom_189_epsilon_0_to_fp16 = const()[name = tensor("denom_189_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_189_cast = rsqrt(epsilon = denom_189_epsilon_0_to_fp16, x = var_6924_cast)[name = tensor("denom_189_cast")]; + tensor out_189_cast = mul(x = zero_mean_189_cast, y = denom_189_cast)[name = tensor("out_189_cast")]; + tensor var_6928_to_fp16 = const()[name = tensor("op_6928_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3930112640)))]; + tensor var_6929_cast = add(x = out_189_cast, y = var_6928_to_fp16)[name = tensor("op_6929_cast")]; + tensor var_6931_to_fp16 = const()[name = tensor("op_6931_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3930115776)))]; + tensor hidden_states_325_cast = mul(x = var_6929_cast, y = var_6931_to_fp16)[name = tensor("hidden_states_325_cast")]; + tensor var_6938 = const()[name = tensor("op_6938"), val = tensor([1, 1])]; + tensor var_6940 = const()[name = tensor("op_6940"), val = tensor([1, 1])]; + tensor q_127_pad_type_0 = const()[name = tensor("q_127_pad_type_0"), val = tensor("custom")]; + tensor q_127_pad_0 = const()[name = tensor("q_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3930118912)))]; + tensor q_127_cast = conv(dilations = var_6940, groups = var_4628, pad = q_127_pad_0, pad_type = q_127_pad_type_0, strides = var_6938, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_325_cast)[name = tensor("q_127_cast")]; + tensor var_6944 = const()[name = tensor("op_6944"), val = tensor([1, 1])]; + tensor var_6946 = const()[name = tensor("op_6946"), val = tensor([1, 1])]; + tensor k_127_pad_type_0 = const()[name = tensor("k_127_pad_type_0"), val = tensor("custom")]; + tensor k_127_pad_0 = const()[name = tensor("k_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3934837568)))]; + tensor k_127_cast = conv(dilations = var_6946, groups = var_4628, pad = k_127_pad_0, pad_type = k_127_pad_type_0, strides = var_6944, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_127_cast")]; + tensor var_6950 = const()[name = tensor("op_6950"), val = tensor([1, 1])]; + tensor var_6952 = const()[name = tensor("op_6952"), val = tensor([1, 1])]; + tensor v_127_pad_type_0 = const()[name = tensor("v_127_pad_type_0"), val = tensor("custom")]; + tensor v_127_pad_0 = const()[name = tensor("v_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3938769792)))]; + tensor v_127_cast = conv(dilations = var_6952, groups = var_4628, pad = v_127_pad_0, pad_type = v_127_pad_type_0, strides = var_6950, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_127_cast")]; + tensor var_6956 = const()[name = tensor("op_6956"), val = tensor([2, 24, 64, -1])]; + tensor var_6957_cast = reshape(shape = var_6956, x = q_127_cast)[name = tensor("op_6957_cast")]; + tensor var_6958 = const()[name = tensor("op_6958"), val = tensor([2, 24, 64, -1])]; + tensor var_6959_cast = reshape(shape = var_6958, x = k_127_cast)[name = tensor("op_6959_cast")]; + tensor var_6960 = const()[name = tensor("op_6960"), val = tensor([2, 24, 64, -1])]; + tensor var_6961_cast = reshape(shape = var_6960, x = v_127_cast)[name = tensor("op_6961_cast")]; + tensor attn_weights_253_transpose_x_0 = const()[name = tensor("attn_weights_253_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_253_transpose_y_0 = const()[name = tensor("attn_weights_253_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_253_cast = matmul(transpose_x = attn_weights_253_transpose_x_0, transpose_y = attn_weights_253_transpose_y_0, x = var_6957_cast, y = var_6959_cast)[name = tensor("attn_weights_253_cast")]; + tensor attn_weights_255_cast = mul(x = attn_weights_253_cast, y = var_4619_to_fp16)[name = tensor("attn_weights_255_cast")]; + tensor var_6965_cast = softmax(axis = var_4612, x = attn_weights_255_cast)[name = tensor("op_6965_cast")]; + tensor attn_127_transpose_x_0 = const()[name = tensor("attn_127_transpose_x_0"), val = tensor(false)]; + tensor attn_127_transpose_y_0 = const()[name = tensor("attn_127_transpose_y_0"), val = tensor(true)]; + tensor attn_127_cast = matmul(transpose_x = attn_127_transpose_x_0, transpose_y = attn_127_transpose_y_0, x = var_6961_cast, y = var_6965_cast)[name = tensor("attn_127_cast")]; + tensor var_6969 = const()[name = tensor("op_6969"), val = tensor([2, 1536, 1, -1])]; + tensor input_529_cast = reshape(shape = var_6969, x = attn_127_cast)[name = tensor("input_529_cast")]; + tensor var_6974 = const()[name = tensor("op_6974"), val = tensor([1, 1])]; + tensor var_6976 = const()[name = tensor("op_6976"), val = tensor([1, 1])]; + tensor var_6978_pad_type_0 = const()[name = tensor("op_6978_pad_type_0"), val = tensor("custom")]; + tensor var_6978_pad_0 = const()[name = tensor("op_6978_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3942702016)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3947420672)))]; + tensor var_6978_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_6976, groups = var_4628, pad = var_6978_pad_0, pad_type = var_6978_pad_type_0, strides = var_6974, weight = up_blocks_1_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_529_cast)[name = tensor("op_6978_cast")]; + tensor inputs_191_cast = add(x = var_6978_cast, y = inputs_189_cast)[name = tensor("inputs_191_cast")]; + tensor var_6982 = const()[name = tensor("op_6982"), val = tensor([1])]; + tensor channels_mean_191_cast = reduce_mean(axes = var_6982, keep_dims = var_4623, x = inputs_191_cast)[name = tensor("channels_mean_191_cast")]; + tensor zero_mean_191_cast = sub(x = inputs_191_cast, y = channels_mean_191_cast)[name = tensor("zero_mean_191_cast")]; + tensor zero_mean_sq_191_cast = mul(x = zero_mean_191_cast, y = zero_mean_191_cast)[name = tensor("zero_mean_sq_191_cast")]; + tensor var_6986 = const()[name = tensor("op_6986"), val = tensor([1])]; + tensor var_6987_cast = reduce_mean(axes = var_6986, keep_dims = var_4623, x = zero_mean_sq_191_cast)[name = tensor("op_6987_cast")]; + tensor var_6988_to_fp16 = const()[name = tensor("op_6988_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6989_cast = add(x = var_6987_cast, y = var_6988_to_fp16)[name = tensor("op_6989_cast")]; + tensor denom_191_epsilon_0_to_fp16 = const()[name = tensor("denom_191_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_191_cast = rsqrt(epsilon = denom_191_epsilon_0_to_fp16, x = var_6989_cast)[name = tensor("denom_191_cast")]; + tensor out_191_cast = mul(x = zero_mean_191_cast, y = denom_191_cast)[name = tensor("out_191_cast")]; + tensor var_6993_to_fp16 = const()[name = tensor("op_6993_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3947423808)))]; + tensor var_6994_cast = add(x = out_191_cast, y = var_6993_to_fp16)[name = tensor("op_6994_cast")]; + tensor var_6996_to_fp16 = const()[name = tensor("op_6996_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3947426944)))]; + tensor input_531_cast = mul(x = var_6994_cast, y = var_6996_to_fp16)[name = tensor("input_531_cast")]; + tensor var_7004 = const()[name = tensor("op_7004"), val = tensor([1, 1])]; + tensor var_7006 = const()[name = tensor("op_7006"), val = tensor([1, 1])]; + tensor var_7008_pad_type_0 = const()[name = tensor("op_7008_pad_type_0"), val = tensor("custom")]; + tensor var_7008_pad_0 = const()[name = tensor("op_7008_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3947430080)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3985178880)))]; + tensor var_7008_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_7006, groups = var_4628, pad = var_7008_pad_0, pad_type = var_7008_pad_type_0, strides = var_7004, weight = up_blocks_1_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_531_cast)[name = tensor("op_7008_cast")]; + tensor var_7009_split_sizes_0 = const()[name = tensor("op_7009_split_sizes_0"), val = tensor([6144, 6144])]; + tensor var_7009_axis_0 = const()[name = tensor("op_7009_axis_0"), val = tensor(1)]; + tensor var_7009_cast_0, tensor var_7009_cast_1 = split(axis = var_7009_axis_0, split_sizes = var_7009_split_sizes_0, x = var_7008_cast)[name = tensor("op_7009_cast")]; + tensor var_7011_mode_0 = const()[name = tensor("op_7011_mode_0"), val = tensor("EXACT")]; + tensor var_7011_cast = gelu(mode = var_7011_mode_0, x = var_7009_cast_1)[name = tensor("op_7011_cast")]; + tensor input_533_cast = mul(x = var_7009_cast_0, y = var_7011_cast)[name = tensor("input_533_cast")]; + tensor var_7015 = const()[name = tensor("op_7015"), val = tensor([1, 1])]; + tensor var_7017 = const()[name = tensor("op_7017"), val = tensor([1, 1])]; + tensor var_7019_pad_type_0 = const()[name = tensor("op_7019_pad_type_0"), val = tensor("custom")]; + tensor var_7019_pad_0 = const()[name = tensor("op_7019_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3985203520)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4004077952)))]; + tensor var_7019_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_7017, groups = var_4628, pad = var_7019_pad_0, pad_type = var_7019_pad_type_0, strides = var_7015, weight = up_blocks_1_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_533_cast)[name = tensor("op_7019_cast")]; + tensor hidden_states_329_cast = add(x = var_7019_cast, y = inputs_191_cast)[name = tensor("hidden_states_329_cast")]; + tensor var_7021 = const()[name = tensor("op_7021"), val = tensor([2, 1536, 32, 32])]; + tensor input_535_cast = reshape(shape = var_7021, x = hidden_states_329_cast)[name = tensor("input_535_cast")]; + tensor var_7025 = const()[name = tensor("op_7025"), val = tensor([1, 1])]; + tensor var_7027 = const()[name = tensor("op_7027"), val = tensor([1, 1])]; + tensor hidden_states_331_pad_type_0 = const()[name = tensor("hidden_states_331_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_331_pad_0 = const()[name = tensor("hidden_states_331_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4004081088)))]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4008799744)))]; + tensor hidden_states_331_cast = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_7027, groups = var_4628, pad = hidden_states_331_pad_0, pad_type = hidden_states_331_pad_type_0, strides = var_7025, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16, x = input_535_cast)[name = tensor("hidden_states_331_cast")]; + tensor input_537_cast = add(x = hidden_states_331_cast, y = hidden_states_301_cast)[name = tensor("input_537_cast")]; + tensor input_539_scale_factor_height_0 = const()[name = tensor("input_539_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_539_scale_factor_width_0 = const()[name = tensor("input_539_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_539_cast = upsample_nearest_neighbor(scale_factor_height = input_539_scale_factor_height_0, scale_factor_width = input_539_scale_factor_width_0, x = input_537_cast)[name = tensor("input_539_cast")]; + tensor var_7036 = const()[name = tensor("op_7036"), val = tensor([1, 1])]; + tensor var_7038 = const()[name = tensor("op_7038"), val = tensor([1, 1])]; + tensor hidden_states_333_pad_type_0 = const()[name = tensor("hidden_states_333_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_333_pad_0 = const()[name = tensor("hidden_states_333_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4008802880)))]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4051270272)))]; + tensor hidden_states_333_cast = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_7038, groups = var_4628, pad = hidden_states_333_pad_0, pad_type = hidden_states_333_pad_type_0, strides = var_7036, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_539_cast)[name = tensor("hidden_states_333_cast")]; + tensor var_7043 = const()[name = tensor("op_7043"), val = tensor(3)]; + tensor var_7054 = const()[name = tensor("op_7054"), val = tensor(true)]; + tensor var_7059 = const()[name = tensor("op_7059"), val = tensor(1)]; + tensor input_541_interleave_0 = const()[name = tensor("input_541_interleave_0"), val = tensor(false)]; + tensor input_541_cast = concat(axis = var_7059, interleave = input_541_interleave_0, values = (hidden_states_333_cast, input_145_cast))[name = tensor("input_541_cast")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 72, 64, 64])]; + tensor reshape_160_cast = reshape(shape = reshape_160_shape_0, x = input_541_cast)[name = tensor("reshape_160_cast")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast)[name = tensor("reduce_mean_120_cast")]; + tensor sub_80_cast = sub(x = reshape_160_cast, y = reduce_mean_120_cast)[name = tensor("sub_80_cast")]; + tensor square_40_cast = square(x = sub_80_cast)[name = tensor("square_40_cast")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast)[name = tensor("reduce_mean_122_cast")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast = add(x = reduce_mean_122_cast, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast")]; + tensor sqrt_40_cast = sqrt(x = add_80_cast)[name = tensor("sqrt_40_cast")]; + tensor real_div_40_cast = real_div(x = sub_80_cast, y = sqrt_40_cast)[name = tensor("real_div_40_cast")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 2304, 64, 64])]; + tensor reshape_161_cast = reshape(shape = reshape_161_shape_0, x = real_div_40_cast)[name = tensor("reshape_161_cast")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4051273408)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4051278080)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_75_mean_0_to_fp16, variance = add_75_variance_0_to_fp16, x = reshape_161_cast)[name = tensor("add_81_cast")]; + tensor input_545_cast = silu(x = add_81_cast)[name = tensor("input_545_cast")]; + tensor var_7088 = const()[name = tensor("op_7088"), val = tensor([1, 1])]; + tensor var_7090 = const()[name = tensor("op_7090"), val = tensor([1, 1])]; + tensor hidden_states_335_pad_type_0 = const()[name = tensor("hidden_states_335_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_335_pad_0 = const()[name = tensor("hidden_states_335_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4051282752)))]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4083133312)))]; + tensor hidden_states_335_cast = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_7090, groups = var_7059, pad = hidden_states_335_pad_0, pad_type = hidden_states_335_pad_type_0, strides = var_7088, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_545_cast)[name = tensor("hidden_states_335_cast")]; + tensor var_7096 = const()[name = tensor("op_7096"), val = tensor([1, 1])]; + tensor var_7098 = const()[name = tensor("op_7098"), val = tensor([1, 1])]; + tensor temb_33_pad_type_0 = const()[name = tensor("temb_33_pad_type_0"), val = tensor("custom")]; + tensor temb_33_pad_0 = const()[name = tensor("temb_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4083134912)))]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085494272)))]; + tensor temb_33_cast = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_7098, groups = var_7059, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_7096, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_33_cast")]; + tensor input_549_cast = add(x = hidden_states_335_cast, y = temb_33_cast)[name = tensor("input_549_cast")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_164_cast = reshape(shape = reshape_164_shape_0, x = input_549_cast)[name = tensor("reshape_164_cast")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast)[name = tensor("reduce_mean_123_cast")]; + tensor sub_82_cast = sub(x = reshape_164_cast, y = reduce_mean_123_cast)[name = tensor("sub_82_cast")]; + tensor square_41_cast = square(x = sub_82_cast)[name = tensor("square_41_cast")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast)[name = tensor("reduce_mean_125_cast")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_82_cast = add(x = reduce_mean_125_cast, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast")]; + tensor sqrt_41_cast = sqrt(x = add_82_cast)[name = tensor("sqrt_41_cast")]; + tensor real_div_41_cast = real_div(x = sub_82_cast, y = sqrt_41_cast)[name = tensor("real_div_41_cast")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_165_cast = reshape(shape = reshape_165_shape_0, x = real_div_41_cast)[name = tensor("reshape_165_cast")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085495872)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085497472)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_165_cast)[name = tensor("add_83_cast")]; + tensor input_553_cast = silu(x = add_83_cast)[name = tensor("input_553_cast")]; + tensor var_7108 = const()[name = tensor("op_7108"), val = tensor([1, 1])]; + tensor var_7110 = const()[name = tensor("op_7110"), val = tensor([1, 1])]; + tensor hidden_states_337_pad_type_0 = const()[name = tensor("hidden_states_337_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_337_pad_0 = const()[name = tensor("hidden_states_337_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085499072)))]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4096115968)))]; + tensor hidden_states_337_cast = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_7110, groups = var_7059, pad = hidden_states_337_pad_0, pad_type = hidden_states_337_pad_type_0, strides = var_7108, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_553_cast)[name = tensor("hidden_states_337_cast")]; + tensor var_7115 = const()[name = tensor("op_7115"), val = tensor([1, 1])]; + tensor var_7117 = const()[name = tensor("op_7117"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4096117568)))]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099656576)))]; + tensor x_17_cast = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_7117, groups = var_7059, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_7115, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_541_cast)[name = tensor("x_17_cast")]; + tensor hidden_states_339_cast = add(x = x_17_cast, y = hidden_states_337_cast)[name = tensor("hidden_states_339_cast")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_168_cast = reshape(shape = reshape_168_shape_0, x = hidden_states_339_cast)[name = tensor("reshape_168_cast")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast)[name = tensor("reduce_mean_126_cast")]; + tensor sub_84_cast = sub(x = reshape_168_cast, y = reduce_mean_126_cast)[name = tensor("sub_84_cast")]; + tensor square_42_cast = square(x = sub_84_cast)[name = tensor("square_42_cast")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast)[name = tensor("reduce_mean_128_cast")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_84_cast = add(x = reduce_mean_128_cast, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast")]; + tensor sqrt_42_cast = sqrt(x = add_84_cast)[name = tensor("sqrt_42_cast")]; + tensor real_div_42_cast = real_div(x = sub_84_cast, y = sqrt_42_cast)[name = tensor("real_div_42_cast")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_169_cast = reshape(shape = reshape_169_shape_0, x = real_div_42_cast)[name = tensor("reshape_169_cast")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099658176)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099659776)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_169_cast)[name = tensor("add_85_cast")]; + tensor var_7143 = const()[name = tensor("op_7143"), val = tensor([1, 1])]; + tensor var_7145 = const()[name = tensor("op_7145"), val = tensor([1, 1])]; + tensor hidden_states_341_pad_type_0 = const()[name = tensor("hidden_states_341_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_341_pad_0 = const()[name = tensor("hidden_states_341_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099661376)))]; + tensor up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4100841088)))]; + tensor hidden_states_341_cast = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_7145, groups = var_7059, pad = hidden_states_341_pad_0, pad_type = hidden_states_341_pad_type_0, strides = var_7143, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_85_cast)[name = tensor("hidden_states_341_cast")]; + tensor var_7150 = const()[name = tensor("op_7150"), val = tensor([2, 768, 1, 4096])]; + tensor inputs_193_cast = reshape(shape = var_7150, x = hidden_states_341_cast)[name = tensor("inputs_193_cast")]; + tensor var_7160 = const()[name = tensor("op_7160"), val = tensor([1])]; + tensor channels_mean_193_cast = reduce_mean(axes = var_7160, keep_dims = var_7054, x = inputs_193_cast)[name = tensor("channels_mean_193_cast")]; + tensor zero_mean_193_cast = sub(x = inputs_193_cast, y = channels_mean_193_cast)[name = tensor("zero_mean_193_cast")]; + tensor zero_mean_sq_193_cast = mul(x = zero_mean_193_cast, y = zero_mean_193_cast)[name = tensor("zero_mean_sq_193_cast")]; + tensor var_7164 = const()[name = tensor("op_7164"), val = tensor([1])]; + tensor var_7165_cast = reduce_mean(axes = var_7164, keep_dims = var_7054, x = zero_mean_sq_193_cast)[name = tensor("op_7165_cast")]; + tensor var_7166_to_fp16 = const()[name = tensor("op_7166_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7167_cast = add(x = var_7165_cast, y = var_7166_to_fp16)[name = tensor("op_7167_cast")]; + tensor denom_193_epsilon_0_to_fp16 = const()[name = tensor("denom_193_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_193_cast = rsqrt(epsilon = denom_193_epsilon_0_to_fp16, x = var_7167_cast)[name = tensor("denom_193_cast")]; + tensor out_193_cast = mul(x = zero_mean_193_cast, y = denom_193_cast)[name = tensor("out_193_cast")]; + tensor var_7171_to_fp16 = const()[name = tensor("op_7171_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4100842688)))]; + tensor var_7172_cast = add(x = out_193_cast, y = var_7171_to_fp16)[name = tensor("op_7172_cast")]; + tensor var_7174_to_fp16 = const()[name = tensor("op_7174_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4100844288)))]; + tensor hidden_states_343_cast = mul(x = var_7172_cast, y = var_7174_to_fp16)[name = tensor("hidden_states_343_cast")]; + tensor var_7181 = const()[name = tensor("op_7181"), val = tensor([1, 1])]; + tensor var_7183 = const()[name = tensor("op_7183"), val = tensor([1, 1])]; + tensor q_129_pad_type_0 = const()[name = tensor("q_129_pad_type_0"), val = tensor("custom")]; + tensor q_129_pad_0 = const()[name = tensor("q_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4100845888)))]; + tensor q_129_cast = conv(dilations = var_7183, groups = var_7059, pad = q_129_pad_0, pad_type = q_129_pad_type_0, strides = var_7181, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_343_cast)[name = tensor("q_129_cast")]; + tensor var_7187 = const()[name = tensor("op_7187"), val = tensor([1, 1])]; + tensor var_7189 = const()[name = tensor("op_7189"), val = tensor([1, 1])]; + tensor k_129_pad_type_0 = const()[name = tensor("k_129_pad_type_0"), val = tensor("custom")]; + tensor k_129_pad_0 = const()[name = tensor("k_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4102025600)))]; + tensor k_129_cast = conv(dilations = var_7189, groups = var_7059, pad = k_129_pad_0, pad_type = k_129_pad_type_0, strides = var_7187, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_343_cast)[name = tensor("k_129_cast")]; + tensor var_7193 = const()[name = tensor("op_7193"), val = tensor([1, 1])]; + tensor var_7195 = const()[name = tensor("op_7195"), val = tensor([1, 1])]; + tensor v_129_pad_type_0 = const()[name = tensor("v_129_pad_type_0"), val = tensor("custom")]; + tensor v_129_pad_0 = const()[name = tensor("v_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4103205312)))]; + tensor v_129_cast = conv(dilations = var_7195, groups = var_7059, pad = v_129_pad_0, pad_type = v_129_pad_type_0, strides = var_7193, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_343_cast)[name = tensor("v_129_cast")]; + tensor var_7199 = const()[name = tensor("op_7199"), val = tensor([2, 12, 64, -1])]; + tensor var_7200_cast = reshape(shape = var_7199, x = q_129_cast)[name = tensor("op_7200_cast")]; + tensor var_7201 = const()[name = tensor("op_7201"), val = tensor([2, 12, 64, -1])]; + tensor var_7202_cast = reshape(shape = var_7201, x = k_129_cast)[name = tensor("op_7202_cast")]; + tensor var_7203 = const()[name = tensor("op_7203"), val = tensor([2, 12, 64, -1])]; + tensor var_7204_cast = reshape(shape = var_7203, x = v_129_cast)[name = tensor("op_7204_cast")]; + tensor attn_weights_257_transpose_x_0 = const()[name = tensor("attn_weights_257_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_257_transpose_y_0 = const()[name = tensor("attn_weights_257_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_257_cast = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_7200_cast, y = var_7202_cast)[name = tensor("attn_weights_257_cast")]; + tensor var_7050_to_fp16 = const()[name = tensor("op_7050_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_259_cast = mul(x = attn_weights_257_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_259_cast")]; + tensor var_7208_cast = softmax(axis = var_7043, x = attn_weights_259_cast)[name = tensor("op_7208_cast")]; + tensor attn_129_transpose_x_0 = const()[name = tensor("attn_129_transpose_x_0"), val = tensor(false)]; + tensor attn_129_transpose_y_0 = const()[name = tensor("attn_129_transpose_y_0"), val = tensor(true)]; + tensor attn_129_cast = matmul(transpose_x = attn_129_transpose_x_0, transpose_y = attn_129_transpose_y_0, x = var_7204_cast, y = var_7208_cast)[name = tensor("attn_129_cast")]; + tensor var_7212 = const()[name = tensor("op_7212"), val = tensor([2, 768, 1, -1])]; + tensor input_557_cast = reshape(shape = var_7212, x = attn_129_cast)[name = tensor("input_557_cast")]; + tensor var_7217 = const()[name = tensor("op_7217"), val = tensor([1, 1])]; + tensor var_7219 = const()[name = tensor("op_7219"), val = tensor([1, 1])]; + tensor var_7221_pad_type_0 = const()[name = tensor("op_7221_pad_type_0"), val = tensor("custom")]; + tensor var_7221_pad_0 = const()[name = tensor("op_7221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4104385024)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4105564736)))]; + tensor var_7221_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_7219, groups = var_7059, pad = var_7221_pad_0, pad_type = var_7221_pad_type_0, strides = var_7217, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_557_cast)[name = tensor("op_7221_cast")]; + tensor inputs_195_cast = add(x = var_7221_cast, y = inputs_193_cast)[name = tensor("inputs_195_cast")]; + tensor var_7225 = const()[name = tensor("op_7225"), val = tensor([1])]; + tensor channels_mean_195_cast = reduce_mean(axes = var_7225, keep_dims = var_7054, x = inputs_195_cast)[name = tensor("channels_mean_195_cast")]; + tensor zero_mean_195_cast = sub(x = inputs_195_cast, y = channels_mean_195_cast)[name = tensor("zero_mean_195_cast")]; + tensor zero_mean_sq_195_cast = mul(x = zero_mean_195_cast, y = zero_mean_195_cast)[name = tensor("zero_mean_sq_195_cast")]; + tensor var_7229 = const()[name = tensor("op_7229"), val = tensor([1])]; + tensor var_7230_cast = reduce_mean(axes = var_7229, keep_dims = var_7054, x = zero_mean_sq_195_cast)[name = tensor("op_7230_cast")]; + tensor var_7231_to_fp16 = const()[name = tensor("op_7231_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7232_cast = add(x = var_7230_cast, y = var_7231_to_fp16)[name = tensor("op_7232_cast")]; + tensor denom_195_epsilon_0_to_fp16 = const()[name = tensor("denom_195_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_195_cast = rsqrt(epsilon = denom_195_epsilon_0_to_fp16, x = var_7232_cast)[name = tensor("denom_195_cast")]; + tensor out_195_cast = mul(x = zero_mean_195_cast, y = denom_195_cast)[name = tensor("out_195_cast")]; + tensor var_7236_to_fp16 = const()[name = tensor("op_7236_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4105566336)))]; + tensor var_7237_cast = add(x = out_195_cast, y = var_7236_to_fp16)[name = tensor("op_7237_cast")]; + tensor var_7239_to_fp16 = const()[name = tensor("op_7239_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4105567936)))]; + tensor hidden_states_345_cast = mul(x = var_7237_cast, y = var_7239_to_fp16)[name = tensor("hidden_states_345_cast")]; + tensor var_7246 = const()[name = tensor("op_7246"), val = tensor([1, 1])]; + tensor var_7248 = const()[name = tensor("op_7248"), val = tensor([1, 1])]; + tensor q_131_pad_type_0 = const()[name = tensor("q_131_pad_type_0"), val = tensor("custom")]; + tensor q_131_pad_0 = const()[name = tensor("q_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4105569536)))]; + tensor q_131_cast = conv(dilations = var_7248, groups = var_7059, pad = q_131_pad_0, pad_type = q_131_pad_type_0, strides = var_7246, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_345_cast)[name = tensor("q_131_cast")]; + tensor var_7252 = const()[name = tensor("op_7252"), val = tensor([1, 1])]; + tensor var_7254 = const()[name = tensor("op_7254"), val = tensor([1, 1])]; + tensor k_131_pad_type_0 = const()[name = tensor("k_131_pad_type_0"), val = tensor("custom")]; + tensor k_131_pad_0 = const()[name = tensor("k_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4106749248)))]; + tensor k_131_cast = conv(dilations = var_7254, groups = var_7059, pad = k_131_pad_0, pad_type = k_131_pad_type_0, strides = var_7252, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_131_cast")]; + tensor var_7258 = const()[name = tensor("op_7258"), val = tensor([1, 1])]; + tensor var_7260 = const()[name = tensor("op_7260"), val = tensor([1, 1])]; + tensor v_131_pad_type_0 = const()[name = tensor("v_131_pad_type_0"), val = tensor("custom")]; + tensor v_131_pad_0 = const()[name = tensor("v_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4108715392)))]; + tensor v_131_cast = conv(dilations = var_7260, groups = var_7059, pad = v_131_pad_0, pad_type = v_131_pad_type_0, strides = var_7258, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_131_cast")]; + tensor var_7264 = const()[name = tensor("op_7264"), val = tensor([2, 12, 64, -1])]; + tensor var_7265_cast = reshape(shape = var_7264, x = q_131_cast)[name = tensor("op_7265_cast")]; + tensor var_7266 = const()[name = tensor("op_7266"), val = tensor([2, 12, 64, -1])]; + tensor var_7267_cast = reshape(shape = var_7266, x = k_131_cast)[name = tensor("op_7267_cast")]; + tensor var_7268 = const()[name = tensor("op_7268"), val = tensor([2, 12, 64, -1])]; + tensor var_7269_cast = reshape(shape = var_7268, x = v_131_cast)[name = tensor("op_7269_cast")]; + tensor attn_weights_261_transpose_x_0 = const()[name = tensor("attn_weights_261_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_261_transpose_y_0 = const()[name = tensor("attn_weights_261_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_261_cast = matmul(transpose_x = attn_weights_261_transpose_x_0, transpose_y = attn_weights_261_transpose_y_0, x = var_7265_cast, y = var_7267_cast)[name = tensor("attn_weights_261_cast")]; + tensor attn_weights_263_cast = mul(x = attn_weights_261_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_263_cast")]; + tensor var_7273_cast = softmax(axis = var_7043, x = attn_weights_263_cast)[name = tensor("op_7273_cast")]; + tensor attn_131_transpose_x_0 = const()[name = tensor("attn_131_transpose_x_0"), val = tensor(false)]; + tensor attn_131_transpose_y_0 = const()[name = tensor("attn_131_transpose_y_0"), val = tensor(true)]; + tensor attn_131_cast = matmul(transpose_x = attn_131_transpose_x_0, transpose_y = attn_131_transpose_y_0, x = var_7269_cast, y = var_7273_cast)[name = tensor("attn_131_cast")]; + tensor var_7277 = const()[name = tensor("op_7277"), val = tensor([2, 768, 1, -1])]; + tensor input_559_cast = reshape(shape = var_7277, x = attn_131_cast)[name = tensor("input_559_cast")]; + tensor var_7282 = const()[name = tensor("op_7282"), val = tensor([1, 1])]; + tensor var_7284 = const()[name = tensor("op_7284"), val = tensor([1, 1])]; + tensor var_7286_pad_type_0 = const()[name = tensor("op_7286_pad_type_0"), val = tensor("custom")]; + tensor var_7286_pad_0 = const()[name = tensor("op_7286_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4110681536)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4111861248)))]; + tensor var_7286_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_7284, groups = var_7059, pad = var_7286_pad_0, pad_type = var_7286_pad_type_0, strides = var_7282, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_559_cast)[name = tensor("op_7286_cast")]; + tensor inputs_197_cast = add(x = var_7286_cast, y = inputs_195_cast)[name = tensor("inputs_197_cast")]; + tensor var_7290 = const()[name = tensor("op_7290"), val = tensor([1])]; + tensor channels_mean_197_cast = reduce_mean(axes = var_7290, keep_dims = var_7054, x = inputs_197_cast)[name = tensor("channels_mean_197_cast")]; + tensor zero_mean_197_cast = sub(x = inputs_197_cast, y = channels_mean_197_cast)[name = tensor("zero_mean_197_cast")]; + tensor zero_mean_sq_197_cast = mul(x = zero_mean_197_cast, y = zero_mean_197_cast)[name = tensor("zero_mean_sq_197_cast")]; + tensor var_7294 = const()[name = tensor("op_7294"), val = tensor([1])]; + tensor var_7295_cast = reduce_mean(axes = var_7294, keep_dims = var_7054, x = zero_mean_sq_197_cast)[name = tensor("op_7295_cast")]; + tensor var_7296_to_fp16 = const()[name = tensor("op_7296_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7297_cast = add(x = var_7295_cast, y = var_7296_to_fp16)[name = tensor("op_7297_cast")]; + tensor denom_197_epsilon_0_to_fp16 = const()[name = tensor("denom_197_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_197_cast = rsqrt(epsilon = denom_197_epsilon_0_to_fp16, x = var_7297_cast)[name = tensor("denom_197_cast")]; + tensor out_197_cast = mul(x = zero_mean_197_cast, y = denom_197_cast)[name = tensor("out_197_cast")]; + tensor var_7301_to_fp16 = const()[name = tensor("op_7301_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4111862848)))]; + tensor var_7302_cast = add(x = out_197_cast, y = var_7301_to_fp16)[name = tensor("op_7302_cast")]; + tensor var_7304_to_fp16 = const()[name = tensor("op_7304_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4111864448)))]; + tensor input_561_cast = mul(x = var_7302_cast, y = var_7304_to_fp16)[name = tensor("input_561_cast")]; + tensor var_7312 = const()[name = tensor("op_7312"), val = tensor([1, 1])]; + tensor var_7314 = const()[name = tensor("op_7314"), val = tensor([1, 1])]; + tensor var_7316_pad_type_0 = const()[name = tensor("op_7316_pad_type_0"), val = tensor("custom")]; + tensor var_7316_pad_0 = const()[name = tensor("op_7316_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4111866048)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4121303296)))]; + tensor var_7316_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_7314, groups = var_7059, pad = var_7316_pad_0, pad_type = var_7316_pad_type_0, strides = var_7312, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_561_cast)[name = tensor("op_7316_cast")]; + tensor var_7317_split_sizes_0 = const()[name = tensor("op_7317_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_7317_axis_0 = const()[name = tensor("op_7317_axis_0"), val = tensor(1)]; + tensor var_7317_cast_0, tensor var_7317_cast_1 = split(axis = var_7317_axis_0, split_sizes = var_7317_split_sizes_0, x = var_7316_cast)[name = tensor("op_7317_cast")]; + tensor var_7319_mode_0 = const()[name = tensor("op_7319_mode_0"), val = tensor("EXACT")]; + tensor var_7319_cast = gelu(mode = var_7319_mode_0, x = var_7317_cast_1)[name = tensor("op_7319_cast")]; + tensor input_563_cast = mul(x = var_7317_cast_0, y = var_7319_cast)[name = tensor("input_563_cast")]; + tensor var_7323 = const()[name = tensor("op_7323"), val = tensor([1, 1])]; + tensor var_7325 = const()[name = tensor("op_7325"), val = tensor([1, 1])]; + tensor var_7327_pad_type_0 = const()[name = tensor("op_7327_pad_type_0"), val = tensor("custom")]; + tensor var_7327_pad_0 = const()[name = tensor("op_7327_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4121315648)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4126034304)))]; + tensor var_7327_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7325, groups = var_7059, pad = var_7327_pad_0, pad_type = var_7327_pad_type_0, strides = var_7323, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_563_cast)[name = tensor("op_7327_cast")]; + tensor inputs_199_cast = add(x = var_7327_cast, y = inputs_197_cast)[name = tensor("inputs_199_cast")]; + tensor var_7337 = const()[name = tensor("op_7337"), val = tensor([1])]; + tensor channels_mean_199_cast = reduce_mean(axes = var_7337, keep_dims = var_7054, x = inputs_199_cast)[name = tensor("channels_mean_199_cast")]; + tensor zero_mean_199_cast = sub(x = inputs_199_cast, y = channels_mean_199_cast)[name = tensor("zero_mean_199_cast")]; + tensor zero_mean_sq_199_cast = mul(x = zero_mean_199_cast, y = zero_mean_199_cast)[name = tensor("zero_mean_sq_199_cast")]; + tensor var_7341 = const()[name = tensor("op_7341"), val = tensor([1])]; + tensor var_7342_cast = reduce_mean(axes = var_7341, keep_dims = var_7054, x = zero_mean_sq_199_cast)[name = tensor("op_7342_cast")]; + tensor var_7343_to_fp16 = const()[name = tensor("op_7343_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7344_cast = add(x = var_7342_cast, y = var_7343_to_fp16)[name = tensor("op_7344_cast")]; + tensor denom_199_epsilon_0_to_fp16 = const()[name = tensor("denom_199_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_199_cast = rsqrt(epsilon = denom_199_epsilon_0_to_fp16, x = var_7344_cast)[name = tensor("denom_199_cast")]; + tensor out_199_cast = mul(x = zero_mean_199_cast, y = denom_199_cast)[name = tensor("out_199_cast")]; + tensor var_7348_to_fp16 = const()[name = tensor("op_7348_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4126035904)))]; + tensor var_7349_cast = add(x = out_199_cast, y = var_7348_to_fp16)[name = tensor("op_7349_cast")]; + tensor var_7351_to_fp16 = const()[name = tensor("op_7351_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4126037504)))]; + tensor hidden_states_349_cast = mul(x = var_7349_cast, y = var_7351_to_fp16)[name = tensor("hidden_states_349_cast")]; + tensor var_7358 = const()[name = tensor("op_7358"), val = tensor([1, 1])]; + tensor var_7360 = const()[name = tensor("op_7360"), val = tensor([1, 1])]; + tensor q_133_pad_type_0 = const()[name = tensor("q_133_pad_type_0"), val = tensor("custom")]; + tensor q_133_pad_0 = const()[name = tensor("q_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4126039104)))]; + tensor q_133_cast = conv(dilations = var_7360, groups = var_7059, pad = q_133_pad_0, pad_type = q_133_pad_type_0, strides = var_7358, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_349_cast)[name = tensor("q_133_cast")]; + tensor var_7364 = const()[name = tensor("op_7364"), val = tensor([1, 1])]; + tensor var_7366 = const()[name = tensor("op_7366"), val = tensor([1, 1])]; + tensor k_133_pad_type_0 = const()[name = tensor("k_133_pad_type_0"), val = tensor("custom")]; + tensor k_133_pad_0 = const()[name = tensor("k_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4127218816)))]; + tensor k_133_cast = conv(dilations = var_7366, groups = var_7059, pad = k_133_pad_0, pad_type = k_133_pad_type_0, strides = var_7364, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_349_cast)[name = tensor("k_133_cast")]; + tensor var_7370 = const()[name = tensor("op_7370"), val = tensor([1, 1])]; + tensor var_7372 = const()[name = tensor("op_7372"), val = tensor([1, 1])]; + tensor v_133_pad_type_0 = const()[name = tensor("v_133_pad_type_0"), val = tensor("custom")]; + tensor v_133_pad_0 = const()[name = tensor("v_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4128398528)))]; + tensor v_133_cast = conv(dilations = var_7372, groups = var_7059, pad = v_133_pad_0, pad_type = v_133_pad_type_0, strides = var_7370, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_349_cast)[name = tensor("v_133_cast")]; + tensor var_7376 = const()[name = tensor("op_7376"), val = tensor([2, 12, 64, -1])]; + tensor var_7377_cast = reshape(shape = var_7376, x = q_133_cast)[name = tensor("op_7377_cast")]; + tensor var_7378 = const()[name = tensor("op_7378"), val = tensor([2, 12, 64, -1])]; + tensor var_7379_cast = reshape(shape = var_7378, x = k_133_cast)[name = tensor("op_7379_cast")]; + tensor var_7380 = const()[name = tensor("op_7380"), val = tensor([2, 12, 64, -1])]; + tensor var_7381_cast = reshape(shape = var_7380, x = v_133_cast)[name = tensor("op_7381_cast")]; + tensor attn_weights_265_transpose_x_0 = const()[name = tensor("attn_weights_265_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_265_transpose_y_0 = const()[name = tensor("attn_weights_265_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_265_cast = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_7377_cast, y = var_7379_cast)[name = tensor("attn_weights_265_cast")]; + tensor attn_weights_267_cast = mul(x = attn_weights_265_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_267_cast")]; + tensor var_7385_cast = softmax(axis = var_7043, x = attn_weights_267_cast)[name = tensor("op_7385_cast")]; + tensor attn_133_transpose_x_0 = const()[name = tensor("attn_133_transpose_x_0"), val = tensor(false)]; + tensor attn_133_transpose_y_0 = const()[name = tensor("attn_133_transpose_y_0"), val = tensor(true)]; + tensor attn_133_cast = matmul(transpose_x = attn_133_transpose_x_0, transpose_y = attn_133_transpose_y_0, x = var_7381_cast, y = var_7385_cast)[name = tensor("attn_133_cast")]; + tensor var_7389 = const()[name = tensor("op_7389"), val = tensor([2, 768, 1, -1])]; + tensor input_565_cast = reshape(shape = var_7389, x = attn_133_cast)[name = tensor("input_565_cast")]; + tensor var_7394 = const()[name = tensor("op_7394"), val = tensor([1, 1])]; + tensor var_7396 = const()[name = tensor("op_7396"), val = tensor([1, 1])]; + tensor var_7398_pad_type_0 = const()[name = tensor("op_7398_pad_type_0"), val = tensor("custom")]; + tensor var_7398_pad_0 = const()[name = tensor("op_7398_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4129578240)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4130757952)))]; + tensor var_7398_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_7396, groups = var_7059, pad = var_7398_pad_0, pad_type = var_7398_pad_type_0, strides = var_7394, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_565_cast)[name = tensor("op_7398_cast")]; + tensor inputs_201_cast = add(x = var_7398_cast, y = inputs_199_cast)[name = tensor("inputs_201_cast")]; + tensor var_7402 = const()[name = tensor("op_7402"), val = tensor([1])]; + tensor channels_mean_201_cast = reduce_mean(axes = var_7402, keep_dims = var_7054, x = inputs_201_cast)[name = tensor("channels_mean_201_cast")]; + tensor zero_mean_201_cast = sub(x = inputs_201_cast, y = channels_mean_201_cast)[name = tensor("zero_mean_201_cast")]; + tensor zero_mean_sq_201_cast = mul(x = zero_mean_201_cast, y = zero_mean_201_cast)[name = tensor("zero_mean_sq_201_cast")]; + tensor var_7406 = const()[name = tensor("op_7406"), val = tensor([1])]; + tensor var_7407_cast = reduce_mean(axes = var_7406, keep_dims = var_7054, x = zero_mean_sq_201_cast)[name = tensor("op_7407_cast")]; + tensor var_7408_to_fp16 = const()[name = tensor("op_7408_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7409_cast = add(x = var_7407_cast, y = var_7408_to_fp16)[name = tensor("op_7409_cast")]; + tensor denom_201_epsilon_0_to_fp16 = const()[name = tensor("denom_201_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_201_cast = rsqrt(epsilon = denom_201_epsilon_0_to_fp16, x = var_7409_cast)[name = tensor("denom_201_cast")]; + tensor out_201_cast = mul(x = zero_mean_201_cast, y = denom_201_cast)[name = tensor("out_201_cast")]; + tensor var_7413_to_fp16 = const()[name = tensor("op_7413_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4130759552)))]; + tensor var_7414_cast = add(x = out_201_cast, y = var_7413_to_fp16)[name = tensor("op_7414_cast")]; + tensor var_7416_to_fp16 = const()[name = tensor("op_7416_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4130761152)))]; + tensor hidden_states_351_cast = mul(x = var_7414_cast, y = var_7416_to_fp16)[name = tensor("hidden_states_351_cast")]; + tensor var_7423 = const()[name = tensor("op_7423"), val = tensor([1, 1])]; + tensor var_7425 = const()[name = tensor("op_7425"), val = tensor([1, 1])]; + tensor q_135_pad_type_0 = const()[name = tensor("q_135_pad_type_0"), val = tensor("custom")]; + tensor q_135_pad_0 = const()[name = tensor("q_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4130762752)))]; + tensor q_135_cast = conv(dilations = var_7425, groups = var_7059, pad = q_135_pad_0, pad_type = q_135_pad_type_0, strides = var_7423, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_351_cast)[name = tensor("q_135_cast")]; + tensor var_7429 = const()[name = tensor("op_7429"), val = tensor([1, 1])]; + tensor var_7431 = const()[name = tensor("op_7431"), val = tensor([1, 1])]; + tensor k_135_pad_type_0 = const()[name = tensor("k_135_pad_type_0"), val = tensor("custom")]; + tensor k_135_pad_0 = const()[name = tensor("k_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4131942464)))]; + tensor k_135_cast = conv(dilations = var_7431, groups = var_7059, pad = k_135_pad_0, pad_type = k_135_pad_type_0, strides = var_7429, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_135_cast")]; + tensor var_7435 = const()[name = tensor("op_7435"), val = tensor([1, 1])]; + tensor var_7437 = const()[name = tensor("op_7437"), val = tensor([1, 1])]; + tensor v_135_pad_type_0 = const()[name = tensor("v_135_pad_type_0"), val = tensor("custom")]; + tensor v_135_pad_0 = const()[name = tensor("v_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4133908608)))]; + tensor v_135_cast = conv(dilations = var_7437, groups = var_7059, pad = v_135_pad_0, pad_type = v_135_pad_type_0, strides = var_7435, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_135_cast")]; + tensor var_7441 = const()[name = tensor("op_7441"), val = tensor([2, 12, 64, -1])]; + tensor var_7442_cast = reshape(shape = var_7441, x = q_135_cast)[name = tensor("op_7442_cast")]; + tensor var_7443 = const()[name = tensor("op_7443"), val = tensor([2, 12, 64, -1])]; + tensor var_7444_cast = reshape(shape = var_7443, x = k_135_cast)[name = tensor("op_7444_cast")]; + tensor var_7445 = const()[name = tensor("op_7445"), val = tensor([2, 12, 64, -1])]; + tensor var_7446_cast = reshape(shape = var_7445, x = v_135_cast)[name = tensor("op_7446_cast")]; + tensor attn_weights_269_transpose_x_0 = const()[name = tensor("attn_weights_269_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_269_transpose_y_0 = const()[name = tensor("attn_weights_269_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_269_cast = matmul(transpose_x = attn_weights_269_transpose_x_0, transpose_y = attn_weights_269_transpose_y_0, x = var_7442_cast, y = var_7444_cast)[name = tensor("attn_weights_269_cast")]; + tensor attn_weights_271_cast = mul(x = attn_weights_269_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_271_cast")]; + tensor var_7450_cast = softmax(axis = var_7043, x = attn_weights_271_cast)[name = tensor("op_7450_cast")]; + tensor attn_135_transpose_x_0 = const()[name = tensor("attn_135_transpose_x_0"), val = tensor(false)]; + tensor attn_135_transpose_y_0 = const()[name = tensor("attn_135_transpose_y_0"), val = tensor(true)]; + tensor attn_135_cast = matmul(transpose_x = attn_135_transpose_x_0, transpose_y = attn_135_transpose_y_0, x = var_7446_cast, y = var_7450_cast)[name = tensor("attn_135_cast")]; + tensor var_7454 = const()[name = tensor("op_7454"), val = tensor([2, 768, 1, -1])]; + tensor input_567_cast = reshape(shape = var_7454, x = attn_135_cast)[name = tensor("input_567_cast")]; + tensor var_7459 = const()[name = tensor("op_7459"), val = tensor([1, 1])]; + tensor var_7461 = const()[name = tensor("op_7461"), val = tensor([1, 1])]; + tensor var_7463_pad_type_0 = const()[name = tensor("op_7463_pad_type_0"), val = tensor("custom")]; + tensor var_7463_pad_0 = const()[name = tensor("op_7463_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4135874752)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4137054464)))]; + tensor var_7463_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_7461, groups = var_7059, pad = var_7463_pad_0, pad_type = var_7463_pad_type_0, strides = var_7459, weight = up_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_567_cast)[name = tensor("op_7463_cast")]; + tensor inputs_203_cast = add(x = var_7463_cast, y = inputs_201_cast)[name = tensor("inputs_203_cast")]; + tensor var_7467 = const()[name = tensor("op_7467"), val = tensor([1])]; + tensor channels_mean_203_cast = reduce_mean(axes = var_7467, keep_dims = var_7054, x = inputs_203_cast)[name = tensor("channels_mean_203_cast")]; + tensor zero_mean_203_cast = sub(x = inputs_203_cast, y = channels_mean_203_cast)[name = tensor("zero_mean_203_cast")]; + tensor zero_mean_sq_203_cast = mul(x = zero_mean_203_cast, y = zero_mean_203_cast)[name = tensor("zero_mean_sq_203_cast")]; + tensor var_7471 = const()[name = tensor("op_7471"), val = tensor([1])]; + tensor var_7472_cast = reduce_mean(axes = var_7471, keep_dims = var_7054, x = zero_mean_sq_203_cast)[name = tensor("op_7472_cast")]; + tensor var_7473_to_fp16 = const()[name = tensor("op_7473_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7474_cast = add(x = var_7472_cast, y = var_7473_to_fp16)[name = tensor("op_7474_cast")]; + tensor denom_203_epsilon_0_to_fp16 = const()[name = tensor("denom_203_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_203_cast = rsqrt(epsilon = denom_203_epsilon_0_to_fp16, x = var_7474_cast)[name = tensor("denom_203_cast")]; + tensor out_203_cast = mul(x = zero_mean_203_cast, y = denom_203_cast)[name = tensor("out_203_cast")]; + tensor var_7478_to_fp16 = const()[name = tensor("op_7478_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4137056064)))]; + tensor var_7479_cast = add(x = out_203_cast, y = var_7478_to_fp16)[name = tensor("op_7479_cast")]; + tensor var_7481_to_fp16 = const()[name = tensor("op_7481_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4137057664)))]; + tensor input_569_cast = mul(x = var_7479_cast, y = var_7481_to_fp16)[name = tensor("input_569_cast")]; + tensor var_7489 = const()[name = tensor("op_7489"), val = tensor([1, 1])]; + tensor var_7491 = const()[name = tensor("op_7491"), val = tensor([1, 1])]; + tensor var_7493_pad_type_0 = const()[name = tensor("op_7493_pad_type_0"), val = tensor("custom")]; + tensor var_7493_pad_0 = const()[name = tensor("op_7493_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4137059264)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4146496512)))]; + tensor var_7493_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_7491, groups = var_7059, pad = var_7493_pad_0, pad_type = var_7493_pad_type_0, strides = var_7489, weight = up_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_569_cast)[name = tensor("op_7493_cast")]; + tensor var_7494_split_sizes_0 = const()[name = tensor("op_7494_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_7494_axis_0 = const()[name = tensor("op_7494_axis_0"), val = tensor(1)]; + tensor var_7494_cast_0, tensor var_7494_cast_1 = split(axis = var_7494_axis_0, split_sizes = var_7494_split_sizes_0, x = var_7493_cast)[name = tensor("op_7494_cast")]; + tensor var_7496_mode_0 = const()[name = tensor("op_7496_mode_0"), val = tensor("EXACT")]; + tensor var_7496_cast = gelu(mode = var_7496_mode_0, x = var_7494_cast_1)[name = tensor("op_7496_cast")]; + tensor input_571_cast = mul(x = var_7494_cast_0, y = var_7496_cast)[name = tensor("input_571_cast")]; + tensor var_7500 = const()[name = tensor("op_7500"), val = tensor([1, 1])]; + tensor var_7502 = const()[name = tensor("op_7502"), val = tensor([1, 1])]; + tensor var_7504_pad_type_0 = const()[name = tensor("op_7504_pad_type_0"), val = tensor("custom")]; + tensor var_7504_pad_0 = const()[name = tensor("op_7504_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4146508864)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4151227520)))]; + tensor var_7504_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_7502, groups = var_7059, pad = var_7504_pad_0, pad_type = var_7504_pad_type_0, strides = var_7500, weight = up_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_571_cast)[name = tensor("op_7504_cast")]; + tensor inputs_205_cast = add(x = var_7504_cast, y = inputs_203_cast)[name = tensor("inputs_205_cast")]; + tensor var_7514 = const()[name = tensor("op_7514"), val = tensor([1])]; + tensor channels_mean_205_cast = reduce_mean(axes = var_7514, keep_dims = var_7054, x = inputs_205_cast)[name = tensor("channels_mean_205_cast")]; + tensor zero_mean_205_cast = sub(x = inputs_205_cast, y = channels_mean_205_cast)[name = tensor("zero_mean_205_cast")]; + tensor zero_mean_sq_205_cast = mul(x = zero_mean_205_cast, y = zero_mean_205_cast)[name = tensor("zero_mean_sq_205_cast")]; + tensor var_7518 = const()[name = tensor("op_7518"), val = tensor([1])]; + tensor var_7519_cast = reduce_mean(axes = var_7518, keep_dims = var_7054, x = zero_mean_sq_205_cast)[name = tensor("op_7519_cast")]; + tensor var_7520_to_fp16 = const()[name = tensor("op_7520_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7521_cast = add(x = var_7519_cast, y = var_7520_to_fp16)[name = tensor("op_7521_cast")]; + tensor denom_205_epsilon_0_to_fp16 = const()[name = tensor("denom_205_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_205_cast = rsqrt(epsilon = denom_205_epsilon_0_to_fp16, x = var_7521_cast)[name = tensor("denom_205_cast")]; + tensor out_205_cast = mul(x = zero_mean_205_cast, y = denom_205_cast)[name = tensor("out_205_cast")]; + tensor var_7525_to_fp16 = const()[name = tensor("op_7525_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4151229120)))]; + tensor var_7526_cast = add(x = out_205_cast, y = var_7525_to_fp16)[name = tensor("op_7526_cast")]; + tensor var_7528_to_fp16 = const()[name = tensor("op_7528_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4151230720)))]; + tensor hidden_states_355_cast = mul(x = var_7526_cast, y = var_7528_to_fp16)[name = tensor("hidden_states_355_cast")]; + tensor var_7535 = const()[name = tensor("op_7535"), val = tensor([1, 1])]; + tensor var_7537 = const()[name = tensor("op_7537"), val = tensor([1, 1])]; + tensor q_137_pad_type_0 = const()[name = tensor("q_137_pad_type_0"), val = tensor("custom")]; + tensor q_137_pad_0 = const()[name = tensor("q_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4151232320)))]; + tensor q_137_cast = conv(dilations = var_7537, groups = var_7059, pad = q_137_pad_0, pad_type = q_137_pad_type_0, strides = var_7535, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_355_cast)[name = tensor("q_137_cast")]; + tensor var_7541 = const()[name = tensor("op_7541"), val = tensor([1, 1])]; + tensor var_7543 = const()[name = tensor("op_7543"), val = tensor([1, 1])]; + tensor k_137_pad_type_0 = const()[name = tensor("k_137_pad_type_0"), val = tensor("custom")]; + tensor k_137_pad_0 = const()[name = tensor("k_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4152412032)))]; + tensor k_137_cast = conv(dilations = var_7543, groups = var_7059, pad = k_137_pad_0, pad_type = k_137_pad_type_0, strides = var_7541, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_355_cast)[name = tensor("k_137_cast")]; + tensor var_7547 = const()[name = tensor("op_7547"), val = tensor([1, 1])]; + tensor var_7549 = const()[name = tensor("op_7549"), val = tensor([1, 1])]; + tensor v_137_pad_type_0 = const()[name = tensor("v_137_pad_type_0"), val = tensor("custom")]; + tensor v_137_pad_0 = const()[name = tensor("v_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4153591744)))]; + tensor v_137_cast = conv(dilations = var_7549, groups = var_7059, pad = v_137_pad_0, pad_type = v_137_pad_type_0, strides = var_7547, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_355_cast)[name = tensor("v_137_cast")]; + tensor var_7553 = const()[name = tensor("op_7553"), val = tensor([2, 12, 64, -1])]; + tensor var_7554_cast = reshape(shape = var_7553, x = q_137_cast)[name = tensor("op_7554_cast")]; + tensor var_7555 = const()[name = tensor("op_7555"), val = tensor([2, 12, 64, -1])]; + tensor var_7556_cast = reshape(shape = var_7555, x = k_137_cast)[name = tensor("op_7556_cast")]; + tensor var_7557 = const()[name = tensor("op_7557"), val = tensor([2, 12, 64, -1])]; + tensor var_7558_cast = reshape(shape = var_7557, x = v_137_cast)[name = tensor("op_7558_cast")]; + tensor attn_weights_273_transpose_x_0 = const()[name = tensor("attn_weights_273_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_273_transpose_y_0 = const()[name = tensor("attn_weights_273_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_273_cast = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_7554_cast, y = var_7556_cast)[name = tensor("attn_weights_273_cast")]; + tensor attn_weights_275_cast = mul(x = attn_weights_273_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_275_cast")]; + tensor var_7562_cast = softmax(axis = var_7043, x = attn_weights_275_cast)[name = tensor("op_7562_cast")]; + tensor attn_137_transpose_x_0 = const()[name = tensor("attn_137_transpose_x_0"), val = tensor(false)]; + tensor attn_137_transpose_y_0 = const()[name = tensor("attn_137_transpose_y_0"), val = tensor(true)]; + tensor attn_137_cast = matmul(transpose_x = attn_137_transpose_x_0, transpose_y = attn_137_transpose_y_0, x = var_7558_cast, y = var_7562_cast)[name = tensor("attn_137_cast")]; + tensor var_7566 = const()[name = tensor("op_7566"), val = tensor([2, 768, 1, -1])]; + tensor input_573_cast = reshape(shape = var_7566, x = attn_137_cast)[name = tensor("input_573_cast")]; + tensor var_7571 = const()[name = tensor("op_7571"), val = tensor([1, 1])]; + tensor var_7573 = const()[name = tensor("op_7573"), val = tensor([1, 1])]; + tensor var_7575_pad_type_0 = const()[name = tensor("op_7575_pad_type_0"), val = tensor("custom")]; + tensor var_7575_pad_0 = const()[name = tensor("op_7575_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4154771456)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4155951168)))]; + tensor var_7575_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_7573, groups = var_7059, pad = var_7575_pad_0, pad_type = var_7575_pad_type_0, strides = var_7571, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_573_cast)[name = tensor("op_7575_cast")]; + tensor inputs_207_cast = add(x = var_7575_cast, y = inputs_205_cast)[name = tensor("inputs_207_cast")]; + tensor var_7579 = const()[name = tensor("op_7579"), val = tensor([1])]; + tensor channels_mean_207_cast = reduce_mean(axes = var_7579, keep_dims = var_7054, x = inputs_207_cast)[name = tensor("channels_mean_207_cast")]; + tensor zero_mean_207_cast = sub(x = inputs_207_cast, y = channels_mean_207_cast)[name = tensor("zero_mean_207_cast")]; + tensor zero_mean_sq_207_cast = mul(x = zero_mean_207_cast, y = zero_mean_207_cast)[name = tensor("zero_mean_sq_207_cast")]; + tensor var_7583 = const()[name = tensor("op_7583"), val = tensor([1])]; + tensor var_7584_cast = reduce_mean(axes = var_7583, keep_dims = var_7054, x = zero_mean_sq_207_cast)[name = tensor("op_7584_cast")]; + tensor var_7585_to_fp16 = const()[name = tensor("op_7585_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7586_cast = add(x = var_7584_cast, y = var_7585_to_fp16)[name = tensor("op_7586_cast")]; + tensor denom_207_epsilon_0_to_fp16 = const()[name = tensor("denom_207_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_207_cast = rsqrt(epsilon = denom_207_epsilon_0_to_fp16, x = var_7586_cast)[name = tensor("denom_207_cast")]; + tensor out_207_cast = mul(x = zero_mean_207_cast, y = denom_207_cast)[name = tensor("out_207_cast")]; + tensor var_7590_to_fp16 = const()[name = tensor("op_7590_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4155952768)))]; + tensor var_7591_cast = add(x = out_207_cast, y = var_7590_to_fp16)[name = tensor("op_7591_cast")]; + tensor var_7593_to_fp16 = const()[name = tensor("op_7593_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4155954368)))]; + tensor hidden_states_357_cast = mul(x = var_7591_cast, y = var_7593_to_fp16)[name = tensor("hidden_states_357_cast")]; + tensor var_7600 = const()[name = tensor("op_7600"), val = tensor([1, 1])]; + tensor var_7602 = const()[name = tensor("op_7602"), val = tensor([1, 1])]; + tensor q_139_pad_type_0 = const()[name = tensor("q_139_pad_type_0"), val = tensor("custom")]; + tensor q_139_pad_0 = const()[name = tensor("q_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4155955968)))]; + tensor q_139_cast = conv(dilations = var_7602, groups = var_7059, pad = q_139_pad_0, pad_type = q_139_pad_type_0, strides = var_7600, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_357_cast)[name = tensor("q_139_cast")]; + tensor var_7606 = const()[name = tensor("op_7606"), val = tensor([1, 1])]; + tensor var_7608 = const()[name = tensor("op_7608"), val = tensor([1, 1])]; + tensor k_139_pad_type_0 = const()[name = tensor("k_139_pad_type_0"), val = tensor("custom")]; + tensor k_139_pad_0 = const()[name = tensor("k_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4157135680)))]; + tensor k_139_cast = conv(dilations = var_7608, groups = var_7059, pad = k_139_pad_0, pad_type = k_139_pad_type_0, strides = var_7606, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_139_cast")]; + tensor var_7612 = const()[name = tensor("op_7612"), val = tensor([1, 1])]; + tensor var_7614 = const()[name = tensor("op_7614"), val = tensor([1, 1])]; + tensor v_139_pad_type_0 = const()[name = tensor("v_139_pad_type_0"), val = tensor("custom")]; + tensor v_139_pad_0 = const()[name = tensor("v_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4159101824)))]; + tensor v_139_cast = conv(dilations = var_7614, groups = var_7059, pad = v_139_pad_0, pad_type = v_139_pad_type_0, strides = var_7612, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_139_cast")]; + tensor var_7618 = const()[name = tensor("op_7618"), val = tensor([2, 12, 64, -1])]; + tensor var_7619_cast = reshape(shape = var_7618, x = q_139_cast)[name = tensor("op_7619_cast")]; + tensor var_7620 = const()[name = tensor("op_7620"), val = tensor([2, 12, 64, -1])]; + tensor var_7621_cast = reshape(shape = var_7620, x = k_139_cast)[name = tensor("op_7621_cast")]; + tensor var_7622 = const()[name = tensor("op_7622"), val = tensor([2, 12, 64, -1])]; + tensor var_7623_cast = reshape(shape = var_7622, x = v_139_cast)[name = tensor("op_7623_cast")]; + tensor attn_weights_277_transpose_x_0 = const()[name = tensor("attn_weights_277_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_277_transpose_y_0 = const()[name = tensor("attn_weights_277_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_277_cast = matmul(transpose_x = attn_weights_277_transpose_x_0, transpose_y = attn_weights_277_transpose_y_0, x = var_7619_cast, y = var_7621_cast)[name = tensor("attn_weights_277_cast")]; + tensor attn_weights_279_cast = mul(x = attn_weights_277_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_279_cast")]; + tensor var_7627_cast = softmax(axis = var_7043, x = attn_weights_279_cast)[name = tensor("op_7627_cast")]; + tensor attn_139_transpose_x_0 = const()[name = tensor("attn_139_transpose_x_0"), val = tensor(false)]; + tensor attn_139_transpose_y_0 = const()[name = tensor("attn_139_transpose_y_0"), val = tensor(true)]; + tensor attn_139_cast = matmul(transpose_x = attn_139_transpose_x_0, transpose_y = attn_139_transpose_y_0, x = var_7623_cast, y = var_7627_cast)[name = tensor("attn_139_cast")]; + tensor var_7631 = const()[name = tensor("op_7631"), val = tensor([2, 768, 1, -1])]; + tensor input_575_cast = reshape(shape = var_7631, x = attn_139_cast)[name = tensor("input_575_cast")]; + tensor var_7636 = const()[name = tensor("op_7636"), val = tensor([1, 1])]; + tensor var_7638 = const()[name = tensor("op_7638"), val = tensor([1, 1])]; + tensor var_7640_pad_type_0 = const()[name = tensor("op_7640_pad_type_0"), val = tensor("custom")]; + tensor var_7640_pad_0 = const()[name = tensor("op_7640_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4161067968)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162247680)))]; + tensor var_7640_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_7638, groups = var_7059, pad = var_7640_pad_0, pad_type = var_7640_pad_type_0, strides = var_7636, weight = up_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_575_cast)[name = tensor("op_7640_cast")]; + tensor inputs_209_cast = add(x = var_7640_cast, y = inputs_207_cast)[name = tensor("inputs_209_cast")]; + tensor var_7644 = const()[name = tensor("op_7644"), val = tensor([1])]; + tensor channels_mean_209_cast = reduce_mean(axes = var_7644, keep_dims = var_7054, x = inputs_209_cast)[name = tensor("channels_mean_209_cast")]; + tensor zero_mean_209_cast = sub(x = inputs_209_cast, y = channels_mean_209_cast)[name = tensor("zero_mean_209_cast")]; + tensor zero_mean_sq_209_cast = mul(x = zero_mean_209_cast, y = zero_mean_209_cast)[name = tensor("zero_mean_sq_209_cast")]; + tensor var_7648 = const()[name = tensor("op_7648"), val = tensor([1])]; + tensor var_7649_cast = reduce_mean(axes = var_7648, keep_dims = var_7054, x = zero_mean_sq_209_cast)[name = tensor("op_7649_cast")]; + tensor var_7650_to_fp16 = const()[name = tensor("op_7650_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7651_cast = add(x = var_7649_cast, y = var_7650_to_fp16)[name = tensor("op_7651_cast")]; + tensor denom_209_epsilon_0_to_fp16 = const()[name = tensor("denom_209_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_209_cast = rsqrt(epsilon = denom_209_epsilon_0_to_fp16, x = var_7651_cast)[name = tensor("denom_209_cast")]; + tensor out_209_cast = mul(x = zero_mean_209_cast, y = denom_209_cast)[name = tensor("out_209_cast")]; + tensor var_7655_to_fp16 = const()[name = tensor("op_7655_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162249280)))]; + tensor var_7656_cast = add(x = out_209_cast, y = var_7655_to_fp16)[name = tensor("op_7656_cast")]; + tensor var_7658_to_fp16 = const()[name = tensor("op_7658_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162250880)))]; + tensor input_577_cast = mul(x = var_7656_cast, y = var_7658_to_fp16)[name = tensor("input_577_cast")]; + tensor var_7666 = const()[name = tensor("op_7666"), val = tensor([1, 1])]; + tensor var_7668 = const()[name = tensor("op_7668"), val = tensor([1, 1])]; + tensor var_7670_pad_type_0 = const()[name = tensor("op_7670_pad_type_0"), val = tensor("custom")]; + tensor var_7670_pad_0 = const()[name = tensor("op_7670_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162252480)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4171689728)))]; + tensor var_7670_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_7668, groups = var_7059, pad = var_7670_pad_0, pad_type = var_7670_pad_type_0, strides = var_7666, weight = up_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_577_cast)[name = tensor("op_7670_cast")]; + tensor var_7671_split_sizes_0 = const()[name = tensor("op_7671_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_7671_axis_0 = const()[name = tensor("op_7671_axis_0"), val = tensor(1)]; + tensor var_7671_cast_0, tensor var_7671_cast_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = var_7670_cast)[name = tensor("op_7671_cast")]; + tensor var_7673_mode_0 = const()[name = tensor("op_7673_mode_0"), val = tensor("EXACT")]; + tensor var_7673_cast = gelu(mode = var_7673_mode_0, x = var_7671_cast_1)[name = tensor("op_7673_cast")]; + tensor input_579_cast = mul(x = var_7671_cast_0, y = var_7673_cast)[name = tensor("input_579_cast")]; + tensor var_7677 = const()[name = tensor("op_7677"), val = tensor([1, 1])]; + tensor var_7679 = const()[name = tensor("op_7679"), val = tensor([1, 1])]; + tensor var_7681_pad_type_0 = const()[name = tensor("op_7681_pad_type_0"), val = tensor("custom")]; + tensor var_7681_pad_0 = const()[name = tensor("op_7681_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4171702080)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4176420736)))]; + tensor var_7681_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_7679, groups = var_7059, pad = var_7681_pad_0, pad_type = var_7681_pad_type_0, strides = var_7677, weight = up_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_579_cast)[name = tensor("op_7681_cast")]; + tensor inputs_211_cast = add(x = var_7681_cast, y = inputs_209_cast)[name = tensor("inputs_211_cast")]; + tensor var_7691 = const()[name = tensor("op_7691"), val = tensor([1])]; + tensor channels_mean_211_cast = reduce_mean(axes = var_7691, keep_dims = var_7054, x = inputs_211_cast)[name = tensor("channels_mean_211_cast")]; + tensor zero_mean_211_cast = sub(x = inputs_211_cast, y = channels_mean_211_cast)[name = tensor("zero_mean_211_cast")]; + tensor zero_mean_sq_211_cast = mul(x = zero_mean_211_cast, y = zero_mean_211_cast)[name = tensor("zero_mean_sq_211_cast")]; + tensor var_7695 = const()[name = tensor("op_7695"), val = tensor([1])]; + tensor var_7696_cast = reduce_mean(axes = var_7695, keep_dims = var_7054, x = zero_mean_sq_211_cast)[name = tensor("op_7696_cast")]; + tensor var_7697_to_fp16 = const()[name = tensor("op_7697_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7698_cast = add(x = var_7696_cast, y = var_7697_to_fp16)[name = tensor("op_7698_cast")]; + tensor denom_211_epsilon_0_to_fp16 = const()[name = tensor("denom_211_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_211_cast = rsqrt(epsilon = denom_211_epsilon_0_to_fp16, x = var_7698_cast)[name = tensor("denom_211_cast")]; + tensor out_211_cast = mul(x = zero_mean_211_cast, y = denom_211_cast)[name = tensor("out_211_cast")]; + tensor var_7702_to_fp16 = const()[name = tensor("op_7702_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4176422336)))]; + tensor var_7703_cast = add(x = out_211_cast, y = var_7702_to_fp16)[name = tensor("op_7703_cast")]; + tensor var_7705_to_fp16 = const()[name = tensor("op_7705_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4176423936)))]; + tensor hidden_states_361_cast = mul(x = var_7703_cast, y = var_7705_to_fp16)[name = tensor("hidden_states_361_cast")]; + tensor var_7712 = const()[name = tensor("op_7712"), val = tensor([1, 1])]; + tensor var_7714 = const()[name = tensor("op_7714"), val = tensor([1, 1])]; + tensor q_141_pad_type_0 = const()[name = tensor("q_141_pad_type_0"), val = tensor("custom")]; + tensor q_141_pad_0 = const()[name = tensor("q_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4176425536)))]; + tensor q_141_cast = conv(dilations = var_7714, groups = var_7059, pad = q_141_pad_0, pad_type = q_141_pad_type_0, strides = var_7712, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("q_141_cast")]; + tensor var_7718 = const()[name = tensor("op_7718"), val = tensor([1, 1])]; + tensor var_7720 = const()[name = tensor("op_7720"), val = tensor([1, 1])]; + tensor k_141_pad_type_0 = const()[name = tensor("k_141_pad_type_0"), val = tensor("custom")]; + tensor k_141_pad_0 = const()[name = tensor("k_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4177605248)))]; + tensor k_141_cast = conv(dilations = var_7720, groups = var_7059, pad = k_141_pad_0, pad_type = k_141_pad_type_0, strides = var_7718, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("k_141_cast")]; + tensor var_7724 = const()[name = tensor("op_7724"), val = tensor([1, 1])]; + tensor var_7726 = const()[name = tensor("op_7726"), val = tensor([1, 1])]; + tensor v_141_pad_type_0 = const()[name = tensor("v_141_pad_type_0"), val = tensor("custom")]; + tensor v_141_pad_0 = const()[name = tensor("v_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4178784960)))]; + tensor v_141_cast = conv(dilations = var_7726, groups = var_7059, pad = v_141_pad_0, pad_type = v_141_pad_type_0, strides = var_7724, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("v_141_cast")]; + tensor var_7730 = const()[name = tensor("op_7730"), val = tensor([2, 12, 64, -1])]; + tensor var_7731_cast = reshape(shape = var_7730, x = q_141_cast)[name = tensor("op_7731_cast")]; + tensor var_7732 = const()[name = tensor("op_7732"), val = tensor([2, 12, 64, -1])]; + tensor var_7733_cast = reshape(shape = var_7732, x = k_141_cast)[name = tensor("op_7733_cast")]; + tensor var_7734 = const()[name = tensor("op_7734"), val = tensor([2, 12, 64, -1])]; + tensor var_7735_cast = reshape(shape = var_7734, x = v_141_cast)[name = tensor("op_7735_cast")]; + tensor attn_weights_281_transpose_x_0 = const()[name = tensor("attn_weights_281_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_281_transpose_y_0 = const()[name = tensor("attn_weights_281_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_281_cast = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_7731_cast, y = var_7733_cast)[name = tensor("attn_weights_281_cast")]; + tensor attn_weights_283_cast = mul(x = attn_weights_281_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_283_cast")]; + tensor var_7739_cast = softmax(axis = var_7043, x = attn_weights_283_cast)[name = tensor("op_7739_cast")]; + tensor attn_141_transpose_x_0 = const()[name = tensor("attn_141_transpose_x_0"), val = tensor(false)]; + tensor attn_141_transpose_y_0 = const()[name = tensor("attn_141_transpose_y_0"), val = tensor(true)]; + tensor attn_141_cast = matmul(transpose_x = attn_141_transpose_x_0, transpose_y = attn_141_transpose_y_0, x = var_7735_cast, y = var_7739_cast)[name = tensor("attn_141_cast")]; + tensor var_7743 = const()[name = tensor("op_7743"), val = tensor([2, 768, 1, -1])]; + tensor input_581_cast = reshape(shape = var_7743, x = attn_141_cast)[name = tensor("input_581_cast")]; + tensor var_7748 = const()[name = tensor("op_7748"), val = tensor([1, 1])]; + tensor var_7750 = const()[name = tensor("op_7750"), val = tensor([1, 1])]; + tensor var_7752_pad_type_0 = const()[name = tensor("op_7752_pad_type_0"), val = tensor("custom")]; + tensor var_7752_pad_0 = const()[name = tensor("op_7752_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4179964672)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4181144384)))]; + tensor var_7752_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_7750, groups = var_7059, pad = var_7752_pad_0, pad_type = var_7752_pad_type_0, strides = var_7748, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_581_cast)[name = tensor("op_7752_cast")]; + tensor inputs_213_cast = add(x = var_7752_cast, y = inputs_211_cast)[name = tensor("inputs_213_cast")]; + tensor var_7756 = const()[name = tensor("op_7756"), val = tensor([1])]; + tensor channels_mean_213_cast = reduce_mean(axes = var_7756, keep_dims = var_7054, x = inputs_213_cast)[name = tensor("channels_mean_213_cast")]; + tensor zero_mean_213_cast = sub(x = inputs_213_cast, y = channels_mean_213_cast)[name = tensor("zero_mean_213_cast")]; + tensor zero_mean_sq_213_cast = mul(x = zero_mean_213_cast, y = zero_mean_213_cast)[name = tensor("zero_mean_sq_213_cast")]; + tensor var_7760 = const()[name = tensor("op_7760"), val = tensor([1])]; + tensor var_7761_cast = reduce_mean(axes = var_7760, keep_dims = var_7054, x = zero_mean_sq_213_cast)[name = tensor("op_7761_cast")]; + tensor var_7762_to_fp16 = const()[name = tensor("op_7762_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7763_cast = add(x = var_7761_cast, y = var_7762_to_fp16)[name = tensor("op_7763_cast")]; + tensor denom_213_epsilon_0_to_fp16 = const()[name = tensor("denom_213_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_213_cast = rsqrt(epsilon = denom_213_epsilon_0_to_fp16, x = var_7763_cast)[name = tensor("denom_213_cast")]; + tensor out_213_cast = mul(x = zero_mean_213_cast, y = denom_213_cast)[name = tensor("out_213_cast")]; + tensor var_7767_to_fp16 = const()[name = tensor("op_7767_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4181145984)))]; + tensor var_7768_cast = add(x = out_213_cast, y = var_7767_to_fp16)[name = tensor("op_7768_cast")]; + tensor var_7770_to_fp16 = const()[name = tensor("op_7770_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4181147584)))]; + tensor hidden_states_363_cast = mul(x = var_7768_cast, y = var_7770_to_fp16)[name = tensor("hidden_states_363_cast")]; + tensor var_7777 = const()[name = tensor("op_7777"), val = tensor([1, 1])]; + tensor var_7779 = const()[name = tensor("op_7779"), val = tensor([1, 1])]; + tensor q_143_pad_type_0 = const()[name = tensor("q_143_pad_type_0"), val = tensor("custom")]; + tensor q_143_pad_0 = const()[name = tensor("q_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4181149184)))]; + tensor q_143_cast = conv(dilations = var_7779, groups = var_7059, pad = q_143_pad_0, pad_type = q_143_pad_type_0, strides = var_7777, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_363_cast)[name = tensor("q_143_cast")]; + tensor var_7783 = const()[name = tensor("op_7783"), val = tensor([1, 1])]; + tensor var_7785 = const()[name = tensor("op_7785"), val = tensor([1, 1])]; + tensor k_143_pad_type_0 = const()[name = tensor("k_143_pad_type_0"), val = tensor("custom")]; + tensor k_143_pad_0 = const()[name = tensor("k_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4182328896)))]; + tensor k_143_cast = conv(dilations = var_7785, groups = var_7059, pad = k_143_pad_0, pad_type = k_143_pad_type_0, strides = var_7783, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_143_cast")]; + tensor var_7789 = const()[name = tensor("op_7789"), val = tensor([1, 1])]; + tensor var_7791 = const()[name = tensor("op_7791"), val = tensor([1, 1])]; + tensor v_143_pad_type_0 = const()[name = tensor("v_143_pad_type_0"), val = tensor("custom")]; + tensor v_143_pad_0 = const()[name = tensor("v_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4184295040)))]; + tensor v_143_cast = conv(dilations = var_7791, groups = var_7059, pad = v_143_pad_0, pad_type = v_143_pad_type_0, strides = var_7789, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_143_cast")]; + tensor var_7795 = const()[name = tensor("op_7795"), val = tensor([2, 12, 64, -1])]; + tensor var_7796_cast = reshape(shape = var_7795, x = q_143_cast)[name = tensor("op_7796_cast")]; + tensor var_7797 = const()[name = tensor("op_7797"), val = tensor([2, 12, 64, -1])]; + tensor var_7798_cast = reshape(shape = var_7797, x = k_143_cast)[name = tensor("op_7798_cast")]; + tensor var_7799 = const()[name = tensor("op_7799"), val = tensor([2, 12, 64, -1])]; + tensor var_7800_cast = reshape(shape = var_7799, x = v_143_cast)[name = tensor("op_7800_cast")]; + tensor attn_weights_285_transpose_x_0 = const()[name = tensor("attn_weights_285_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_285_transpose_y_0 = const()[name = tensor("attn_weights_285_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_285_cast = matmul(transpose_x = attn_weights_285_transpose_x_0, transpose_y = attn_weights_285_transpose_y_0, x = var_7796_cast, y = var_7798_cast)[name = tensor("attn_weights_285_cast")]; + tensor attn_weights_287_cast = mul(x = attn_weights_285_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_287_cast")]; + tensor var_7804_cast = softmax(axis = var_7043, x = attn_weights_287_cast)[name = tensor("op_7804_cast")]; + tensor attn_143_transpose_x_0 = const()[name = tensor("attn_143_transpose_x_0"), val = tensor(false)]; + tensor attn_143_transpose_y_0 = const()[name = tensor("attn_143_transpose_y_0"), val = tensor(true)]; + tensor attn_143_cast = matmul(transpose_x = attn_143_transpose_x_0, transpose_y = attn_143_transpose_y_0, x = var_7800_cast, y = var_7804_cast)[name = tensor("attn_143_cast")]; + tensor var_7808 = const()[name = tensor("op_7808"), val = tensor([2, 768, 1, -1])]; + tensor input_583_cast = reshape(shape = var_7808, x = attn_143_cast)[name = tensor("input_583_cast")]; + tensor var_7813 = const()[name = tensor("op_7813"), val = tensor([1, 1])]; + tensor var_7815 = const()[name = tensor("op_7815"), val = tensor([1, 1])]; + tensor var_7817_pad_type_0 = const()[name = tensor("op_7817_pad_type_0"), val = tensor("custom")]; + tensor var_7817_pad_0 = const()[name = tensor("op_7817_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4186261184)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4187440896)))]; + tensor var_7817_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_7815, groups = var_7059, pad = var_7817_pad_0, pad_type = var_7817_pad_type_0, strides = var_7813, weight = up_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_583_cast)[name = tensor("op_7817_cast")]; + tensor inputs_215_cast = add(x = var_7817_cast, y = inputs_213_cast)[name = tensor("inputs_215_cast")]; + tensor var_7821 = const()[name = tensor("op_7821"), val = tensor([1])]; + tensor channels_mean_215_cast = reduce_mean(axes = var_7821, keep_dims = var_7054, x = inputs_215_cast)[name = tensor("channels_mean_215_cast")]; + tensor zero_mean_215_cast = sub(x = inputs_215_cast, y = channels_mean_215_cast)[name = tensor("zero_mean_215_cast")]; + tensor zero_mean_sq_215_cast = mul(x = zero_mean_215_cast, y = zero_mean_215_cast)[name = tensor("zero_mean_sq_215_cast")]; + tensor var_7825 = const()[name = tensor("op_7825"), val = tensor([1])]; + tensor var_7826_cast = reduce_mean(axes = var_7825, keep_dims = var_7054, x = zero_mean_sq_215_cast)[name = tensor("op_7826_cast")]; + tensor var_7827_to_fp16 = const()[name = tensor("op_7827_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7828_cast = add(x = var_7826_cast, y = var_7827_to_fp16)[name = tensor("op_7828_cast")]; + tensor denom_215_epsilon_0_to_fp16 = const()[name = tensor("denom_215_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_215_cast = rsqrt(epsilon = denom_215_epsilon_0_to_fp16, x = var_7828_cast)[name = tensor("denom_215_cast")]; + tensor out_215_cast = mul(x = zero_mean_215_cast, y = denom_215_cast)[name = tensor("out_215_cast")]; + tensor var_7832_to_fp16 = const()[name = tensor("op_7832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4187442496)))]; + tensor var_7833_cast = add(x = out_215_cast, y = var_7832_to_fp16)[name = tensor("op_7833_cast")]; + tensor var_7835_to_fp16 = const()[name = tensor("op_7835_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4187444096)))]; + tensor input_585_cast = mul(x = var_7833_cast, y = var_7835_to_fp16)[name = tensor("input_585_cast")]; + tensor var_7843 = const()[name = tensor("op_7843"), val = tensor([1, 1])]; + tensor var_7845 = const()[name = tensor("op_7845"), val = tensor([1, 1])]; + tensor var_7847_pad_type_0 = const()[name = tensor("op_7847_pad_type_0"), val = tensor("custom")]; + tensor var_7847_pad_0 = const()[name = tensor("op_7847_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4187445696)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4196882944)))]; + tensor var_7847_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_7845, groups = var_7059, pad = var_7847_pad_0, pad_type = var_7847_pad_type_0, strides = var_7843, weight = up_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_585_cast)[name = tensor("op_7847_cast")]; + tensor var_7848_split_sizes_0 = const()[name = tensor("op_7848_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_7848_axis_0 = const()[name = tensor("op_7848_axis_0"), val = tensor(1)]; + tensor var_7848_cast_0, tensor var_7848_cast_1 = split(axis = var_7848_axis_0, split_sizes = var_7848_split_sizes_0, x = var_7847_cast)[name = tensor("op_7848_cast")]; + tensor var_7850_mode_0 = const()[name = tensor("op_7850_mode_0"), val = tensor("EXACT")]; + tensor var_7850_cast = gelu(mode = var_7850_mode_0, x = var_7848_cast_1)[name = tensor("op_7850_cast")]; + tensor input_587_cast = mul(x = var_7848_cast_0, y = var_7850_cast)[name = tensor("input_587_cast")]; + tensor var_7854 = const()[name = tensor("op_7854"), val = tensor([1, 1])]; + tensor var_7856 = const()[name = tensor("op_7856"), val = tensor([1, 1])]; + tensor var_7858_pad_type_0 = const()[name = tensor("op_7858_pad_type_0"), val = tensor("custom")]; + tensor var_7858_pad_0 = const()[name = tensor("op_7858_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4196895296)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4201613952)))]; + tensor var_7858_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_7856, groups = var_7059, pad = var_7858_pad_0, pad_type = var_7858_pad_type_0, strides = var_7854, weight = up_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_587_cast)[name = tensor("op_7858_cast")]; + tensor hidden_states_367_cast = add(x = var_7858_cast, y = inputs_215_cast)[name = tensor("hidden_states_367_cast")]; + tensor var_7860 = const()[name = tensor("op_7860"), val = tensor([2, 768, 64, 64])]; + tensor input_589_cast = reshape(shape = var_7860, x = hidden_states_367_cast)[name = tensor("input_589_cast")]; + tensor var_7864 = const()[name = tensor("op_7864"), val = tensor([1, 1])]; + tensor var_7866 = const()[name = tensor("op_7866"), val = tensor([1, 1])]; + tensor hidden_states_369_pad_type_0 = const()[name = tensor("hidden_states_369_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_369_pad_0 = const()[name = tensor("hidden_states_369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4201615552)))]; + tensor up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202795264)))]; + tensor hidden_states_369_cast = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_7866, groups = var_7059, pad = hidden_states_369_pad_0, pad_type = hidden_states_369_pad_type_0, strides = var_7864, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_589_cast)[name = tensor("hidden_states_369_cast")]; + tensor hidden_states_371_cast = add(x = hidden_states_369_cast, y = hidden_states_339_cast)[name = tensor("hidden_states_371_cast")]; + tensor input_591_interleave_0 = const()[name = tensor("input_591_interleave_0"), val = tensor(false)]; + tensor input_591_cast = concat(axis = var_7059, interleave = input_591_interleave_0, values = (hidden_states_371_cast, input_95_cast))[name = tensor("input_591_cast")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 48, 64, 64])]; + tensor reshape_172_cast = reshape(shape = reshape_172_shape_0, x = input_591_cast)[name = tensor("reshape_172_cast")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast)[name = tensor("reduce_mean_129_cast")]; + tensor sub_86_cast = sub(x = reshape_172_cast, y = reduce_mean_129_cast)[name = tensor("sub_86_cast")]; + tensor square_43_cast = square(x = sub_86_cast)[name = tensor("square_43_cast")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast)[name = tensor("reduce_mean_131_cast")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast = add(x = reduce_mean_131_cast, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast")]; + tensor sqrt_43_cast = sqrt(x = add_86_cast)[name = tensor("sqrt_43_cast")]; + tensor real_div_43_cast = real_div(x = sub_86_cast, y = sqrt_43_cast)[name = tensor("real_div_43_cast")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 1536, 64, 64])]; + tensor reshape_173_cast = reshape(shape = reshape_173_shape_0, x = real_div_43_cast)[name = tensor("reshape_173_cast")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202796864)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202800000)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_173_cast)[name = tensor("add_87_cast")]; + tensor input_595_cast = silu(x = add_87_cast)[name = tensor("input_595_cast")]; + tensor var_7884 = const()[name = tensor("op_7884"), val = tensor([1, 1])]; + tensor var_7886 = const()[name = tensor("op_7886"), val = tensor([1, 1])]; + tensor hidden_states_373_pad_type_0 = const()[name = tensor("hidden_states_373_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_373_pad_0 = const()[name = tensor("hidden_states_373_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202803136)))]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4224036864)))]; + tensor hidden_states_373_cast = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_7886, groups = var_7059, pad = hidden_states_373_pad_0, pad_type = hidden_states_373_pad_type_0, strides = var_7884, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_595_cast)[name = tensor("hidden_states_373_cast")]; + tensor var_7892 = const()[name = tensor("op_7892"), val = tensor([1, 1])]; + tensor var_7894 = const()[name = tensor("op_7894"), val = tensor([1, 1])]; + tensor temb_35_pad_type_0 = const()[name = tensor("temb_35_pad_type_0"), val = tensor("custom")]; + tensor temb_35_pad_0 = const()[name = tensor("temb_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4224038464)))]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4226397824)))]; + tensor temb_35_cast = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_7894, groups = var_7059, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_7892, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_35_cast")]; + tensor input_599_cast = add(x = hidden_states_373_cast, y = temb_35_cast)[name = tensor("input_599_cast")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_176_cast = reshape(shape = reshape_176_shape_0, x = input_599_cast)[name = tensor("reshape_176_cast")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast)[name = tensor("reduce_mean_132_cast")]; + tensor sub_88_cast = sub(x = reshape_176_cast, y = reduce_mean_132_cast)[name = tensor("sub_88_cast")]; + tensor square_44_cast = square(x = sub_88_cast)[name = tensor("square_44_cast")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast)[name = tensor("reduce_mean_134_cast")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_88_cast = add(x = reduce_mean_134_cast, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast")]; + tensor sqrt_44_cast = sqrt(x = add_88_cast)[name = tensor("sqrt_44_cast")]; + tensor real_div_44_cast = real_div(x = sub_88_cast, y = sqrt_44_cast)[name = tensor("real_div_44_cast")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_177_cast = reshape(shape = reshape_177_shape_0, x = real_div_44_cast)[name = tensor("reshape_177_cast")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4226399424)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4226401024)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_177_cast)[name = tensor("add_89_cast")]; + tensor input_603_cast = silu(x = add_89_cast)[name = tensor("input_603_cast")]; + tensor var_7904 = const()[name = tensor("op_7904"), val = tensor([1, 1])]; + tensor var_7906 = const()[name = tensor("op_7906"), val = tensor([1, 1])]; + tensor hidden_states_375_pad_type_0 = const()[name = tensor("hidden_states_375_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_375_pad_0 = const()[name = tensor("hidden_states_375_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4226402624)))]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4237019520)))]; + tensor hidden_states_375_cast = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_7906, groups = var_7059, pad = hidden_states_375_pad_0, pad_type = hidden_states_375_pad_type_0, strides = var_7904, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_603_cast)[name = tensor("hidden_states_375_cast")]; + tensor var_7911 = const()[name = tensor("op_7911"), val = tensor([1, 1])]; + tensor var_7913 = const()[name = tensor("op_7913"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4237021120)))]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4239380480)))]; + tensor x_19_cast = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_7913, groups = var_7059, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_7911, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16, x = input_591_cast)[name = tensor("x_19_cast")]; + tensor hidden_states_377_cast = add(x = x_19_cast, y = hidden_states_375_cast)[name = tensor("hidden_states_377_cast")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_180_cast = reshape(shape = reshape_180_shape_0, x = hidden_states_377_cast)[name = tensor("reshape_180_cast")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast)[name = tensor("reduce_mean_135_cast")]; + tensor sub_90_cast = sub(x = reshape_180_cast, y = reduce_mean_135_cast)[name = tensor("sub_90_cast")]; + tensor square_45_cast = square(x = sub_90_cast)[name = tensor("square_45_cast")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast)[name = tensor("reduce_mean_137_cast")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_90_cast = add(x = reduce_mean_137_cast, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast")]; + tensor sqrt_45_cast = sqrt(x = add_90_cast)[name = tensor("sqrt_45_cast")]; + tensor real_div_45_cast = real_div(x = sub_90_cast, y = sqrt_45_cast)[name = tensor("real_div_45_cast")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_181_cast = reshape(shape = reshape_181_shape_0, x = real_div_45_cast)[name = tensor("reshape_181_cast")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4239382080)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4239383680)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_181_cast)[name = tensor("add_91_cast")]; + tensor var_7939 = const()[name = tensor("op_7939"), val = tensor([1, 1])]; + tensor var_7941 = const()[name = tensor("op_7941"), val = tensor([1, 1])]; + tensor hidden_states_379_pad_type_0 = const()[name = tensor("hidden_states_379_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_379_pad_0 = const()[name = tensor("hidden_states_379_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4239385280)))]; + tensor up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4240564992)))]; + tensor hidden_states_379_cast = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_7941, groups = var_7059, pad = hidden_states_379_pad_0, pad_type = hidden_states_379_pad_type_0, strides = var_7939, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_91_cast)[name = tensor("hidden_states_379_cast")]; + tensor var_7946 = const()[name = tensor("op_7946"), val = tensor([2, 768, 1, 4096])]; + tensor inputs_217_cast = reshape(shape = var_7946, x = hidden_states_379_cast)[name = tensor("inputs_217_cast")]; + tensor var_7956 = const()[name = tensor("op_7956"), val = tensor([1])]; + tensor channels_mean_217_cast = reduce_mean(axes = var_7956, keep_dims = var_7054, x = inputs_217_cast)[name = tensor("channels_mean_217_cast")]; + tensor zero_mean_217_cast = sub(x = inputs_217_cast, y = channels_mean_217_cast)[name = tensor("zero_mean_217_cast")]; + tensor zero_mean_sq_217_cast = mul(x = zero_mean_217_cast, y = zero_mean_217_cast)[name = tensor("zero_mean_sq_217_cast")]; + tensor var_7960 = const()[name = tensor("op_7960"), val = tensor([1])]; + tensor var_7961_cast = reduce_mean(axes = var_7960, keep_dims = var_7054, x = zero_mean_sq_217_cast)[name = tensor("op_7961_cast")]; + tensor var_7962_to_fp16 = const()[name = tensor("op_7962_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7963_cast = add(x = var_7961_cast, y = var_7962_to_fp16)[name = tensor("op_7963_cast")]; + tensor denom_217_epsilon_0_to_fp16 = const()[name = tensor("denom_217_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_217_cast = rsqrt(epsilon = denom_217_epsilon_0_to_fp16, x = var_7963_cast)[name = tensor("denom_217_cast")]; + tensor out_217_cast = mul(x = zero_mean_217_cast, y = denom_217_cast)[name = tensor("out_217_cast")]; + tensor var_7967_to_fp16 = const()[name = tensor("op_7967_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4240566592)))]; + tensor var_7968_cast = add(x = out_217_cast, y = var_7967_to_fp16)[name = tensor("op_7968_cast")]; + tensor var_7970_to_fp16 = const()[name = tensor("op_7970_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4240568192)))]; + tensor hidden_states_381_cast = mul(x = var_7968_cast, y = var_7970_to_fp16)[name = tensor("hidden_states_381_cast")]; + tensor var_7977 = const()[name = tensor("op_7977"), val = tensor([1, 1])]; + tensor var_7979 = const()[name = tensor("op_7979"), val = tensor([1, 1])]; + tensor q_145_pad_type_0 = const()[name = tensor("q_145_pad_type_0"), val = tensor("custom")]; + tensor q_145_pad_0 = const()[name = tensor("q_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4240569792)))]; + tensor q_145_cast = conv(dilations = var_7979, groups = var_7059, pad = q_145_pad_0, pad_type = q_145_pad_type_0, strides = var_7977, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_381_cast)[name = tensor("q_145_cast")]; + tensor var_7983 = const()[name = tensor("op_7983"), val = tensor([1, 1])]; + tensor var_7985 = const()[name = tensor("op_7985"), val = tensor([1, 1])]; + tensor k_145_pad_type_0 = const()[name = tensor("k_145_pad_type_0"), val = tensor("custom")]; + tensor k_145_pad_0 = const()[name = tensor("k_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241749504)))]; + tensor k_145_cast = conv(dilations = var_7985, groups = var_7059, pad = k_145_pad_0, pad_type = k_145_pad_type_0, strides = var_7983, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_381_cast)[name = tensor("k_145_cast")]; + tensor var_7989 = const()[name = tensor("op_7989"), val = tensor([1, 1])]; + tensor var_7991 = const()[name = tensor("op_7991"), val = tensor([1, 1])]; + tensor v_145_pad_type_0 = const()[name = tensor("v_145_pad_type_0"), val = tensor("custom")]; + tensor v_145_pad_0 = const()[name = tensor("v_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4242929216)))]; + tensor v_145_cast = conv(dilations = var_7991, groups = var_7059, pad = v_145_pad_0, pad_type = v_145_pad_type_0, strides = var_7989, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_381_cast)[name = tensor("v_145_cast")]; + tensor var_7995 = const()[name = tensor("op_7995"), val = tensor([2, 12, 64, -1])]; + tensor var_7996_cast = reshape(shape = var_7995, x = q_145_cast)[name = tensor("op_7996_cast")]; + tensor var_7997 = const()[name = tensor("op_7997"), val = tensor([2, 12, 64, -1])]; + tensor var_7998_cast = reshape(shape = var_7997, x = k_145_cast)[name = tensor("op_7998_cast")]; + tensor var_7999 = const()[name = tensor("op_7999"), val = tensor([2, 12, 64, -1])]; + tensor var_8000_cast = reshape(shape = var_7999, x = v_145_cast)[name = tensor("op_8000_cast")]; + tensor attn_weights_289_transpose_x_0 = const()[name = tensor("attn_weights_289_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_289_transpose_y_0 = const()[name = tensor("attn_weights_289_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_289_cast = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_7996_cast, y = var_7998_cast)[name = tensor("attn_weights_289_cast")]; + tensor attn_weights_291_cast = mul(x = attn_weights_289_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_291_cast")]; + tensor var_8004_cast = softmax(axis = var_7043, x = attn_weights_291_cast)[name = tensor("op_8004_cast")]; + tensor attn_145_transpose_x_0 = const()[name = tensor("attn_145_transpose_x_0"), val = tensor(false)]; + tensor attn_145_transpose_y_0 = const()[name = tensor("attn_145_transpose_y_0"), val = tensor(true)]; + tensor attn_145_cast = matmul(transpose_x = attn_145_transpose_x_0, transpose_y = attn_145_transpose_y_0, x = var_8000_cast, y = var_8004_cast)[name = tensor("attn_145_cast")]; + tensor var_8008 = const()[name = tensor("op_8008"), val = tensor([2, 768, 1, -1])]; + tensor input_607_cast = reshape(shape = var_8008, x = attn_145_cast)[name = tensor("input_607_cast")]; + tensor var_8013 = const()[name = tensor("op_8013"), val = tensor([1, 1])]; + tensor var_8015 = const()[name = tensor("op_8015"), val = tensor([1, 1])]; + tensor var_8017_pad_type_0 = const()[name = tensor("op_8017_pad_type_0"), val = tensor("custom")]; + tensor var_8017_pad_0 = const()[name = tensor("op_8017_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4244108928)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4245288640)))]; + tensor var_8017_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8015, groups = var_7059, pad = var_8017_pad_0, pad_type = var_8017_pad_type_0, strides = var_8013, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_607_cast)[name = tensor("op_8017_cast")]; + tensor inputs_219_cast = add(x = var_8017_cast, y = inputs_217_cast)[name = tensor("inputs_219_cast")]; + tensor var_8021 = const()[name = tensor("op_8021"), val = tensor([1])]; + tensor channels_mean_219_cast = reduce_mean(axes = var_8021, keep_dims = var_7054, x = inputs_219_cast)[name = tensor("channels_mean_219_cast")]; + tensor zero_mean_219_cast = sub(x = inputs_219_cast, y = channels_mean_219_cast)[name = tensor("zero_mean_219_cast")]; + tensor zero_mean_sq_219_cast = mul(x = zero_mean_219_cast, y = zero_mean_219_cast)[name = tensor("zero_mean_sq_219_cast")]; + tensor var_8025 = const()[name = tensor("op_8025"), val = tensor([1])]; + tensor var_8026_cast = reduce_mean(axes = var_8025, keep_dims = var_7054, x = zero_mean_sq_219_cast)[name = tensor("op_8026_cast")]; + tensor var_8027_to_fp16 = const()[name = tensor("op_8027_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8028_cast = add(x = var_8026_cast, y = var_8027_to_fp16)[name = tensor("op_8028_cast")]; + tensor denom_219_epsilon_0_to_fp16 = const()[name = tensor("denom_219_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_219_cast = rsqrt(epsilon = denom_219_epsilon_0_to_fp16, x = var_8028_cast)[name = tensor("denom_219_cast")]; + tensor out_219_cast = mul(x = zero_mean_219_cast, y = denom_219_cast)[name = tensor("out_219_cast")]; + tensor var_8032_to_fp16 = const()[name = tensor("op_8032_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4245290240)))]; + tensor var_8033_cast = add(x = out_219_cast, y = var_8032_to_fp16)[name = tensor("op_8033_cast")]; + tensor var_8035_to_fp16 = const()[name = tensor("op_8035_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4245291840)))]; + tensor hidden_states_383_cast = mul(x = var_8033_cast, y = var_8035_to_fp16)[name = tensor("hidden_states_383_cast")]; + tensor var_8042 = const()[name = tensor("op_8042"), val = tensor([1, 1])]; + tensor var_8044 = const()[name = tensor("op_8044"), val = tensor([1, 1])]; + tensor q_147_pad_type_0 = const()[name = tensor("q_147_pad_type_0"), val = tensor("custom")]; + tensor q_147_pad_0 = const()[name = tensor("q_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4245293440)))]; + tensor q_147_cast = conv(dilations = var_8044, groups = var_7059, pad = q_147_pad_0, pad_type = q_147_pad_type_0, strides = var_8042, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_383_cast)[name = tensor("q_147_cast")]; + tensor var_8048 = const()[name = tensor("op_8048"), val = tensor([1, 1])]; + tensor var_8050 = const()[name = tensor("op_8050"), val = tensor([1, 1])]; + tensor k_147_pad_type_0 = const()[name = tensor("k_147_pad_type_0"), val = tensor("custom")]; + tensor k_147_pad_0 = const()[name = tensor("k_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4246473152)))]; + tensor k_147_cast = conv(dilations = var_8050, groups = var_7059, pad = k_147_pad_0, pad_type = k_147_pad_type_0, strides = var_8048, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_147_cast")]; + tensor var_8054 = const()[name = tensor("op_8054"), val = tensor([1, 1])]; + tensor var_8056 = const()[name = tensor("op_8056"), val = tensor([1, 1])]; + tensor v_147_pad_type_0 = const()[name = tensor("v_147_pad_type_0"), val = tensor("custom")]; + tensor v_147_pad_0 = const()[name = tensor("v_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4248439296)))]; + tensor v_147_cast = conv(dilations = var_8056, groups = var_7059, pad = v_147_pad_0, pad_type = v_147_pad_type_0, strides = var_8054, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_147_cast")]; + tensor var_8060 = const()[name = tensor("op_8060"), val = tensor([2, 12, 64, -1])]; + tensor var_8061_cast = reshape(shape = var_8060, x = q_147_cast)[name = tensor("op_8061_cast")]; + tensor var_8062 = const()[name = tensor("op_8062"), val = tensor([2, 12, 64, -1])]; + tensor var_8063_cast = reshape(shape = var_8062, x = k_147_cast)[name = tensor("op_8063_cast")]; + tensor var_8064 = const()[name = tensor("op_8064"), val = tensor([2, 12, 64, -1])]; + tensor var_8065_cast = reshape(shape = var_8064, x = v_147_cast)[name = tensor("op_8065_cast")]; + tensor attn_weights_293_transpose_x_0 = const()[name = tensor("attn_weights_293_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_293_transpose_y_0 = const()[name = tensor("attn_weights_293_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_293_cast = matmul(transpose_x = attn_weights_293_transpose_x_0, transpose_y = attn_weights_293_transpose_y_0, x = var_8061_cast, y = var_8063_cast)[name = tensor("attn_weights_293_cast")]; + tensor attn_weights_295_cast = mul(x = attn_weights_293_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_295_cast")]; + tensor var_8069_cast = softmax(axis = var_7043, x = attn_weights_295_cast)[name = tensor("op_8069_cast")]; + tensor attn_147_transpose_x_0 = const()[name = tensor("attn_147_transpose_x_0"), val = tensor(false)]; + tensor attn_147_transpose_y_0 = const()[name = tensor("attn_147_transpose_y_0"), val = tensor(true)]; + tensor attn_147_cast = matmul(transpose_x = attn_147_transpose_x_0, transpose_y = attn_147_transpose_y_0, x = var_8065_cast, y = var_8069_cast)[name = tensor("attn_147_cast")]; + tensor var_8073 = const()[name = tensor("op_8073"), val = tensor([2, 768, 1, -1])]; + tensor input_609_cast = reshape(shape = var_8073, x = attn_147_cast)[name = tensor("input_609_cast")]; + tensor var_8078 = const()[name = tensor("op_8078"), val = tensor([1, 1])]; + tensor var_8080 = const()[name = tensor("op_8080"), val = tensor([1, 1])]; + tensor var_8082_pad_type_0 = const()[name = tensor("op_8082_pad_type_0"), val = tensor("custom")]; + tensor var_8082_pad_0 = const()[name = tensor("op_8082_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4250405440)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4251585152)))]; + tensor var_8082_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8080, groups = var_7059, pad = var_8082_pad_0, pad_type = var_8082_pad_type_0, strides = var_8078, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_609_cast)[name = tensor("op_8082_cast")]; + tensor inputs_221_cast = add(x = var_8082_cast, y = inputs_219_cast)[name = tensor("inputs_221_cast")]; + tensor var_8086 = const()[name = tensor("op_8086"), val = tensor([1])]; + tensor channels_mean_221_cast = reduce_mean(axes = var_8086, keep_dims = var_7054, x = inputs_221_cast)[name = tensor("channels_mean_221_cast")]; + tensor zero_mean_221_cast = sub(x = inputs_221_cast, y = channels_mean_221_cast)[name = tensor("zero_mean_221_cast")]; + tensor zero_mean_sq_221_cast = mul(x = zero_mean_221_cast, y = zero_mean_221_cast)[name = tensor("zero_mean_sq_221_cast")]; + tensor var_8090 = const()[name = tensor("op_8090"), val = tensor([1])]; + tensor var_8091_cast = reduce_mean(axes = var_8090, keep_dims = var_7054, x = zero_mean_sq_221_cast)[name = tensor("op_8091_cast")]; + tensor var_8092_to_fp16 = const()[name = tensor("op_8092_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8093_cast = add(x = var_8091_cast, y = var_8092_to_fp16)[name = tensor("op_8093_cast")]; + tensor denom_221_epsilon_0_to_fp16 = const()[name = tensor("denom_221_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_221_cast = rsqrt(epsilon = denom_221_epsilon_0_to_fp16, x = var_8093_cast)[name = tensor("denom_221_cast")]; + tensor out_221_cast = mul(x = zero_mean_221_cast, y = denom_221_cast)[name = tensor("out_221_cast")]; + tensor var_8097_to_fp16 = const()[name = tensor("op_8097_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4251586752)))]; + tensor var_8098_cast = add(x = out_221_cast, y = var_8097_to_fp16)[name = tensor("op_8098_cast")]; + tensor var_8100_to_fp16 = const()[name = tensor("op_8100_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4251588352)))]; + tensor input_611_cast = mul(x = var_8098_cast, y = var_8100_to_fp16)[name = tensor("input_611_cast")]; + tensor var_8108 = const()[name = tensor("op_8108"), val = tensor([1, 1])]; + tensor var_8110 = const()[name = tensor("op_8110"), val = tensor([1, 1])]; + tensor var_8112_pad_type_0 = const()[name = tensor("op_8112_pad_type_0"), val = tensor("custom")]; + tensor var_8112_pad_0 = const()[name = tensor("op_8112_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4251589952)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4261027200)))]; + tensor var_8112_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_8110, groups = var_7059, pad = var_8112_pad_0, pad_type = var_8112_pad_type_0, strides = var_8108, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_611_cast)[name = tensor("op_8112_cast")]; + tensor var_8113_split_sizes_0 = const()[name = tensor("op_8113_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_8113_axis_0 = const()[name = tensor("op_8113_axis_0"), val = tensor(1)]; + tensor var_8113_cast_0, tensor var_8113_cast_1 = split(axis = var_8113_axis_0, split_sizes = var_8113_split_sizes_0, x = var_8112_cast)[name = tensor("op_8113_cast")]; + tensor var_8115_mode_0 = const()[name = tensor("op_8115_mode_0"), val = tensor("EXACT")]; + tensor var_8115_cast = gelu(mode = var_8115_mode_0, x = var_8113_cast_1)[name = tensor("op_8115_cast")]; + tensor input_613_cast = mul(x = var_8113_cast_0, y = var_8115_cast)[name = tensor("input_613_cast")]; + tensor var_8119 = const()[name = tensor("op_8119"), val = tensor([1, 1])]; + tensor var_8121 = const()[name = tensor("op_8121"), val = tensor([1, 1])]; + tensor var_8123_pad_type_0 = const()[name = tensor("op_8123_pad_type_0"), val = tensor("custom")]; + tensor var_8123_pad_0 = const()[name = tensor("op_8123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4261039552)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4265758208)))]; + tensor var_8123_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_8121, groups = var_7059, pad = var_8123_pad_0, pad_type = var_8123_pad_type_0, strides = var_8119, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_613_cast)[name = tensor("op_8123_cast")]; + tensor inputs_223_cast = add(x = var_8123_cast, y = inputs_221_cast)[name = tensor("inputs_223_cast")]; + tensor var_8133 = const()[name = tensor("op_8133"), val = tensor([1])]; + tensor channels_mean_223_cast = reduce_mean(axes = var_8133, keep_dims = var_7054, x = inputs_223_cast)[name = tensor("channels_mean_223_cast")]; + tensor zero_mean_223_cast = sub(x = inputs_223_cast, y = channels_mean_223_cast)[name = tensor("zero_mean_223_cast")]; + tensor zero_mean_sq_223_cast = mul(x = zero_mean_223_cast, y = zero_mean_223_cast)[name = tensor("zero_mean_sq_223_cast")]; + tensor var_8137 = const()[name = tensor("op_8137"), val = tensor([1])]; + tensor var_8138_cast = reduce_mean(axes = var_8137, keep_dims = var_7054, x = zero_mean_sq_223_cast)[name = tensor("op_8138_cast")]; + tensor var_8139_to_fp16 = const()[name = tensor("op_8139_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8140_cast = add(x = var_8138_cast, y = var_8139_to_fp16)[name = tensor("op_8140_cast")]; + tensor denom_223_epsilon_0_to_fp16 = const()[name = tensor("denom_223_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_223_cast = rsqrt(epsilon = denom_223_epsilon_0_to_fp16, x = var_8140_cast)[name = tensor("denom_223_cast")]; + tensor out_223_cast = mul(x = zero_mean_223_cast, y = denom_223_cast)[name = tensor("out_223_cast")]; + tensor var_8144_to_fp16 = const()[name = tensor("op_8144_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4265759808)))]; + tensor var_8145_cast = add(x = out_223_cast, y = var_8144_to_fp16)[name = tensor("op_8145_cast")]; + tensor var_8147_to_fp16 = const()[name = tensor("op_8147_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4265761408)))]; + tensor hidden_states_387_cast = mul(x = var_8145_cast, y = var_8147_to_fp16)[name = tensor("hidden_states_387_cast")]; + tensor var_8154 = const()[name = tensor("op_8154"), val = tensor([1, 1])]; + tensor var_8156 = const()[name = tensor("op_8156"), val = tensor([1, 1])]; + tensor q_149_pad_type_0 = const()[name = tensor("q_149_pad_type_0"), val = tensor("custom")]; + tensor q_149_pad_0 = const()[name = tensor("q_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4265763008)))]; + tensor q_149_cast = conv(dilations = var_8156, groups = var_7059, pad = q_149_pad_0, pad_type = q_149_pad_type_0, strides = var_8154, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_387_cast)[name = tensor("q_149_cast")]; + tensor var_8160 = const()[name = tensor("op_8160"), val = tensor([1, 1])]; + tensor var_8162 = const()[name = tensor("op_8162"), val = tensor([1, 1])]; + tensor k_149_pad_type_0 = const()[name = tensor("k_149_pad_type_0"), val = tensor("custom")]; + tensor k_149_pad_0 = const()[name = tensor("k_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4266942720)))]; + tensor k_149_cast = conv(dilations = var_8162, groups = var_7059, pad = k_149_pad_0, pad_type = k_149_pad_type_0, strides = var_8160, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_387_cast)[name = tensor("k_149_cast")]; + tensor var_8166 = const()[name = tensor("op_8166"), val = tensor([1, 1])]; + tensor var_8168 = const()[name = tensor("op_8168"), val = tensor([1, 1])]; + tensor v_149_pad_type_0 = const()[name = tensor("v_149_pad_type_0"), val = tensor("custom")]; + tensor v_149_pad_0 = const()[name = tensor("v_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4268122432)))]; + tensor v_149_cast = conv(dilations = var_8168, groups = var_7059, pad = v_149_pad_0, pad_type = v_149_pad_type_0, strides = var_8166, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_387_cast)[name = tensor("v_149_cast")]; + tensor var_8172 = const()[name = tensor("op_8172"), val = tensor([2, 12, 64, -1])]; + tensor var_8173_cast = reshape(shape = var_8172, x = q_149_cast)[name = tensor("op_8173_cast")]; + tensor var_8174 = const()[name = tensor("op_8174"), val = tensor([2, 12, 64, -1])]; + tensor var_8175_cast = reshape(shape = var_8174, x = k_149_cast)[name = tensor("op_8175_cast")]; + tensor var_8176 = const()[name = tensor("op_8176"), val = tensor([2, 12, 64, -1])]; + tensor var_8177_cast = reshape(shape = var_8176, x = v_149_cast)[name = tensor("op_8177_cast")]; + tensor attn_weights_297_transpose_x_0 = const()[name = tensor("attn_weights_297_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_297_transpose_y_0 = const()[name = tensor("attn_weights_297_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_297_cast = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_8173_cast, y = var_8175_cast)[name = tensor("attn_weights_297_cast")]; + tensor attn_weights_299_cast = mul(x = attn_weights_297_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_299_cast")]; + tensor var_8181_cast = softmax(axis = var_7043, x = attn_weights_299_cast)[name = tensor("op_8181_cast")]; + tensor attn_149_transpose_x_0 = const()[name = tensor("attn_149_transpose_x_0"), val = tensor(false)]; + tensor attn_149_transpose_y_0 = const()[name = tensor("attn_149_transpose_y_0"), val = tensor(true)]; + tensor attn_149_cast = matmul(transpose_x = attn_149_transpose_x_0, transpose_y = attn_149_transpose_y_0, x = var_8177_cast, y = var_8181_cast)[name = tensor("attn_149_cast")]; + tensor var_8185 = const()[name = tensor("op_8185"), val = tensor([2, 768, 1, -1])]; + tensor input_615_cast = reshape(shape = var_8185, x = attn_149_cast)[name = tensor("input_615_cast")]; + tensor var_8190 = const()[name = tensor("op_8190"), val = tensor([1, 1])]; + tensor var_8192 = const()[name = tensor("op_8192"), val = tensor([1, 1])]; + tensor var_8194_pad_type_0 = const()[name = tensor("op_8194_pad_type_0"), val = tensor("custom")]; + tensor var_8194_pad_0 = const()[name = tensor("op_8194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4269302144)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4270481856)))]; + tensor var_8194_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_8192, groups = var_7059, pad = var_8194_pad_0, pad_type = var_8194_pad_type_0, strides = var_8190, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_615_cast)[name = tensor("op_8194_cast")]; + tensor inputs_225_cast = add(x = var_8194_cast, y = inputs_223_cast)[name = tensor("inputs_225_cast")]; + tensor var_8198 = const()[name = tensor("op_8198"), val = tensor([1])]; + tensor channels_mean_225_cast = reduce_mean(axes = var_8198, keep_dims = var_7054, x = inputs_225_cast)[name = tensor("channels_mean_225_cast")]; + tensor zero_mean_225_cast = sub(x = inputs_225_cast, y = channels_mean_225_cast)[name = tensor("zero_mean_225_cast")]; + tensor zero_mean_sq_225_cast = mul(x = zero_mean_225_cast, y = zero_mean_225_cast)[name = tensor("zero_mean_sq_225_cast")]; + tensor var_8202 = const()[name = tensor("op_8202"), val = tensor([1])]; + tensor var_8203_cast = reduce_mean(axes = var_8202, keep_dims = var_7054, x = zero_mean_sq_225_cast)[name = tensor("op_8203_cast")]; + tensor var_8204_to_fp16 = const()[name = tensor("op_8204_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8205_cast = add(x = var_8203_cast, y = var_8204_to_fp16)[name = tensor("op_8205_cast")]; + tensor denom_225_epsilon_0_to_fp16 = const()[name = tensor("denom_225_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_225_cast = rsqrt(epsilon = denom_225_epsilon_0_to_fp16, x = var_8205_cast)[name = tensor("denom_225_cast")]; + tensor out_225_cast = mul(x = zero_mean_225_cast, y = denom_225_cast)[name = tensor("out_225_cast")]; + tensor var_8209_to_fp16 = const()[name = tensor("op_8209_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4270483456)))]; + tensor var_8210_cast = add(x = out_225_cast, y = var_8209_to_fp16)[name = tensor("op_8210_cast")]; + tensor var_8212_to_fp16 = const()[name = tensor("op_8212_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4270485056)))]; + tensor hidden_states_389_cast = mul(x = var_8210_cast, y = var_8212_to_fp16)[name = tensor("hidden_states_389_cast")]; + tensor var_8219 = const()[name = tensor("op_8219"), val = tensor([1, 1])]; + tensor var_8221 = const()[name = tensor("op_8221"), val = tensor([1, 1])]; + tensor q_151_pad_type_0 = const()[name = tensor("q_151_pad_type_0"), val = tensor("custom")]; + tensor q_151_pad_0 = const()[name = tensor("q_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4270486656)))]; + tensor q_151_cast = conv(dilations = var_8221, groups = var_7059, pad = q_151_pad_0, pad_type = q_151_pad_type_0, strides = var_8219, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_389_cast)[name = tensor("q_151_cast")]; + tensor var_8225 = const()[name = tensor("op_8225"), val = tensor([1, 1])]; + tensor var_8227 = const()[name = tensor("op_8227"), val = tensor([1, 1])]; + tensor k_151_pad_type_0 = const()[name = tensor("k_151_pad_type_0"), val = tensor("custom")]; + tensor k_151_pad_0 = const()[name = tensor("k_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271666368)))]; + tensor k_151_cast = conv(dilations = var_8227, groups = var_7059, pad = k_151_pad_0, pad_type = k_151_pad_type_0, strides = var_8225, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_151_cast")]; + tensor var_8231 = const()[name = tensor("op_8231"), val = tensor([1, 1])]; + tensor var_8233 = const()[name = tensor("op_8233"), val = tensor([1, 1])]; + tensor v_151_pad_type_0 = const()[name = tensor("v_151_pad_type_0"), val = tensor("custom")]; + tensor v_151_pad_0 = const()[name = tensor("v_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4273632512)))]; + tensor v_151_cast = conv(dilations = var_8233, groups = var_7059, pad = v_151_pad_0, pad_type = v_151_pad_type_0, strides = var_8231, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_151_cast")]; + tensor var_8237 = const()[name = tensor("op_8237"), val = tensor([2, 12, 64, -1])]; + tensor var_8238_cast = reshape(shape = var_8237, x = q_151_cast)[name = tensor("op_8238_cast")]; + tensor var_8239 = const()[name = tensor("op_8239"), val = tensor([2, 12, 64, -1])]; + tensor var_8240_cast = reshape(shape = var_8239, x = k_151_cast)[name = tensor("op_8240_cast")]; + tensor var_8241 = const()[name = tensor("op_8241"), val = tensor([2, 12, 64, -1])]; + tensor var_8242_cast = reshape(shape = var_8241, x = v_151_cast)[name = tensor("op_8242_cast")]; + tensor attn_weights_301_transpose_x_0 = const()[name = tensor("attn_weights_301_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_301_transpose_y_0 = const()[name = tensor("attn_weights_301_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_301_cast = matmul(transpose_x = attn_weights_301_transpose_x_0, transpose_y = attn_weights_301_transpose_y_0, x = var_8238_cast, y = var_8240_cast)[name = tensor("attn_weights_301_cast")]; + tensor attn_weights_303_cast = mul(x = attn_weights_301_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_303_cast")]; + tensor var_8246_cast = softmax(axis = var_7043, x = attn_weights_303_cast)[name = tensor("op_8246_cast")]; + tensor attn_151_transpose_x_0 = const()[name = tensor("attn_151_transpose_x_0"), val = tensor(false)]; + tensor attn_151_transpose_y_0 = const()[name = tensor("attn_151_transpose_y_0"), val = tensor(true)]; + tensor attn_151_cast = matmul(transpose_x = attn_151_transpose_x_0, transpose_y = attn_151_transpose_y_0, x = var_8242_cast, y = var_8246_cast)[name = tensor("attn_151_cast")]; + tensor var_8250 = const()[name = tensor("op_8250"), val = tensor([2, 768, 1, -1])]; + tensor input_617_cast = reshape(shape = var_8250, x = attn_151_cast)[name = tensor("input_617_cast")]; + tensor var_8255 = const()[name = tensor("op_8255"), val = tensor([1, 1])]; + tensor var_8257 = const()[name = tensor("op_8257"), val = tensor([1, 1])]; + tensor var_8259_pad_type_0 = const()[name = tensor("op_8259_pad_type_0"), val = tensor("custom")]; + tensor var_8259_pad_0 = const()[name = tensor("op_8259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4275598656)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4276778368)))]; + tensor var_8259_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_8257, groups = var_7059, pad = var_8259_pad_0, pad_type = var_8259_pad_type_0, strides = var_8255, weight = up_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_617_cast)[name = tensor("op_8259_cast")]; + tensor inputs_227_cast = add(x = var_8259_cast, y = inputs_225_cast)[name = tensor("inputs_227_cast")]; + tensor var_8263 = const()[name = tensor("op_8263"), val = tensor([1])]; + tensor channels_mean_227_cast = reduce_mean(axes = var_8263, keep_dims = var_7054, x = inputs_227_cast)[name = tensor("channels_mean_227_cast")]; + tensor zero_mean_227_cast = sub(x = inputs_227_cast, y = channels_mean_227_cast)[name = tensor("zero_mean_227_cast")]; + tensor zero_mean_sq_227_cast = mul(x = zero_mean_227_cast, y = zero_mean_227_cast)[name = tensor("zero_mean_sq_227_cast")]; + tensor var_8267 = const()[name = tensor("op_8267"), val = tensor([1])]; + tensor var_8268_cast = reduce_mean(axes = var_8267, keep_dims = var_7054, x = zero_mean_sq_227_cast)[name = tensor("op_8268_cast")]; + tensor var_8269_to_fp16 = const()[name = tensor("op_8269_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8270_cast = add(x = var_8268_cast, y = var_8269_to_fp16)[name = tensor("op_8270_cast")]; + tensor denom_227_epsilon_0_to_fp16 = const()[name = tensor("denom_227_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_227_cast = rsqrt(epsilon = denom_227_epsilon_0_to_fp16, x = var_8270_cast)[name = tensor("denom_227_cast")]; + tensor out_227_cast = mul(x = zero_mean_227_cast, y = denom_227_cast)[name = tensor("out_227_cast")]; + tensor var_8274_to_fp16 = const()[name = tensor("op_8274_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4276779968)))]; + tensor var_8275_cast = add(x = out_227_cast, y = var_8274_to_fp16)[name = tensor("op_8275_cast")]; + tensor var_8277_to_fp16 = const()[name = tensor("op_8277_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4276781568)))]; + tensor input_619_cast = mul(x = var_8275_cast, y = var_8277_to_fp16)[name = tensor("input_619_cast")]; + tensor var_8285 = const()[name = tensor("op_8285"), val = tensor([1, 1])]; + tensor var_8287 = const()[name = tensor("op_8287"), val = tensor([1, 1])]; + tensor var_8289_pad_type_0 = const()[name = tensor("op_8289_pad_type_0"), val = tensor("custom")]; + tensor var_8289_pad_0 = const()[name = tensor("op_8289_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4276783168)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4286220416)))]; + tensor var_8289_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_8287, groups = var_7059, pad = var_8289_pad_0, pad_type = var_8289_pad_type_0, strides = var_8285, weight = up_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_619_cast)[name = tensor("op_8289_cast")]; + tensor var_8290_split_sizes_0 = const()[name = tensor("op_8290_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_8290_axis_0 = const()[name = tensor("op_8290_axis_0"), val = tensor(1)]; + tensor var_8290_cast_0, tensor var_8290_cast_1 = split(axis = var_8290_axis_0, split_sizes = var_8290_split_sizes_0, x = var_8289_cast)[name = tensor("op_8290_cast")]; + tensor var_8292_mode_0 = const()[name = tensor("op_8292_mode_0"), val = tensor("EXACT")]; + tensor var_8292_cast = gelu(mode = var_8292_mode_0, x = var_8290_cast_1)[name = tensor("op_8292_cast")]; + tensor input_621_cast = mul(x = var_8290_cast_0, y = var_8292_cast)[name = tensor("input_621_cast")]; + tensor var_8296 = const()[name = tensor("op_8296"), val = tensor([1, 1])]; + tensor var_8298 = const()[name = tensor("op_8298"), val = tensor([1, 1])]; + tensor var_8300_pad_type_0 = const()[name = tensor("op_8300_pad_type_0"), val = tensor("custom")]; + tensor var_8300_pad_0 = const()[name = tensor("op_8300_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4286232768)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4290951424)))]; + tensor var_8300_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_8298, groups = var_7059, pad = var_8300_pad_0, pad_type = var_8300_pad_type_0, strides = var_8296, weight = up_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_621_cast)[name = tensor("op_8300_cast")]; + tensor inputs_229_cast = add(x = var_8300_cast, y = inputs_227_cast)[name = tensor("inputs_229_cast")]; + tensor var_8310 = const()[name = tensor("op_8310"), val = tensor([1])]; + tensor channels_mean_229_cast = reduce_mean(axes = var_8310, keep_dims = var_7054, x = inputs_229_cast)[name = tensor("channels_mean_229_cast")]; + tensor zero_mean_229_cast = sub(x = inputs_229_cast, y = channels_mean_229_cast)[name = tensor("zero_mean_229_cast")]; + tensor zero_mean_sq_229_cast = mul(x = zero_mean_229_cast, y = zero_mean_229_cast)[name = tensor("zero_mean_sq_229_cast")]; + tensor var_8314 = const()[name = tensor("op_8314"), val = tensor([1])]; + tensor var_8315_cast = reduce_mean(axes = var_8314, keep_dims = var_7054, x = zero_mean_sq_229_cast)[name = tensor("op_8315_cast")]; + tensor var_8316_to_fp16 = const()[name = tensor("op_8316_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8317_cast = add(x = var_8315_cast, y = var_8316_to_fp16)[name = tensor("op_8317_cast")]; + tensor denom_229_epsilon_0_to_fp16 = const()[name = tensor("denom_229_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_229_cast = rsqrt(epsilon = denom_229_epsilon_0_to_fp16, x = var_8317_cast)[name = tensor("denom_229_cast")]; + tensor out_229_cast = mul(x = zero_mean_229_cast, y = denom_229_cast)[name = tensor("out_229_cast")]; + tensor var_8321_to_fp16 = const()[name = tensor("op_8321_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4290953024)))]; + tensor var_8322_cast = add(x = out_229_cast, y = var_8321_to_fp16)[name = tensor("op_8322_cast")]; + tensor var_8324_to_fp16 = const()[name = tensor("op_8324_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4290954624)))]; + tensor hidden_states_393_cast = mul(x = var_8322_cast, y = var_8324_to_fp16)[name = tensor("hidden_states_393_cast")]; + tensor var_8331 = const()[name = tensor("op_8331"), val = tensor([1, 1])]; + tensor var_8333 = const()[name = tensor("op_8333"), val = tensor([1, 1])]; + tensor q_153_pad_type_0 = const()[name = tensor("q_153_pad_type_0"), val = tensor("custom")]; + tensor q_153_pad_0 = const()[name = tensor("q_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4290956224)))]; + tensor q_153_cast = conv(dilations = var_8333, groups = var_7059, pad = q_153_pad_0, pad_type = q_153_pad_type_0, strides = var_8331, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_393_cast)[name = tensor("q_153_cast")]; + tensor var_8337 = const()[name = tensor("op_8337"), val = tensor([1, 1])]; + tensor var_8339 = const()[name = tensor("op_8339"), val = tensor([1, 1])]; + tensor k_153_pad_type_0 = const()[name = tensor("k_153_pad_type_0"), val = tensor("custom")]; + tensor k_153_pad_0 = const()[name = tensor("k_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4292135936)))]; + tensor k_153_cast = conv(dilations = var_8339, groups = var_7059, pad = k_153_pad_0, pad_type = k_153_pad_type_0, strides = var_8337, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_393_cast)[name = tensor("k_153_cast")]; + tensor var_8343 = const()[name = tensor("op_8343"), val = tensor([1, 1])]; + tensor var_8345 = const()[name = tensor("op_8345"), val = tensor([1, 1])]; + tensor v_153_pad_type_0 = const()[name = tensor("v_153_pad_type_0"), val = tensor("custom")]; + tensor v_153_pad_0 = const()[name = tensor("v_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4293315648)))]; + tensor v_153_cast = conv(dilations = var_8345, groups = var_7059, pad = v_153_pad_0, pad_type = v_153_pad_type_0, strides = var_8343, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_393_cast)[name = tensor("v_153_cast")]; + tensor var_8349 = const()[name = tensor("op_8349"), val = tensor([2, 12, 64, -1])]; + tensor var_8350_cast = reshape(shape = var_8349, x = q_153_cast)[name = tensor("op_8350_cast")]; + tensor var_8351 = const()[name = tensor("op_8351"), val = tensor([2, 12, 64, -1])]; + tensor var_8352_cast = reshape(shape = var_8351, x = k_153_cast)[name = tensor("op_8352_cast")]; + tensor var_8353 = const()[name = tensor("op_8353"), val = tensor([2, 12, 64, -1])]; + tensor var_8354_cast = reshape(shape = var_8353, x = v_153_cast)[name = tensor("op_8354_cast")]; + tensor attn_weights_305_transpose_x_0 = const()[name = tensor("attn_weights_305_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_305_transpose_y_0 = const()[name = tensor("attn_weights_305_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_305_cast = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_8350_cast, y = var_8352_cast)[name = tensor("attn_weights_305_cast")]; + tensor attn_weights_307_cast = mul(x = attn_weights_305_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_307_cast")]; + tensor var_8358_cast = softmax(axis = var_7043, x = attn_weights_307_cast)[name = tensor("op_8358_cast")]; + tensor attn_153_transpose_x_0 = const()[name = tensor("attn_153_transpose_x_0"), val = tensor(false)]; + tensor attn_153_transpose_y_0 = const()[name = tensor("attn_153_transpose_y_0"), val = tensor(true)]; + tensor attn_153_cast = matmul(transpose_x = attn_153_transpose_x_0, transpose_y = attn_153_transpose_y_0, x = var_8354_cast, y = var_8358_cast)[name = tensor("attn_153_cast")]; + tensor var_8362 = const()[name = tensor("op_8362"), val = tensor([2, 768, 1, -1])]; + tensor input_623_cast = reshape(shape = var_8362, x = attn_153_cast)[name = tensor("input_623_cast")]; + tensor var_8367 = const()[name = tensor("op_8367"), val = tensor([1, 1])]; + tensor var_8369 = const()[name = tensor("op_8369"), val = tensor([1, 1])]; + tensor var_8371_pad_type_0 = const()[name = tensor("op_8371_pad_type_0"), val = tensor("custom")]; + tensor var_8371_pad_0 = const()[name = tensor("op_8371_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4294495360)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4295675072)))]; + tensor var_8371_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_8369, groups = var_7059, pad = var_8371_pad_0, pad_type = var_8371_pad_type_0, strides = var_8367, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_623_cast)[name = tensor("op_8371_cast")]; + tensor inputs_231_cast = add(x = var_8371_cast, y = inputs_229_cast)[name = tensor("inputs_231_cast")]; + tensor var_8375 = const()[name = tensor("op_8375"), val = tensor([1])]; + tensor channels_mean_231_cast = reduce_mean(axes = var_8375, keep_dims = var_7054, x = inputs_231_cast)[name = tensor("channels_mean_231_cast")]; + tensor zero_mean_231_cast = sub(x = inputs_231_cast, y = channels_mean_231_cast)[name = tensor("zero_mean_231_cast")]; + tensor zero_mean_sq_231_cast = mul(x = zero_mean_231_cast, y = zero_mean_231_cast)[name = tensor("zero_mean_sq_231_cast")]; + tensor var_8379 = const()[name = tensor("op_8379"), val = tensor([1])]; + tensor var_8380_cast = reduce_mean(axes = var_8379, keep_dims = var_7054, x = zero_mean_sq_231_cast)[name = tensor("op_8380_cast")]; + tensor var_8381_to_fp16 = const()[name = tensor("op_8381_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8382_cast = add(x = var_8380_cast, y = var_8381_to_fp16)[name = tensor("op_8382_cast")]; + tensor denom_231_epsilon_0_to_fp16 = const()[name = tensor("denom_231_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_231_cast = rsqrt(epsilon = denom_231_epsilon_0_to_fp16, x = var_8382_cast)[name = tensor("denom_231_cast")]; + tensor out_231_cast = mul(x = zero_mean_231_cast, y = denom_231_cast)[name = tensor("out_231_cast")]; + tensor var_8386_to_fp16 = const()[name = tensor("op_8386_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4295676672)))]; + tensor var_8387_cast = add(x = out_231_cast, y = var_8386_to_fp16)[name = tensor("op_8387_cast")]; + tensor var_8389_to_fp16 = const()[name = tensor("op_8389_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4295678272)))]; + tensor hidden_states_395_cast = mul(x = var_8387_cast, y = var_8389_to_fp16)[name = tensor("hidden_states_395_cast")]; + tensor var_8396 = const()[name = tensor("op_8396"), val = tensor([1, 1])]; + tensor var_8398 = const()[name = tensor("op_8398"), val = tensor([1, 1])]; + tensor q_155_pad_type_0 = const()[name = tensor("q_155_pad_type_0"), val = tensor("custom")]; + tensor q_155_pad_0 = const()[name = tensor("q_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4295679872)))]; + tensor q_155_cast = conv(dilations = var_8398, groups = var_7059, pad = q_155_pad_0, pad_type = q_155_pad_type_0, strides = var_8396, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_395_cast)[name = tensor("q_155_cast")]; + tensor var_8402 = const()[name = tensor("op_8402"), val = tensor([1, 1])]; + tensor var_8404 = const()[name = tensor("op_8404"), val = tensor([1, 1])]; + tensor k_155_pad_type_0 = const()[name = tensor("k_155_pad_type_0"), val = tensor("custom")]; + tensor k_155_pad_0 = const()[name = tensor("k_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4296859584)))]; + tensor k_155_cast = conv(dilations = var_8404, groups = var_7059, pad = k_155_pad_0, pad_type = k_155_pad_type_0, strides = var_8402, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_155_cast")]; + tensor var_8408 = const()[name = tensor("op_8408"), val = tensor([1, 1])]; + tensor var_8410 = const()[name = tensor("op_8410"), val = tensor([1, 1])]; + tensor v_155_pad_type_0 = const()[name = tensor("v_155_pad_type_0"), val = tensor("custom")]; + tensor v_155_pad_0 = const()[name = tensor("v_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4298825728)))]; + tensor v_155_cast = conv(dilations = var_8410, groups = var_7059, pad = v_155_pad_0, pad_type = v_155_pad_type_0, strides = var_8408, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_155_cast")]; + tensor var_8414 = const()[name = tensor("op_8414"), val = tensor([2, 12, 64, -1])]; + tensor var_8415_cast = reshape(shape = var_8414, x = q_155_cast)[name = tensor("op_8415_cast")]; + tensor var_8416 = const()[name = tensor("op_8416"), val = tensor([2, 12, 64, -1])]; + tensor var_8417_cast = reshape(shape = var_8416, x = k_155_cast)[name = tensor("op_8417_cast")]; + tensor var_8418 = const()[name = tensor("op_8418"), val = tensor([2, 12, 64, -1])]; + tensor var_8419_cast = reshape(shape = var_8418, x = v_155_cast)[name = tensor("op_8419_cast")]; + tensor attn_weights_309_transpose_x_0 = const()[name = tensor("attn_weights_309_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_309_transpose_y_0 = const()[name = tensor("attn_weights_309_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_309_cast = matmul(transpose_x = attn_weights_309_transpose_x_0, transpose_y = attn_weights_309_transpose_y_0, x = var_8415_cast, y = var_8417_cast)[name = tensor("attn_weights_309_cast")]; + tensor attn_weights_311_cast = mul(x = attn_weights_309_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_311_cast")]; + tensor var_8423_cast = softmax(axis = var_7043, x = attn_weights_311_cast)[name = tensor("op_8423_cast")]; + tensor attn_155_transpose_x_0 = const()[name = tensor("attn_155_transpose_x_0"), val = tensor(false)]; + tensor attn_155_transpose_y_0 = const()[name = tensor("attn_155_transpose_y_0"), val = tensor(true)]; + tensor attn_155_cast = matmul(transpose_x = attn_155_transpose_x_0, transpose_y = attn_155_transpose_y_0, x = var_8419_cast, y = var_8423_cast)[name = tensor("attn_155_cast")]; + tensor var_8427 = const()[name = tensor("op_8427"), val = tensor([2, 768, 1, -1])]; + tensor input_625_cast = reshape(shape = var_8427, x = attn_155_cast)[name = tensor("input_625_cast")]; + tensor var_8432 = const()[name = tensor("op_8432"), val = tensor([1, 1])]; + tensor var_8434 = const()[name = tensor("op_8434"), val = tensor([1, 1])]; + tensor var_8436_pad_type_0 = const()[name = tensor("op_8436_pad_type_0"), val = tensor("custom")]; + tensor var_8436_pad_0 = const()[name = tensor("op_8436_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4300791872)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4301971584)))]; + tensor var_8436_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_8434, groups = var_7059, pad = var_8436_pad_0, pad_type = var_8436_pad_type_0, strides = var_8432, weight = up_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_625_cast)[name = tensor("op_8436_cast")]; + tensor inputs_233_cast = add(x = var_8436_cast, y = inputs_231_cast)[name = tensor("inputs_233_cast")]; + tensor var_8440 = const()[name = tensor("op_8440"), val = tensor([1])]; + tensor channels_mean_233_cast = reduce_mean(axes = var_8440, keep_dims = var_7054, x = inputs_233_cast)[name = tensor("channels_mean_233_cast")]; + tensor zero_mean_233_cast = sub(x = inputs_233_cast, y = channels_mean_233_cast)[name = tensor("zero_mean_233_cast")]; + tensor zero_mean_sq_233_cast = mul(x = zero_mean_233_cast, y = zero_mean_233_cast)[name = tensor("zero_mean_sq_233_cast")]; + tensor var_8444 = const()[name = tensor("op_8444"), val = tensor([1])]; + tensor var_8445_cast = reduce_mean(axes = var_8444, keep_dims = var_7054, x = zero_mean_sq_233_cast)[name = tensor("op_8445_cast")]; + tensor var_8446_to_fp16 = const()[name = tensor("op_8446_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8447_cast = add(x = var_8445_cast, y = var_8446_to_fp16)[name = tensor("op_8447_cast")]; + tensor denom_233_epsilon_0_to_fp16 = const()[name = tensor("denom_233_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_233_cast = rsqrt(epsilon = denom_233_epsilon_0_to_fp16, x = var_8447_cast)[name = tensor("denom_233_cast")]; + tensor out_233_cast = mul(x = zero_mean_233_cast, y = denom_233_cast)[name = tensor("out_233_cast")]; + tensor var_8451_to_fp16 = const()[name = tensor("op_8451_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4301973184)))]; + tensor var_8452_cast = add(x = out_233_cast, y = var_8451_to_fp16)[name = tensor("op_8452_cast")]; + tensor var_8454_to_fp16 = const()[name = tensor("op_8454_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4301974784)))]; + tensor input_627_cast = mul(x = var_8452_cast, y = var_8454_to_fp16)[name = tensor("input_627_cast")]; + tensor var_8462 = const()[name = tensor("op_8462"), val = tensor([1, 1])]; + tensor var_8464 = const()[name = tensor("op_8464"), val = tensor([1, 1])]; + tensor var_8466_pad_type_0 = const()[name = tensor("op_8466_pad_type_0"), val = tensor("custom")]; + tensor var_8466_pad_0 = const()[name = tensor("op_8466_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4301976384)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311413632)))]; + tensor var_8466_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_8464, groups = var_7059, pad = var_8466_pad_0, pad_type = var_8466_pad_type_0, strides = var_8462, weight = up_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_627_cast)[name = tensor("op_8466_cast")]; + tensor var_8467_split_sizes_0 = const()[name = tensor("op_8467_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_8467_axis_0 = const()[name = tensor("op_8467_axis_0"), val = tensor(1)]; + tensor var_8467_cast_0, tensor var_8467_cast_1 = split(axis = var_8467_axis_0, split_sizes = var_8467_split_sizes_0, x = var_8466_cast)[name = tensor("op_8467_cast")]; + tensor var_8469_mode_0 = const()[name = tensor("op_8469_mode_0"), val = tensor("EXACT")]; + tensor var_8469_cast = gelu(mode = var_8469_mode_0, x = var_8467_cast_1)[name = tensor("op_8469_cast")]; + tensor input_629_cast = mul(x = var_8467_cast_0, y = var_8469_cast)[name = tensor("input_629_cast")]; + tensor var_8473 = const()[name = tensor("op_8473"), val = tensor([1, 1])]; + tensor var_8475 = const()[name = tensor("op_8475"), val = tensor([1, 1])]; + tensor var_8477_pad_type_0 = const()[name = tensor("op_8477_pad_type_0"), val = tensor("custom")]; + tensor var_8477_pad_0 = const()[name = tensor("op_8477_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311425984)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4316144640)))]; + tensor var_8477_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_8475, groups = var_7059, pad = var_8477_pad_0, pad_type = var_8477_pad_type_0, strides = var_8473, weight = up_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_629_cast)[name = tensor("op_8477_cast")]; + tensor inputs_235_cast = add(x = var_8477_cast, y = inputs_233_cast)[name = tensor("inputs_235_cast")]; + tensor var_8487 = const()[name = tensor("op_8487"), val = tensor([1])]; + tensor channels_mean_235_cast = reduce_mean(axes = var_8487, keep_dims = var_7054, x = inputs_235_cast)[name = tensor("channels_mean_235_cast")]; + tensor zero_mean_235_cast = sub(x = inputs_235_cast, y = channels_mean_235_cast)[name = tensor("zero_mean_235_cast")]; + tensor zero_mean_sq_235_cast = mul(x = zero_mean_235_cast, y = zero_mean_235_cast)[name = tensor("zero_mean_sq_235_cast")]; + tensor var_8491 = const()[name = tensor("op_8491"), val = tensor([1])]; + tensor var_8492_cast = reduce_mean(axes = var_8491, keep_dims = var_7054, x = zero_mean_sq_235_cast)[name = tensor("op_8492_cast")]; + tensor var_8493_to_fp16 = const()[name = tensor("op_8493_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8494_cast = add(x = var_8492_cast, y = var_8493_to_fp16)[name = tensor("op_8494_cast")]; + tensor denom_235_epsilon_0_to_fp16 = const()[name = tensor("denom_235_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_235_cast = rsqrt(epsilon = denom_235_epsilon_0_to_fp16, x = var_8494_cast)[name = tensor("denom_235_cast")]; + tensor out_235_cast = mul(x = zero_mean_235_cast, y = denom_235_cast)[name = tensor("out_235_cast")]; + tensor var_8498_to_fp16 = const()[name = tensor("op_8498_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4316146240)))]; + tensor var_8499_cast = add(x = out_235_cast, y = var_8498_to_fp16)[name = tensor("op_8499_cast")]; + tensor var_8501_to_fp16 = const()[name = tensor("op_8501_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4316147840)))]; + tensor hidden_states_399_cast = mul(x = var_8499_cast, y = var_8501_to_fp16)[name = tensor("hidden_states_399_cast")]; + tensor var_8508 = const()[name = tensor("op_8508"), val = tensor([1, 1])]; + tensor var_8510 = const()[name = tensor("op_8510"), val = tensor([1, 1])]; + tensor q_157_pad_type_0 = const()[name = tensor("q_157_pad_type_0"), val = tensor("custom")]; + tensor q_157_pad_0 = const()[name = tensor("q_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4316149440)))]; + tensor q_157_cast = conv(dilations = var_8510, groups = var_7059, pad = q_157_pad_0, pad_type = q_157_pad_type_0, strides = var_8508, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_399_cast)[name = tensor("q_157_cast")]; + tensor var_8514 = const()[name = tensor("op_8514"), val = tensor([1, 1])]; + tensor var_8516 = const()[name = tensor("op_8516"), val = tensor([1, 1])]; + tensor k_157_pad_type_0 = const()[name = tensor("k_157_pad_type_0"), val = tensor("custom")]; + tensor k_157_pad_0 = const()[name = tensor("k_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4317329152)))]; + tensor k_157_cast = conv(dilations = var_8516, groups = var_7059, pad = k_157_pad_0, pad_type = k_157_pad_type_0, strides = var_8514, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_399_cast)[name = tensor("k_157_cast")]; + tensor var_8520 = const()[name = tensor("op_8520"), val = tensor([1, 1])]; + tensor var_8522 = const()[name = tensor("op_8522"), val = tensor([1, 1])]; + tensor v_157_pad_type_0 = const()[name = tensor("v_157_pad_type_0"), val = tensor("custom")]; + tensor v_157_pad_0 = const()[name = tensor("v_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4318508864)))]; + tensor v_157_cast = conv(dilations = var_8522, groups = var_7059, pad = v_157_pad_0, pad_type = v_157_pad_type_0, strides = var_8520, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_399_cast)[name = tensor("v_157_cast")]; + tensor var_8526 = const()[name = tensor("op_8526"), val = tensor([2, 12, 64, -1])]; + tensor var_8527_cast = reshape(shape = var_8526, x = q_157_cast)[name = tensor("op_8527_cast")]; + tensor var_8528 = const()[name = tensor("op_8528"), val = tensor([2, 12, 64, -1])]; + tensor var_8529_cast = reshape(shape = var_8528, x = k_157_cast)[name = tensor("op_8529_cast")]; + tensor var_8530 = const()[name = tensor("op_8530"), val = tensor([2, 12, 64, -1])]; + tensor var_8531_cast = reshape(shape = var_8530, x = v_157_cast)[name = tensor("op_8531_cast")]; + tensor attn_weights_313_transpose_x_0 = const()[name = tensor("attn_weights_313_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_313_transpose_y_0 = const()[name = tensor("attn_weights_313_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_313_cast = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_8527_cast, y = var_8529_cast)[name = tensor("attn_weights_313_cast")]; + tensor attn_weights_315_cast = mul(x = attn_weights_313_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_315_cast")]; + tensor var_8535_cast = softmax(axis = var_7043, x = attn_weights_315_cast)[name = tensor("op_8535_cast")]; + tensor attn_157_transpose_x_0 = const()[name = tensor("attn_157_transpose_x_0"), val = tensor(false)]; + tensor attn_157_transpose_y_0 = const()[name = tensor("attn_157_transpose_y_0"), val = tensor(true)]; + tensor attn_157_cast = matmul(transpose_x = attn_157_transpose_x_0, transpose_y = attn_157_transpose_y_0, x = var_8531_cast, y = var_8535_cast)[name = tensor("attn_157_cast")]; + tensor var_8539 = const()[name = tensor("op_8539"), val = tensor([2, 768, 1, -1])]; + tensor input_631_cast = reshape(shape = var_8539, x = attn_157_cast)[name = tensor("input_631_cast")]; + tensor var_8544 = const()[name = tensor("op_8544"), val = tensor([1, 1])]; + tensor var_8546 = const()[name = tensor("op_8546"), val = tensor([1, 1])]; + tensor var_8548_pad_type_0 = const()[name = tensor("op_8548_pad_type_0"), val = tensor("custom")]; + tensor var_8548_pad_0 = const()[name = tensor("op_8548_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4319688576)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4320868288)))]; + tensor var_8548_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_8546, groups = var_7059, pad = var_8548_pad_0, pad_type = var_8548_pad_type_0, strides = var_8544, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_631_cast)[name = tensor("op_8548_cast")]; + tensor inputs_237_cast = add(x = var_8548_cast, y = inputs_235_cast)[name = tensor("inputs_237_cast")]; + tensor var_8552 = const()[name = tensor("op_8552"), val = tensor([1])]; + tensor channels_mean_237_cast = reduce_mean(axes = var_8552, keep_dims = var_7054, x = inputs_237_cast)[name = tensor("channels_mean_237_cast")]; + tensor zero_mean_237_cast = sub(x = inputs_237_cast, y = channels_mean_237_cast)[name = tensor("zero_mean_237_cast")]; + tensor zero_mean_sq_237_cast = mul(x = zero_mean_237_cast, y = zero_mean_237_cast)[name = tensor("zero_mean_sq_237_cast")]; + tensor var_8556 = const()[name = tensor("op_8556"), val = tensor([1])]; + tensor var_8557_cast = reduce_mean(axes = var_8556, keep_dims = var_7054, x = zero_mean_sq_237_cast)[name = tensor("op_8557_cast")]; + tensor var_8558_to_fp16 = const()[name = tensor("op_8558_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8559_cast = add(x = var_8557_cast, y = var_8558_to_fp16)[name = tensor("op_8559_cast")]; + tensor denom_237_epsilon_0_to_fp16 = const()[name = tensor("denom_237_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_237_cast = rsqrt(epsilon = denom_237_epsilon_0_to_fp16, x = var_8559_cast)[name = tensor("denom_237_cast")]; + tensor out_237_cast = mul(x = zero_mean_237_cast, y = denom_237_cast)[name = tensor("out_237_cast")]; + tensor var_8563_to_fp16 = const()[name = tensor("op_8563_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4320869888)))]; + tensor var_8564_cast = add(x = out_237_cast, y = var_8563_to_fp16)[name = tensor("op_8564_cast")]; + tensor var_8566_to_fp16 = const()[name = tensor("op_8566_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4320871488)))]; + tensor hidden_states_401_cast = mul(x = var_8564_cast, y = var_8566_to_fp16)[name = tensor("hidden_states_401_cast")]; + tensor var_8573 = const()[name = tensor("op_8573"), val = tensor([1, 1])]; + tensor var_8575 = const()[name = tensor("op_8575"), val = tensor([1, 1])]; + tensor q_159_pad_type_0 = const()[name = tensor("q_159_pad_type_0"), val = tensor("custom")]; + tensor q_159_pad_0 = const()[name = tensor("q_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4320873088)))]; + tensor q_159_cast = conv(dilations = var_8575, groups = var_7059, pad = q_159_pad_0, pad_type = q_159_pad_type_0, strides = var_8573, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_401_cast)[name = tensor("q_159_cast")]; + tensor var_8579 = const()[name = tensor("op_8579"), val = tensor([1, 1])]; + tensor var_8581 = const()[name = tensor("op_8581"), val = tensor([1, 1])]; + tensor k_159_pad_type_0 = const()[name = tensor("k_159_pad_type_0"), val = tensor("custom")]; + tensor k_159_pad_0 = const()[name = tensor("k_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4322052800)))]; + tensor k_159_cast = conv(dilations = var_8581, groups = var_7059, pad = k_159_pad_0, pad_type = k_159_pad_type_0, strides = var_8579, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_159_cast")]; + tensor var_8585 = const()[name = tensor("op_8585"), val = tensor([1, 1])]; + tensor var_8587 = const()[name = tensor("op_8587"), val = tensor([1, 1])]; + tensor v_159_pad_type_0 = const()[name = tensor("v_159_pad_type_0"), val = tensor("custom")]; + tensor v_159_pad_0 = const()[name = tensor("v_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324018944)))]; + tensor v_159_cast = conv(dilations = var_8587, groups = var_7059, pad = v_159_pad_0, pad_type = v_159_pad_type_0, strides = var_8585, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_159_cast")]; + tensor var_8591 = const()[name = tensor("op_8591"), val = tensor([2, 12, 64, -1])]; + tensor var_8592_cast = reshape(shape = var_8591, x = q_159_cast)[name = tensor("op_8592_cast")]; + tensor var_8593 = const()[name = tensor("op_8593"), val = tensor([2, 12, 64, -1])]; + tensor var_8594_cast = reshape(shape = var_8593, x = k_159_cast)[name = tensor("op_8594_cast")]; + tensor var_8595 = const()[name = tensor("op_8595"), val = tensor([2, 12, 64, -1])]; + tensor var_8596_cast = reshape(shape = var_8595, x = v_159_cast)[name = tensor("op_8596_cast")]; + tensor attn_weights_317_transpose_x_0 = const()[name = tensor("attn_weights_317_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_317_transpose_y_0 = const()[name = tensor("attn_weights_317_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_317_cast = matmul(transpose_x = attn_weights_317_transpose_x_0, transpose_y = attn_weights_317_transpose_y_0, x = var_8592_cast, y = var_8594_cast)[name = tensor("attn_weights_317_cast")]; + tensor attn_weights_319_cast = mul(x = attn_weights_317_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_319_cast")]; + tensor var_8600_cast = softmax(axis = var_7043, x = attn_weights_319_cast)[name = tensor("op_8600_cast")]; + tensor attn_159_transpose_x_0 = const()[name = tensor("attn_159_transpose_x_0"), val = tensor(false)]; + tensor attn_159_transpose_y_0 = const()[name = tensor("attn_159_transpose_y_0"), val = tensor(true)]; + tensor attn_159_cast = matmul(transpose_x = attn_159_transpose_x_0, transpose_y = attn_159_transpose_y_0, x = var_8596_cast, y = var_8600_cast)[name = tensor("attn_159_cast")]; + tensor var_8604 = const()[name = tensor("op_8604"), val = tensor([2, 768, 1, -1])]; + tensor input_633_cast = reshape(shape = var_8604, x = attn_159_cast)[name = tensor("input_633_cast")]; + tensor var_8609 = const()[name = tensor("op_8609"), val = tensor([1, 1])]; + tensor var_8611 = const()[name = tensor("op_8611"), val = tensor([1, 1])]; + tensor var_8613_pad_type_0 = const()[name = tensor("op_8613_pad_type_0"), val = tensor("custom")]; + tensor var_8613_pad_0 = const()[name = tensor("op_8613_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4325985088)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4327164800)))]; + tensor var_8613_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_8611, groups = var_7059, pad = var_8613_pad_0, pad_type = var_8613_pad_type_0, strides = var_8609, weight = up_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_633_cast)[name = tensor("op_8613_cast")]; + tensor inputs_239_cast = add(x = var_8613_cast, y = inputs_237_cast)[name = tensor("inputs_239_cast")]; + tensor var_8617 = const()[name = tensor("op_8617"), val = tensor([1])]; + tensor channels_mean_239_cast = reduce_mean(axes = var_8617, keep_dims = var_7054, x = inputs_239_cast)[name = tensor("channels_mean_239_cast")]; + tensor zero_mean_239_cast = sub(x = inputs_239_cast, y = channels_mean_239_cast)[name = tensor("zero_mean_239_cast")]; + tensor zero_mean_sq_239_cast = mul(x = zero_mean_239_cast, y = zero_mean_239_cast)[name = tensor("zero_mean_sq_239_cast")]; + tensor var_8621 = const()[name = tensor("op_8621"), val = tensor([1])]; + tensor var_8622_cast = reduce_mean(axes = var_8621, keep_dims = var_7054, x = zero_mean_sq_239_cast)[name = tensor("op_8622_cast")]; + tensor var_8623_to_fp16 = const()[name = tensor("op_8623_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8624_cast = add(x = var_8622_cast, y = var_8623_to_fp16)[name = tensor("op_8624_cast")]; + tensor denom_239_epsilon_0_to_fp16 = const()[name = tensor("denom_239_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_239_cast = rsqrt(epsilon = denom_239_epsilon_0_to_fp16, x = var_8624_cast)[name = tensor("denom_239_cast")]; + tensor out_239_cast = mul(x = zero_mean_239_cast, y = denom_239_cast)[name = tensor("out_239_cast")]; + tensor var_8628_to_fp16 = const()[name = tensor("op_8628_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4327166400)))]; + tensor var_8629_cast = add(x = out_239_cast, y = var_8628_to_fp16)[name = tensor("op_8629_cast")]; + tensor var_8631_to_fp16 = const()[name = tensor("op_8631_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4327168000)))]; + tensor input_635_cast = mul(x = var_8629_cast, y = var_8631_to_fp16)[name = tensor("input_635_cast")]; + tensor var_8639 = const()[name = tensor("op_8639"), val = tensor([1, 1])]; + tensor var_8641 = const()[name = tensor("op_8641"), val = tensor([1, 1])]; + tensor var_8643_pad_type_0 = const()[name = tensor("op_8643_pad_type_0"), val = tensor("custom")]; + tensor var_8643_pad_0 = const()[name = tensor("op_8643_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4327169600)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4336606848)))]; + tensor var_8643_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_8641, groups = var_7059, pad = var_8643_pad_0, pad_type = var_8643_pad_type_0, strides = var_8639, weight = up_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_635_cast)[name = tensor("op_8643_cast")]; + tensor var_8644_split_sizes_0 = const()[name = tensor("op_8644_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_8644_axis_0 = const()[name = tensor("op_8644_axis_0"), val = tensor(1)]; + tensor var_8644_cast_0, tensor var_8644_cast_1 = split(axis = var_8644_axis_0, split_sizes = var_8644_split_sizes_0, x = var_8643_cast)[name = tensor("op_8644_cast")]; + tensor var_8646_mode_0 = const()[name = tensor("op_8646_mode_0"), val = tensor("EXACT")]; + tensor var_8646_cast = gelu(mode = var_8646_mode_0, x = var_8644_cast_1)[name = tensor("op_8646_cast")]; + tensor input_637_cast = mul(x = var_8644_cast_0, y = var_8646_cast)[name = tensor("input_637_cast")]; + tensor var_8650 = const()[name = tensor("op_8650"), val = tensor([1, 1])]; + tensor var_8652 = const()[name = tensor("op_8652"), val = tensor([1, 1])]; + tensor var_8654_pad_type_0 = const()[name = tensor("op_8654_pad_type_0"), val = tensor("custom")]; + tensor var_8654_pad_0 = const()[name = tensor("op_8654_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4336619200)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341337856)))]; + tensor var_8654_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_8652, groups = var_7059, pad = var_8654_pad_0, pad_type = var_8654_pad_type_0, strides = var_8650, weight = up_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_637_cast)[name = tensor("op_8654_cast")]; + tensor hidden_states_405_cast = add(x = var_8654_cast, y = inputs_239_cast)[name = tensor("hidden_states_405_cast")]; + tensor var_8656 = const()[name = tensor("op_8656"), val = tensor([2, 768, 64, 64])]; + tensor input_639_cast = reshape(shape = var_8656, x = hidden_states_405_cast)[name = tensor("input_639_cast")]; + tensor var_8660 = const()[name = tensor("op_8660"), val = tensor([1, 1])]; + tensor var_8662 = const()[name = tensor("op_8662"), val = tensor([1, 1])]; + tensor hidden_states_407_pad_type_0 = const()[name = tensor("hidden_states_407_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_407_pad_0 = const()[name = tensor("hidden_states_407_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341339456)))]; + tensor up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4342519168)))]; + tensor hidden_states_407_cast = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_8662, groups = var_7059, pad = hidden_states_407_pad_0, pad_type = hidden_states_407_pad_type_0, strides = var_8660, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_639_cast)[name = tensor("hidden_states_407_cast")]; + tensor hidden_states_409_cast = add(x = hidden_states_407_cast, y = hidden_states_377_cast)[name = tensor("hidden_states_409_cast")]; + tensor input_641_interleave_0 = const()[name = tensor("input_641_interleave_0"), val = tensor(false)]; + tensor input_641_cast = concat(axis = var_7059, interleave = input_641_interleave_0, values = (hidden_states_409_cast, input_45_cast))[name = tensor("input_641_cast")]; + tensor reshape_184_shape_0 = const()[name = tensor("reshape_184_shape_0"), val = tensor([2, 32, 36, 64, 64])]; + tensor reshape_184_cast = reshape(shape = reshape_184_shape_0, x = input_641_cast)[name = tensor("reshape_184_cast")]; + tensor reduce_mean_138_axes_0 = const()[name = tensor("reduce_mean_138_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_138_keep_dims_0 = const()[name = tensor("reduce_mean_138_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_138_cast = reduce_mean(axes = reduce_mean_138_axes_0, keep_dims = reduce_mean_138_keep_dims_0, x = reshape_184_cast)[name = tensor("reduce_mean_138_cast")]; + tensor sub_92_cast = sub(x = reshape_184_cast, y = reduce_mean_138_cast)[name = tensor("sub_92_cast")]; + tensor square_46_cast = square(x = sub_92_cast)[name = tensor("square_46_cast")]; + tensor reduce_mean_140_axes_0 = const()[name = tensor("reduce_mean_140_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_140_keep_dims_0 = const()[name = tensor("reduce_mean_140_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_140_cast = reduce_mean(axes = reduce_mean_140_axes_0, keep_dims = reduce_mean_140_keep_dims_0, x = square_46_cast)[name = tensor("reduce_mean_140_cast")]; + tensor add_92_y_0_to_fp16 = const()[name = tensor("add_92_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_92_cast = add(x = reduce_mean_140_cast, y = add_92_y_0_to_fp16)[name = tensor("add_92_cast")]; + tensor sqrt_46_cast = sqrt(x = add_92_cast)[name = tensor("sqrt_46_cast")]; + tensor real_div_46_cast = real_div(x = sub_92_cast, y = sqrt_46_cast)[name = tensor("real_div_46_cast")]; + tensor reshape_185_shape_0 = const()[name = tensor("reshape_185_shape_0"), val = tensor([2, 1152, 64, 64])]; + tensor reshape_185_cast = reshape(shape = reshape_185_shape_0, x = real_div_46_cast)[name = tensor("reshape_185_cast")]; + tensor add_93_mean_0_to_fp16 = const()[name = tensor("add_93_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4342520768)))]; + tensor add_93_variance_0_to_fp16 = const()[name = tensor("add_93_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4342523136)))]; + tensor add_93_gamma_0_to_fp16 = const()[name = tensor("add_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4342525504)))]; + tensor add_93_beta_0_to_fp16 = const()[name = tensor("add_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4342527872)))]; + tensor add_93_epsilon_0_to_fp16 = const()[name = tensor("add_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_93_cast = batch_norm(beta = add_93_beta_0_to_fp16, epsilon = add_93_epsilon_0_to_fp16, gamma = add_93_gamma_0_to_fp16, mean = add_93_mean_0_to_fp16, variance = add_93_variance_0_to_fp16, x = reshape_185_cast)[name = tensor("add_93_cast")]; + tensor input_645_cast = silu(x = add_93_cast)[name = tensor("input_645_cast")]; + tensor var_8680 = const()[name = tensor("op_8680"), val = tensor([1, 1])]; + tensor var_8682 = const()[name = tensor("op_8682"), val = tensor([1, 1])]; + tensor hidden_states_411_pad_type_0 = const()[name = tensor("hidden_states_411_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_411_pad_0 = const()[name = tensor("hidden_states_411_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4342530240)))]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4358455552)))]; + tensor hidden_states_411_cast = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_8682, groups = var_7059, pad = hidden_states_411_pad_0, pad_type = hidden_states_411_pad_type_0, strides = var_8680, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_645_cast)[name = tensor("hidden_states_411_cast")]; + tensor var_8688 = const()[name = tensor("op_8688"), val = tensor([1, 1])]; + tensor var_8690 = const()[name = tensor("op_8690"), val = tensor([1, 1])]; + tensor temb_37_pad_type_0 = const()[name = tensor("temb_37_pad_type_0"), val = tensor("custom")]; + tensor temb_37_pad_0 = const()[name = tensor("temb_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4358457152)))]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4360816512)))]; + tensor temb_37_cast = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_8690, groups = var_7059, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_8688, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_37_cast")]; + tensor input_649_cast = add(x = hidden_states_411_cast, y = temb_37_cast)[name = tensor("input_649_cast")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_188_cast = reshape(shape = reshape_188_shape_0, x = input_649_cast)[name = tensor("reshape_188_cast")]; + tensor reduce_mean_141_axes_0 = const()[name = tensor("reduce_mean_141_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_141_keep_dims_0 = const()[name = tensor("reduce_mean_141_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_141_cast = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = reshape_188_cast)[name = tensor("reduce_mean_141_cast")]; + tensor sub_94_cast = sub(x = reshape_188_cast, y = reduce_mean_141_cast)[name = tensor("sub_94_cast")]; + tensor square_47_cast = square(x = sub_94_cast)[name = tensor("square_47_cast")]; + tensor reduce_mean_143_axes_0 = const()[name = tensor("reduce_mean_143_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_143_keep_dims_0 = const()[name = tensor("reduce_mean_143_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_143_cast = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_47_cast)[name = tensor("reduce_mean_143_cast")]; + tensor add_94_y_0_to_fp16 = const()[name = tensor("add_94_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_94_cast = add(x = reduce_mean_143_cast, y = add_94_y_0_to_fp16)[name = tensor("add_94_cast")]; + tensor sqrt_47_cast = sqrt(x = add_94_cast)[name = tensor("sqrt_47_cast")]; + tensor real_div_47_cast = real_div(x = sub_94_cast, y = sqrt_47_cast)[name = tensor("real_div_47_cast")]; + tensor reshape_189_shape_0 = const()[name = tensor("reshape_189_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_189_cast = reshape(shape = reshape_189_shape_0, x = real_div_47_cast)[name = tensor("reshape_189_cast")]; + tensor add_95_gamma_0_to_fp16 = const()[name = tensor("add_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4360818112)))]; + tensor add_95_beta_0_to_fp16 = const()[name = tensor("add_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4360819712)))]; + tensor add_95_epsilon_0_to_fp16 = const()[name = tensor("add_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_95_cast = batch_norm(beta = add_95_beta_0_to_fp16, epsilon = add_95_epsilon_0_to_fp16, gamma = add_95_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_189_cast)[name = tensor("add_95_cast")]; + tensor input_653_cast = silu(x = add_95_cast)[name = tensor("input_653_cast")]; + tensor var_8700 = const()[name = tensor("op_8700"), val = tensor([1, 1])]; + tensor var_8702 = const()[name = tensor("op_8702"), val = tensor([1, 1])]; + tensor hidden_states_413_pad_type_0 = const()[name = tensor("hidden_states_413_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_413_pad_0 = const()[name = tensor("hidden_states_413_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4360821312)))]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4371438208)))]; + tensor hidden_states_413_cast = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_8702, groups = var_7059, pad = hidden_states_413_pad_0, pad_type = hidden_states_413_pad_type_0, strides = var_8700, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_653_cast)[name = tensor("hidden_states_413_cast")]; + tensor var_8707 = const()[name = tensor("op_8707"), val = tensor([1, 1])]; + tensor var_8709 = const()[name = tensor("op_8709"), val = tensor([1, 1])]; + tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("custom")]; + tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4371439808)))]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4373209344)))]; + tensor x_21_cast = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_8709, groups = var_7059, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_8707, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16, x = input_641_cast)[name = tensor("x_21_cast")]; + tensor hidden_states_415_cast = add(x = x_21_cast, y = hidden_states_413_cast)[name = tensor("hidden_states_415_cast")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([2, 32, 24, 64, 64])]; + tensor reshape_192_cast = reshape(shape = reshape_192_shape_0, x = hidden_states_415_cast)[name = tensor("reshape_192_cast")]; + tensor reduce_mean_144_axes_0 = const()[name = tensor("reduce_mean_144_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_144_keep_dims_0 = const()[name = tensor("reduce_mean_144_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_144_cast = reduce_mean(axes = reduce_mean_144_axes_0, keep_dims = reduce_mean_144_keep_dims_0, x = reshape_192_cast)[name = tensor("reduce_mean_144_cast")]; + tensor sub_96_cast = sub(x = reshape_192_cast, y = reduce_mean_144_cast)[name = tensor("sub_96_cast")]; + tensor square_48_cast = square(x = sub_96_cast)[name = tensor("square_48_cast")]; + tensor reduce_mean_146_axes_0 = const()[name = tensor("reduce_mean_146_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_146_keep_dims_0 = const()[name = tensor("reduce_mean_146_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_146_cast = reduce_mean(axes = reduce_mean_146_axes_0, keep_dims = reduce_mean_146_keep_dims_0, x = square_48_cast)[name = tensor("reduce_mean_146_cast")]; + tensor add_96_y_0_to_fp16 = const()[name = tensor("add_96_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_96_cast = add(x = reduce_mean_146_cast, y = add_96_y_0_to_fp16)[name = tensor("add_96_cast")]; + tensor sqrt_48_cast = sqrt(x = add_96_cast)[name = tensor("sqrt_48_cast")]; + tensor real_div_48_cast = real_div(x = sub_96_cast, y = sqrt_48_cast)[name = tensor("real_div_48_cast")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([2, 768, 64, 64])]; + tensor reshape_193_cast = reshape(shape = reshape_193_shape_0, x = real_div_48_cast)[name = tensor("reshape_193_cast")]; + tensor add_97_gamma_0_to_fp16 = const()[name = tensor("add_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4373210944)))]; + tensor add_97_beta_0_to_fp16 = const()[name = tensor("add_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4373212544)))]; + tensor add_97_epsilon_0_to_fp16 = const()[name = tensor("add_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_97_cast = batch_norm(beta = add_97_beta_0_to_fp16, epsilon = add_97_epsilon_0_to_fp16, gamma = add_97_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_193_cast)[name = tensor("add_97_cast")]; + tensor var_8735 = const()[name = tensor("op_8735"), val = tensor([1, 1])]; + tensor var_8737 = const()[name = tensor("op_8737"), val = tensor([1, 1])]; + tensor hidden_states_417_pad_type_0 = const()[name = tensor("hidden_states_417_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_417_pad_0 = const()[name = tensor("hidden_states_417_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4373214144)))]; + tensor up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4374393856)))]; + tensor hidden_states_417_cast = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_8737, groups = var_7059, pad = hidden_states_417_pad_0, pad_type = hidden_states_417_pad_type_0, strides = var_8735, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16, x = add_97_cast)[name = tensor("hidden_states_417_cast")]; + tensor var_8742 = const()[name = tensor("op_8742"), val = tensor([2, 768, 1, 4096])]; + tensor inputs_241_cast = reshape(shape = var_8742, x = hidden_states_417_cast)[name = tensor("inputs_241_cast")]; + tensor var_8752 = const()[name = tensor("op_8752"), val = tensor([1])]; + tensor channels_mean_241_cast = reduce_mean(axes = var_8752, keep_dims = var_7054, x = inputs_241_cast)[name = tensor("channels_mean_241_cast")]; + tensor zero_mean_241_cast = sub(x = inputs_241_cast, y = channels_mean_241_cast)[name = tensor("zero_mean_241_cast")]; + tensor zero_mean_sq_241_cast = mul(x = zero_mean_241_cast, y = zero_mean_241_cast)[name = tensor("zero_mean_sq_241_cast")]; + tensor var_8756 = const()[name = tensor("op_8756"), val = tensor([1])]; + tensor var_8757_cast = reduce_mean(axes = var_8756, keep_dims = var_7054, x = zero_mean_sq_241_cast)[name = tensor("op_8757_cast")]; + tensor var_8758_to_fp16 = const()[name = tensor("op_8758_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8759_cast = add(x = var_8757_cast, y = var_8758_to_fp16)[name = tensor("op_8759_cast")]; + tensor denom_241_epsilon_0_to_fp16 = const()[name = tensor("denom_241_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_241_cast = rsqrt(epsilon = denom_241_epsilon_0_to_fp16, x = var_8759_cast)[name = tensor("denom_241_cast")]; + tensor out_241_cast = mul(x = zero_mean_241_cast, y = denom_241_cast)[name = tensor("out_241_cast")]; + tensor var_8763_to_fp16 = const()[name = tensor("op_8763_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4374395456)))]; + tensor var_8764_cast = add(x = out_241_cast, y = var_8763_to_fp16)[name = tensor("op_8764_cast")]; + tensor var_8766_to_fp16 = const()[name = tensor("op_8766_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4374397056)))]; + tensor hidden_states_419_cast = mul(x = var_8764_cast, y = var_8766_to_fp16)[name = tensor("hidden_states_419_cast")]; + tensor var_8773 = const()[name = tensor("op_8773"), val = tensor([1, 1])]; + tensor var_8775 = const()[name = tensor("op_8775"), val = tensor([1, 1])]; + tensor q_161_pad_type_0 = const()[name = tensor("q_161_pad_type_0"), val = tensor("custom")]; + tensor q_161_pad_0 = const()[name = tensor("q_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4374398656)))]; + tensor q_161_cast = conv(dilations = var_8775, groups = var_7059, pad = q_161_pad_0, pad_type = q_161_pad_type_0, strides = var_8773, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_419_cast)[name = tensor("q_161_cast")]; + tensor var_8779 = const()[name = tensor("op_8779"), val = tensor([1, 1])]; + tensor var_8781 = const()[name = tensor("op_8781"), val = tensor([1, 1])]; + tensor k_161_pad_type_0 = const()[name = tensor("k_161_pad_type_0"), val = tensor("custom")]; + tensor k_161_pad_0 = const()[name = tensor("k_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4375578368)))]; + tensor k_161_cast = conv(dilations = var_8781, groups = var_7059, pad = k_161_pad_0, pad_type = k_161_pad_type_0, strides = var_8779, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_419_cast)[name = tensor("k_161_cast")]; + tensor var_8785 = const()[name = tensor("op_8785"), val = tensor([1, 1])]; + tensor var_8787 = const()[name = tensor("op_8787"), val = tensor([1, 1])]; + tensor v_161_pad_type_0 = const()[name = tensor("v_161_pad_type_0"), val = tensor("custom")]; + tensor v_161_pad_0 = const()[name = tensor("v_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4376758080)))]; + tensor v_161_cast = conv(dilations = var_8787, groups = var_7059, pad = v_161_pad_0, pad_type = v_161_pad_type_0, strides = var_8785, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_419_cast)[name = tensor("v_161_cast")]; + tensor var_8791 = const()[name = tensor("op_8791"), val = tensor([2, 12, 64, -1])]; + tensor var_8792_cast = reshape(shape = var_8791, x = q_161_cast)[name = tensor("op_8792_cast")]; + tensor var_8793 = const()[name = tensor("op_8793"), val = tensor([2, 12, 64, -1])]; + tensor var_8794_cast = reshape(shape = var_8793, x = k_161_cast)[name = tensor("op_8794_cast")]; + tensor var_8795 = const()[name = tensor("op_8795"), val = tensor([2, 12, 64, -1])]; + tensor var_8796_cast = reshape(shape = var_8795, x = v_161_cast)[name = tensor("op_8796_cast")]; + tensor attn_weights_321_transpose_x_0 = const()[name = tensor("attn_weights_321_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_321_transpose_y_0 = const()[name = tensor("attn_weights_321_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_321_cast = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_8792_cast, y = var_8794_cast)[name = tensor("attn_weights_321_cast")]; + tensor attn_weights_323_cast = mul(x = attn_weights_321_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_323_cast")]; + tensor var_8800_cast = softmax(axis = var_7043, x = attn_weights_323_cast)[name = tensor("op_8800_cast")]; + tensor attn_161_transpose_x_0 = const()[name = tensor("attn_161_transpose_x_0"), val = tensor(false)]; + tensor attn_161_transpose_y_0 = const()[name = tensor("attn_161_transpose_y_0"), val = tensor(true)]; + tensor attn_161_cast = matmul(transpose_x = attn_161_transpose_x_0, transpose_y = attn_161_transpose_y_0, x = var_8796_cast, y = var_8800_cast)[name = tensor("attn_161_cast")]; + tensor var_8804 = const()[name = tensor("op_8804"), val = tensor([2, 768, 1, -1])]; + tensor input_657_cast = reshape(shape = var_8804, x = attn_161_cast)[name = tensor("input_657_cast")]; + tensor var_8809 = const()[name = tensor("op_8809"), val = tensor([1, 1])]; + tensor var_8811 = const()[name = tensor("op_8811"), val = tensor([1, 1])]; + tensor var_8813_pad_type_0 = const()[name = tensor("op_8813_pad_type_0"), val = tensor("custom")]; + tensor var_8813_pad_0 = const()[name = tensor("op_8813_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4377937792)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4379117504)))]; + tensor var_8813_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8811, groups = var_7059, pad = var_8813_pad_0, pad_type = var_8813_pad_type_0, strides = var_8809, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_657_cast)[name = tensor("op_8813_cast")]; + tensor inputs_243_cast = add(x = var_8813_cast, y = inputs_241_cast)[name = tensor("inputs_243_cast")]; + tensor var_8817 = const()[name = tensor("op_8817"), val = tensor([1])]; + tensor channels_mean_243_cast = reduce_mean(axes = var_8817, keep_dims = var_7054, x = inputs_243_cast)[name = tensor("channels_mean_243_cast")]; + tensor zero_mean_243_cast = sub(x = inputs_243_cast, y = channels_mean_243_cast)[name = tensor("zero_mean_243_cast")]; + tensor zero_mean_sq_243_cast = mul(x = zero_mean_243_cast, y = zero_mean_243_cast)[name = tensor("zero_mean_sq_243_cast")]; + tensor var_8821 = const()[name = tensor("op_8821"), val = tensor([1])]; + tensor var_8822_cast = reduce_mean(axes = var_8821, keep_dims = var_7054, x = zero_mean_sq_243_cast)[name = tensor("op_8822_cast")]; + tensor var_8823_to_fp16 = const()[name = tensor("op_8823_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8824_cast = add(x = var_8822_cast, y = var_8823_to_fp16)[name = tensor("op_8824_cast")]; + tensor denom_243_epsilon_0_to_fp16 = const()[name = tensor("denom_243_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_243_cast = rsqrt(epsilon = denom_243_epsilon_0_to_fp16, x = var_8824_cast)[name = tensor("denom_243_cast")]; + tensor out_243_cast = mul(x = zero_mean_243_cast, y = denom_243_cast)[name = tensor("out_243_cast")]; + tensor var_8828_to_fp16 = const()[name = tensor("op_8828_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4379119104)))]; + tensor var_8829_cast = add(x = out_243_cast, y = var_8828_to_fp16)[name = tensor("op_8829_cast")]; + tensor var_8831_to_fp16 = const()[name = tensor("op_8831_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4379120704)))]; + tensor hidden_states_421_cast = mul(x = var_8829_cast, y = var_8831_to_fp16)[name = tensor("hidden_states_421_cast")]; + tensor var_8838 = const()[name = tensor("op_8838"), val = tensor([1, 1])]; + tensor var_8840 = const()[name = tensor("op_8840"), val = tensor([1, 1])]; + tensor q_163_pad_type_0 = const()[name = tensor("q_163_pad_type_0"), val = tensor("custom")]; + tensor q_163_pad_0 = const()[name = tensor("q_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4379122304)))]; + tensor q_163_cast = conv(dilations = var_8840, groups = var_7059, pad = q_163_pad_0, pad_type = q_163_pad_type_0, strides = var_8838, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_421_cast)[name = tensor("q_163_cast")]; + tensor var_8844 = const()[name = tensor("op_8844"), val = tensor([1, 1])]; + tensor var_8846 = const()[name = tensor("op_8846"), val = tensor([1, 1])]; + tensor k_163_pad_type_0 = const()[name = tensor("k_163_pad_type_0"), val = tensor("custom")]; + tensor k_163_pad_0 = const()[name = tensor("k_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380302016)))]; + tensor k_163_cast = conv(dilations = var_8846, groups = var_7059, pad = k_163_pad_0, pad_type = k_163_pad_type_0, strides = var_8844, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_163_cast")]; + tensor var_8850 = const()[name = tensor("op_8850"), val = tensor([1, 1])]; + tensor var_8852 = const()[name = tensor("op_8852"), val = tensor([1, 1])]; + tensor v_163_pad_type_0 = const()[name = tensor("v_163_pad_type_0"), val = tensor("custom")]; + tensor v_163_pad_0 = const()[name = tensor("v_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4382268160)))]; + tensor v_163_cast = conv(dilations = var_8852, groups = var_7059, pad = v_163_pad_0, pad_type = v_163_pad_type_0, strides = var_8850, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_163_cast")]; + tensor var_8856 = const()[name = tensor("op_8856"), val = tensor([2, 12, 64, -1])]; + tensor var_8857_cast = reshape(shape = var_8856, x = q_163_cast)[name = tensor("op_8857_cast")]; + tensor var_8858 = const()[name = tensor("op_8858"), val = tensor([2, 12, 64, -1])]; + tensor var_8859_cast = reshape(shape = var_8858, x = k_163_cast)[name = tensor("op_8859_cast")]; + tensor var_8860 = const()[name = tensor("op_8860"), val = tensor([2, 12, 64, -1])]; + tensor var_8861_cast = reshape(shape = var_8860, x = v_163_cast)[name = tensor("op_8861_cast")]; + tensor attn_weights_325_transpose_x_0 = const()[name = tensor("attn_weights_325_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_325_transpose_y_0 = const()[name = tensor("attn_weights_325_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_325_cast = matmul(transpose_x = attn_weights_325_transpose_x_0, transpose_y = attn_weights_325_transpose_y_0, x = var_8857_cast, y = var_8859_cast)[name = tensor("attn_weights_325_cast")]; + tensor attn_weights_327_cast = mul(x = attn_weights_325_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_327_cast")]; + tensor var_8865_cast = softmax(axis = var_7043, x = attn_weights_327_cast)[name = tensor("op_8865_cast")]; + tensor attn_163_transpose_x_0 = const()[name = tensor("attn_163_transpose_x_0"), val = tensor(false)]; + tensor attn_163_transpose_y_0 = const()[name = tensor("attn_163_transpose_y_0"), val = tensor(true)]; + tensor attn_163_cast = matmul(transpose_x = attn_163_transpose_x_0, transpose_y = attn_163_transpose_y_0, x = var_8861_cast, y = var_8865_cast)[name = tensor("attn_163_cast")]; + tensor var_8869 = const()[name = tensor("op_8869"), val = tensor([2, 768, 1, -1])]; + tensor input_659_cast = reshape(shape = var_8869, x = attn_163_cast)[name = tensor("input_659_cast")]; + tensor var_8874 = const()[name = tensor("op_8874"), val = tensor([1, 1])]; + tensor var_8876 = const()[name = tensor("op_8876"), val = tensor([1, 1])]; + tensor var_8878_pad_type_0 = const()[name = tensor("op_8878_pad_type_0"), val = tensor("custom")]; + tensor var_8878_pad_0 = const()[name = tensor("op_8878_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4384234304)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4385414016)))]; + tensor var_8878_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8876, groups = var_7059, pad = var_8878_pad_0, pad_type = var_8878_pad_type_0, strides = var_8874, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_659_cast)[name = tensor("op_8878_cast")]; + tensor inputs_245_cast = add(x = var_8878_cast, y = inputs_243_cast)[name = tensor("inputs_245_cast")]; + tensor var_8882 = const()[name = tensor("op_8882"), val = tensor([1])]; + tensor channels_mean_245_cast = reduce_mean(axes = var_8882, keep_dims = var_7054, x = inputs_245_cast)[name = tensor("channels_mean_245_cast")]; + tensor zero_mean_245_cast = sub(x = inputs_245_cast, y = channels_mean_245_cast)[name = tensor("zero_mean_245_cast")]; + tensor zero_mean_sq_245_cast = mul(x = zero_mean_245_cast, y = zero_mean_245_cast)[name = tensor("zero_mean_sq_245_cast")]; + tensor var_8886 = const()[name = tensor("op_8886"), val = tensor([1])]; + tensor var_8887_cast = reduce_mean(axes = var_8886, keep_dims = var_7054, x = zero_mean_sq_245_cast)[name = tensor("op_8887_cast")]; + tensor var_8888_to_fp16 = const()[name = tensor("op_8888_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8889_cast = add(x = var_8887_cast, y = var_8888_to_fp16)[name = tensor("op_8889_cast")]; + tensor denom_245_epsilon_0_to_fp16 = const()[name = tensor("denom_245_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_245_cast = rsqrt(epsilon = denom_245_epsilon_0_to_fp16, x = var_8889_cast)[name = tensor("denom_245_cast")]; + tensor out_245_cast = mul(x = zero_mean_245_cast, y = denom_245_cast)[name = tensor("out_245_cast")]; + tensor var_8893_to_fp16 = const()[name = tensor("op_8893_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4385415616)))]; + tensor var_8894_cast = add(x = out_245_cast, y = var_8893_to_fp16)[name = tensor("op_8894_cast")]; + tensor var_8896_to_fp16 = const()[name = tensor("op_8896_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4385417216)))]; + tensor input_661_cast = mul(x = var_8894_cast, y = var_8896_to_fp16)[name = tensor("input_661_cast")]; + tensor var_8904 = const()[name = tensor("op_8904"), val = tensor([1, 1])]; + tensor var_8906 = const()[name = tensor("op_8906"), val = tensor([1, 1])]; + tensor var_8908_pad_type_0 = const()[name = tensor("op_8908_pad_type_0"), val = tensor("custom")]; + tensor var_8908_pad_0 = const()[name = tensor("op_8908_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4385418816)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4394856064)))]; + tensor var_8908_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_8906, groups = var_7059, pad = var_8908_pad_0, pad_type = var_8908_pad_type_0, strides = var_8904, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_661_cast)[name = tensor("op_8908_cast")]; + tensor var_8909_split_sizes_0 = const()[name = tensor("op_8909_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_8909_axis_0 = const()[name = tensor("op_8909_axis_0"), val = tensor(1)]; + tensor var_8909_cast_0, tensor var_8909_cast_1 = split(axis = var_8909_axis_0, split_sizes = var_8909_split_sizes_0, x = var_8908_cast)[name = tensor("op_8909_cast")]; + tensor var_8911_mode_0 = const()[name = tensor("op_8911_mode_0"), val = tensor("EXACT")]; + tensor var_8911_cast = gelu(mode = var_8911_mode_0, x = var_8909_cast_1)[name = tensor("op_8911_cast")]; + tensor input_663_cast = mul(x = var_8909_cast_0, y = var_8911_cast)[name = tensor("input_663_cast")]; + tensor var_8915 = const()[name = tensor("op_8915"), val = tensor([1, 1])]; + tensor var_8917 = const()[name = tensor("op_8917"), val = tensor([1, 1])]; + tensor var_8919_pad_type_0 = const()[name = tensor("op_8919_pad_type_0"), val = tensor("custom")]; + tensor var_8919_pad_0 = const()[name = tensor("op_8919_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4394868416)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4399587072)))]; + tensor var_8919_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_8917, groups = var_7059, pad = var_8919_pad_0, pad_type = var_8919_pad_type_0, strides = var_8915, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_663_cast)[name = tensor("op_8919_cast")]; + tensor inputs_247_cast = add(x = var_8919_cast, y = inputs_245_cast)[name = tensor("inputs_247_cast")]; + tensor var_8929 = const()[name = tensor("op_8929"), val = tensor([1])]; + tensor channels_mean_247_cast = reduce_mean(axes = var_8929, keep_dims = var_7054, x = inputs_247_cast)[name = tensor("channels_mean_247_cast")]; + tensor zero_mean_247_cast = sub(x = inputs_247_cast, y = channels_mean_247_cast)[name = tensor("zero_mean_247_cast")]; + tensor zero_mean_sq_247_cast = mul(x = zero_mean_247_cast, y = zero_mean_247_cast)[name = tensor("zero_mean_sq_247_cast")]; + tensor var_8933 = const()[name = tensor("op_8933"), val = tensor([1])]; + tensor var_8934_cast = reduce_mean(axes = var_8933, keep_dims = var_7054, x = zero_mean_sq_247_cast)[name = tensor("op_8934_cast")]; + tensor var_8935_to_fp16 = const()[name = tensor("op_8935_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8936_cast = add(x = var_8934_cast, y = var_8935_to_fp16)[name = tensor("op_8936_cast")]; + tensor denom_247_epsilon_0_to_fp16 = const()[name = tensor("denom_247_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_247_cast = rsqrt(epsilon = denom_247_epsilon_0_to_fp16, x = var_8936_cast)[name = tensor("denom_247_cast")]; + tensor out_247_cast = mul(x = zero_mean_247_cast, y = denom_247_cast)[name = tensor("out_247_cast")]; + tensor var_8940_to_fp16 = const()[name = tensor("op_8940_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4399588672)))]; + tensor var_8941_cast = add(x = out_247_cast, y = var_8940_to_fp16)[name = tensor("op_8941_cast")]; + tensor var_8943_to_fp16 = const()[name = tensor("op_8943_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4399590272)))]; + tensor hidden_states_425_cast = mul(x = var_8941_cast, y = var_8943_to_fp16)[name = tensor("hidden_states_425_cast")]; + tensor var_8950 = const()[name = tensor("op_8950"), val = tensor([1, 1])]; + tensor var_8952 = const()[name = tensor("op_8952"), val = tensor([1, 1])]; + tensor q_165_pad_type_0 = const()[name = tensor("q_165_pad_type_0"), val = tensor("custom")]; + tensor q_165_pad_0 = const()[name = tensor("q_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4399591872)))]; + tensor q_165_cast = conv(dilations = var_8952, groups = var_7059, pad = q_165_pad_0, pad_type = q_165_pad_type_0, strides = var_8950, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_425_cast)[name = tensor("q_165_cast")]; + tensor var_8956 = const()[name = tensor("op_8956"), val = tensor([1, 1])]; + tensor var_8958 = const()[name = tensor("op_8958"), val = tensor([1, 1])]; + tensor k_165_pad_type_0 = const()[name = tensor("k_165_pad_type_0"), val = tensor("custom")]; + tensor k_165_pad_0 = const()[name = tensor("k_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4400771584)))]; + tensor k_165_cast = conv(dilations = var_8958, groups = var_7059, pad = k_165_pad_0, pad_type = k_165_pad_type_0, strides = var_8956, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_425_cast)[name = tensor("k_165_cast")]; + tensor var_8962 = const()[name = tensor("op_8962"), val = tensor([1, 1])]; + tensor var_8964 = const()[name = tensor("op_8964"), val = tensor([1, 1])]; + tensor v_165_pad_type_0 = const()[name = tensor("v_165_pad_type_0"), val = tensor("custom")]; + tensor v_165_pad_0 = const()[name = tensor("v_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4401951296)))]; + tensor v_165_cast = conv(dilations = var_8964, groups = var_7059, pad = v_165_pad_0, pad_type = v_165_pad_type_0, strides = var_8962, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_425_cast)[name = tensor("v_165_cast")]; + tensor var_8968 = const()[name = tensor("op_8968"), val = tensor([2, 12, 64, -1])]; + tensor var_8969_cast = reshape(shape = var_8968, x = q_165_cast)[name = tensor("op_8969_cast")]; + tensor var_8970 = const()[name = tensor("op_8970"), val = tensor([2, 12, 64, -1])]; + tensor var_8971_cast = reshape(shape = var_8970, x = k_165_cast)[name = tensor("op_8971_cast")]; + tensor var_8972 = const()[name = tensor("op_8972"), val = tensor([2, 12, 64, -1])]; + tensor var_8973_cast = reshape(shape = var_8972, x = v_165_cast)[name = tensor("op_8973_cast")]; + tensor attn_weights_329_transpose_x_0 = const()[name = tensor("attn_weights_329_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_329_transpose_y_0 = const()[name = tensor("attn_weights_329_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_329_cast = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_8969_cast, y = var_8971_cast)[name = tensor("attn_weights_329_cast")]; + tensor attn_weights_331_cast = mul(x = attn_weights_329_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_331_cast")]; + tensor var_8977_cast = softmax(axis = var_7043, x = attn_weights_331_cast)[name = tensor("op_8977_cast")]; + tensor attn_165_transpose_x_0 = const()[name = tensor("attn_165_transpose_x_0"), val = tensor(false)]; + tensor attn_165_transpose_y_0 = const()[name = tensor("attn_165_transpose_y_0"), val = tensor(true)]; + tensor attn_165_cast = matmul(transpose_x = attn_165_transpose_x_0, transpose_y = attn_165_transpose_y_0, x = var_8973_cast, y = var_8977_cast)[name = tensor("attn_165_cast")]; + tensor var_8981 = const()[name = tensor("op_8981"), val = tensor([2, 768, 1, -1])]; + tensor input_665_cast = reshape(shape = var_8981, x = attn_165_cast)[name = tensor("input_665_cast")]; + tensor var_8986 = const()[name = tensor("op_8986"), val = tensor([1, 1])]; + tensor var_8988 = const()[name = tensor("op_8988"), val = tensor([1, 1])]; + tensor var_8990_pad_type_0 = const()[name = tensor("op_8990_pad_type_0"), val = tensor("custom")]; + tensor var_8990_pad_0 = const()[name = tensor("op_8990_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4403131008)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4404310720)))]; + tensor var_8990_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_8988, groups = var_7059, pad = var_8990_pad_0, pad_type = var_8990_pad_type_0, strides = var_8986, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_665_cast)[name = tensor("op_8990_cast")]; + tensor inputs_249_cast = add(x = var_8990_cast, y = inputs_247_cast)[name = tensor("inputs_249_cast")]; + tensor var_8994 = const()[name = tensor("op_8994"), val = tensor([1])]; + tensor channels_mean_249_cast = reduce_mean(axes = var_8994, keep_dims = var_7054, x = inputs_249_cast)[name = tensor("channels_mean_249_cast")]; + tensor zero_mean_249_cast = sub(x = inputs_249_cast, y = channels_mean_249_cast)[name = tensor("zero_mean_249_cast")]; + tensor zero_mean_sq_249_cast = mul(x = zero_mean_249_cast, y = zero_mean_249_cast)[name = tensor("zero_mean_sq_249_cast")]; + tensor var_8998 = const()[name = tensor("op_8998"), val = tensor([1])]; + tensor var_8999_cast = reduce_mean(axes = var_8998, keep_dims = var_7054, x = zero_mean_sq_249_cast)[name = tensor("op_8999_cast")]; + tensor var_9000_to_fp16 = const()[name = tensor("op_9000_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9001_cast = add(x = var_8999_cast, y = var_9000_to_fp16)[name = tensor("op_9001_cast")]; + tensor denom_249_epsilon_0_to_fp16 = const()[name = tensor("denom_249_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_249_cast = rsqrt(epsilon = denom_249_epsilon_0_to_fp16, x = var_9001_cast)[name = tensor("denom_249_cast")]; + tensor out_249_cast = mul(x = zero_mean_249_cast, y = denom_249_cast)[name = tensor("out_249_cast")]; + tensor var_9005_to_fp16 = const()[name = tensor("op_9005_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4404312320)))]; + tensor var_9006_cast = add(x = out_249_cast, y = var_9005_to_fp16)[name = tensor("op_9006_cast")]; + tensor var_9008_to_fp16 = const()[name = tensor("op_9008_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4404313920)))]; + tensor hidden_states_427_cast = mul(x = var_9006_cast, y = var_9008_to_fp16)[name = tensor("hidden_states_427_cast")]; + tensor var_9015 = const()[name = tensor("op_9015"), val = tensor([1, 1])]; + tensor var_9017 = const()[name = tensor("op_9017"), val = tensor([1, 1])]; + tensor q_167_pad_type_0 = const()[name = tensor("q_167_pad_type_0"), val = tensor("custom")]; + tensor q_167_pad_0 = const()[name = tensor("q_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4404315520)))]; + tensor q_167_cast = conv(dilations = var_9017, groups = var_7059, pad = q_167_pad_0, pad_type = q_167_pad_type_0, strides = var_9015, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_427_cast)[name = tensor("q_167_cast")]; + tensor var_9021 = const()[name = tensor("op_9021"), val = tensor([1, 1])]; + tensor var_9023 = const()[name = tensor("op_9023"), val = tensor([1, 1])]; + tensor k_167_pad_type_0 = const()[name = tensor("k_167_pad_type_0"), val = tensor("custom")]; + tensor k_167_pad_0 = const()[name = tensor("k_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4405495232)))]; + tensor k_167_cast = conv(dilations = var_9023, groups = var_7059, pad = k_167_pad_0, pad_type = k_167_pad_type_0, strides = var_9021, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_167_cast")]; + tensor var_9027 = const()[name = tensor("op_9027"), val = tensor([1, 1])]; + tensor var_9029 = const()[name = tensor("op_9029"), val = tensor([1, 1])]; + tensor v_167_pad_type_0 = const()[name = tensor("v_167_pad_type_0"), val = tensor("custom")]; + tensor v_167_pad_0 = const()[name = tensor("v_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4407461376)))]; + tensor v_167_cast = conv(dilations = var_9029, groups = var_7059, pad = v_167_pad_0, pad_type = v_167_pad_type_0, strides = var_9027, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_167_cast")]; + tensor var_9033 = const()[name = tensor("op_9033"), val = tensor([2, 12, 64, -1])]; + tensor var_9034_cast = reshape(shape = var_9033, x = q_167_cast)[name = tensor("op_9034_cast")]; + tensor var_9035 = const()[name = tensor("op_9035"), val = tensor([2, 12, 64, -1])]; + tensor var_9036_cast = reshape(shape = var_9035, x = k_167_cast)[name = tensor("op_9036_cast")]; + tensor var_9037 = const()[name = tensor("op_9037"), val = tensor([2, 12, 64, -1])]; + tensor var_9038_cast = reshape(shape = var_9037, x = v_167_cast)[name = tensor("op_9038_cast")]; + tensor attn_weights_333_transpose_x_0 = const()[name = tensor("attn_weights_333_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_333_transpose_y_0 = const()[name = tensor("attn_weights_333_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_333_cast = matmul(transpose_x = attn_weights_333_transpose_x_0, transpose_y = attn_weights_333_transpose_y_0, x = var_9034_cast, y = var_9036_cast)[name = tensor("attn_weights_333_cast")]; + tensor attn_weights_335_cast = mul(x = attn_weights_333_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_335_cast")]; + tensor var_9042_cast = softmax(axis = var_7043, x = attn_weights_335_cast)[name = tensor("op_9042_cast")]; + tensor attn_167_transpose_x_0 = const()[name = tensor("attn_167_transpose_x_0"), val = tensor(false)]; + tensor attn_167_transpose_y_0 = const()[name = tensor("attn_167_transpose_y_0"), val = tensor(true)]; + tensor attn_167_cast = matmul(transpose_x = attn_167_transpose_x_0, transpose_y = attn_167_transpose_y_0, x = var_9038_cast, y = var_9042_cast)[name = tensor("attn_167_cast")]; + tensor var_9046 = const()[name = tensor("op_9046"), val = tensor([2, 768, 1, -1])]; + tensor input_667_cast = reshape(shape = var_9046, x = attn_167_cast)[name = tensor("input_667_cast")]; + tensor var_9051 = const()[name = tensor("op_9051"), val = tensor([1, 1])]; + tensor var_9053 = const()[name = tensor("op_9053"), val = tensor([1, 1])]; + tensor var_9055_pad_type_0 = const()[name = tensor("op_9055_pad_type_0"), val = tensor("custom")]; + tensor var_9055_pad_0 = const()[name = tensor("op_9055_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4409427520)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410607232)))]; + tensor var_9055_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_9053, groups = var_7059, pad = var_9055_pad_0, pad_type = var_9055_pad_type_0, strides = var_9051, weight = up_blocks_2_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_667_cast)[name = tensor("op_9055_cast")]; + tensor inputs_251_cast = add(x = var_9055_cast, y = inputs_249_cast)[name = tensor("inputs_251_cast")]; + tensor var_9059 = const()[name = tensor("op_9059"), val = tensor([1])]; + tensor channels_mean_251_cast = reduce_mean(axes = var_9059, keep_dims = var_7054, x = inputs_251_cast)[name = tensor("channels_mean_251_cast")]; + tensor zero_mean_251_cast = sub(x = inputs_251_cast, y = channels_mean_251_cast)[name = tensor("zero_mean_251_cast")]; + tensor zero_mean_sq_251_cast = mul(x = zero_mean_251_cast, y = zero_mean_251_cast)[name = tensor("zero_mean_sq_251_cast")]; + tensor var_9063 = const()[name = tensor("op_9063"), val = tensor([1])]; + tensor var_9064_cast = reduce_mean(axes = var_9063, keep_dims = var_7054, x = zero_mean_sq_251_cast)[name = tensor("op_9064_cast")]; + tensor var_9065_to_fp16 = const()[name = tensor("op_9065_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9066_cast = add(x = var_9064_cast, y = var_9065_to_fp16)[name = tensor("op_9066_cast")]; + tensor denom_251_epsilon_0_to_fp16 = const()[name = tensor("denom_251_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_251_cast = rsqrt(epsilon = denom_251_epsilon_0_to_fp16, x = var_9066_cast)[name = tensor("denom_251_cast")]; + tensor out_251_cast = mul(x = zero_mean_251_cast, y = denom_251_cast)[name = tensor("out_251_cast")]; + tensor var_9070_to_fp16 = const()[name = tensor("op_9070_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410608832)))]; + tensor var_9071_cast = add(x = out_251_cast, y = var_9070_to_fp16)[name = tensor("op_9071_cast")]; + tensor var_9073_to_fp16 = const()[name = tensor("op_9073_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410610432)))]; + tensor input_669_cast = mul(x = var_9071_cast, y = var_9073_to_fp16)[name = tensor("input_669_cast")]; + tensor var_9081 = const()[name = tensor("op_9081"), val = tensor([1, 1])]; + tensor var_9083 = const()[name = tensor("op_9083"), val = tensor([1, 1])]; + tensor var_9085_pad_type_0 = const()[name = tensor("op_9085_pad_type_0"), val = tensor("custom")]; + tensor var_9085_pad_0 = const()[name = tensor("op_9085_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410612032)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4420049280)))]; + tensor var_9085_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_9083, groups = var_7059, pad = var_9085_pad_0, pad_type = var_9085_pad_type_0, strides = var_9081, weight = up_blocks_2_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_669_cast)[name = tensor("op_9085_cast")]; + tensor var_9086_split_sizes_0 = const()[name = tensor("op_9086_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_9086_axis_0 = const()[name = tensor("op_9086_axis_0"), val = tensor(1)]; + tensor var_9086_cast_0, tensor var_9086_cast_1 = split(axis = var_9086_axis_0, split_sizes = var_9086_split_sizes_0, x = var_9085_cast)[name = tensor("op_9086_cast")]; + tensor var_9088_mode_0 = const()[name = tensor("op_9088_mode_0"), val = tensor("EXACT")]; + tensor var_9088_cast = gelu(mode = var_9088_mode_0, x = var_9086_cast_1)[name = tensor("op_9088_cast")]; + tensor input_671_cast = mul(x = var_9086_cast_0, y = var_9088_cast)[name = tensor("input_671_cast")]; + tensor var_9092 = const()[name = tensor("op_9092"), val = tensor([1, 1])]; + tensor var_9094 = const()[name = tensor("op_9094"), val = tensor([1, 1])]; + tensor var_9096_pad_type_0 = const()[name = tensor("op_9096_pad_type_0"), val = tensor("custom")]; + tensor var_9096_pad_0 = const()[name = tensor("op_9096_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4420061632)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4424780288)))]; + tensor var_9096_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_9094, groups = var_7059, pad = var_9096_pad_0, pad_type = var_9096_pad_type_0, strides = var_9092, weight = up_blocks_2_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_671_cast)[name = tensor("op_9096_cast")]; + tensor inputs_253_cast = add(x = var_9096_cast, y = inputs_251_cast)[name = tensor("inputs_253_cast")]; + tensor var_9106 = const()[name = tensor("op_9106"), val = tensor([1])]; + tensor channels_mean_253_cast = reduce_mean(axes = var_9106, keep_dims = var_7054, x = inputs_253_cast)[name = tensor("channels_mean_253_cast")]; + tensor zero_mean_253_cast = sub(x = inputs_253_cast, y = channels_mean_253_cast)[name = tensor("zero_mean_253_cast")]; + tensor zero_mean_sq_253_cast = mul(x = zero_mean_253_cast, y = zero_mean_253_cast)[name = tensor("zero_mean_sq_253_cast")]; + tensor var_9110 = const()[name = tensor("op_9110"), val = tensor([1])]; + tensor var_9111_cast = reduce_mean(axes = var_9110, keep_dims = var_7054, x = zero_mean_sq_253_cast)[name = tensor("op_9111_cast")]; + tensor var_9112_to_fp16 = const()[name = tensor("op_9112_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9113_cast = add(x = var_9111_cast, y = var_9112_to_fp16)[name = tensor("op_9113_cast")]; + tensor denom_253_epsilon_0_to_fp16 = const()[name = tensor("denom_253_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_253_cast = rsqrt(epsilon = denom_253_epsilon_0_to_fp16, x = var_9113_cast)[name = tensor("denom_253_cast")]; + tensor out_253_cast = mul(x = zero_mean_253_cast, y = denom_253_cast)[name = tensor("out_253_cast")]; + tensor var_9117_to_fp16 = const()[name = tensor("op_9117_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4424781888)))]; + tensor var_9118_cast = add(x = out_253_cast, y = var_9117_to_fp16)[name = tensor("op_9118_cast")]; + tensor var_9120_to_fp16 = const()[name = tensor("op_9120_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4424783488)))]; + tensor hidden_states_431_cast = mul(x = var_9118_cast, y = var_9120_to_fp16)[name = tensor("hidden_states_431_cast")]; + tensor var_9127 = const()[name = tensor("op_9127"), val = tensor([1, 1])]; + tensor var_9129 = const()[name = tensor("op_9129"), val = tensor([1, 1])]; + tensor q_169_pad_type_0 = const()[name = tensor("q_169_pad_type_0"), val = tensor("custom")]; + tensor q_169_pad_0 = const()[name = tensor("q_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4424785088)))]; + tensor q_169_cast = conv(dilations = var_9129, groups = var_7059, pad = q_169_pad_0, pad_type = q_169_pad_type_0, strides = var_9127, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_431_cast)[name = tensor("q_169_cast")]; + tensor var_9133 = const()[name = tensor("op_9133"), val = tensor([1, 1])]; + tensor var_9135 = const()[name = tensor("op_9135"), val = tensor([1, 1])]; + tensor k_169_pad_type_0 = const()[name = tensor("k_169_pad_type_0"), val = tensor("custom")]; + tensor k_169_pad_0 = const()[name = tensor("k_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4425964800)))]; + tensor k_169_cast = conv(dilations = var_9135, groups = var_7059, pad = k_169_pad_0, pad_type = k_169_pad_type_0, strides = var_9133, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_431_cast)[name = tensor("k_169_cast")]; + tensor var_9139 = const()[name = tensor("op_9139"), val = tensor([1, 1])]; + tensor var_9141 = const()[name = tensor("op_9141"), val = tensor([1, 1])]; + tensor v_169_pad_type_0 = const()[name = tensor("v_169_pad_type_0"), val = tensor("custom")]; + tensor v_169_pad_0 = const()[name = tensor("v_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4427144512)))]; + tensor v_169_cast = conv(dilations = var_9141, groups = var_7059, pad = v_169_pad_0, pad_type = v_169_pad_type_0, strides = var_9139, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_431_cast)[name = tensor("v_169_cast")]; + tensor var_9145 = const()[name = tensor("op_9145"), val = tensor([2, 12, 64, -1])]; + tensor var_9146_cast = reshape(shape = var_9145, x = q_169_cast)[name = tensor("op_9146_cast")]; + tensor var_9147 = const()[name = tensor("op_9147"), val = tensor([2, 12, 64, -1])]; + tensor var_9148_cast = reshape(shape = var_9147, x = k_169_cast)[name = tensor("op_9148_cast")]; + tensor var_9149 = const()[name = tensor("op_9149"), val = tensor([2, 12, 64, -1])]; + tensor var_9150_cast = reshape(shape = var_9149, x = v_169_cast)[name = tensor("op_9150_cast")]; + tensor attn_weights_337_transpose_x_0 = const()[name = tensor("attn_weights_337_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_337_transpose_y_0 = const()[name = tensor("attn_weights_337_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_337_cast = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_9146_cast, y = var_9148_cast)[name = tensor("attn_weights_337_cast")]; + tensor attn_weights_339_cast = mul(x = attn_weights_337_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_339_cast")]; + tensor var_9154_cast = softmax(axis = var_7043, x = attn_weights_339_cast)[name = tensor("op_9154_cast")]; + tensor attn_169_transpose_x_0 = const()[name = tensor("attn_169_transpose_x_0"), val = tensor(false)]; + tensor attn_169_transpose_y_0 = const()[name = tensor("attn_169_transpose_y_0"), val = tensor(true)]; + tensor attn_169_cast = matmul(transpose_x = attn_169_transpose_x_0, transpose_y = attn_169_transpose_y_0, x = var_9150_cast, y = var_9154_cast)[name = tensor("attn_169_cast")]; + tensor var_9158 = const()[name = tensor("op_9158"), val = tensor([2, 768, 1, -1])]; + tensor input_673_cast = reshape(shape = var_9158, x = attn_169_cast)[name = tensor("input_673_cast")]; + tensor var_9163 = const()[name = tensor("op_9163"), val = tensor([1, 1])]; + tensor var_9165 = const()[name = tensor("op_9165"), val = tensor([1, 1])]; + tensor var_9167_pad_type_0 = const()[name = tensor("op_9167_pad_type_0"), val = tensor("custom")]; + tensor var_9167_pad_0 = const()[name = tensor("op_9167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4428324224)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4429503936)))]; + tensor var_9167_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_9165, groups = var_7059, pad = var_9167_pad_0, pad_type = var_9167_pad_type_0, strides = var_9163, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_673_cast)[name = tensor("op_9167_cast")]; + tensor inputs_255_cast = add(x = var_9167_cast, y = inputs_253_cast)[name = tensor("inputs_255_cast")]; + tensor var_9171 = const()[name = tensor("op_9171"), val = tensor([1])]; + tensor channels_mean_255_cast = reduce_mean(axes = var_9171, keep_dims = var_7054, x = inputs_255_cast)[name = tensor("channels_mean_255_cast")]; + tensor zero_mean_255_cast = sub(x = inputs_255_cast, y = channels_mean_255_cast)[name = tensor("zero_mean_255_cast")]; + tensor zero_mean_sq_255_cast = mul(x = zero_mean_255_cast, y = zero_mean_255_cast)[name = tensor("zero_mean_sq_255_cast")]; + tensor var_9175 = const()[name = tensor("op_9175"), val = tensor([1])]; + tensor var_9176_cast = reduce_mean(axes = var_9175, keep_dims = var_7054, x = zero_mean_sq_255_cast)[name = tensor("op_9176_cast")]; + tensor var_9177_to_fp16 = const()[name = tensor("op_9177_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9178_cast = add(x = var_9176_cast, y = var_9177_to_fp16)[name = tensor("op_9178_cast")]; + tensor denom_255_epsilon_0_to_fp16 = const()[name = tensor("denom_255_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_255_cast = rsqrt(epsilon = denom_255_epsilon_0_to_fp16, x = var_9178_cast)[name = tensor("denom_255_cast")]; + tensor out_255_cast = mul(x = zero_mean_255_cast, y = denom_255_cast)[name = tensor("out_255_cast")]; + tensor var_9182_to_fp16 = const()[name = tensor("op_9182_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4429505536)))]; + tensor var_9183_cast = add(x = out_255_cast, y = var_9182_to_fp16)[name = tensor("op_9183_cast")]; + tensor var_9185_to_fp16 = const()[name = tensor("op_9185_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4429507136)))]; + tensor hidden_states_433_cast = mul(x = var_9183_cast, y = var_9185_to_fp16)[name = tensor("hidden_states_433_cast")]; + tensor var_9192 = const()[name = tensor("op_9192"), val = tensor([1, 1])]; + tensor var_9194 = const()[name = tensor("op_9194"), val = tensor([1, 1])]; + tensor q_171_pad_type_0 = const()[name = tensor("q_171_pad_type_0"), val = tensor("custom")]; + tensor q_171_pad_0 = const()[name = tensor("q_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4429508736)))]; + tensor q_171_cast = conv(dilations = var_9194, groups = var_7059, pad = q_171_pad_0, pad_type = q_171_pad_type_0, strides = var_9192, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_433_cast)[name = tensor("q_171_cast")]; + tensor var_9198 = const()[name = tensor("op_9198"), val = tensor([1, 1])]; + tensor var_9200 = const()[name = tensor("op_9200"), val = tensor([1, 1])]; + tensor k_171_pad_type_0 = const()[name = tensor("k_171_pad_type_0"), val = tensor("custom")]; + tensor k_171_pad_0 = const()[name = tensor("k_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4430688448)))]; + tensor k_171_cast = conv(dilations = var_9200, groups = var_7059, pad = k_171_pad_0, pad_type = k_171_pad_type_0, strides = var_9198, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_171_cast")]; + tensor var_9204 = const()[name = tensor("op_9204"), val = tensor([1, 1])]; + tensor var_9206 = const()[name = tensor("op_9206"), val = tensor([1, 1])]; + tensor v_171_pad_type_0 = const()[name = tensor("v_171_pad_type_0"), val = tensor("custom")]; + tensor v_171_pad_0 = const()[name = tensor("v_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4432654592)))]; + tensor v_171_cast = conv(dilations = var_9206, groups = var_7059, pad = v_171_pad_0, pad_type = v_171_pad_type_0, strides = var_9204, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_171_cast")]; + tensor var_9210 = const()[name = tensor("op_9210"), val = tensor([2, 12, 64, -1])]; + tensor var_9211_cast = reshape(shape = var_9210, x = q_171_cast)[name = tensor("op_9211_cast")]; + tensor var_9212 = const()[name = tensor("op_9212"), val = tensor([2, 12, 64, -1])]; + tensor var_9213_cast = reshape(shape = var_9212, x = k_171_cast)[name = tensor("op_9213_cast")]; + tensor var_9214 = const()[name = tensor("op_9214"), val = tensor([2, 12, 64, -1])]; + tensor var_9215_cast = reshape(shape = var_9214, x = v_171_cast)[name = tensor("op_9215_cast")]; + tensor attn_weights_341_transpose_x_0 = const()[name = tensor("attn_weights_341_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_341_transpose_y_0 = const()[name = tensor("attn_weights_341_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_341_cast = matmul(transpose_x = attn_weights_341_transpose_x_0, transpose_y = attn_weights_341_transpose_y_0, x = var_9211_cast, y = var_9213_cast)[name = tensor("attn_weights_341_cast")]; + tensor attn_weights_343_cast = mul(x = attn_weights_341_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_343_cast")]; + tensor var_9219_cast = softmax(axis = var_7043, x = attn_weights_343_cast)[name = tensor("op_9219_cast")]; + tensor attn_171_transpose_x_0 = const()[name = tensor("attn_171_transpose_x_0"), val = tensor(false)]; + tensor attn_171_transpose_y_0 = const()[name = tensor("attn_171_transpose_y_0"), val = tensor(true)]; + tensor attn_171_cast = matmul(transpose_x = attn_171_transpose_x_0, transpose_y = attn_171_transpose_y_0, x = var_9215_cast, y = var_9219_cast)[name = tensor("attn_171_cast")]; + tensor var_9223 = const()[name = tensor("op_9223"), val = tensor([2, 768, 1, -1])]; + tensor input_675_cast = reshape(shape = var_9223, x = attn_171_cast)[name = tensor("input_675_cast")]; + tensor var_9228 = const()[name = tensor("op_9228"), val = tensor([1, 1])]; + tensor var_9230 = const()[name = tensor("op_9230"), val = tensor([1, 1])]; + tensor var_9232_pad_type_0 = const()[name = tensor("op_9232_pad_type_0"), val = tensor("custom")]; + tensor var_9232_pad_0 = const()[name = tensor("op_9232_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4434620736)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4435800448)))]; + tensor var_9232_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_9230, groups = var_7059, pad = var_9232_pad_0, pad_type = var_9232_pad_type_0, strides = var_9228, weight = up_blocks_2_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_675_cast)[name = tensor("op_9232_cast")]; + tensor inputs_257_cast = add(x = var_9232_cast, y = inputs_255_cast)[name = tensor("inputs_257_cast")]; + tensor var_9236 = const()[name = tensor("op_9236"), val = tensor([1])]; + tensor channels_mean_257_cast = reduce_mean(axes = var_9236, keep_dims = var_7054, x = inputs_257_cast)[name = tensor("channels_mean_257_cast")]; + tensor zero_mean_257_cast = sub(x = inputs_257_cast, y = channels_mean_257_cast)[name = tensor("zero_mean_257_cast")]; + tensor zero_mean_sq_257_cast = mul(x = zero_mean_257_cast, y = zero_mean_257_cast)[name = tensor("zero_mean_sq_257_cast")]; + tensor var_9240 = const()[name = tensor("op_9240"), val = tensor([1])]; + tensor var_9241_cast = reduce_mean(axes = var_9240, keep_dims = var_7054, x = zero_mean_sq_257_cast)[name = tensor("op_9241_cast")]; + tensor var_9242_to_fp16 = const()[name = tensor("op_9242_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9243_cast = add(x = var_9241_cast, y = var_9242_to_fp16)[name = tensor("op_9243_cast")]; + tensor denom_257_epsilon_0_to_fp16 = const()[name = tensor("denom_257_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_257_cast = rsqrt(epsilon = denom_257_epsilon_0_to_fp16, x = var_9243_cast)[name = tensor("denom_257_cast")]; + tensor out_257_cast = mul(x = zero_mean_257_cast, y = denom_257_cast)[name = tensor("out_257_cast")]; + tensor var_9247_to_fp16 = const()[name = tensor("op_9247_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4435802048)))]; + tensor var_9248_cast = add(x = out_257_cast, y = var_9247_to_fp16)[name = tensor("op_9248_cast")]; + tensor var_9250_to_fp16 = const()[name = tensor("op_9250_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4435803648)))]; + tensor input_677_cast = mul(x = var_9248_cast, y = var_9250_to_fp16)[name = tensor("input_677_cast")]; + tensor var_9258 = const()[name = tensor("op_9258"), val = tensor([1, 1])]; + tensor var_9260 = const()[name = tensor("op_9260"), val = tensor([1, 1])]; + tensor var_9262_pad_type_0 = const()[name = tensor("op_9262_pad_type_0"), val = tensor("custom")]; + tensor var_9262_pad_0 = const()[name = tensor("op_9262_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4435805248)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4445242496)))]; + tensor var_9262_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_9260, groups = var_7059, pad = var_9262_pad_0, pad_type = var_9262_pad_type_0, strides = var_9258, weight = up_blocks_2_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_677_cast)[name = tensor("op_9262_cast")]; + tensor var_9263_split_sizes_0 = const()[name = tensor("op_9263_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_9263_axis_0 = const()[name = tensor("op_9263_axis_0"), val = tensor(1)]; + tensor var_9263_cast_0, tensor var_9263_cast_1 = split(axis = var_9263_axis_0, split_sizes = var_9263_split_sizes_0, x = var_9262_cast)[name = tensor("op_9263_cast")]; + tensor var_9265_mode_0 = const()[name = tensor("op_9265_mode_0"), val = tensor("EXACT")]; + tensor var_9265_cast = gelu(mode = var_9265_mode_0, x = var_9263_cast_1)[name = tensor("op_9265_cast")]; + tensor input_679_cast = mul(x = var_9263_cast_0, y = var_9265_cast)[name = tensor("input_679_cast")]; + tensor var_9269 = const()[name = tensor("op_9269"), val = tensor([1, 1])]; + tensor var_9271 = const()[name = tensor("op_9271"), val = tensor([1, 1])]; + tensor var_9273_pad_type_0 = const()[name = tensor("op_9273_pad_type_0"), val = tensor("custom")]; + tensor var_9273_pad_0 = const()[name = tensor("op_9273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4445254848)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4449973504)))]; + tensor var_9273_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_9271, groups = var_7059, pad = var_9273_pad_0, pad_type = var_9273_pad_type_0, strides = var_9269, weight = up_blocks_2_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_679_cast)[name = tensor("op_9273_cast")]; + tensor inputs_259_cast = add(x = var_9273_cast, y = inputs_257_cast)[name = tensor("inputs_259_cast")]; + tensor var_9283 = const()[name = tensor("op_9283"), val = tensor([1])]; + tensor channels_mean_259_cast = reduce_mean(axes = var_9283, keep_dims = var_7054, x = inputs_259_cast)[name = tensor("channels_mean_259_cast")]; + tensor zero_mean_259_cast = sub(x = inputs_259_cast, y = channels_mean_259_cast)[name = tensor("zero_mean_259_cast")]; + tensor zero_mean_sq_259_cast = mul(x = zero_mean_259_cast, y = zero_mean_259_cast)[name = tensor("zero_mean_sq_259_cast")]; + tensor var_9287 = const()[name = tensor("op_9287"), val = tensor([1])]; + tensor var_9288_cast = reduce_mean(axes = var_9287, keep_dims = var_7054, x = zero_mean_sq_259_cast)[name = tensor("op_9288_cast")]; + tensor var_9289_to_fp16 = const()[name = tensor("op_9289_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9290_cast = add(x = var_9288_cast, y = var_9289_to_fp16)[name = tensor("op_9290_cast")]; + tensor denom_259_epsilon_0_to_fp16 = const()[name = tensor("denom_259_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_259_cast = rsqrt(epsilon = denom_259_epsilon_0_to_fp16, x = var_9290_cast)[name = tensor("denom_259_cast")]; + tensor out_259_cast = mul(x = zero_mean_259_cast, y = denom_259_cast)[name = tensor("out_259_cast")]; + tensor var_9294_to_fp16 = const()[name = tensor("op_9294_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4449975104)))]; + tensor var_9295_cast = add(x = out_259_cast, y = var_9294_to_fp16)[name = tensor("op_9295_cast")]; + tensor var_9297_to_fp16 = const()[name = tensor("op_9297_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4449976704)))]; + tensor hidden_states_437_cast = mul(x = var_9295_cast, y = var_9297_to_fp16)[name = tensor("hidden_states_437_cast")]; + tensor var_9304 = const()[name = tensor("op_9304"), val = tensor([1, 1])]; + tensor var_9306 = const()[name = tensor("op_9306"), val = tensor([1, 1])]; + tensor q_173_pad_type_0 = const()[name = tensor("q_173_pad_type_0"), val = tensor("custom")]; + tensor q_173_pad_0 = const()[name = tensor("q_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4449978304)))]; + tensor q_173_cast = conv(dilations = var_9306, groups = var_7059, pad = q_173_pad_0, pad_type = q_173_pad_type_0, strides = var_9304, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_437_cast)[name = tensor("q_173_cast")]; + tensor var_9310 = const()[name = tensor("op_9310"), val = tensor([1, 1])]; + tensor var_9312 = const()[name = tensor("op_9312"), val = tensor([1, 1])]; + tensor k_173_pad_type_0 = const()[name = tensor("k_173_pad_type_0"), val = tensor("custom")]; + tensor k_173_pad_0 = const()[name = tensor("k_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4451158016)))]; + tensor k_173_cast = conv(dilations = var_9312, groups = var_7059, pad = k_173_pad_0, pad_type = k_173_pad_type_0, strides = var_9310, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_437_cast)[name = tensor("k_173_cast")]; + tensor var_9316 = const()[name = tensor("op_9316"), val = tensor([1, 1])]; + tensor var_9318 = const()[name = tensor("op_9318"), val = tensor([1, 1])]; + tensor v_173_pad_type_0 = const()[name = tensor("v_173_pad_type_0"), val = tensor("custom")]; + tensor v_173_pad_0 = const()[name = tensor("v_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4452337728)))]; + tensor v_173_cast = conv(dilations = var_9318, groups = var_7059, pad = v_173_pad_0, pad_type = v_173_pad_type_0, strides = var_9316, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_437_cast)[name = tensor("v_173_cast")]; + tensor var_9322 = const()[name = tensor("op_9322"), val = tensor([2, 12, 64, -1])]; + tensor var_9323_cast = reshape(shape = var_9322, x = q_173_cast)[name = tensor("op_9323_cast")]; + tensor var_9324 = const()[name = tensor("op_9324"), val = tensor([2, 12, 64, -1])]; + tensor var_9325_cast = reshape(shape = var_9324, x = k_173_cast)[name = tensor("op_9325_cast")]; + tensor var_9326 = const()[name = tensor("op_9326"), val = tensor([2, 12, 64, -1])]; + tensor var_9327_cast = reshape(shape = var_9326, x = v_173_cast)[name = tensor("op_9327_cast")]; + tensor attn_weights_345_transpose_x_0 = const()[name = tensor("attn_weights_345_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_345_transpose_y_0 = const()[name = tensor("attn_weights_345_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_345_cast = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_9323_cast, y = var_9325_cast)[name = tensor("attn_weights_345_cast")]; + tensor attn_weights_347_cast = mul(x = attn_weights_345_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_347_cast")]; + tensor var_9331_cast = softmax(axis = var_7043, x = attn_weights_347_cast)[name = tensor("op_9331_cast")]; + tensor attn_173_transpose_x_0 = const()[name = tensor("attn_173_transpose_x_0"), val = tensor(false)]; + tensor attn_173_transpose_y_0 = const()[name = tensor("attn_173_transpose_y_0"), val = tensor(true)]; + tensor attn_173_cast = matmul(transpose_x = attn_173_transpose_x_0, transpose_y = attn_173_transpose_y_0, x = var_9327_cast, y = var_9331_cast)[name = tensor("attn_173_cast")]; + tensor var_9335 = const()[name = tensor("op_9335"), val = tensor([2, 768, 1, -1])]; + tensor input_681_cast = reshape(shape = var_9335, x = attn_173_cast)[name = tensor("input_681_cast")]; + tensor var_9340 = const()[name = tensor("op_9340"), val = tensor([1, 1])]; + tensor var_9342 = const()[name = tensor("op_9342"), val = tensor([1, 1])]; + tensor var_9344_pad_type_0 = const()[name = tensor("op_9344_pad_type_0"), val = tensor("custom")]; + tensor var_9344_pad_0 = const()[name = tensor("op_9344_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4453517440)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4454697152)))]; + tensor var_9344_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_9342, groups = var_7059, pad = var_9344_pad_0, pad_type = var_9344_pad_type_0, strides = var_9340, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_681_cast)[name = tensor("op_9344_cast")]; + tensor inputs_261_cast = add(x = var_9344_cast, y = inputs_259_cast)[name = tensor("inputs_261_cast")]; + tensor var_9348 = const()[name = tensor("op_9348"), val = tensor([1])]; + tensor channels_mean_261_cast = reduce_mean(axes = var_9348, keep_dims = var_7054, x = inputs_261_cast)[name = tensor("channels_mean_261_cast")]; + tensor zero_mean_261_cast = sub(x = inputs_261_cast, y = channels_mean_261_cast)[name = tensor("zero_mean_261_cast")]; + tensor zero_mean_sq_261_cast = mul(x = zero_mean_261_cast, y = zero_mean_261_cast)[name = tensor("zero_mean_sq_261_cast")]; + tensor var_9352 = const()[name = tensor("op_9352"), val = tensor([1])]; + tensor var_9353_cast = reduce_mean(axes = var_9352, keep_dims = var_7054, x = zero_mean_sq_261_cast)[name = tensor("op_9353_cast")]; + tensor var_9354_to_fp16 = const()[name = tensor("op_9354_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9355_cast = add(x = var_9353_cast, y = var_9354_to_fp16)[name = tensor("op_9355_cast")]; + tensor denom_261_epsilon_0_to_fp16 = const()[name = tensor("denom_261_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_261_cast = rsqrt(epsilon = denom_261_epsilon_0_to_fp16, x = var_9355_cast)[name = tensor("denom_261_cast")]; + tensor out_261_cast = mul(x = zero_mean_261_cast, y = denom_261_cast)[name = tensor("out_261_cast")]; + tensor var_9359_to_fp16 = const()[name = tensor("op_9359_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4454698752)))]; + tensor var_9360_cast = add(x = out_261_cast, y = var_9359_to_fp16)[name = tensor("op_9360_cast")]; + tensor var_9362_to_fp16 = const()[name = tensor("op_9362_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4454700352)))]; + tensor hidden_states_439_cast = mul(x = var_9360_cast, y = var_9362_to_fp16)[name = tensor("hidden_states_439_cast")]; + tensor var_9369 = const()[name = tensor("op_9369"), val = tensor([1, 1])]; + tensor var_9371 = const()[name = tensor("op_9371"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4454701952)))]; + tensor q_cast = conv(dilations = var_9371, groups = var_7059, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_9369, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_439_cast)[name = tensor("q_cast")]; + tensor var_9375 = const()[name = tensor("op_9375"), val = tensor([1, 1])]; + tensor var_9377 = const()[name = tensor("op_9377"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4455881664)))]; + tensor k_cast = conv(dilations = var_9377, groups = var_7059, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_9375, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_cast")]; + tensor var_9381 = const()[name = tensor("op_9381"), val = tensor([1, 1])]; + tensor var_9383 = const()[name = tensor("op_9383"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4457847808)))]; + tensor v_cast = conv(dilations = var_9383, groups = var_7059, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_9381, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_cast")]; + tensor var_9387 = const()[name = tensor("op_9387"), val = tensor([2, 12, 64, -1])]; + tensor var_9388_cast = reshape(shape = var_9387, x = q_cast)[name = tensor("op_9388_cast")]; + tensor var_9389 = const()[name = tensor("op_9389"), val = tensor([2, 12, 64, -1])]; + tensor var_9390_cast = reshape(shape = var_9389, x = k_cast)[name = tensor("op_9390_cast")]; + tensor var_9391 = const()[name = tensor("op_9391"), val = tensor([2, 12, 64, -1])]; + tensor var_9392_cast = reshape(shape = var_9391, x = v_cast)[name = tensor("op_9392_cast")]; + tensor attn_weights_349_transpose_x_0 = const()[name = tensor("attn_weights_349_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_349_transpose_y_0 = const()[name = tensor("attn_weights_349_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_349_cast = matmul(transpose_x = attn_weights_349_transpose_x_0, transpose_y = attn_weights_349_transpose_y_0, x = var_9388_cast, y = var_9390_cast)[name = tensor("attn_weights_349_cast")]; + tensor attn_weights_cast = mul(x = attn_weights_349_cast, y = var_7050_to_fp16)[name = tensor("attn_weights_cast")]; + tensor var_9396_cast = softmax(axis = var_7043, x = attn_weights_cast)[name = tensor("op_9396_cast")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_9392_cast, y = var_9396_cast)[name = tensor("attn_cast")]; + tensor var_9400 = const()[name = tensor("op_9400"), val = tensor([2, 768, 1, -1])]; + tensor input_683_cast = reshape(shape = var_9400, x = attn_cast)[name = tensor("input_683_cast")]; + tensor var_9405 = const()[name = tensor("op_9405"), val = tensor([1, 1])]; + tensor var_9407 = const()[name = tensor("op_9407"), val = tensor([1, 1])]; + tensor var_9409_pad_type_0 = const()[name = tensor("op_9409_pad_type_0"), val = tensor("custom")]; + tensor var_9409_pad_0 = const()[name = tensor("op_9409_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4459813952)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4460993664)))]; + tensor var_9409_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_9407, groups = var_7059, pad = var_9409_pad_0, pad_type = var_9409_pad_type_0, strides = var_9405, weight = up_blocks_2_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_683_cast)[name = tensor("op_9409_cast")]; + tensor inputs_cast = add(x = var_9409_cast, y = inputs_261_cast)[name = tensor("inputs_cast")]; + tensor var_9413 = const()[name = tensor("op_9413"), val = tensor([1])]; + tensor channels_mean_cast = reduce_mean(axes = var_9413, keep_dims = var_7054, x = inputs_cast)[name = tensor("channels_mean_cast")]; + tensor zero_mean_cast = sub(x = inputs_cast, y = channels_mean_cast)[name = tensor("zero_mean_cast")]; + tensor zero_mean_sq_cast = mul(x = zero_mean_cast, y = zero_mean_cast)[name = tensor("zero_mean_sq_cast")]; + tensor var_9417 = const()[name = tensor("op_9417"), val = tensor([1])]; + tensor var_9418_cast = reduce_mean(axes = var_9417, keep_dims = var_7054, x = zero_mean_sq_cast)[name = tensor("op_9418_cast")]; + tensor var_9419_to_fp16 = const()[name = tensor("op_9419_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9420_cast = add(x = var_9418_cast, y = var_9419_to_fp16)[name = tensor("op_9420_cast")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_9420_cast)[name = tensor("denom_cast")]; + tensor out_cast = mul(x = zero_mean_cast, y = denom_cast)[name = tensor("out_cast")]; + tensor var_9424_to_fp16 = const()[name = tensor("op_9424_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4460995264)))]; + tensor var_9425_cast = add(x = out_cast, y = var_9424_to_fp16)[name = tensor("op_9425_cast")]; + tensor var_9427_to_fp16 = const()[name = tensor("op_9427_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4460996864)))]; + tensor input_685_cast = mul(x = var_9425_cast, y = var_9427_to_fp16)[name = tensor("input_685_cast")]; + tensor var_9435 = const()[name = tensor("op_9435"), val = tensor([1, 1])]; + tensor var_9437 = const()[name = tensor("op_9437"), val = tensor([1, 1])]; + tensor var_9439_pad_type_0 = const()[name = tensor("op_9439_pad_type_0"), val = tensor("custom")]; + tensor var_9439_pad_0 = const()[name = tensor("op_9439_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4460998464)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4470435712)))]; + tensor var_9439_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_9437, groups = var_7059, pad = var_9439_pad_0, pad_type = var_9439_pad_type_0, strides = var_9435, weight = up_blocks_2_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_685_cast)[name = tensor("op_9439_cast")]; + tensor var_9440_split_sizes_0 = const()[name = tensor("op_9440_split_sizes_0"), val = tensor([3072, 3072])]; + tensor var_9440_axis_0 = const()[name = tensor("op_9440_axis_0"), val = tensor(1)]; + tensor var_9440_cast_0, tensor var_9440_cast_1 = split(axis = var_9440_axis_0, split_sizes = var_9440_split_sizes_0, x = var_9439_cast)[name = tensor("op_9440_cast")]; + tensor var_9442_mode_0 = const()[name = tensor("op_9442_mode_0"), val = tensor("EXACT")]; + tensor var_9442_cast = gelu(mode = var_9442_mode_0, x = var_9440_cast_1)[name = tensor("op_9442_cast")]; + tensor input_687_cast = mul(x = var_9440_cast_0, y = var_9442_cast)[name = tensor("input_687_cast")]; + tensor var_9446 = const()[name = tensor("op_9446"), val = tensor([1, 1])]; + tensor var_9448 = const()[name = tensor("op_9448"), val = tensor([1, 1])]; + tensor var_9450_pad_type_0 = const()[name = tensor("op_9450_pad_type_0"), val = tensor("custom")]; + tensor var_9450_pad_0 = const()[name = tensor("op_9450_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4470448064)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4475166720)))]; + tensor var_9450_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_9448, groups = var_7059, pad = var_9450_pad_0, pad_type = var_9450_pad_type_0, strides = var_9446, weight = up_blocks_2_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_687_cast)[name = tensor("op_9450_cast")]; + tensor hidden_states_443_cast = add(x = var_9450_cast, y = inputs_cast)[name = tensor("hidden_states_443_cast")]; + tensor var_9452 = const()[name = tensor("op_9452"), val = tensor([2, 768, 64, 64])]; + tensor input_689_cast = reshape(shape = var_9452, x = hidden_states_443_cast)[name = tensor("input_689_cast")]; + tensor var_9456 = const()[name = tensor("op_9456"), val = tensor([1, 1])]; + tensor var_9458 = const()[name = tensor("op_9458"), val = tensor([1, 1])]; + tensor hidden_states_445_pad_type_0 = const()[name = tensor("hidden_states_445_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_445_pad_0 = const()[name = tensor("hidden_states_445_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4475168320)))]; + tensor up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4476348032)))]; + tensor hidden_states_445_cast = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_9458, groups = var_7059, pad = hidden_states_445_pad_0, pad_type = hidden_states_445_pad_type_0, strides = var_9456, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16, x = input_689_cast)[name = tensor("hidden_states_445_cast")]; + tensor input_691_cast = add(x = hidden_states_445_cast, y = hidden_states_415_cast)[name = tensor("input_691_cast")]; + tensor input_693_scale_factor_height_0 = const()[name = tensor("input_693_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_693_scale_factor_width_0 = const()[name = tensor("input_693_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_693_cast = upsample_nearest_neighbor(scale_factor_height = input_693_scale_factor_height_0, scale_factor_width = input_693_scale_factor_width_0, x = input_691_cast)[name = tensor("input_693_cast")]; + tensor var_9467 = const()[name = tensor("op_9467"), val = tensor([1, 1])]; + tensor var_9469 = const()[name = tensor("op_9469"), val = tensor([1, 1])]; + tensor hidden_states_447_pad_type_0 = const()[name = tensor("hidden_states_447_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_447_pad_0 = const()[name = tensor("hidden_states_447_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4476349632)))]; + tensor up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4486966528)))]; + tensor hidden_states_447_cast = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_9469, groups = var_7059, pad = hidden_states_447_pad_0, pad_type = hidden_states_447_pad_type_0, strides = var_9467, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_693_cast)[name = tensor("hidden_states_447_cast")]; + tensor var_9477 = const()[name = tensor("op_9477"), val = tensor(1)]; + tensor input_695_interleave_0 = const()[name = tensor("input_695_interleave_0"), val = tensor(false)]; + tensor input_695_cast = concat(axis = var_9477, interleave = input_695_interleave_0, values = (hidden_states_447_cast, input_43_cast))[name = tensor("input_695_cast")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([2, 32, 36, 128, 128])]; + tensor reshape_196_cast = reshape(shape = reshape_196_shape_0, x = input_695_cast)[name = tensor("reshape_196_cast")]; + tensor reduce_mean_147_axes_0 = const()[name = tensor("reduce_mean_147_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_147_keep_dims_0 = const()[name = tensor("reduce_mean_147_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_147_cast = reduce_mean(axes = reduce_mean_147_axes_0, keep_dims = reduce_mean_147_keep_dims_0, x = reshape_196_cast)[name = tensor("reduce_mean_147_cast")]; + tensor sub_98_cast = sub(x = reshape_196_cast, y = reduce_mean_147_cast)[name = tensor("sub_98_cast")]; + tensor square_49_cast = square(x = sub_98_cast)[name = tensor("square_49_cast")]; + tensor reduce_mean_149_axes_0 = const()[name = tensor("reduce_mean_149_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_149_keep_dims_0 = const()[name = tensor("reduce_mean_149_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_149_cast = reduce_mean(axes = reduce_mean_149_axes_0, keep_dims = reduce_mean_149_keep_dims_0, x = square_49_cast)[name = tensor("reduce_mean_149_cast")]; + tensor add_98_y_0_to_fp16 = const()[name = tensor("add_98_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_98_cast = add(x = reduce_mean_149_cast, y = add_98_y_0_to_fp16)[name = tensor("add_98_cast")]; + tensor sqrt_49_cast = sqrt(x = add_98_cast)[name = tensor("sqrt_49_cast")]; + tensor real_div_49_cast = real_div(x = sub_98_cast, y = sqrt_49_cast)[name = tensor("real_div_49_cast")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([2, 1152, 128, 128])]; + tensor reshape_197_cast = reshape(shape = reshape_197_shape_0, x = real_div_49_cast)[name = tensor("reshape_197_cast")]; + tensor add_99_gamma_0_to_fp16 = const()[name = tensor("add_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4486968128)))]; + tensor add_99_beta_0_to_fp16 = const()[name = tensor("add_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4486970496)))]; + tensor add_99_epsilon_0_to_fp16 = const()[name = tensor("add_99_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_99_cast = batch_norm(beta = add_99_beta_0_to_fp16, epsilon = add_99_epsilon_0_to_fp16, gamma = add_99_gamma_0_to_fp16, mean = add_93_mean_0_to_fp16, variance = add_93_variance_0_to_fp16, x = reshape_197_cast)[name = tensor("add_99_cast")]; + tensor input_699_cast = silu(x = add_99_cast)[name = tensor("input_699_cast")]; + tensor var_9498 = const()[name = tensor("op_9498"), val = tensor([1, 1])]; + tensor var_9500 = const()[name = tensor("op_9500"), val = tensor([1, 1])]; + tensor hidden_states_449_pad_type_0 = const()[name = tensor("hidden_states_449_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_449_pad_0 = const()[name = tensor("hidden_states_449_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4486972864)))]; + tensor up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4494935552)))]; + tensor hidden_states_449_cast = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_9500, groups = var_9477, pad = hidden_states_449_pad_0, pad_type = hidden_states_449_pad_type_0, strides = var_9498, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_699_cast)[name = tensor("hidden_states_449_cast")]; + tensor var_9506 = const()[name = tensor("op_9506"), val = tensor([1, 1])]; + tensor var_9508 = const()[name = tensor("op_9508"), val = tensor([1, 1])]; + tensor temb_39_pad_type_0 = const()[name = tensor("temb_39_pad_type_0"), val = tensor("custom")]; + tensor temb_39_pad_0 = const()[name = tensor("temb_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4494936384)))]; + tensor up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4496116096)))]; + tensor temb_39_cast = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_9508, groups = var_9477, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_9506, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_39_cast")]; + tensor input_703_cast = add(x = hidden_states_449_cast, y = temb_39_cast)[name = tensor("input_703_cast")]; + tensor reshape_200_shape_0 = const()[name = tensor("reshape_200_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_200_cast = reshape(shape = reshape_200_shape_0, x = input_703_cast)[name = tensor("reshape_200_cast")]; + tensor reduce_mean_150_axes_0 = const()[name = tensor("reduce_mean_150_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_150_keep_dims_0 = const()[name = tensor("reduce_mean_150_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_150_cast = reduce_mean(axes = reduce_mean_150_axes_0, keep_dims = reduce_mean_150_keep_dims_0, x = reshape_200_cast)[name = tensor("reduce_mean_150_cast")]; + tensor sub_100_cast = sub(x = reshape_200_cast, y = reduce_mean_150_cast)[name = tensor("sub_100_cast")]; + tensor square_50_cast = square(x = sub_100_cast)[name = tensor("square_50_cast")]; + tensor reduce_mean_152_axes_0 = const()[name = tensor("reduce_mean_152_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_152_keep_dims_0 = const()[name = tensor("reduce_mean_152_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_152_cast = reduce_mean(axes = reduce_mean_152_axes_0, keep_dims = reduce_mean_152_keep_dims_0, x = square_50_cast)[name = tensor("reduce_mean_152_cast")]; + tensor add_100_y_0_to_fp16 = const()[name = tensor("add_100_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_100_cast = add(x = reduce_mean_152_cast, y = add_100_y_0_to_fp16)[name = tensor("add_100_cast")]; + tensor sqrt_50_cast = sqrt(x = add_100_cast)[name = tensor("sqrt_50_cast")]; + tensor real_div_50_cast = real_div(x = sub_100_cast, y = sqrt_50_cast)[name = tensor("real_div_50_cast")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_201_cast = reshape(shape = reshape_201_shape_0, x = real_div_50_cast)[name = tensor("reshape_201_cast")]; + tensor add_101_gamma_0_to_fp16 = const()[name = tensor("add_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4496116928)))]; + tensor add_101_beta_0_to_fp16 = const()[name = tensor("add_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4496117760)))]; + tensor add_101_epsilon_0_to_fp16 = const()[name = tensor("add_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_101_cast = batch_norm(beta = add_101_beta_0_to_fp16, epsilon = add_101_epsilon_0_to_fp16, gamma = add_101_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_201_cast)[name = tensor("add_101_cast")]; + tensor input_707_cast = silu(x = add_101_cast)[name = tensor("input_707_cast")]; + tensor var_9518 = const()[name = tensor("op_9518"), val = tensor([1, 1])]; + tensor var_9520 = const()[name = tensor("op_9520"), val = tensor([1, 1])]; + tensor hidden_states_451_pad_type_0 = const()[name = tensor("hidden_states_451_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_451_pad_0 = const()[name = tensor("hidden_states_451_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4496118592)))]; + tensor up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4498772864)))]; + tensor hidden_states_451_cast = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_9520, groups = var_9477, pad = hidden_states_451_pad_0, pad_type = hidden_states_451_pad_type_0, strides = var_9518, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_707_cast)[name = tensor("hidden_states_451_cast")]; + tensor var_9525 = const()[name = tensor("op_9525"), val = tensor([1, 1])]; + tensor var_9527 = const()[name = tensor("op_9527"), val = tensor([1, 1])]; + tensor x_23_pad_type_0 = const()[name = tensor("x_23_pad_type_0"), val = tensor("custom")]; + tensor x_23_pad_0 = const()[name = tensor("x_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4498773696)))]; + tensor up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4499658496)))]; + tensor x_23_cast = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_9527, groups = var_9477, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_9525, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_695_cast)[name = tensor("x_23_cast")]; + tensor hidden_states_453_cast = add(x = x_23_cast, y = hidden_states_451_cast)[name = tensor("hidden_states_453_cast")]; + tensor input_709_interleave_0 = const()[name = tensor("input_709_interleave_0"), val = tensor(false)]; + tensor input_709_cast = concat(axis = var_9477, interleave = input_709_interleave_0, values = (hidden_states_453_cast, input_29_cast))[name = tensor("input_709_cast")]; + tensor reshape_204_shape_0 = const()[name = tensor("reshape_204_shape_0"), val = tensor([2, 32, 24, 128, 128])]; + tensor reshape_204_cast = reshape(shape = reshape_204_shape_0, x = input_709_cast)[name = tensor("reshape_204_cast")]; + tensor reduce_mean_153_axes_0 = const()[name = tensor("reduce_mean_153_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_153_keep_dims_0 = const()[name = tensor("reduce_mean_153_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_153_cast = reduce_mean(axes = reduce_mean_153_axes_0, keep_dims = reduce_mean_153_keep_dims_0, x = reshape_204_cast)[name = tensor("reduce_mean_153_cast")]; + tensor sub_102_cast = sub(x = reshape_204_cast, y = reduce_mean_153_cast)[name = tensor("sub_102_cast")]; + tensor square_51_cast = square(x = sub_102_cast)[name = tensor("square_51_cast")]; + tensor reduce_mean_155_axes_0 = const()[name = tensor("reduce_mean_155_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_155_keep_dims_0 = const()[name = tensor("reduce_mean_155_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_155_cast = reduce_mean(axes = reduce_mean_155_axes_0, keep_dims = reduce_mean_155_keep_dims_0, x = square_51_cast)[name = tensor("reduce_mean_155_cast")]; + tensor add_102_y_0_to_fp16 = const()[name = tensor("add_102_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_102_cast = add(x = reduce_mean_155_cast, y = add_102_y_0_to_fp16)[name = tensor("add_102_cast")]; + tensor sqrt_51_cast = sqrt(x = add_102_cast)[name = tensor("sqrt_51_cast")]; + tensor real_div_51_cast = real_div(x = sub_102_cast, y = sqrt_51_cast)[name = tensor("real_div_51_cast")]; + tensor reshape_205_shape_0 = const()[name = tensor("reshape_205_shape_0"), val = tensor([2, 768, 128, 128])]; + tensor reshape_205_cast = reshape(shape = reshape_205_shape_0, x = real_div_51_cast)[name = tensor("reshape_205_cast")]; + tensor add_103_gamma_0_to_fp16 = const()[name = tensor("add_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4499659328)))]; + tensor add_103_beta_0_to_fp16 = const()[name = tensor("add_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4499660928)))]; + tensor add_103_epsilon_0_to_fp16 = const()[name = tensor("add_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_103_cast = batch_norm(beta = add_103_beta_0_to_fp16, epsilon = add_103_epsilon_0_to_fp16, gamma = add_103_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_205_cast)[name = tensor("add_103_cast")]; + tensor input_713_cast = silu(x = add_103_cast)[name = tensor("input_713_cast")]; + tensor var_9545 = const()[name = tensor("op_9545"), val = tensor([1, 1])]; + tensor var_9547 = const()[name = tensor("op_9547"), val = tensor([1, 1])]; + tensor hidden_states_455_pad_type_0 = const()[name = tensor("hidden_states_455_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_455_pad_0 = const()[name = tensor("hidden_states_455_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4499662528)))]; + tensor up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4504971008)))]; + tensor hidden_states_455_cast = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_9547, groups = var_9477, pad = hidden_states_455_pad_0, pad_type = hidden_states_455_pad_type_0, strides = var_9545, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_713_cast)[name = tensor("hidden_states_455_cast")]; + tensor var_9553 = const()[name = tensor("op_9553"), val = tensor([1, 1])]; + tensor var_9555 = const()[name = tensor("op_9555"), val = tensor([1, 1])]; + tensor temb_41_pad_type_0 = const()[name = tensor("temb_41_pad_type_0"), val = tensor("custom")]; + tensor temb_41_pad_0 = const()[name = tensor("temb_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4504971840)))]; + tensor up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506151552)))]; + tensor temb_41_cast = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_9555, groups = var_9477, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_9553, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_41_cast")]; + tensor input_717_cast = add(x = hidden_states_455_cast, y = temb_41_cast)[name = tensor("input_717_cast")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_208_cast = reshape(shape = reshape_208_shape_0, x = input_717_cast)[name = tensor("reshape_208_cast")]; + tensor reduce_mean_156_axes_0 = const()[name = tensor("reduce_mean_156_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_156_keep_dims_0 = const()[name = tensor("reduce_mean_156_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_156_cast = reduce_mean(axes = reduce_mean_156_axes_0, keep_dims = reduce_mean_156_keep_dims_0, x = reshape_208_cast)[name = tensor("reduce_mean_156_cast")]; + tensor sub_104_cast = sub(x = reshape_208_cast, y = reduce_mean_156_cast)[name = tensor("sub_104_cast")]; + tensor square_52_cast = square(x = sub_104_cast)[name = tensor("square_52_cast")]; + tensor reduce_mean_158_axes_0 = const()[name = tensor("reduce_mean_158_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_158_keep_dims_0 = const()[name = tensor("reduce_mean_158_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_158_cast = reduce_mean(axes = reduce_mean_158_axes_0, keep_dims = reduce_mean_158_keep_dims_0, x = square_52_cast)[name = tensor("reduce_mean_158_cast")]; + tensor add_104_y_0_to_fp16 = const()[name = tensor("add_104_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_104_cast = add(x = reduce_mean_158_cast, y = add_104_y_0_to_fp16)[name = tensor("add_104_cast")]; + tensor sqrt_52_cast = sqrt(x = add_104_cast)[name = tensor("sqrt_52_cast")]; + tensor real_div_52_cast = real_div(x = sub_104_cast, y = sqrt_52_cast)[name = tensor("real_div_52_cast")]; + tensor reshape_209_shape_0 = const()[name = tensor("reshape_209_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_209_cast = reshape(shape = reshape_209_shape_0, x = real_div_52_cast)[name = tensor("reshape_209_cast")]; + tensor add_105_gamma_0_to_fp16 = const()[name = tensor("add_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506152384)))]; + tensor add_105_beta_0_to_fp16 = const()[name = tensor("add_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506153216)))]; + tensor add_105_epsilon_0_to_fp16 = const()[name = tensor("add_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_105_cast = batch_norm(beta = add_105_beta_0_to_fp16, epsilon = add_105_epsilon_0_to_fp16, gamma = add_105_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_209_cast)[name = tensor("add_105_cast")]; + tensor input_721_cast = silu(x = add_105_cast)[name = tensor("input_721_cast")]; + tensor var_9565 = const()[name = tensor("op_9565"), val = tensor([1, 1])]; + tensor var_9567 = const()[name = tensor("op_9567"), val = tensor([1, 1])]; + tensor hidden_states_457_pad_type_0 = const()[name = tensor("hidden_states_457_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_457_pad_0 = const()[name = tensor("hidden_states_457_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506154048)))]; + tensor up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508808320)))]; + tensor hidden_states_457_cast = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_9567, groups = var_9477, pad = hidden_states_457_pad_0, pad_type = hidden_states_457_pad_type_0, strides = var_9565, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_721_cast)[name = tensor("hidden_states_457_cast")]; + tensor var_9572 = const()[name = tensor("op_9572"), val = tensor([1, 1])]; + tensor var_9574 = const()[name = tensor("op_9574"), val = tensor([1, 1])]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("custom")]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508809152)))]; + tensor up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4509399040)))]; + tensor x_25_cast = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_9574, groups = var_9477, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_9572, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16, x = input_709_cast)[name = tensor("x_25_cast")]; + tensor hidden_states_459_cast = add(x = x_25_cast, y = hidden_states_457_cast)[name = tensor("hidden_states_459_cast")]; + tensor input_723_interleave_0 = const()[name = tensor("input_723_interleave_0"), val = tensor(false)]; + tensor input_723_cast = concat(axis = var_9477, interleave = input_723_interleave_0, values = (hidden_states_459_cast, input_13_cast))[name = tensor("input_723_cast")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([2, 32, 24, 128, 128])]; + tensor reshape_212_cast = reshape(shape = reshape_212_shape_0, x = input_723_cast)[name = tensor("reshape_212_cast")]; + tensor reduce_mean_159_axes_0 = const()[name = tensor("reduce_mean_159_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_159_keep_dims_0 = const()[name = tensor("reduce_mean_159_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_159_cast = reduce_mean(axes = reduce_mean_159_axes_0, keep_dims = reduce_mean_159_keep_dims_0, x = reshape_212_cast)[name = tensor("reduce_mean_159_cast")]; + tensor sub_106_cast = sub(x = reshape_212_cast, y = reduce_mean_159_cast)[name = tensor("sub_106_cast")]; + tensor square_53_cast = square(x = sub_106_cast)[name = tensor("square_53_cast")]; + tensor reduce_mean_161_axes_0 = const()[name = tensor("reduce_mean_161_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_161_keep_dims_0 = const()[name = tensor("reduce_mean_161_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_161_cast = reduce_mean(axes = reduce_mean_161_axes_0, keep_dims = reduce_mean_161_keep_dims_0, x = square_53_cast)[name = tensor("reduce_mean_161_cast")]; + tensor add_106_y_0_to_fp16 = const()[name = tensor("add_106_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_106_cast = add(x = reduce_mean_161_cast, y = add_106_y_0_to_fp16)[name = tensor("add_106_cast")]; + tensor sqrt_53_cast = sqrt(x = add_106_cast)[name = tensor("sqrt_53_cast")]; + tensor real_div_53_cast = real_div(x = sub_106_cast, y = sqrt_53_cast)[name = tensor("real_div_53_cast")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([2, 768, 128, 128])]; + tensor reshape_213_cast = reshape(shape = reshape_213_shape_0, x = real_div_53_cast)[name = tensor("reshape_213_cast")]; + tensor add_107_gamma_0_to_fp16 = const()[name = tensor("add_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4509399872)))]; + tensor add_107_beta_0_to_fp16 = const()[name = tensor("add_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4509401472)))]; + tensor add_107_epsilon_0_to_fp16 = const()[name = tensor("add_107_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_107_cast = batch_norm(beta = add_107_beta_0_to_fp16, epsilon = add_107_epsilon_0_to_fp16, gamma = add_107_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_213_cast)[name = tensor("add_107_cast")]; + tensor input_727_cast = silu(x = add_107_cast)[name = tensor("input_727_cast")]; + tensor var_9592 = const()[name = tensor("op_9592"), val = tensor([1, 1])]; + tensor var_9594 = const()[name = tensor("op_9594"), val = tensor([1, 1])]; + tensor hidden_states_461_pad_type_0 = const()[name = tensor("hidden_states_461_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_461_pad_0 = const()[name = tensor("hidden_states_461_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4509403072)))]; + tensor up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4514711552)))]; + tensor hidden_states_461_cast = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_9594, groups = var_9477, pad = hidden_states_461_pad_0, pad_type = hidden_states_461_pad_type_0, strides = var_9592, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_727_cast)[name = tensor("hidden_states_461_cast")]; + tensor var_9600 = const()[name = tensor("op_9600"), val = tensor([1, 1])]; + tensor var_9602 = const()[name = tensor("op_9602"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4514712384)))]; + tensor up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4515892096)))]; + tensor temb_cast = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_9602, groups = var_9477, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_9600, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_cast")]; + tensor input_731_cast = add(x = hidden_states_461_cast, y = temb_cast)[name = tensor("input_731_cast")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_216_cast = reshape(shape = reshape_216_shape_0, x = input_731_cast)[name = tensor("reshape_216_cast")]; + tensor reduce_mean_162_axes_0 = const()[name = tensor("reduce_mean_162_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_162_keep_dims_0 = const()[name = tensor("reduce_mean_162_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_162_cast = reduce_mean(axes = reduce_mean_162_axes_0, keep_dims = reduce_mean_162_keep_dims_0, x = reshape_216_cast)[name = tensor("reduce_mean_162_cast")]; + tensor sub_108_cast = sub(x = reshape_216_cast, y = reduce_mean_162_cast)[name = tensor("sub_108_cast")]; + tensor square_54_cast = square(x = sub_108_cast)[name = tensor("square_54_cast")]; + tensor reduce_mean_164_axes_0 = const()[name = tensor("reduce_mean_164_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_164_keep_dims_0 = const()[name = tensor("reduce_mean_164_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_164_cast = reduce_mean(axes = reduce_mean_164_axes_0, keep_dims = reduce_mean_164_keep_dims_0, x = square_54_cast)[name = tensor("reduce_mean_164_cast")]; + tensor add_108_y_0_to_fp16 = const()[name = tensor("add_108_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_108_cast = add(x = reduce_mean_164_cast, y = add_108_y_0_to_fp16)[name = tensor("add_108_cast")]; + tensor sqrt_54_cast = sqrt(x = add_108_cast)[name = tensor("sqrt_54_cast")]; + tensor real_div_54_cast = real_div(x = sub_108_cast, y = sqrt_54_cast)[name = tensor("real_div_54_cast")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_217_cast = reshape(shape = reshape_217_shape_0, x = real_div_54_cast)[name = tensor("reshape_217_cast")]; + tensor add_109_gamma_0_to_fp16 = const()[name = tensor("add_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4515892928)))]; + tensor add_109_beta_0_to_fp16 = const()[name = tensor("add_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4515893760)))]; + tensor add_109_epsilon_0_to_fp16 = const()[name = tensor("add_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_109_cast = batch_norm(beta = add_109_beta_0_to_fp16, epsilon = add_109_epsilon_0_to_fp16, gamma = add_109_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_217_cast)[name = tensor("add_109_cast")]; + tensor input_735_cast = silu(x = add_109_cast)[name = tensor("input_735_cast")]; + tensor var_9612 = const()[name = tensor("op_9612"), val = tensor([1, 1])]; + tensor var_9614 = const()[name = tensor("op_9614"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4515894592)))]; + tensor up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4518548864)))]; + tensor hidden_states_cast = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_9614, groups = var_9477, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_9612, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_735_cast)[name = tensor("hidden_states_cast")]; + tensor var_9619 = const()[name = tensor("op_9619"), val = tensor([1, 1])]; + tensor var_9621 = const()[name = tensor("op_9621"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4518549696)))]; + tensor up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519139584)))]; + tensor x_cast = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_9621, groups = var_9477, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_9619, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16, x = input_723_cast)[name = tensor("x_cast")]; + tensor input_737_cast = add(x = x_cast, y = hidden_states_cast)[name = tensor("input_737_cast")]; + tensor reshape_220_shape_0 = const()[name = tensor("reshape_220_shape_0"), val = tensor([2, 32, 12, 128, 128])]; + tensor reshape_220_cast = reshape(shape = reshape_220_shape_0, x = input_737_cast)[name = tensor("reshape_220_cast")]; + tensor reduce_mean_165_axes_0 = const()[name = tensor("reduce_mean_165_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_165_keep_dims_0 = const()[name = tensor("reduce_mean_165_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_165_cast = reduce_mean(axes = reduce_mean_165_axes_0, keep_dims = reduce_mean_165_keep_dims_0, x = reshape_220_cast)[name = tensor("reduce_mean_165_cast")]; + tensor sub_110_cast = sub(x = reshape_220_cast, y = reduce_mean_165_cast)[name = tensor("sub_110_cast")]; + tensor square_55_cast = square(x = sub_110_cast)[name = tensor("square_55_cast")]; + tensor reduce_mean_167_axes_0 = const()[name = tensor("reduce_mean_167_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_167_keep_dims_0 = const()[name = tensor("reduce_mean_167_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_167_cast = reduce_mean(axes = reduce_mean_167_axes_0, keep_dims = reduce_mean_167_keep_dims_0, x = square_55_cast)[name = tensor("reduce_mean_167_cast")]; + tensor add_110_y_0_to_fp16 = const()[name = tensor("add_110_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_110_cast = add(x = reduce_mean_167_cast, y = add_110_y_0_to_fp16)[name = tensor("add_110_cast")]; + tensor sqrt_55_cast = sqrt(x = add_110_cast)[name = tensor("sqrt_55_cast")]; + tensor real_div_55_cast = real_div(x = sub_110_cast, y = sqrt_55_cast)[name = tensor("real_div_55_cast")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([2, 384, 128, 128])]; + tensor reshape_221_cast = reshape(shape = reshape_221_shape_0, x = real_div_55_cast)[name = tensor("reshape_221_cast")]; + tensor add_111_gamma_0_to_fp16 = const()[name = tensor("add_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519140416)))]; + tensor add_111_beta_0_to_fp16 = const()[name = tensor("add_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519141248)))]; + tensor add_111_epsilon_0_to_fp16 = const()[name = tensor("add_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_111_cast = batch_norm(beta = add_111_beta_0_to_fp16, epsilon = add_111_epsilon_0_to_fp16, gamma = add_111_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_221_cast)[name = tensor("add_111_cast")]; + tensor input_cast = silu(x = add_111_cast)[name = tensor("input_cast")]; + tensor var_9635 = const()[name = tensor("op_9635"), val = tensor(1)]; + tensor var_9638 = const()[name = tensor("op_9638"), val = tensor([1, 1])]; + tensor var_9640 = const()[name = tensor("op_9640"), val = tensor([1, 1])]; + tensor var_9642_pad_type_0 = const()[name = tensor("op_9642_pad_type_0"), val = tensor("custom")]; + tensor var_9642_pad_0 = const()[name = tensor("op_9642_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16 = const()[name = tensor("conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519142080)))]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([-0x1.c78p-11, -0x1.0c8p-10, 0x1.1bp-10, -0x1.dc8p-14])]; + tensor var_9642_cast = conv(bias = conv_out_bias_to_fp16, dilations = var_9640, groups = var_9635, pad = var_9642_pad_0, pad_type = var_9642_pad_type_0, strides = var_9638, weight = conv_out_weight_to_fp16, x = input_cast)[name = tensor("op_9642_cast")]; + tensor var_9642_cast_to_fp32_dtype_0 = const()[name = tensor("op_9642_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_9642_cast_to_fp32_dtype_0, x = var_9642_cast)[name = tensor("cast_649")]; + } -> (noise_pred); +} \ No newline at end of file