program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "4.28.4"}, {"coremlc-version", "1429.0.0"}})] { func main(tensor encoder_hidden_states, tensor sample, tensor timestep) { tensor var_25 = const()[name = tensor("op_25"), val = tensor(-1)]; tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; tensor var_42_cast = expand_dims(axes = var_42_axes_0, x = timestep); tensor var_44_to_fp16 = const()[name = tensor("op_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor emb_3_cast = mul(x = var_42_cast, y = var_44_to_fp16); tensor var_49_cast = sin(x = emb_3_cast); tensor var_50_cast = cos(x = emb_3_cast); tensor emb_interleave_0 = const()[name = tensor("emb_interleave_0"), val = tensor(false)]; tensor emb_cast = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast, var_50_cast)); tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 160])]; tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([2, 320])]; tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, true])]; tensor var_54_cast = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast); tensor var_56_begin_0 = const()[name = tensor("op_56_begin_0"), val = tensor([0, 0])]; tensor var_56_end_0 = const()[name = tensor("op_56_end_0"), val = tensor([2, 160])]; tensor var_56_end_mask_0 = const()[name = tensor("op_56_end_mask_0"), val = tensor([true, false])]; tensor var_56_cast = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast); tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; tensor sample_cast = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast, var_56_cast)); tensor var_59 = const()[name = tensor("op_59"), val = tensor(1)]; tensor var_66_axes_0 = const()[name = tensor("op_66_axes_0"), val = tensor([-1])]; tensor var_66_cast = expand_dims(axes = var_66_axes_0, x = sample_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_66_cast); tensor var_70 = const()[name = tensor("op_70"), val = tensor([1, 1])]; tensor var_72 = const()[name = tensor("op_72"), val = tensor([1, 1])]; tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; tensor time_embedding_linear_1_weight_to_fp16 = const()[name = tensor("time_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(819712)))]; tensor input_3_cast = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, weight = time_embedding_linear_1_weight_to_fp16, x = input_1_cast); tensor input_5_cast = silu(x = input_3_cast); tensor var_78 = const()[name = tensor("op_78"), val = tensor([1, 1])]; tensor var_80 = const()[name = tensor("op_80"), 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([0, 0, 0, 0])]; tensor time_embedding_linear_2_weight_to_fp16 = const()[name = tensor("time_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822336)))]; tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099200)))]; tensor input_13_cast = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_80, groups = var_59, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight_to_fp16, x = input_5_cast); tensor var_86 = const()[name = tensor("op_86"), val = tensor(1)]; tensor var_89 = const()[name = tensor("op_89"), val = tensor([1, 1])]; tensor var_91 = const()[name = tensor("op_91"), val = tensor([1, 1])]; tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(4101824)))]; 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(4124928)))]; tensor input_7_cast_1 = conv(bias = conv_in_bias_to_fp16, dilations = var_91, groups = var_86, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_89, weight = conv_in_weight_to_fp16, x = sample); tensor var_95 = const()[name = tensor("op_95"), val = tensor(3)]; tensor var_106 = const()[name = tensor("op_106"), val = tensor(true)]; tensor var_111 = const()[name = tensor("op_111"), val = tensor(1)]; tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 64, 64])]; tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_7_cast_1); 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); tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast); tensor square_0_cast = square(x = sub_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); 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); tensor sqrt_0_cast = sqrt(x = add_0_cast); tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast); tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 64, 64])]; tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_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(4125632)))]; 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(4126336)))]; 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(4127040)))]; 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(4127744)))]; 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); tensor input_11_cast = silu(x = add_1_cast); tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; tensor var_135 = const()[name = tensor("op_135"), 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(4128448)))]; 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(5971712)))]; tensor hidden_states_1_cast = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_111, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_133, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_11_cast); tensor input_15_cast_1 = silu(x = input_13_cast); tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, 1])]; tensor var_143 = const()[name = tensor("op_143"), 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(5972416)))]; 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(6791680)))]; tensor temb_1_cast = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_143, groups = var_111, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_141, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_17_cast = add(x = hidden_states_1_cast, y = temb_1_cast); tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 64, 64])]; tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_17_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); tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast); tensor square_1_cast = square(x = sub_2_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); 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); tensor sqrt_1_cast = sqrt(x = add_2_cast); tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast); tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 64, 64])]; tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast); tensor add_3_mean_0_to_fp16 = const()[name = tensor("add_3_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6792384)))]; tensor add_3_variance_0_to_fp16 = const()[name = tensor("add_3_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6793088)))]; 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(6793792)))]; 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(6794496)))]; 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_3_mean_0_to_fp16, variance = add_3_variance_0_to_fp16, x = reshape_5_cast); tensor input_21_cast = silu(x = add_3_cast); tensor var_153 = const()[name = tensor("op_153"), val = tensor([1, 1])]; tensor var_155 = const()[name = tensor("op_155"), 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(6795200)))]; 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(8638464)))]; tensor hidden_states_3_cast = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_155, groups = var_111, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_153, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_21_cast); tensor hidden_states_5_cast = add(x = input_7_cast_1, y = hidden_states_3_cast); tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 64, 64])]; tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = hidden_states_5_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); tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast); tensor square_2_cast = square(x = sub_4_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); tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16); tensor sqrt_2_cast = sqrt(x = add_4_cast); tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast); tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 64, 64])]; tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast); tensor add_5_mean_0_to_fp16 = const()[name = tensor("add_5_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8639168)))]; tensor add_5_variance_0_to_fp16 = const()[name = tensor("add_5_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8639872)))]; 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(8640576)))]; 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(8641280)))]; 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_5_mean_0_to_fp16, variance = add_5_variance_0_to_fp16, x = reshape_9_cast); tensor var_175 = const()[name = tensor("op_175"), val = tensor([1, 1])]; tensor var_177 = const()[name = tensor("op_177"), 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([0, 0, 0, 0])]; tensor down_blocks_0_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8641984)))]; tensor down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8846848)))]; tensor hidden_states_7_cast = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_177, groups = var_111, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_175, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16, x = add_5_cast); tensor var_182 = const()[name = tensor("op_182"), val = tensor([2, 320, 1, 4096])]; tensor inputs_1_cast = reshape(shape = var_182, x = hidden_states_7_cast); tensor var_192 = const()[name = tensor("op_192"), val = tensor([1])]; tensor channels_mean_1_cast = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast); tensor zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast); tensor zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast); tensor var_196 = const()[name = tensor("op_196"), val = tensor([1])]; tensor var_197_cast = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast); tensor var_198_to_fp16 = const()[name = tensor("op_198_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_199_cast = add(x = var_197_cast, y = var_198_to_fp16); 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_199_cast); tensor out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast); tensor var_203_to_fp16 = const()[name = tensor("op_203_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8847552)))]; tensor var_204_cast = add(x = out_1_cast, y = var_203_to_fp16); tensor var_206_to_fp16 = const()[name = tensor("op_206_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8848256)))]; tensor hidden_states_9_cast = mul(x = var_204_cast, y = var_206_to_fp16); tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; tensor var_215 = const()[name = tensor("op_215"), 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_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8848960)))]; tensor q_1_cast = conv(dilations = var_215, groups = var_111, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_213, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_9_cast); tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; tensor var_221 = const()[name = tensor("op_221"), 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_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9053824)))]; tensor k_1_cast = conv(dilations = var_221, groups = var_111, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_219, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_9_cast); tensor var_225 = const()[name = tensor("op_225"), val = tensor([1, 1])]; tensor var_227 = const()[name = tensor("op_227"), 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_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9258688)))]; tensor v_1_cast = conv(dilations = var_227, groups = var_111, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_225, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_9_cast); tensor var_231 = const()[name = tensor("op_231"), val = tensor([2, 8, 40, -1])]; tensor var_232_cast = reshape(shape = var_231, x = q_1_cast); tensor var_233 = const()[name = tensor("op_233"), val = tensor([2, 8, 40, -1])]; tensor var_234_cast = reshape(shape = var_233, x = k_1_cast); tensor var_235 = const()[name = tensor("op_235"), val = tensor([2, 8, 40, -1])]; tensor var_236_cast = reshape(shape = var_235, x = v_1_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_232_cast, y = var_234_cast); tensor var_102_to_fp16 = const()[name = tensor("op_102_to_fp16"), val = tensor(0x1.43cp-3)]; tensor attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_102_to_fp16); tensor var_240_cast = softmax(axis = var_95, x = attn_weights_3_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_236_cast, y = var_240_cast); tensor var_244 = const()[name = tensor("op_244"), val = tensor([2, 320, 1, -1])]; tensor input_25_cast = reshape(shape = var_244, x = attn_1_cast); tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, 1])]; tensor var_251 = const()[name = tensor("op_251"), val = tensor([1, 1])]; tensor var_253_pad_type_0 = const()[name = tensor("op_253_pad_type_0"), val = tensor("custom")]; tensor var_253_pad_0 = const()[name = tensor("op_253_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9463552)))]; tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9668416)))]; tensor var_253_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_251, groups = var_111, pad = var_253_pad_0, pad_type = var_253_pad_type_0, strides = var_249, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_25_cast); tensor inputs_3_cast = add(x = var_253_cast, y = inputs_1_cast); tensor var_257 = const()[name = tensor("op_257"), val = tensor([1])]; tensor channels_mean_3_cast = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast); tensor zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast); tensor zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast); tensor var_261 = const()[name = tensor("op_261"), val = tensor([1])]; tensor var_262_cast = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast); tensor var_263_to_fp16 = const()[name = tensor("op_263_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_264_cast = add(x = var_262_cast, y = var_263_to_fp16); 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_264_cast); tensor out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast); tensor var_268_to_fp16 = const()[name = tensor("op_268_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9669120)))]; tensor var_269_cast = add(x = out_3_cast, y = var_268_to_fp16); tensor var_271_to_fp16 = const()[name = tensor("op_271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9669824)))]; tensor hidden_states_11_cast = mul(x = var_269_cast, y = var_271_to_fp16); tensor var_278 = const()[name = tensor("op_278"), val = tensor([1, 1])]; tensor var_280 = const()[name = tensor("op_280"), 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_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9670528)))]; tensor q_3_cast = conv(dilations = var_280, groups = var_111, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_278, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_11_cast); tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; tensor 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_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9875392)))]; tensor k_3_cast = conv(dilations = var_286, groups = var_111, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_284, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states); tensor var_290 = const()[name = tensor("op_290"), val = tensor([1, 1])]; tensor var_292 = const()[name = tensor("op_292"), 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_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10366976)))]; tensor v_3_cast = conv(dilations = var_292, groups = var_111, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_290, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states); tensor var_296 = const()[name = tensor("op_296"), val = tensor([2, 8, 40, -1])]; tensor var_297_cast = reshape(shape = var_296, x = q_3_cast); tensor var_298 = const()[name = tensor("op_298"), val = tensor([2, 8, 40, -1])]; tensor var_299_cast = reshape(shape = var_298, x = k_3_cast); tensor var_300 = const()[name = tensor("op_300"), val = tensor([2, 8, 40, -1])]; tensor var_301_cast = reshape(shape = var_300, x = v_3_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_297_cast, y = var_299_cast); tensor attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_102_to_fp16); tensor var_305_cast = softmax(axis = var_95, x = attn_weights_7_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_301_cast, y = var_305_cast); tensor var_309 = const()[name = tensor("op_309"), val = tensor([2, 320, 1, -1])]; tensor input_27_cast = reshape(shape = var_309, x = attn_3_cast); tensor var_314 = const()[name = tensor("op_314"), val = tensor([1, 1])]; tensor var_316 = const()[name = tensor("op_316"), val = tensor([1, 1])]; tensor var_318_pad_type_0 = const()[name = tensor("op_318_pad_type_0"), val = tensor("custom")]; tensor var_318_pad_0 = const()[name = tensor("op_318_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10858560)))]; tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11063424)))]; tensor var_318_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_316, groups = var_111, pad = var_318_pad_0, pad_type = var_318_pad_type_0, strides = var_314, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_27_cast); tensor inputs_5_cast = add(x = var_318_cast, y = inputs_3_cast); tensor var_322 = const()[name = tensor("op_322"), val = tensor([1])]; tensor channels_mean_5_cast = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast); tensor zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast); tensor zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast); tensor var_326 = const()[name = tensor("op_326"), val = tensor([1])]; tensor var_327_cast = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast); tensor var_328_to_fp16 = const()[name = tensor("op_328_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_329_cast = add(x = var_327_cast, y = var_328_to_fp16); 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_329_cast); tensor out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast); tensor var_333_to_fp16 = const()[name = tensor("op_333_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11064128)))]; tensor var_334_cast = add(x = out_5_cast, y = var_333_to_fp16); tensor var_336_to_fp16 = const()[name = tensor("op_336_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11064832)))]; tensor input_29_cast = mul(x = var_334_cast, y = var_336_to_fp16); tensor var_344 = const()[name = tensor("op_344"), val = tensor([1, 1])]; tensor var_346 = const()[name = tensor("op_346"), val = tensor([1, 1])]; tensor var_348_pad_type_0 = const()[name = tensor("op_348_pad_type_0"), val = tensor("custom")]; tensor var_348_pad_0 = const()[name = tensor("op_348_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11065536)))]; tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12704000)))]; tensor var_348_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_346, groups = var_111, pad = var_348_pad_0, pad_type = var_348_pad_type_0, strides = var_344, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_29_cast); tensor var_349_split_sizes_0 = const()[name = tensor("op_349_split_sizes_0"), val = tensor([1280, 1280])]; tensor var_349_axis_0 = const()[name = tensor("op_349_axis_0"), val = tensor(1)]; tensor var_349_cast_0, tensor var_349_cast_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast); tensor var_351_mode_0 = const()[name = tensor("op_351_mode_0"), val = tensor("EXACT")]; tensor var_351_cast = gelu(mode = var_351_mode_0, x = var_349_cast_1); tensor input_31_cast = mul(x = var_349_cast_0, y = var_351_cast); tensor var_355 = const()[name = tensor("op_355"), val = tensor([1, 1])]; tensor var_357 = const()[name = tensor("op_357"), val = tensor([1, 1])]; tensor var_359_pad_type_0 = const()[name = tensor("op_359_pad_type_0"), val = tensor("custom")]; tensor var_359_pad_0 = const()[name = tensor("op_359_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12709184)))]; tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13528448)))]; tensor var_359_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_357, groups = var_111, pad = var_359_pad_0, pad_type = var_359_pad_type_0, strides = var_355, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_31_cast); tensor hidden_states_15_cast = add(x = var_359_cast, y = inputs_5_cast); tensor var_361 = const()[name = tensor("op_361"), val = tensor([2, 320, 64, 64])]; tensor input_33_cast = reshape(shape = var_361, x = hidden_states_15_cast); tensor var_365 = const()[name = tensor("op_365"), val = tensor([1, 1])]; tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13529152)))]; tensor down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13734016)))]; tensor hidden_states_17_cast = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_367, groups = var_111, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_365, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16, x = input_33_cast); tensor input_35_cast_1 = add(x = hidden_states_17_cast, y = hidden_states_5_cast); tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 64, 64])]; tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_35_cast_1); 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); tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast); tensor square_3_cast = square(x = sub_6_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); 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); tensor sqrt_3_cast = sqrt(x = add_6_cast); tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast); tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 64, 64])]; tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast); tensor add_7_mean_0_to_fp16 = const()[name = tensor("add_7_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13734720)))]; tensor add_7_variance_0_to_fp16 = const()[name = tensor("add_7_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13735424)))]; 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(13736128)))]; 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(13736832)))]; 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_7_mean_0_to_fp16, variance = add_7_variance_0_to_fp16, x = reshape_13_cast); tensor input_39_cast = silu(x = add_7_cast); tensor var_382 = const()[name = tensor("op_382"), val = tensor([1, 1])]; tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(13737536)))]; 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(15580800)))]; tensor hidden_states_19_cast = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_384, groups = var_111, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_382, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_39_cast); tensor var_390 = const()[name = tensor("op_390"), val = tensor([1, 1])]; tensor var_392 = const()[name = tensor("op_392"), 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(15581504)))]; 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(16400768)))]; tensor temb_3_cast = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_392, groups = var_111, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_390, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_43_cast = add(x = hidden_states_19_cast, y = temb_3_cast); tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 64, 64])]; tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_43_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); tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast); tensor square_4_cast = square(x = sub_8_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); 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); tensor sqrt_4_cast = sqrt(x = add_8_cast); tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast); tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 64, 64])]; tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast); tensor add_9_mean_0_to_fp16 = const()[name = tensor("add_9_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16401472)))]; tensor add_9_variance_0_to_fp16 = const()[name = tensor("add_9_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16402176)))]; 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(16402880)))]; 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(16403584)))]; 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_9_mean_0_to_fp16, variance = add_9_variance_0_to_fp16, x = reshape_17_cast); tensor input_47_cast = silu(x = add_9_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 hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(16404288)))]; 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(18247552)))]; tensor hidden_states_21_cast = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_404, groups = var_111, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_402, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_47_cast); tensor hidden_states_23_cast = add(x = input_35_cast_1, y = hidden_states_21_cast); tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 10, 64, 64])]; tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = hidden_states_23_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); tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast); tensor square_5_cast = square(x = sub_10_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); tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16); tensor sqrt_5_cast = sqrt(x = add_10_cast); tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast); tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 320, 64, 64])]; tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_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(18248256)))]; 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(18248960)))]; 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(18249664)))]; 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(18250368)))]; 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); tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 1])]; tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, 1])]; tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18251072)))]; tensor down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18455936)))]; tensor hidden_states_25_cast = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_426, groups = var_111, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_424, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16, x = add_11_cast); tensor var_431 = const()[name = tensor("op_431"), val = tensor([2, 320, 1, 4096])]; tensor inputs_7_cast = reshape(shape = var_431, x = hidden_states_25_cast); tensor var_441 = const()[name = tensor("op_441"), val = tensor([1])]; tensor channels_mean_7_cast = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast); tensor zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast); tensor zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast); tensor var_445 = const()[name = tensor("op_445"), val = tensor([1])]; tensor var_446_cast = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast); tensor var_447_to_fp16 = const()[name = tensor("op_447_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_448_cast = add(x = var_446_cast, y = var_447_to_fp16); 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_448_cast); tensor out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast); tensor var_452_to_fp16 = const()[name = tensor("op_452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18456640)))]; tensor var_453_cast = add(x = out_7_cast, y = var_452_to_fp16); tensor var_455_to_fp16 = const()[name = tensor("op_455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18457344)))]; tensor hidden_states_27_cast = mul(x = var_453_cast, y = var_455_to_fp16); tensor var_462 = const()[name = tensor("op_462"), val = tensor([1, 1])]; tensor var_464 = const()[name = tensor("op_464"), 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_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18458048)))]; tensor q_5_cast = conv(dilations = var_464, groups = var_111, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_462, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_27_cast); tensor var_468 = const()[name = tensor("op_468"), val = tensor([1, 1])]; tensor var_470 = const()[name = tensor("op_470"), 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_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18662912)))]; tensor k_5_cast = conv(dilations = var_470, groups = var_111, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_468, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_27_cast); tensor var_474 = const()[name = tensor("op_474"), val = tensor([1, 1])]; tensor var_476 = const()[name = tensor("op_476"), 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_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18867776)))]; tensor v_5_cast = conv(dilations = var_476, groups = var_111, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_474, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_27_cast); tensor var_480 = const()[name = tensor("op_480"), val = tensor([2, 8, 40, -1])]; tensor var_481_cast = reshape(shape = var_480, x = q_5_cast); tensor var_482 = const()[name = tensor("op_482"), val = tensor([2, 8, 40, -1])]; tensor var_483_cast = reshape(shape = var_482, x = k_5_cast); tensor var_484 = const()[name = tensor("op_484"), val = tensor([2, 8, 40, -1])]; tensor var_485_cast = reshape(shape = var_484, x = v_5_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_481_cast, y = var_483_cast); tensor attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_102_to_fp16); tensor var_489_cast = softmax(axis = var_95, x = attn_weights_11_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_485_cast, y = var_489_cast); tensor var_493 = const()[name = tensor("op_493"), val = tensor([2, 320, 1, -1])]; tensor input_51_cast = reshape(shape = var_493, x = attn_5_cast); tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 1])]; tensor var_500 = const()[name = tensor("op_500"), val = tensor([1, 1])]; tensor var_502_pad_type_0 = const()[name = tensor("op_502_pad_type_0"), val = tensor("custom")]; tensor var_502_pad_0 = const()[name = tensor("op_502_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19072640)))]; tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19277504)))]; tensor var_502_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_500, groups = var_111, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_498, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_51_cast); tensor inputs_9_cast = add(x = var_502_cast, y = inputs_7_cast); tensor var_506 = const()[name = tensor("op_506"), val = tensor([1])]; tensor channels_mean_9_cast = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast); tensor zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast); tensor zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast); tensor var_510 = const()[name = tensor("op_510"), val = tensor([1])]; tensor var_511_cast = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast); tensor var_512_to_fp16 = const()[name = tensor("op_512_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_513_cast = add(x = var_511_cast, y = var_512_to_fp16); 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_513_cast); tensor out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast); tensor var_517_to_fp16 = const()[name = tensor("op_517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19278208)))]; tensor var_518_cast = add(x = out_9_cast, y = var_517_to_fp16); tensor var_520_to_fp16 = const()[name = tensor("op_520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19278912)))]; tensor hidden_states_29_cast = mul(x = var_518_cast, y = var_520_to_fp16); tensor var_527 = const()[name = tensor("op_527"), val = tensor([1, 1])]; tensor var_529 = const()[name = tensor("op_529"), 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_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19279616)))]; tensor q_7_cast = conv(dilations = var_529, groups = var_111, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_527, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_29_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 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_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19484480)))]; tensor k_7_cast = conv(dilations = var_535, groups = var_111, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_533, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states); tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 1])]; tensor var_541 = const()[name = tensor("op_541"), 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_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19976064)))]; tensor v_7_cast = conv(dilations = var_541, groups = var_111, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_539, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states); tensor var_545 = const()[name = tensor("op_545"), val = tensor([2, 8, 40, -1])]; tensor var_546_cast = reshape(shape = var_545, x = q_7_cast); tensor var_547 = const()[name = tensor("op_547"), val = tensor([2, 8, 40, -1])]; tensor var_548_cast = reshape(shape = var_547, x = k_7_cast); tensor var_549 = const()[name = tensor("op_549"), val = tensor([2, 8, 40, -1])]; tensor var_550_cast = reshape(shape = var_549, x = v_7_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_546_cast, y = var_548_cast); tensor attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_102_to_fp16); tensor var_554_cast = softmax(axis = var_95, x = attn_weights_15_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_550_cast, y = var_554_cast); tensor var_558 = const()[name = tensor("op_558"), val = tensor([2, 320, 1, -1])]; tensor input_53_cast = reshape(shape = var_558, x = attn_7_cast); tensor var_563 = const()[name = tensor("op_563"), val = tensor([1, 1])]; tensor var_565 = const()[name = tensor("op_565"), val = tensor([1, 1])]; tensor var_567_pad_type_0 = const()[name = tensor("op_567_pad_type_0"), val = tensor("custom")]; tensor var_567_pad_0 = const()[name = tensor("op_567_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20467648)))]; tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20672512)))]; tensor var_567_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_565, groups = var_111, pad = var_567_pad_0, pad_type = var_567_pad_type_0, strides = var_563, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_53_cast); tensor inputs_11_cast = add(x = var_567_cast, y = inputs_9_cast); tensor var_571 = const()[name = tensor("op_571"), val = tensor([1])]; tensor channels_mean_11_cast = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast); tensor zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast); tensor zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast); tensor var_575 = const()[name = tensor("op_575"), val = tensor([1])]; tensor var_576_cast = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast); tensor var_577_to_fp16 = const()[name = tensor("op_577_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_578_cast = add(x = var_576_cast, y = var_577_to_fp16); 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_578_cast); tensor out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast); tensor var_582_to_fp16 = const()[name = tensor("op_582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20673216)))]; tensor var_583_cast = add(x = out_11_cast, y = var_582_to_fp16); tensor var_585_to_fp16 = const()[name = tensor("op_585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20673920)))]; tensor input_55_cast = mul(x = var_583_cast, y = var_585_to_fp16); tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1])]; tensor var_595 = const()[name = tensor("op_595"), val = tensor([1, 1])]; tensor var_597_pad_type_0 = const()[name = tensor("op_597_pad_type_0"), val = tensor("custom")]; tensor var_597_pad_0 = const()[name = tensor("op_597_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20674624)))]; tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22313088)))]; tensor var_597_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_595, groups = var_111, pad = var_597_pad_0, pad_type = var_597_pad_type_0, strides = var_593, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_55_cast); tensor var_598_split_sizes_0 = const()[name = tensor("op_598_split_sizes_0"), val = tensor([1280, 1280])]; tensor var_598_axis_0 = const()[name = tensor("op_598_axis_0"), val = tensor(1)]; tensor var_598_cast_0, tensor var_598_cast_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast); tensor var_600_mode_0 = const()[name = tensor("op_600_mode_0"), val = tensor("EXACT")]; tensor var_600_cast = gelu(mode = var_600_mode_0, x = var_598_cast_1); tensor input_57_cast = mul(x = var_598_cast_0, y = var_600_cast); tensor var_604 = const()[name = tensor("op_604"), val = tensor([1, 1])]; tensor var_606 = const()[name = tensor("op_606"), val = tensor([1, 1])]; tensor var_608_pad_type_0 = const()[name = tensor("op_608_pad_type_0"), val = tensor("custom")]; tensor var_608_pad_0 = const()[name = tensor("op_608_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22318272)))]; tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23137536)))]; tensor var_608_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_606, groups = var_111, pad = var_608_pad_0, pad_type = var_608_pad_type_0, strides = var_604, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_57_cast); tensor hidden_states_33_cast = add(x = var_608_cast, y = inputs_11_cast); tensor var_610 = const()[name = tensor("op_610"), val = tensor([2, 320, 64, 64])]; tensor input_59_cast = reshape(shape = var_610, x = hidden_states_33_cast); tensor var_614 = const()[name = tensor("op_614"), val = tensor([1, 1])]; tensor var_616 = const()[name = tensor("op_616"), val = tensor([1, 1])]; tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_0_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23138240)))]; tensor down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23343104)))]; tensor hidden_states_35_cast = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_616, groups = var_111, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_614, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16, x = input_59_cast); tensor input_61_cast_1 = add(x = hidden_states_35_cast, y = hidden_states_23_cast); tensor var_623 = const()[name = tensor("op_623"), val = tensor([2, 2])]; tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(23343808)))]; 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(25187072)))]; tensor input_63_cast_1 = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_625, groups = var_111, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_623, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = input_61_cast_1); tensor var_633 = const()[name = tensor("op_633"), val = tensor(3)]; tensor var_644 = const()[name = tensor("op_644"), val = tensor(true)]; tensor var_649 = const()[name = tensor("op_649"), val = tensor(1)]; tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 10, 32, 32])]; tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = input_63_cast_1); 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); tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast); tensor square_6_cast = square(x = sub_12_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); tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16); tensor sqrt_6_cast = sqrt(x = add_12_cast); tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast); tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 320, 32, 32])]; tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast); tensor add_13_mean_0_to_fp16 = const()[name = tensor("add_13_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25187776)))]; tensor add_13_variance_0_to_fp16 = const()[name = tensor("add_13_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25188480)))]; 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(25189184)))]; 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(25189888)))]; 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_13_mean_0_to_fp16, variance = add_13_variance_0_to_fp16, x = reshape_25_cast); tensor input_67_cast = silu(x = add_13_cast); tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; tensor var_674 = const()[name = tensor("op_674"), val = tensor([1, 1])]; tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(25190592)))]; 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(28877056)))]; tensor hidden_states_37_cast = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_674, groups = var_649, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_672, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_67_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 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(28878400)))]; 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(30516864)))]; tensor temb_5_cast = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_682, groups = var_649, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_680, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_71_cast = add(x = hidden_states_37_cast, y = temb_5_cast); tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 32, 32])]; tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_71_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); tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast); tensor square_7_cast = square(x = sub_14_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); 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); tensor sqrt_7_cast = sqrt(x = add_14_cast); tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast); tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 32, 32])]; tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast); tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30518208)))]; tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30519552)))]; 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(30520896)))]; 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(30522240)))]; 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast); tensor input_75_cast = silu(x = add_15_cast); tensor var_692 = const()[name = tensor("op_692"), val = tensor([1, 1])]; tensor var_694 = const()[name = tensor("op_694"), val = tensor([1, 1])]; tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(30523584)))]; 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(37896448)))]; tensor hidden_states_39_cast = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_694, groups = var_649, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_692, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_75_cast); tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 1])]; tensor var_701 = const()[name = tensor("op_701"), 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(37897792)))]; 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(38307456)))]; tensor x_1_cast = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_701, groups = var_649, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_699, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_63_cast_1); tensor hidden_states_41_cast = add(x = x_1_cast, y = hidden_states_39_cast); tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 32, 32])]; tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = hidden_states_41_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); tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast); tensor square_8_cast = square(x = sub_16_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); tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16); tensor sqrt_8_cast = sqrt(x = add_16_cast); tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast); tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 32, 32])]; tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast); tensor add_17_mean_0_to_fp16 = const()[name = tensor("add_17_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38308800)))]; tensor add_17_variance_0_to_fp16 = const()[name = tensor("add_17_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38310144)))]; 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(38311488)))]; 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(38312832)))]; 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_17_mean_0_to_fp16, variance = add_17_variance_0_to_fp16, x = reshape_33_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 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_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(38314176)))]; 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(39133440)))]; tensor hidden_states_43_cast = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_723, groups = var_649, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_17_cast); tensor var_728 = const()[name = tensor("op_728"), val = tensor([2, 640, 1, 1024])]; tensor inputs_13_cast = reshape(shape = var_728, x = hidden_states_43_cast); tensor var_738 = const()[name = tensor("op_738"), val = tensor([1])]; tensor channels_mean_13_cast = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast); tensor zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast); tensor zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast); tensor var_742 = const()[name = tensor("op_742"), val = tensor([1])]; tensor var_743_cast = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast); tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_745_cast = add(x = var_743_cast, y = var_744_to_fp16); 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_745_cast); tensor out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_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(39134784)))]; tensor var_750_cast = add(x = out_13_cast, y = var_749_to_fp16); tensor var_752_to_fp16 = const()[name = tensor("op_752_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39136128)))]; tensor hidden_states_45_cast = mul(x = var_750_cast, y = var_752_to_fp16); tensor var_759 = const()[name = tensor("op_759"), val = tensor([1, 1])]; tensor var_761 = const()[name = tensor("op_761"), 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_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(39137472)))]; tensor q_9_cast = conv(dilations = var_761, groups = var_649, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_759, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_45_cast); tensor var_765 = const()[name = tensor("op_765"), val = tensor([1, 1])]; tensor var_767 = const()[name = tensor("op_767"), 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_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(39956736)))]; tensor k_9_cast = conv(dilations = var_767, groups = var_649, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_765, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_45_cast); tensor var_771 = const()[name = tensor("op_771"), val = tensor([1, 1])]; tensor var_773 = const()[name = tensor("op_773"), 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_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(40776000)))]; tensor v_9_cast = conv(dilations = var_773, groups = var_649, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_771, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_45_cast); tensor var_777 = const()[name = tensor("op_777"), val = tensor([2, 8, 80, -1])]; tensor var_778_cast = reshape(shape = var_777, x = q_9_cast); tensor var_779 = const()[name = tensor("op_779"), val = tensor([2, 8, 80, -1])]; tensor var_780_cast = reshape(shape = var_779, x = k_9_cast); tensor var_781 = const()[name = tensor("op_781"), val = tensor([2, 8, 80, -1])]; tensor var_782_cast = reshape(shape = var_781, x = v_9_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_778_cast, y = var_780_cast); tensor var_640_to_fp16 = const()[name = tensor("op_640_to_fp16"), val = tensor(0x1.cap-4)]; tensor attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_640_to_fp16); tensor var_786_cast = softmax(axis = var_633, x = attn_weights_19_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_782_cast, y = var_786_cast); tensor var_790 = const()[name = tensor("op_790"), val = tensor([2, 640, 1, -1])]; tensor input_79_cast = reshape(shape = var_790, x = attn_9_cast); tensor var_795 = const()[name = tensor("op_795"), val = tensor([1, 1])]; tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 1])]; tensor var_799_pad_type_0 = const()[name = tensor("op_799_pad_type_0"), val = tensor("custom")]; tensor var_799_pad_0 = const()[name = tensor("op_799_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(41595264)))]; 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(42414528)))]; tensor var_799_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_797, groups = var_649, pad = var_799_pad_0, pad_type = var_799_pad_type_0, strides = var_795, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_79_cast); tensor inputs_15_cast = add(x = var_799_cast, y = inputs_13_cast); tensor var_803 = const()[name = tensor("op_803"), val = tensor([1])]; tensor channels_mean_15_cast = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast); tensor zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast); tensor zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast); tensor var_807 = const()[name = tensor("op_807"), val = tensor([1])]; tensor var_808_cast = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast); tensor var_809_to_fp16 = const()[name = tensor("op_809_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_810_cast = add(x = var_808_cast, y = var_809_to_fp16); 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_810_cast); tensor out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_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(42415872)))]; tensor var_815_cast = add(x = out_15_cast, y = var_814_to_fp16); tensor var_817_to_fp16 = const()[name = tensor("op_817_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42417216)))]; tensor hidden_states_47_cast = mul(x = var_815_cast, y = var_817_to_fp16); tensor var_824 = const()[name = tensor("op_824"), val = tensor([1, 1])]; tensor var_826 = const()[name = tensor("op_826"), 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_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(42418560)))]; tensor q_11_cast = conv(dilations = var_826, groups = var_649, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_824, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_47_cast); tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; tensor var_832 = const()[name = tensor("op_832"), 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_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(43237824)))]; tensor k_11_cast = conv(dilations = var_832, groups = var_649, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_830, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states); tensor var_836 = const()[name = tensor("op_836"), val = tensor([1, 1])]; tensor var_838 = const()[name = tensor("op_838"), 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_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(44220928)))]; tensor v_11_cast = conv(dilations = var_838, groups = var_649, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_836, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states); tensor var_842 = const()[name = tensor("op_842"), val = tensor([2, 8, 80, -1])]; tensor var_843_cast = reshape(shape = var_842, x = q_11_cast); tensor var_844 = const()[name = tensor("op_844"), val = tensor([2, 8, 80, -1])]; tensor var_845_cast = reshape(shape = var_844, x = k_11_cast); tensor var_846 = const()[name = tensor("op_846"), val = tensor([2, 8, 80, -1])]; tensor var_847_cast = reshape(shape = var_846, x = v_11_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_843_cast, y = var_845_cast); tensor attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_640_to_fp16); tensor var_851_cast = softmax(axis = var_633, x = attn_weights_23_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_847_cast, y = var_851_cast); tensor var_855 = const()[name = tensor("op_855"), val = tensor([2, 640, 1, -1])]; tensor input_81_cast = reshape(shape = var_855, x = attn_11_cast); tensor var_860 = const()[name = tensor("op_860"), val = tensor([1, 1])]; tensor var_862 = const()[name = tensor("op_862"), val = tensor([1, 1])]; tensor var_864_pad_type_0 = const()[name = tensor("op_864_pad_type_0"), val = tensor("custom")]; tensor var_864_pad_0 = const()[name = tensor("op_864_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(45204032)))]; 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(46023296)))]; tensor var_864_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_862, groups = var_649, pad = var_864_pad_0, pad_type = var_864_pad_type_0, strides = var_860, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_81_cast); tensor inputs_17_cast = add(x = var_864_cast, y = inputs_15_cast); tensor var_868 = const()[name = tensor("op_868"), val = tensor([1])]; tensor channels_mean_17_cast = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast); tensor zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast); tensor zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast); tensor var_872 = const()[name = tensor("op_872"), val = tensor([1])]; tensor var_873_cast = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast); tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_875_cast = add(x = var_873_cast, y = var_874_to_fp16); 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_875_cast); tensor out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_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(46024640)))]; tensor var_880_cast = add(x = out_17_cast, y = var_879_to_fp16); tensor var_882_to_fp16 = const()[name = tensor("op_882_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46025984)))]; tensor input_83_cast = mul(x = var_880_cast, y = var_882_to_fp16); tensor var_890 = const()[name = tensor("op_890"), val = tensor([1, 1])]; tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, 1])]; tensor var_894_pad_type_0 = const()[name = tensor("op_894_pad_type_0"), val = tensor("custom")]; tensor var_894_pad_0 = const()[name = tensor("op_894_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(46027328)))]; 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(52580992)))]; tensor var_894_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_892, groups = var_649, pad = var_894_pad_0, pad_type = var_894_pad_type_0, strides = var_890, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_83_cast); tensor var_895_split_sizes_0 = const()[name = tensor("op_895_split_sizes_0"), val = tensor([2560, 2560])]; tensor var_895_axis_0 = const()[name = tensor("op_895_axis_0"), val = tensor(1)]; tensor var_895_cast_0, tensor var_895_cast_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast); tensor var_897_mode_0 = const()[name = tensor("op_897_mode_0"), val = tensor("EXACT")]; tensor var_897_cast = gelu(mode = var_897_mode_0, x = var_895_cast_1); tensor input_85_cast = mul(x = var_895_cast_0, y = var_897_cast); tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, 1])]; tensor var_903 = const()[name = tensor("op_903"), val = tensor([1, 1])]; tensor var_905_pad_type_0 = const()[name = tensor("op_905_pad_type_0"), val = tensor("custom")]; tensor var_905_pad_0 = const()[name = tensor("op_905_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(52591296)))]; 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(55868160)))]; tensor var_905_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_903, groups = var_649, pad = var_905_pad_0, pad_type = var_905_pad_type_0, strides = var_901, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_85_cast); tensor hidden_states_51_cast = add(x = var_905_cast, y = inputs_17_cast); tensor var_907 = const()[name = tensor("op_907"), val = tensor([2, 640, 32, 32])]; tensor input_87_cast = reshape(shape = var_907, x = hidden_states_51_cast); tensor var_911 = const()[name = tensor("op_911"), val = tensor([1, 1])]; tensor var_913 = const()[name = tensor("op_913"), val = tensor([1, 1])]; tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_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(55869504)))]; 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(56688768)))]; tensor hidden_states_53_cast = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_913, groups = var_649, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_911, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_87_cast); tensor input_89_cast_1 = add(x = hidden_states_53_cast, y = hidden_states_41_cast); tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 32, 32])]; tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_89_cast_1); 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); tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast); tensor square_9_cast = square(x = sub_18_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); tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16); tensor sqrt_9_cast = sqrt(x = add_18_cast); tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast); tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 32, 32])]; tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast); tensor add_19_mean_0_to_fp16 = const()[name = tensor("add_19_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56690112)))]; tensor add_19_variance_0_to_fp16 = const()[name = tensor("add_19_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56691456)))]; 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(56692800)))]; 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(56694144)))]; tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_37_cast); tensor input_93_cast = silu(x = add_19_cast); tensor var_928 = const()[name = tensor("op_928"), val = tensor([1, 1])]; tensor var_930 = const()[name = tensor("op_930"), val = tensor([1, 1])]; tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([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(56695488)))]; 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(64068352)))]; tensor hidden_states_55_cast = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_930, groups = var_649, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_928, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_93_cast); tensor var_936 = const()[name = tensor("op_936"), val = tensor([1, 1])]; tensor var_938 = const()[name = tensor("op_938"), 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(64069696)))]; 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(65708160)))]; tensor temb_7_cast = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_938, groups = var_649, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_936, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_97_cast = add(x = hidden_states_55_cast, y = temb_7_cast); tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 32, 32])]; tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_97_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); tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast); tensor square_10_cast = square(x = sub_20_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); 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); tensor sqrt_10_cast = sqrt(x = add_20_cast); tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast); tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 32, 32])]; tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast); tensor add_21_mean_0_to_fp16 = const()[name = tensor("add_21_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65709504)))]; tensor add_21_variance_0_to_fp16 = const()[name = tensor("add_21_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65710848)))]; 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(65712192)))]; 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(65713536)))]; 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_21_mean_0_to_fp16, variance = add_21_variance_0_to_fp16, x = reshape_41_cast); tensor input_101_cast = silu(x = add_21_cast); tensor var_948 = const()[name = tensor("op_948"), val = tensor([1, 1])]; tensor var_950 = const()[name = tensor("op_950"), val = tensor([1, 1])]; tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; tensor down_blocks_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(65714880)))]; 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(73087744)))]; tensor hidden_states_57_cast = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_950, groups = var_649, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_948, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_101_cast); tensor hidden_states_59_cast = add(x = input_89_cast_1, y = hidden_states_57_cast); tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 20, 32, 32])]; tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = hidden_states_59_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); tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast); tensor square_11_cast = square(x = sub_22_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); tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16); tensor sqrt_11_cast = sqrt(x = add_22_cast); tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast); tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 640, 32, 32])]; tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_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(73089088)))]; 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(73090432)))]; 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(73091776)))]; 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(73093120)))]; 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); tensor var_970 = const()[name = tensor("op_970"), val = tensor([1, 1])]; tensor var_972 = const()[name = tensor("op_972"), val = tensor([1, 1])]; tensor hidden_states_61_pad_type_0 = const()[name = tensor("hidden_states_61_pad_type_0"), val = tensor("custom")]; tensor hidden_states_61_pad_0 = const()[name = tensor("hidden_states_61_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(73094464)))]; 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(73913728)))]; tensor hidden_states_61_cast = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_972, groups = var_649, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_970, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_23_cast); tensor var_977 = const()[name = tensor("op_977"), val = tensor([2, 640, 1, 1024])]; tensor inputs_19_cast = reshape(shape = var_977, x = hidden_states_61_cast); tensor var_987 = const()[name = tensor("op_987"), val = tensor([1])]; tensor channels_mean_19_cast = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast); tensor zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast); tensor zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast); tensor var_991 = const()[name = tensor("op_991"), val = tensor([1])]; tensor var_992_cast = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast); tensor var_993_to_fp16 = const()[name = tensor("op_993_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_994_cast = add(x = var_992_cast, y = var_993_to_fp16); 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_994_cast); tensor out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast); tensor var_998_to_fp16 = const()[name = tensor("op_998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73915072)))]; tensor var_999_cast = add(x = out_19_cast, y = var_998_to_fp16); tensor var_1001_to_fp16 = const()[name = tensor("op_1001_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73916416)))]; tensor hidden_states_63_cast = mul(x = var_999_cast, y = var_1001_to_fp16); tensor var_1008 = const()[name = tensor("op_1008"), val = tensor([1, 1])]; tensor var_1010 = const()[name = tensor("op_1010"), val = tensor([1, 1])]; tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_1_attentions_1_transformer_blocks_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(73917760)))]; tensor q_13_cast = conv(dilations = var_1010, groups = var_649, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1008, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_63_cast); tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([1, 1])]; tensor var_1016 = const()[name = tensor("op_1016"), val = tensor([1, 1])]; tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_1_attentions_1_transformer_blocks_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(74737024)))]; tensor k_13_cast = conv(dilations = var_1016, groups = var_649, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1014, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_63_cast); tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 1])]; tensor var_1022 = const()[name = tensor("op_1022"), val = tensor([1, 1])]; tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_1_attentions_1_transformer_blocks_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(75556288)))]; tensor v_13_cast = conv(dilations = var_1022, groups = var_649, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1020, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_63_cast); tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([2, 8, 80, -1])]; tensor var_1027_cast = reshape(shape = var_1026, x = q_13_cast); tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([2, 8, 80, -1])]; tensor var_1029_cast = reshape(shape = var_1028, x = k_13_cast); tensor var_1030 = const()[name = tensor("op_1030"), val = tensor([2, 8, 80, -1])]; tensor var_1031_cast = reshape(shape = var_1030, x = v_13_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_1027_cast, y = var_1029_cast); tensor attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_640_to_fp16); tensor var_1035_cast = softmax(axis = var_633, x = attn_weights_27_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_1031_cast, y = var_1035_cast); tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([2, 640, 1, -1])]; tensor input_105_cast = reshape(shape = var_1039, x = attn_13_cast); tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 1])]; tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1, 1])]; tensor var_1048_pad_type_0 = const()[name = tensor("op_1048_pad_type_0"), val = tensor("custom")]; tensor var_1048_pad_0 = const()[name = tensor("op_1048_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(76375552)))]; 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(77194816)))]; tensor var_1048_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1046, groups = var_649, pad = var_1048_pad_0, pad_type = var_1048_pad_type_0, strides = var_1044, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_105_cast); tensor inputs_21_cast = add(x = var_1048_cast, y = inputs_19_cast); tensor var_1052 = const()[name = tensor("op_1052"), val = tensor([1])]; tensor channels_mean_21_cast = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast); tensor zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast); tensor zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast); tensor var_1056 = const()[name = tensor("op_1056"), val = tensor([1])]; tensor var_1057_cast = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_cast); tensor var_1058_to_fp16 = const()[name = tensor("op_1058_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1059_cast = add(x = var_1057_cast, y = var_1058_to_fp16); 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_1059_cast); tensor out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast); tensor var_1063_to_fp16 = const()[name = tensor("op_1063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77196160)))]; tensor var_1064_cast = add(x = out_21_cast, y = var_1063_to_fp16); tensor var_1066_to_fp16 = const()[name = tensor("op_1066_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77197504)))]; tensor hidden_states_65_cast = mul(x = var_1064_cast, y = var_1066_to_fp16); tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 1])]; tensor var_1075 = const()[name = tensor("op_1075"), val = tensor([1, 1])]; tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_1_attentions_1_transformer_blocks_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(77198848)))]; tensor q_15_cast = conv(dilations = var_1075, groups = var_649, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1073, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_65_cast); tensor var_1079 = const()[name = tensor("op_1079"), val = tensor([1, 1])]; tensor var_1081 = const()[name = tensor("op_1081"), val = tensor([1, 1])]; tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_1_attentions_1_transformer_blocks_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(78018112)))]; tensor k_15_cast = conv(dilations = var_1081, groups = var_649, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1079, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states); 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 v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_1_attentions_1_transformer_blocks_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(79001216)))]; tensor v_15_cast = conv(dilations = var_1087, groups = var_649, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states); tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([2, 8, 80, -1])]; tensor var_1092_cast = reshape(shape = var_1091, x = q_15_cast); tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([2, 8, 80, -1])]; tensor var_1094_cast = reshape(shape = var_1093, x = k_15_cast); tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([2, 8, 80, -1])]; tensor var_1096_cast = reshape(shape = var_1095, x = v_15_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_1092_cast, y = var_1094_cast); tensor attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_640_to_fp16); tensor var_1100_cast = softmax(axis = var_633, x = attn_weights_31_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_1096_cast, y = var_1100_cast); tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([2, 640, 1, -1])]; tensor input_107_cast = reshape(shape = var_1104, x = attn_15_cast); tensor var_1109 = const()[name = tensor("op_1109"), val = tensor([1, 1])]; tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([1, 1])]; tensor var_1113_pad_type_0 = const()[name = tensor("op_1113_pad_type_0"), val = tensor("custom")]; tensor var_1113_pad_0 = const()[name = tensor("op_1113_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(79984320)))]; 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(80803584)))]; tensor var_1113_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1111, groups = var_649, pad = var_1113_pad_0, pad_type = var_1113_pad_type_0, strides = var_1109, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_107_cast); tensor inputs_23_cast = add(x = var_1113_cast, y = inputs_21_cast); tensor var_1117 = const()[name = tensor("op_1117"), val = tensor([1])]; tensor channels_mean_23_cast = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast); tensor zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast); tensor zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast); tensor var_1121 = const()[name = tensor("op_1121"), val = tensor([1])]; tensor var_1122_cast = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast); tensor var_1123_to_fp16 = const()[name = tensor("op_1123_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1124_cast = add(x = var_1122_cast, y = var_1123_to_fp16); 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_1124_cast); tensor out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast); tensor var_1128_to_fp16 = const()[name = tensor("op_1128_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80804928)))]; tensor var_1129_cast = add(x = out_23_cast, y = var_1128_to_fp16); tensor var_1131_to_fp16 = const()[name = tensor("op_1131_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80806272)))]; tensor input_109_cast = mul(x = var_1129_cast, y = var_1131_to_fp16); tensor var_1139 = const()[name = tensor("op_1139"), val = tensor([1, 1])]; tensor var_1141 = const()[name = tensor("op_1141"), val = tensor([1, 1])]; tensor var_1143_pad_type_0 = const()[name = tensor("op_1143_pad_type_0"), val = tensor("custom")]; tensor var_1143_pad_0 = const()[name = tensor("op_1143_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(80807616)))]; 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(87361280)))]; tensor var_1143_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1141, groups = var_649, pad = var_1143_pad_0, pad_type = var_1143_pad_type_0, strides = var_1139, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_109_cast); tensor var_1144_split_sizes_0 = const()[name = tensor("op_1144_split_sizes_0"), val = tensor([2560, 2560])]; tensor var_1144_axis_0 = const()[name = tensor("op_1144_axis_0"), val = tensor(1)]; tensor var_1144_cast_0, tensor var_1144_cast_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast); tensor var_1146_mode_0 = const()[name = tensor("op_1146_mode_0"), val = tensor("EXACT")]; tensor var_1146_cast = gelu(mode = var_1146_mode_0, x = var_1144_cast_1); tensor input_111_cast = mul(x = var_1144_cast_0, y = var_1146_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 var_1154_pad_type_0 = const()[name = tensor("op_1154_pad_type_0"), val = tensor("custom")]; tensor var_1154_pad_0 = const()[name = tensor("op_1154_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(87371584)))]; 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(90648448)))]; tensor var_1154_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1152, groups = var_649, pad = var_1154_pad_0, pad_type = var_1154_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_111_cast); tensor hidden_states_69_cast = add(x = var_1154_cast, y = inputs_23_cast); tensor var_1156 = const()[name = tensor("op_1156"), val = tensor([2, 640, 32, 32])]; tensor input_113_cast = reshape(shape = var_1156, x = hidden_states_69_cast); tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; tensor hidden_states_71_pad_type_0 = const()[name = tensor("hidden_states_71_pad_type_0"), val = tensor("custom")]; tensor hidden_states_71_pad_0 = const()[name = tensor("hidden_states_71_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(90649792)))]; 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(91469056)))]; tensor hidden_states_71_cast = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1162, groups = var_649, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_1160, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_113_cast); tensor input_115_cast_1 = add(x = hidden_states_71_cast, y = hidden_states_59_cast); tensor var_1169 = const()[name = tensor("op_1169"), val = tensor([2, 2])]; tensor var_1171 = const()[name = tensor("op_1171"), val = tensor([1, 1])]; tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(91470400)))]; 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(98843264)))]; tensor input_117_cast_1 = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1171, groups = var_649, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1169, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = input_115_cast_1); tensor var_1179 = const()[name = tensor("op_1179"), val = tensor(3)]; tensor var_1190 = const()[name = tensor("op_1190"), val = tensor(true)]; tensor var_1195 = const()[name = tensor("op_1195"), val = tensor(1)]; tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 20, 16, 16])]; tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_117_cast_1); 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); tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast); tensor square_12_cast = square(x = sub_24_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); tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16); tensor sqrt_12_cast = sqrt(x = add_24_cast); tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast); tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 640, 16, 16])]; tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast); tensor add_25_mean_0_to_fp16 = const()[name = tensor("add_25_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98844608)))]; tensor add_25_variance_0_to_fp16 = const()[name = tensor("add_25_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98845952)))]; 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(98847296)))]; 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(98848640)))]; 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_25_mean_0_to_fp16, variance = add_25_variance_0_to_fp16, x = reshape_49_cast); tensor input_121_cast = silu(x = add_25_cast); tensor var_1218 = const()[name = tensor("op_1218"), val = tensor([1, 1])]; tensor var_1220 = const()[name = tensor("op_1220"), val = tensor([1, 1])]; tensor hidden_states_73_pad_type_0 = const()[name = tensor("hidden_states_73_pad_type_0"), val = tensor("custom")]; tensor hidden_states_73_pad_0 = const()[name = tensor("hidden_states_73_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(98849984)))]; 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(113595648)))]; tensor hidden_states_73_cast = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1220, groups = var_1195, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_1218, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_121_cast); tensor var_1226 = const()[name = tensor("op_1226"), val = tensor([1, 1])]; tensor var_1228 = const()[name = tensor("op_1228"), 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(113598272)))]; 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(116875136)))]; tensor temb_9_cast = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1228, groups = var_1195, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1226, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_125_cast = add(x = hidden_states_73_cast, y = temb_9_cast); tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 16, 16])]; tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_125_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); tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast); tensor square_13_cast = square(x = sub_26_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); 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); tensor sqrt_13_cast = sqrt(x = add_26_cast); tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast); tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 16, 16])]; tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast); tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116877760)))]; tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116880384)))]; 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(116883008)))]; 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(116885632)))]; 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast); tensor input_129_cast = silu(x = add_27_cast); tensor var_1238 = const()[name = tensor("op_1238"), val = tensor([1, 1])]; tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, 1])]; tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_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(116888256)))]; 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(146379520)))]; tensor hidden_states_75_cast = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1240, groups = var_1195, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_1238, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_129_cast); tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, 1])]; tensor var_1247 = const()[name = tensor("op_1247"), 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(146382144)))]; 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(148020608)))]; tensor x_3_cast = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1247, groups = var_1195, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1245, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_117_cast_1); tensor hidden_states_77_cast = add(x = x_3_cast, y = hidden_states_75_cast); tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 16, 16])]; tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = hidden_states_77_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); tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast); tensor square_14_cast = square(x = sub_28_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); tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16); tensor sqrt_14_cast = sqrt(x = add_28_cast); tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast); tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 16, 16])]; tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast); tensor add_29_mean_0_to_fp16 = const()[name = tensor("add_29_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148023232)))]; tensor add_29_variance_0_to_fp16 = const()[name = tensor("add_29_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148025856)))]; 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(148028480)))]; 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(148031104)))]; 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_29_mean_0_to_fp16, variance = add_29_variance_0_to_fp16, x = reshape_57_cast); tensor var_1267 = const()[name = tensor("op_1267"), val = tensor([1, 1])]; tensor var_1269 = const()[name = tensor("op_1269"), 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_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(148033728)))]; 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(151310592)))]; tensor hidden_states_79_cast = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1269, groups = var_1195, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1267, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_29_cast); tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([2, 1280, 1, 256])]; tensor inputs_25_cast = reshape(shape = var_1274, x = hidden_states_79_cast); tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1])]; tensor channels_mean_25_cast = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast); tensor zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast); tensor zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast); tensor var_1288 = const()[name = tensor("op_1288"), val = tensor([1])]; tensor var_1289_cast = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast); tensor var_1290_to_fp16 = const()[name = tensor("op_1290_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1291_cast = add(x = var_1289_cast, y = var_1290_to_fp16); 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_1291_cast); tensor out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast); tensor var_1295_to_fp16 = const()[name = tensor("op_1295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151313216)))]; tensor var_1296_cast = add(x = out_25_cast, y = var_1295_to_fp16); tensor var_1298_to_fp16 = const()[name = tensor("op_1298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151315840)))]; tensor hidden_states_81_cast = mul(x = var_1296_cast, y = var_1298_to_fp16); tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1, 1])]; tensor var_1307 = const()[name = tensor("op_1307"), val = tensor([1, 1])]; tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151318464)))]; tensor q_17_cast = conv(dilations = var_1307, groups = var_1195, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1305, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_81_cast); tensor var_1311 = const()[name = tensor("op_1311"), val = tensor([1, 1])]; tensor var_1313 = const()[name = tensor("op_1313"), val = tensor([1, 1])]; tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154595328)))]; tensor k_17_cast = conv(dilations = var_1313, groups = var_1195, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1311, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_81_cast); tensor var_1317 = const()[name = tensor("op_1317"), val = tensor([1, 1])]; tensor var_1319 = const()[name = tensor("op_1319"), val = tensor([1, 1])]; tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157872192)))]; tensor v_17_cast = conv(dilations = var_1319, groups = var_1195, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1317, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_81_cast); tensor var_1323 = const()[name = tensor("op_1323"), val = tensor([2, 8, 160, -1])]; tensor var_1324_cast = reshape(shape = var_1323, x = q_17_cast); tensor var_1325 = const()[name = tensor("op_1325"), val = tensor([2, 8, 160, -1])]; tensor var_1326_cast = reshape(shape = var_1325, x = k_17_cast); tensor var_1327 = const()[name = tensor("op_1327"), val = tensor([2, 8, 160, -1])]; tensor var_1328_cast = reshape(shape = var_1327, x = v_17_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_1324_cast, y = var_1326_cast); tensor var_1186_to_fp16 = const()[name = tensor("op_1186_to_fp16"), val = tensor(0x1.43cp-4)]; tensor attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_1186_to_fp16); tensor var_1332_cast = softmax(axis = var_1179, x = attn_weights_35_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_1328_cast, y = var_1332_cast); tensor var_1336 = const()[name = tensor("op_1336"), val = tensor([2, 1280, 1, -1])]; tensor input_133_cast = reshape(shape = var_1336, x = attn_17_cast); tensor var_1341 = const()[name = tensor("op_1341"), val = tensor([1, 1])]; tensor var_1343 = const()[name = tensor("op_1343"), val = tensor([1, 1])]; tensor var_1345_pad_type_0 = const()[name = tensor("op_1345_pad_type_0"), val = tensor("custom")]; tensor var_1345_pad_0 = const()[name = tensor("op_1345_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(161149056)))]; 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(164425920)))]; tensor var_1345_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1343, groups = var_1195, pad = var_1345_pad_0, pad_type = var_1345_pad_type_0, strides = var_1341, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_133_cast); tensor inputs_27_cast = add(x = var_1345_cast, y = inputs_25_cast); tensor var_1349 = const()[name = tensor("op_1349"), val = tensor([1])]; tensor channels_mean_27_cast = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast); tensor zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast); tensor zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast); tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([1])]; tensor var_1354_cast = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast); tensor var_1355_to_fp16 = const()[name = tensor("op_1355_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1356_cast = add(x = var_1354_cast, y = var_1355_to_fp16); 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_1356_cast); tensor out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast); tensor var_1360_to_fp16 = const()[name = tensor("op_1360_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164428544)))]; tensor var_1361_cast = add(x = out_27_cast, y = var_1360_to_fp16); tensor var_1363_to_fp16 = const()[name = tensor("op_1363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164431168)))]; tensor hidden_states_83_cast = mul(x = var_1361_cast, y = var_1363_to_fp16); tensor var_1370 = const()[name = tensor("op_1370"), val = tensor([1, 1])]; tensor var_1372 = const()[name = tensor("op_1372"), val = tensor([1, 1])]; tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164433792)))]; tensor q_19_cast = conv(dilations = var_1372, groups = var_1195, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1370, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_83_cast); tensor var_1376 = const()[name = tensor("op_1376"), val = tensor([1, 1])]; tensor var_1378 = const()[name = tensor("op_1378"), val = tensor([1, 1])]; tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167710656)))]; tensor k_19_cast = conv(dilations = var_1378, groups = var_1195, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1376, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states); tensor var_1382 = const()[name = tensor("op_1382"), val = tensor([1, 1])]; tensor var_1384 = const()[name = tensor("op_1384"), val = tensor([1, 1])]; tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169676800)))]; tensor v_19_cast = conv(dilations = var_1384, groups = var_1195, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1382, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states); tensor var_1388 = const()[name = tensor("op_1388"), val = tensor([2, 8, 160, -1])]; tensor var_1389_cast = reshape(shape = var_1388, x = q_19_cast); tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([2, 8, 160, -1])]; tensor var_1391_cast = reshape(shape = var_1390, x = k_19_cast); tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([2, 8, 160, -1])]; tensor var_1393_cast = reshape(shape = var_1392, x = v_19_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_1389_cast, y = var_1391_cast); tensor attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_1186_to_fp16); tensor var_1397_cast = softmax(axis = var_1179, x = attn_weights_39_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_1393_cast, y = var_1397_cast); tensor var_1401 = const()[name = tensor("op_1401"), val = tensor([2, 1280, 1, -1])]; tensor input_135_cast = reshape(shape = var_1401, x = attn_19_cast); tensor var_1406 = const()[name = tensor("op_1406"), val = tensor([1, 1])]; tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([1, 1])]; tensor var_1410_pad_type_0 = const()[name = tensor("op_1410_pad_type_0"), val = tensor("custom")]; tensor var_1410_pad_0 = const()[name = tensor("op_1410_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(171642944)))]; 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(174919808)))]; tensor var_1410_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1408, groups = var_1195, pad = var_1410_pad_0, pad_type = var_1410_pad_type_0, strides = var_1406, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_135_cast); tensor inputs_29_cast = add(x = var_1410_cast, y = inputs_27_cast); tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1])]; tensor channels_mean_29_cast = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast); tensor zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast); tensor zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast); tensor var_1418 = const()[name = tensor("op_1418"), val = tensor([1])]; tensor var_1419_cast = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast); tensor var_1420_to_fp16 = const()[name = tensor("op_1420_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1421_cast = add(x = var_1419_cast, y = var_1420_to_fp16); 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_1421_cast); tensor out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast); tensor var_1425_to_fp16 = const()[name = tensor("op_1425_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174922432)))]; tensor var_1426_cast = add(x = out_29_cast, y = var_1425_to_fp16); tensor var_1428_to_fp16 = const()[name = tensor("op_1428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174925056)))]; tensor input_137_cast = mul(x = var_1426_cast, y = var_1428_to_fp16); 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 var_1440_pad_type_0 = const()[name = tensor("op_1440_pad_type_0"), val = tensor("custom")]; tensor var_1440_pad_0 = const()[name = tensor("op_1440_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(174927680)))]; 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(201142144)))]; tensor var_1440_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1438, groups = var_1195, pad = var_1440_pad_0, pad_type = var_1440_pad_type_0, strides = var_1436, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_137_cast); tensor var_1441_split_sizes_0 = const()[name = tensor("op_1441_split_sizes_0"), val = tensor([5120, 5120])]; tensor var_1441_axis_0 = const()[name = tensor("op_1441_axis_0"), val = tensor(1)]; tensor var_1441_cast_0, tensor var_1441_cast_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast); tensor var_1443_mode_0 = const()[name = tensor("op_1443_mode_0"), val = tensor("EXACT")]; tensor var_1443_cast = gelu(mode = var_1443_mode_0, x = var_1441_cast_1); tensor input_139_cast = mul(x = var_1441_cast_0, y = var_1443_cast); tensor var_1447 = const()[name = tensor("op_1447"), val = tensor([1, 1])]; tensor var_1449 = const()[name = tensor("op_1449"), val = tensor([1, 1])]; tensor var_1451_pad_type_0 = const()[name = tensor("op_1451_pad_type_0"), val = tensor("custom")]; tensor var_1451_pad_0 = const()[name = tensor("op_1451_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(201162688)))]; 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(214269952)))]; tensor var_1451_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1449, groups = var_1195, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1447, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_139_cast); tensor hidden_states_87_cast = add(x = var_1451_cast, y = inputs_29_cast); tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([2, 1280, 16, 16])]; tensor input_141_cast = reshape(shape = var_1453, x = hidden_states_87_cast); tensor var_1457 = const()[name = tensor("op_1457"), val = tensor([1, 1])]; tensor var_1459 = const()[name = tensor("op_1459"), val = tensor([1, 1])]; tensor hidden_states_89_pad_type_0 = const()[name = tensor("hidden_states_89_pad_type_0"), val = tensor("custom")]; tensor hidden_states_89_pad_0 = const()[name = tensor("hidden_states_89_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(214272576)))]; 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(217549440)))]; tensor hidden_states_89_cast = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_1459, groups = var_1195, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_1457, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_141_cast); tensor input_143_cast_1 = add(x = hidden_states_89_cast, y = hidden_states_77_cast); tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 16, 16])]; tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_143_cast_1); 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); tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast); tensor square_15_cast = square(x = sub_30_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); tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16); tensor sqrt_15_cast = sqrt(x = add_30_cast); tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast); tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 16, 16])]; tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast); tensor add_31_mean_0_to_fp16 = const()[name = tensor("add_31_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217552064)))]; tensor add_31_variance_0_to_fp16 = const()[name = tensor("add_31_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217554688)))]; 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(217557312)))]; 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(217559936)))]; 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_31_mean_0_to_fp16, variance = add_31_variance_0_to_fp16, x = reshape_61_cast); tensor input_147_cast = silu(x = add_31_cast); tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([1, 1])]; tensor var_1476 = const()[name = tensor("op_1476"), val = tensor([1, 1])]; tensor hidden_states_91_pad_type_0 = const()[name = tensor("hidden_states_91_pad_type_0"), val = tensor("custom")]; tensor hidden_states_91_pad_0 = const()[name = tensor("hidden_states_91_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(217562560)))]; 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(247053824)))]; tensor hidden_states_91_cast = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_1476, groups = var_1195, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_1474, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_147_cast); tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 1])]; tensor var_1484 = const()[name = tensor("op_1484"), 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(247056448)))]; 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(250333312)))]; tensor temb_11_cast = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1484, groups = var_1195, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_1482, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_151_cast = add(x = hidden_states_91_cast, y = temb_11_cast); tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 16, 16])]; tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_151_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); tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast); tensor square_16_cast = square(x = sub_32_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); 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); tensor sqrt_16_cast = sqrt(x = add_32_cast); tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast); tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 16, 16])]; tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast); tensor add_33_mean_0_to_fp16 = const()[name = tensor("add_33_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250335936)))]; tensor add_33_variance_0_to_fp16 = const()[name = tensor("add_33_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250338560)))]; 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(250341184)))]; 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(250343808)))]; 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_33_mean_0_to_fp16, variance = add_33_variance_0_to_fp16, x = reshape_65_cast); tensor input_155_cast = silu(x = add_33_cast); tensor var_1494 = const()[name = tensor("op_1494"), val = tensor([1, 1])]; tensor var_1496 = const()[name = tensor("op_1496"), val = tensor([1, 1])]; tensor hidden_states_93_pad_type_0 = const()[name = tensor("hidden_states_93_pad_type_0"), val = tensor("custom")]; tensor hidden_states_93_pad_0 = const()[name = tensor("hidden_states_93_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(250346432)))]; 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(279837696)))]; tensor hidden_states_93_cast = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_1496, groups = var_1195, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_1494, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_155_cast); tensor hidden_states_95_cast = add(x = input_143_cast_1, y = hidden_states_93_cast); tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 16, 16])]; tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = hidden_states_95_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); tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast); tensor square_17_cast = square(x = sub_34_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); tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16); tensor sqrt_17_cast = sqrt(x = add_34_cast); tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast); tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 16, 16])]; tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast); tensor add_35_mean_0_to_fp16 = const()[name = tensor("add_35_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279840320)))]; tensor add_35_variance_0_to_fp16 = const()[name = tensor("add_35_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279842944)))]; 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(279845568)))]; 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(279848192)))]; 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_35_mean_0_to_fp16, variance = add_35_variance_0_to_fp16, x = reshape_69_cast); tensor var_1516 = const()[name = tensor("op_1516"), val = tensor([1, 1])]; tensor var_1518 = const()[name = tensor("op_1518"), val = tensor([1, 1])]; tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_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(279850816)))]; 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(283127680)))]; tensor hidden_states_97_cast = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_1518, groups = var_1195, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_1516, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_35_cast); tensor var_1523 = const()[name = tensor("op_1523"), val = tensor([2, 1280, 1, 256])]; tensor inputs_31_cast = reshape(shape = var_1523, x = hidden_states_97_cast); tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1])]; tensor channels_mean_31_cast = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast); tensor zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast); tensor zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast); tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1])]; tensor var_1538_cast = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_cast); tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1540_cast = add(x = var_1538_cast, y = var_1539_to_fp16); 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_1540_cast); tensor out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast); tensor var_1544_to_fp16 = const()[name = tensor("op_1544_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283130304)))]; tensor var_1545_cast = add(x = out_31_cast, y = var_1544_to_fp16); tensor var_1547_to_fp16 = const()[name = tensor("op_1547_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283132928)))]; tensor hidden_states_99_cast = mul(x = var_1545_cast, y = var_1547_to_fp16); tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_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(283135552)))]; tensor q_21_cast = conv(dilations = var_1556, groups = var_1195, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_99_cast); tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([1, 1])]; tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([1, 1])]; tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_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(286412416)))]; tensor k_21_cast = conv(dilations = var_1562, groups = var_1195, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_99_cast); tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 1])]; tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 1])]; tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_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(289689280)))]; tensor v_21_cast = conv(dilations = var_1568, groups = var_1195, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_99_cast); tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([2, 8, 160, -1])]; tensor var_1573_cast = reshape(shape = var_1572, x = q_21_cast); tensor var_1574 = const()[name = tensor("op_1574"), val = tensor([2, 8, 160, -1])]; tensor var_1575_cast = reshape(shape = var_1574, x = k_21_cast); tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([2, 8, 160, -1])]; tensor var_1577_cast = reshape(shape = var_1576, x = v_21_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_1573_cast, y = var_1575_cast); tensor attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_1186_to_fp16); tensor var_1581_cast = softmax(axis = var_1179, x = attn_weights_43_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_1577_cast, y = var_1581_cast); tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([2, 1280, 1, -1])]; tensor input_159_cast = reshape(shape = var_1585, x = attn_21_cast); tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 1])]; tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1])]; tensor var_1594_pad_type_0 = const()[name = tensor("op_1594_pad_type_0"), val = tensor("custom")]; tensor var_1594_pad_0 = const()[name = tensor("op_1594_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_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(292966144)))]; 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(296243008)))]; tensor var_1594_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1195, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_159_cast); tensor inputs_33_cast = add(x = var_1594_cast, y = inputs_31_cast); tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1])]; tensor channels_mean_33_cast = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast); tensor zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast); tensor zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast); tensor var_1602 = const()[name = tensor("op_1602"), val = tensor([1])]; tensor var_1603_cast = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_cast); tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1605_cast = add(x = var_1603_cast, y = var_1604_to_fp16); 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_1605_cast); tensor out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast); tensor var_1609_to_fp16 = const()[name = tensor("op_1609_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296245632)))]; tensor var_1610_cast = add(x = out_33_cast, y = var_1609_to_fp16); tensor var_1612_to_fp16 = const()[name = tensor("op_1612_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296248256)))]; tensor hidden_states_101_cast = mul(x = var_1610_cast, y = var_1612_to_fp16); 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 q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_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(296250880)))]; tensor q_23_cast = conv(dilations = var_1621, groups = var_1195, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1619, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_101_cast); tensor var_1625 = const()[name = tensor("op_1625"), val = tensor([1, 1])]; tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 1])]; tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_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(299527744)))]; tensor k_23_cast = conv(dilations = var_1627, groups = var_1195, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1625, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states); tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([1, 1])]; tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([1, 1])]; tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; tensor down_blocks_2_attentions_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(301493888)))]; tensor v_23_cast = conv(dilations = var_1633, groups = var_1195, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states); tensor var_1637 = const()[name = tensor("op_1637"), val = tensor([2, 8, 160, -1])]; tensor var_1638_cast = reshape(shape = var_1637, x = q_23_cast); tensor var_1639 = const()[name = tensor("op_1639"), val = tensor([2, 8, 160, -1])]; tensor var_1640_cast = reshape(shape = var_1639, x = k_23_cast); tensor var_1641 = const()[name = tensor("op_1641"), val = tensor([2, 8, 160, -1])]; tensor var_1642_cast = reshape(shape = var_1641, x = v_23_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_1638_cast, y = var_1640_cast); tensor attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_1186_to_fp16); tensor var_1646_cast = softmax(axis = var_1179, x = attn_weights_47_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_1642_cast, y = var_1646_cast); tensor var_1650 = const()[name = tensor("op_1650"), val = tensor([2, 1280, 1, -1])]; tensor input_161_cast = reshape(shape = var_1650, x = attn_23_cast); tensor var_1655 = const()[name = tensor("op_1655"), val = tensor([1, 1])]; tensor var_1657 = const()[name = tensor("op_1657"), val = tensor([1, 1])]; tensor var_1659_pad_type_0 = const()[name = tensor("op_1659_pad_type_0"), val = tensor("custom")]; tensor var_1659_pad_0 = const()[name = tensor("op_1659_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(303460032)))]; 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(306736896)))]; tensor var_1659_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1657, groups = var_1195, pad = var_1659_pad_0, pad_type = var_1659_pad_type_0, strides = var_1655, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_161_cast); tensor inputs_35_cast = add(x = var_1659_cast, y = inputs_33_cast); tensor var_1663 = const()[name = tensor("op_1663"), val = tensor([1])]; tensor channels_mean_35_cast = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast); tensor zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast); tensor zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast); tensor var_1667 = const()[name = tensor("op_1667"), val = tensor([1])]; tensor var_1668_cast = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast); tensor var_1669_to_fp16 = const()[name = tensor("op_1669_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1670_cast = add(x = var_1668_cast, y = var_1669_to_fp16); 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_1670_cast); tensor out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast); tensor var_1674_to_fp16 = const()[name = tensor("op_1674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306739520)))]; tensor var_1675_cast = add(x = out_35_cast, y = var_1674_to_fp16); tensor var_1677_to_fp16 = const()[name = tensor("op_1677_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306742144)))]; tensor input_163_cast = mul(x = var_1675_cast, y = var_1677_to_fp16); tensor var_1685 = const()[name = tensor("op_1685"), val = tensor([1, 1])]; tensor var_1687 = const()[name = tensor("op_1687"), val = tensor([1, 1])]; tensor var_1689_pad_type_0 = const()[name = tensor("op_1689_pad_type_0"), val = tensor("custom")]; tensor var_1689_pad_0 = const()[name = tensor("op_1689_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(306744768)))]; 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(332959232)))]; tensor var_1689_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1687, groups = var_1195, pad = var_1689_pad_0, pad_type = var_1689_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_163_cast); tensor var_1690_split_sizes_0 = const()[name = tensor("op_1690_split_sizes_0"), val = tensor([5120, 5120])]; tensor var_1690_axis_0 = const()[name = tensor("op_1690_axis_0"), val = tensor(1)]; tensor var_1690_cast_0, tensor var_1690_cast_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast); tensor var_1692_mode_0 = const()[name = tensor("op_1692_mode_0"), val = tensor("EXACT")]; tensor var_1692_cast = gelu(mode = var_1692_mode_0, x = var_1690_cast_1); tensor input_165_cast = mul(x = var_1690_cast_0, y = var_1692_cast); tensor var_1696 = const()[name = tensor("op_1696"), val = tensor([1, 1])]; tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([1, 1])]; tensor var_1700_pad_type_0 = const()[name = tensor("op_1700_pad_type_0"), val = tensor("custom")]; tensor var_1700_pad_0 = const()[name = tensor("op_1700_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(332979776)))]; 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(346087040)))]; tensor var_1700_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1698, groups = var_1195, pad = var_1700_pad_0, pad_type = var_1700_pad_type_0, strides = var_1696, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_165_cast); tensor hidden_states_105_cast = add(x = var_1700_cast, y = inputs_35_cast); tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([2, 1280, 16, 16])]; tensor input_167_cast = reshape(shape = var_1702, x = hidden_states_105_cast); tensor var_1706 = const()[name = tensor("op_1706"), val = tensor([1, 1])]; tensor var_1708 = const()[name = tensor("op_1708"), val = tensor([1, 1])]; tensor hidden_states_107_pad_type_0 = const()[name = tensor("hidden_states_107_pad_type_0"), val = tensor("custom")]; tensor hidden_states_107_pad_0 = const()[name = tensor("hidden_states_107_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(346089664)))]; 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(349366528)))]; tensor hidden_states_107_cast = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_1708, groups = var_1195, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_1706, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_167_cast); tensor input_169_cast_1 = add(x = hidden_states_107_cast, y = hidden_states_95_cast); tensor var_1715 = const()[name = tensor("op_1715"), val = tensor([2, 2])]; tensor var_1717 = const()[name = tensor("op_1717"), val = tensor([1, 1])]; tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; tensor 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(349369152)))]; 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(378860416)))]; tensor input_171_cast_1 = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_1717, groups = var_1195, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1715, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = input_169_cast_1); tensor var_1729 = const()[name = tensor("op_1729"), val = tensor(1)]; tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = input_171_cast_1); 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); tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast); tensor square_18_cast = square(x = sub_36_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); 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); tensor sqrt_18_cast = sqrt(x = add_36_cast); tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast); tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast); tensor add_37_mean_0_to_fp16 = const()[name = tensor("add_37_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378863040)))]; tensor add_37_variance_0_to_fp16 = const()[name = tensor("add_37_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378865664)))]; 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(378868288)))]; 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(378870912)))]; 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_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast); tensor input_175_cast = silu(x = add_37_cast); tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; tensor var_1747 = const()[name = tensor("op_1747"), val = tensor([1, 1])]; tensor hidden_states_109_pad_type_0 = const()[name = tensor("hidden_states_109_pad_type_0"), val = tensor("custom")]; tensor hidden_states_109_pad_0 = const()[name = tensor("hidden_states_109_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(378873536)))]; 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(408364800)))]; tensor hidden_states_109_cast = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_1747, groups = var_1729, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_1745, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_175_cast); tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 1])]; tensor var_1755 = const()[name = tensor("op_1755"), 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(408367424)))]; 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(411644288)))]; tensor temb_13_cast = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1755, groups = var_1729, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_1753, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_179_cast = add(x = hidden_states_109_cast, y = temb_13_cast); tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_179_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); tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast); tensor square_19_cast = square(x = sub_38_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); 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); tensor sqrt_19_cast = sqrt(x = add_38_cast); tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast); tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast); tensor add_39_mean_0_to_fp16 = const()[name = tensor("add_39_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411646912)))]; tensor add_39_variance_0_to_fp16 = const()[name = tensor("add_39_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411649536)))]; 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(411652160)))]; 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(411654784)))]; 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_39_mean_0_to_fp16, variance = add_39_variance_0_to_fp16, x = reshape_77_cast); tensor input_183_cast = silu(x = add_39_cast); tensor var_1765 = const()[name = tensor("op_1765"), val = tensor([1, 1])]; tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, 1])]; tensor hidden_states_111_pad_type_0 = const()[name = tensor("hidden_states_111_pad_type_0"), val = tensor("custom")]; tensor hidden_states_111_pad_0 = const()[name = tensor("hidden_states_111_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(411657408)))]; 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(441148672)))]; tensor hidden_states_111_cast = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_1767, groups = var_1729, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_1765, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_183_cast); tensor input_185_cast = add(x = input_171_cast_1, y = hidden_states_111_cast); tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_185_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); tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast); tensor square_20_cast = square(x = sub_40_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); 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); tensor sqrt_20_cast = sqrt(x = add_40_cast); tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast); tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast); tensor add_41_mean_0_to_fp16 = const()[name = tensor("add_41_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441151296)))]; tensor add_41_variance_0_to_fp16 = const()[name = tensor("add_41_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441153920)))]; 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(441156544)))]; 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(441159168)))]; 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_41_mean_0_to_fp16, variance = add_41_variance_0_to_fp16, x = reshape_81_cast); tensor input_189_cast = silu(x = add_41_cast); tensor var_1782 = const()[name = tensor("op_1782"), val = tensor([1, 1])]; tensor var_1784 = const()[name = tensor("op_1784"), val = tensor([1, 1])]; tensor hidden_states_113_pad_type_0 = const()[name = tensor("hidden_states_113_pad_type_0"), val = tensor("custom")]; tensor hidden_states_113_pad_0 = const()[name = tensor("hidden_states_113_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(441161792)))]; 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(470653056)))]; tensor hidden_states_113_cast = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_1784, groups = var_1729, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_1782, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_189_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 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(470655680)))]; 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(473932544)))]; tensor temb_15_cast = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1792, groups = var_1729, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_1790, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_193_cast = add(x = hidden_states_113_cast, y = temb_15_cast); tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_193_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); tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast); tensor square_21_cast = square(x = sub_42_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); 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); tensor sqrt_21_cast = sqrt(x = add_42_cast); tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast); tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast); tensor add_43_mean_0_to_fp16 = const()[name = tensor("add_43_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(473935168)))]; tensor add_43_variance_0_to_fp16 = const()[name = tensor("add_43_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(473937792)))]; 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(473940416)))]; 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(473943040)))]; tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_43_cast = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_85_cast); tensor input_197_cast = silu(x = add_43_cast); tensor var_1802 = const()[name = tensor("op_1802"), val = tensor([1, 1])]; tensor var_1804 = const()[name = tensor("op_1804"), 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([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(473945664)))]; 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(503436928)))]; tensor hidden_states_115_cast = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_1804, groups = var_1729, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_1802, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_197_cast); tensor input_199_cast = add(x = input_185_cast, y = hidden_states_115_cast); tensor var_1812 = const()[name = tensor("op_1812"), val = tensor(3)]; tensor var_1823 = const()[name = tensor("op_1823"), val = tensor(true)]; tensor var_1828 = const()[name = tensor("op_1828"), val = tensor(1)]; tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_199_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); tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast); tensor square_22_cast = square(x = sub_44_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); tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_44_cast = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16); tensor sqrt_22_cast = sqrt(x = add_44_cast); tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast); tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast); tensor add_45_mean_0_to_fp16 = const()[name = tensor("add_45_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503439552)))]; tensor add_45_variance_0_to_fp16 = const()[name = tensor("add_45_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503442176)))]; 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(503444800)))]; 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(503447424)))]; 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_45_mean_0_to_fp16, variance = add_45_variance_0_to_fp16, x = reshape_89_cast); tensor input_203_cast = silu(x = add_45_cast); tensor var_1846 = const()[name = tensor("op_1846"), val = tensor([1, 1])]; tensor var_1848 = const()[name = tensor("op_1848"), 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 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(503450048)))]; 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(532941312)))]; tensor hidden_states_117_cast = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_1848, groups = var_1828, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_1846, weight = mid_block_resnets_0_conv1_weight_to_fp16, x = input_203_cast); tensor var_1854 = const()[name = tensor("op_1854"), val = tensor([1, 1])]; tensor var_1856 = const()[name = tensor("op_1856"), 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(532943936)))]; 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(536220800)))]; tensor temb_17_cast = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1856, groups = var_1828, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_1854, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_207_cast = add(x = hidden_states_117_cast, y = temb_17_cast); tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_92_cast = reshape(shape = reshape_92_shape_0, x = input_207_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); tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast); tensor square_23_cast = square(x = sub_46_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); 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); tensor sqrt_23_cast = sqrt(x = add_46_cast); tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast); tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast); tensor add_47_mean_0_to_fp16 = const()[name = tensor("add_47_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(536223424)))]; tensor add_47_variance_0_to_fp16 = const()[name = tensor("add_47_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(536226048)))]; 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(536228672)))]; 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(536231296)))]; 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_47_mean_0_to_fp16, variance = add_47_variance_0_to_fp16, x = reshape_93_cast); tensor input_211_cast = silu(x = add_47_cast); tensor var_1866 = const()[name = tensor("op_1866"), val = tensor([1, 1])]; tensor var_1868 = const()[name = tensor("op_1868"), 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 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(536233920)))]; 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(565725184)))]; tensor hidden_states_119_cast = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_1868, groups = var_1828, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_1866, weight = mid_block_resnets_0_conv2_weight_to_fp16, x = input_211_cast); tensor hidden_states_121_cast = add(x = input_199_cast, y = hidden_states_119_cast); tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_96_cast = reshape(shape = reshape_96_shape_0, x = hidden_states_121_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); tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast); tensor square_24_cast = square(x = sub_48_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); tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_48_cast = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16); tensor sqrt_24_cast = sqrt(x = add_48_cast); tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast); tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast); tensor add_49_mean_0_to_fp16 = const()[name = tensor("add_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565727808)))]; tensor add_49_variance_0_to_fp16 = const()[name = tensor("add_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565730432)))]; 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(565733056)))]; 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(565735680)))]; 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_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_97_cast); tensor var_1888 = const()[name = tensor("op_1888"), val = tensor([1, 1])]; tensor var_1890 = const()[name = tensor("op_1890"), 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 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(565738304)))]; 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(569015168)))]; tensor hidden_states_123_cast = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_1890, groups = var_1828, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_1888, weight = mid_block_attentions_0_proj_in_weight_to_fp16, x = add_49_cast); tensor var_1895 = const()[name = tensor("op_1895"), val = tensor([2, 1280, 1, 64])]; tensor inputs_37_cast = reshape(shape = var_1895, x = hidden_states_123_cast); tensor var_1905 = const()[name = tensor("op_1905"), val = tensor([1])]; tensor channels_mean_37_cast = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast); tensor zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast); tensor zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast); tensor var_1909 = const()[name = tensor("op_1909"), val = tensor([1])]; tensor var_1910_cast = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast); tensor var_1911_to_fp16 = const()[name = tensor("op_1911_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1912_cast = add(x = var_1910_cast, y = var_1911_to_fp16); 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_1912_cast); tensor out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast); tensor var_1916_to_fp16 = const()[name = tensor("op_1916_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569017792)))]; tensor var_1917_cast = add(x = out_37_cast, y = var_1916_to_fp16); tensor var_1919_to_fp16 = const()[name = tensor("op_1919_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569020416)))]; tensor hidden_states_125_cast = mul(x = var_1917_cast, y = var_1919_to_fp16); tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([1, 1])]; tensor var_1928 = const()[name = tensor("op_1928"), 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 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(569023040)))]; tensor q_25_cast = conv(dilations = var_1928, groups = var_1828, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1926, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_125_cast); tensor var_1932 = const()[name = tensor("op_1932"), val = tensor([1, 1])]; tensor var_1934 = const()[name = tensor("op_1934"), 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 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(572299904)))]; tensor k_25_cast = conv(dilations = var_1934, groups = var_1828, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1932, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_125_cast); tensor var_1938 = const()[name = tensor("op_1938"), val = tensor([1, 1])]; tensor var_1940 = const()[name = tensor("op_1940"), 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 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(575576768)))]; tensor v_25_cast = conv(dilations = var_1940, groups = var_1828, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1938, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_125_cast); tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([2, 8, 160, -1])]; tensor var_1945_cast = reshape(shape = var_1944, x = q_25_cast); tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([2, 8, 160, -1])]; tensor var_1947_cast = reshape(shape = var_1946, x = k_25_cast); tensor var_1948 = const()[name = tensor("op_1948"), val = tensor([2, 8, 160, -1])]; tensor var_1949_cast = reshape(shape = var_1948, x = v_25_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_1945_cast, y = var_1947_cast); tensor var_1819_to_fp16 = const()[name = tensor("op_1819_to_fp16"), val = tensor(0x1.43cp-4)]; tensor attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_1819_to_fp16); tensor var_1953_cast = softmax(axis = var_1812, x = attn_weights_51_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_1949_cast, y = var_1953_cast); tensor var_1957 = const()[name = tensor("op_1957"), val = tensor([2, 1280, 1, -1])]; tensor input_215_cast = reshape(shape = var_1957, x = attn_25_cast); tensor var_1962 = const()[name = tensor("op_1962"), val = tensor([1, 1])]; tensor var_1964 = const()[name = tensor("op_1964"), val = tensor([1, 1])]; tensor var_1966_pad_type_0 = const()[name = tensor("op_1966_pad_type_0"), val = tensor("custom")]; tensor var_1966_pad_0 = const()[name = tensor("op_1966_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(578853632)))]; 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(582130496)))]; tensor var_1966_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1964, groups = var_1828, pad = var_1966_pad_0, pad_type = var_1966_pad_type_0, strides = var_1962, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_215_cast); tensor inputs_39_cast = add(x = var_1966_cast, y = inputs_37_cast); tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1])]; tensor channels_mean_39_cast = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast); tensor zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast); tensor zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast); tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([1])]; tensor var_1975_cast = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast); tensor var_1976_to_fp16 = const()[name = tensor("op_1976_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_1977_cast = add(x = var_1975_cast, y = var_1976_to_fp16); 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_1977_cast); tensor out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast); tensor var_1981_to_fp16 = const()[name = tensor("op_1981_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582133120)))]; tensor var_1982_cast = add(x = out_39_cast, y = var_1981_to_fp16); tensor var_1984_to_fp16 = const()[name = tensor("op_1984_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582135744)))]; tensor hidden_states_127_cast = mul(x = var_1982_cast, y = var_1984_to_fp16); tensor var_1991 = const()[name = tensor("op_1991"), val = tensor([1, 1])]; tensor var_1993 = const()[name = tensor("op_1993"), 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 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(582138368)))]; tensor q_27_cast = conv(dilations = var_1993, groups = var_1828, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1991, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_127_cast); tensor var_1997 = const()[name = tensor("op_1997"), val = tensor([1, 1])]; tensor var_1999 = const()[name = tensor("op_1999"), 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 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(585415232)))]; tensor k_27_cast = conv(dilations = var_1999, groups = var_1828, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1997, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states); tensor var_2003 = const()[name = tensor("op_2003"), val = tensor([1, 1])]; tensor var_2005 = const()[name = tensor("op_2005"), 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 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(587381376)))]; tensor v_27_cast = conv(dilations = var_2005, groups = var_1828, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_2003, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states); tensor var_2009 = const()[name = tensor("op_2009"), val = tensor([2, 8, 160, -1])]; tensor var_2010_cast = reshape(shape = var_2009, x = q_27_cast); tensor var_2011 = const()[name = tensor("op_2011"), val = tensor([2, 8, 160, -1])]; tensor var_2012_cast = reshape(shape = var_2011, x = k_27_cast); tensor var_2013 = const()[name = tensor("op_2013"), val = tensor([2, 8, 160, -1])]; tensor var_2014_cast = reshape(shape = var_2013, x = v_27_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_2010_cast, y = var_2012_cast); tensor attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_1819_to_fp16); tensor var_2018_cast = softmax(axis = var_1812, x = attn_weights_55_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_2014_cast, y = var_2018_cast); tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([2, 1280, 1, -1])]; tensor input_217_cast = reshape(shape = var_2022, x = attn_27_cast); tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1])]; tensor var_2029 = const()[name = tensor("op_2029"), val = tensor([1, 1])]; tensor var_2031_pad_type_0 = const()[name = tensor("op_2031_pad_type_0"), val = tensor("custom")]; tensor var_2031_pad_0 = const()[name = tensor("op_2031_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(589347520)))]; 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(592624384)))]; tensor var_2031_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2029, groups = var_1828, pad = var_2031_pad_0, pad_type = var_2031_pad_type_0, strides = var_2027, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_217_cast); tensor inputs_41_cast = add(x = var_2031_cast, y = inputs_39_cast); tensor var_2035 = const()[name = tensor("op_2035"), val = tensor([1])]; tensor channels_mean_41_cast = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast); tensor zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast); tensor zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast); tensor var_2039 = const()[name = tensor("op_2039"), val = tensor([1])]; tensor var_2040_cast = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast); tensor var_2041_to_fp16 = const()[name = tensor("op_2041_to_fp16"), val = tensor(0x1.5p-17)]; tensor var_2042_cast = add(x = var_2040_cast, y = var_2041_to_fp16); 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_2042_cast); tensor out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast); tensor var_2046_to_fp16 = const()[name = tensor("op_2046_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592627008)))]; tensor var_2047_cast = add(x = out_41_cast, y = var_2046_to_fp16); tensor var_2049_to_fp16 = const()[name = tensor("op_2049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592629632)))]; tensor input_219_cast = mul(x = var_2047_cast, y = var_2049_to_fp16); tensor var_2057 = const()[name = tensor("op_2057"), val = tensor([1, 1])]; tensor var_2059 = const()[name = tensor("op_2059"), val = tensor([1, 1])]; tensor var_2061_pad_type_0 = const()[name = tensor("op_2061_pad_type_0"), val = tensor("custom")]; tensor var_2061_pad_0 = const()[name = tensor("op_2061_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(592632256)))]; 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(618846720)))]; tensor var_2061_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2059, groups = var_1828, pad = var_2061_pad_0, pad_type = var_2061_pad_type_0, strides = var_2057, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_219_cast); tensor var_2062_split_sizes_0 = const()[name = tensor("op_2062_split_sizes_0"), val = tensor([5120, 5120])]; tensor var_2062_axis_0 = const()[name = tensor("op_2062_axis_0"), val = tensor(1)]; tensor var_2062_cast_0, tensor var_2062_cast_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast); tensor var_2064_mode_0 = const()[name = tensor("op_2064_mode_0"), val = tensor("EXACT")]; tensor var_2064_cast = gelu(mode = var_2064_mode_0, x = var_2062_cast_1); tensor input_221_cast = mul(x = var_2062_cast_0, y = var_2064_cast); tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1, 1])]; tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([1, 1])]; tensor var_2072_pad_type_0 = const()[name = tensor("op_2072_pad_type_0"), val = tensor("custom")]; tensor var_2072_pad_0 = const()[name = tensor("op_2072_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(618867264)))]; 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(631974528)))]; tensor var_2072_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2070, groups = var_1828, pad = var_2072_pad_0, pad_type = var_2072_pad_type_0, strides = var_2068, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_221_cast); tensor hidden_states_131_cast = add(x = var_2072_cast, y = inputs_41_cast); tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([2, 1280, 8, 8])]; tensor input_223_cast = reshape(shape = var_2074, x = hidden_states_131_cast); tensor var_2078 = const()[name = tensor("op_2078"), val = tensor([1, 1])]; tensor var_2080 = const()[name = tensor("op_2080"), val = tensor([1, 1])]; tensor hidden_states_133_pad_type_0 = const()[name = tensor("hidden_states_133_pad_type_0"), val = tensor("custom")]; tensor hidden_states_133_pad_0 = const()[name = tensor("hidden_states_133_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(631977152)))]; 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(635254016)))]; tensor hidden_states_133_cast = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_2080, groups = var_1828, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_2078, weight = mid_block_attentions_0_proj_out_weight_to_fp16, x = input_223_cast); tensor input_225_cast = add(x = hidden_states_133_cast, y = hidden_states_121_cast); tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_225_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); tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast); tensor square_25_cast = square(x = sub_50_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); 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); tensor sqrt_25_cast = sqrt(x = add_50_cast); tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast); tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_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(635256640)))]; 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(635259264)))]; 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(635261888)))]; 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(635264512)))]; 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); tensor input_229_cast = silu(x = add_51_cast); tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1])]; tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1])]; tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([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(635267136)))]; 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(664758400)))]; tensor hidden_states_135_cast = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_2097, groups = var_1828, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_2095, weight = mid_block_resnets_1_conv1_weight_to_fp16, x = input_229_cast); tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([1, 1])]; tensor var_2105 = const()[name = tensor("op_2105"), 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(664761024)))]; 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(668037888)))]; tensor temb_19_cast = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2105, groups = var_1828, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_2103, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_233_cast = add(x = hidden_states_135_cast, y = temb_19_cast); tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_104_cast = reshape(shape = reshape_104_shape_0, x = input_233_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); tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast); tensor square_26_cast = square(x = sub_52_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); 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); tensor sqrt_26_cast = sqrt(x = add_52_cast); tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast); tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast); tensor add_53_mean_0_to_fp16 = const()[name = tensor("add_53_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668040512)))]; tensor add_53_variance_0_to_fp16 = const()[name = tensor("add_53_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668043136)))]; 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(668045760)))]; 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(668048384)))]; 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_53_mean_0_to_fp16, variance = add_53_variance_0_to_fp16, x = reshape_105_cast); tensor input_237_cast = silu(x = add_53_cast); tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, 1])]; tensor var_2117 = const()[name = tensor("op_2117"), val = tensor([1, 1])]; tensor hidden_states_137_pad_type_0 = const()[name = tensor("hidden_states_137_pad_type_0"), val = tensor("custom")]; tensor hidden_states_137_pad_0 = const()[name = tensor("hidden_states_137_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(668051008)))]; 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(697542272)))]; tensor hidden_states_137_cast = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_2117, groups = var_1828, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_2115, weight = mid_block_resnets_1_conv2_weight_to_fp16, x = input_237_cast); tensor hidden_states_139_cast = add(x = input_225_cast, y = hidden_states_137_cast); tensor var_2128 = const()[name = tensor("op_2128"), val = tensor(1)]; tensor input_239_interleave_0 = const()[name = tensor("input_239_interleave_0"), val = tensor(false)]; tensor input_239_cast = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast, input_199_cast)); tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 80, 8, 8])]; tensor reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_239_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); tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast); tensor square_27_cast = square(x = sub_54_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); 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); tensor sqrt_27_cast = sqrt(x = add_54_cast); tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast); tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 2560, 8, 8])]; tensor reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast); tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697544896)))]; tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697550080)))]; 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(697555264)))]; 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(697560448)))]; tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_55_cast = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast); tensor input_243_cast = silu(x = add_55_cast); tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 1])]; tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; tensor hidden_states_141_pad_type_0 = const()[name = tensor("hidden_states_141_pad_type_0"), val = tensor("custom")]; tensor hidden_states_141_pad_0 = const()[name = tensor("hidden_states_141_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(697565632)))]; 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(756548096)))]; tensor hidden_states_141_cast = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_2153, groups = var_2128, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_2151, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_243_cast); tensor var_2159 = const()[name = tensor("op_2159"), val = tensor([1, 1])]; tensor var_2161 = const()[name = tensor("op_2161"), 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(756550720)))]; 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(759827584)))]; tensor temb_21_cast = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2161, groups = var_2128, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_2159, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_247_cast = add(x = hidden_states_141_cast, y = temb_21_cast); tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_247_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); tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast); tensor square_28_cast = square(x = sub_56_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); 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); tensor sqrt_28_cast = sqrt(x = add_56_cast); tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast); tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast); tensor add_57_mean_0_to_fp16 = const()[name = tensor("add_57_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(759830208)))]; tensor add_57_variance_0_to_fp16 = const()[name = tensor("add_57_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(759832832)))]; 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(759835456)))]; 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(759838080)))]; 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_57_mean_0_to_fp16, variance = add_57_variance_0_to_fp16, x = reshape_113_cast); tensor input_251_cast = silu(x = add_57_cast); tensor var_2171 = const()[name = tensor("op_2171"), val = tensor([1, 1])]; tensor var_2173 = const()[name = tensor("op_2173"), val = tensor([1, 1])]; tensor hidden_states_143_pad_type_0 = const()[name = tensor("hidden_states_143_pad_type_0"), val = tensor("custom")]; tensor hidden_states_143_pad_0 = const()[name = tensor("hidden_states_143_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(759840704)))]; 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(789331968)))]; tensor hidden_states_143_cast = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_2173, groups = var_2128, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_2171, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_251_cast); tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1, 1])]; tensor var_2180 = const()[name = tensor("op_2180"), 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(789334592)))]; 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(795888256)))]; tensor x_5_cast = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2180, groups = var_2128, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_2178, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16, x = input_239_cast); tensor hidden_states_145_cast = add(x = x_5_cast, y = hidden_states_143_cast); tensor input_253_interleave_0 = const()[name = tensor("input_253_interleave_0"), val = tensor(false)]; tensor input_253_cast_1 = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast, input_185_cast)); tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 80, 8, 8])]; tensor reshape_116_cast = reshape(shape = reshape_116_shape_0, x = input_253_cast_1); 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); tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast); tensor square_29_cast = square(x = sub_58_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); 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); tensor sqrt_29_cast = sqrt(x = add_58_cast); tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast); tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 2560, 8, 8])]; tensor reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast); tensor add_59_mean_0_to_fp16 = const()[name = tensor("add_59_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795890880)))]; tensor add_59_variance_0_to_fp16 = const()[name = tensor("add_59_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795896064)))]; 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(795901248)))]; 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(795906432)))]; 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_59_mean_0_to_fp16, variance = add_59_variance_0_to_fp16, x = reshape_117_cast); tensor input_257_cast = silu(x = add_59_cast); tensor var_2198 = const()[name = tensor("op_2198"), val = tensor([1, 1])]; tensor var_2200 = const()[name = tensor("op_2200"), val = tensor([1, 1])]; tensor hidden_states_147_pad_type_0 = const()[name = tensor("hidden_states_147_pad_type_0"), val = tensor("custom")]; tensor hidden_states_147_pad_0 = const()[name = tensor("hidden_states_147_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(795911616)))]; 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(854894080)))]; tensor hidden_states_147_cast = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_2200, groups = var_2128, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_2198, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_257_cast); tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 1])]; tensor var_2208 = const()[name = tensor("op_2208"), 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(854896704)))]; 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(858173568)))]; tensor temb_23_cast = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2208, groups = var_2128, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_2206, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_1); tensor input_261_cast = add(x = hidden_states_147_cast, y = temb_23_cast); tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 40, 8, 8])]; tensor reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_261_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); tensor sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast); tensor square_30_cast = square(x = sub_60_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); 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); tensor sqrt_30_cast = sqrt(x = add_60_cast); tensor real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast); tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1280, 8, 8])]; tensor reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast); tensor add_61_mean_0_to_fp16 = const()[name = tensor("add_61_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858176192)))]; tensor add_61_variance_0_to_fp16 = const()[name = tensor("add_61_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858178816)))]; 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(858181440)))]; 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(858184064)))]; 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_61_mean_0_to_fp16, variance = add_61_variance_0_to_fp16, x = reshape_121_cast); tensor input_265_cast = silu(x = add_61_cast); tensor var_2218 = const()[name = tensor("op_2218"), val = tensor([1, 1])]; tensor var_2220 = const()[name = tensor("op_2220"), val = tensor([1, 1])]; tensor hidden_states_149_pad_type_0 = const()[name = tensor("hidden_states_149_pad_type_0"), val = tensor("custom")]; tensor hidden_states_149_pad_0 = const()[name = tensor("hidden_states_149_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(858186688)))]; 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(887677952)))]; tensor hidden_states_149_cast_1 = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_2220, groups = var_2128, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_2218, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_265_cast); tensor input_171_cast_dtype_0 = const()[name = tensor("input_171_cast_dtype_0"), val = tensor("fp32")]; tensor input_15_cast_dtype_0 = const()[name = tensor("input_15_cast_dtype_0"), val = tensor("fp32")]; tensor input_115_cast_dtype_0 = const()[name = tensor("input_115_cast_dtype_0"), val = tensor("fp32")]; tensor input_253_cast_dtype_0 = const()[name = tensor("input_253_cast_dtype_0"), val = tensor("fp32")]; tensor input_61_cast_dtype_0 = const()[name = tensor("input_61_cast_dtype_0"), val = tensor("fp32")]; tensor input_117_cast_dtype_0 = const()[name = tensor("input_117_cast_dtype_0"), val = tensor("fp32")]; tensor input_143_cast_dtype_0 = const()[name = tensor("input_143_cast_dtype_0"), val = tensor("fp32")]; tensor input_169_cast_dtype_0 = const()[name = tensor("input_169_cast_dtype_0"), val = tensor("fp32")]; tensor input_63_cast_dtype_0 = const()[name = tensor("input_63_cast_dtype_0"), val = tensor("fp32")]; tensor input_89_cast_dtype_0 = const()[name = tensor("input_89_cast_dtype_0"), val = tensor("fp32")]; tensor input_7_cast_dtype_0 = const()[name = tensor("input_7_cast_dtype_0"), val = tensor("fp32")]; tensor input_35_cast_dtype_0 = const()[name = tensor("input_35_cast_dtype_0"), val = tensor("fp32")]; tensor hidden_states_149_cast_dtype_0 = const()[name = tensor("hidden_states_149_cast_dtype_0"), val = tensor("fp32")]; tensor hidden_states_149_cast = cast(dtype = hidden_states_149_cast_dtype_0, x = hidden_states_149_cast_1); tensor input_35_cast = cast(dtype = input_35_cast_dtype_0, x = input_35_cast_1); tensor input_7_cast = cast(dtype = input_7_cast_dtype_0, x = input_7_cast_1); tensor input_89_cast = cast(dtype = input_89_cast_dtype_0, x = input_89_cast_1); tensor input_63_cast = cast(dtype = input_63_cast_dtype_0, x = input_63_cast_1); tensor input_169_cast = cast(dtype = input_169_cast_dtype_0, x = input_169_cast_1); tensor input_143_cast = cast(dtype = input_143_cast_dtype_0, x = input_143_cast_1); tensor input_117_cast = cast(dtype = input_117_cast_dtype_0, x = input_117_cast_1); tensor input_61_cast = cast(dtype = input_61_cast_dtype_0, x = input_61_cast_1); tensor input_253_cast = cast(dtype = input_253_cast_dtype_0, x = input_253_cast_1); tensor input_115_cast = cast(dtype = input_115_cast_dtype_0, x = input_115_cast_1); tensor input_15_cast = cast(dtype = input_15_cast_dtype_0, x = input_15_cast_1); tensor input_171_cast = cast(dtype = input_171_cast_dtype_0, x = input_171_cast_1); } -> (input_171_cast, input_15_cast, input_115_cast, input_253_cast, input_61_cast, input_117_cast, input_143_cast, input_169_cast, input_63_cast, input_89_cast, input_7_cast, input_35_cast, hidden_states_149_cast); }