diff --git "a/TextEncoder_v2.mlmodelc/model.mil" "b/TextEncoder_v2.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/TextEncoder_v2.mlmodelc/model.mil" @@ -0,0 +1,984 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor style_ttl, tensor text_ids, tensor text_mask) { + int32 var_29 = const()[name = string("op_29"), val = int32(-1)]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor m_char_embedder_weight_to_fp16 = const()[name = string("m_char_embedder_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string text_ids_to_int16_dtype_0 = const()[name = string("text_ids_to_int16_dtype_0"), val = string("int16")]; + string cast_45_dtype_0 = const()[name = string("cast_45_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_ids_to_int16 = cast(dtype = text_ids_to_int16_dtype_0, x = text_ids)[name = string("cast_51")]; + tensor cast_45 = cast(dtype = cast_45_dtype_0, x = text_ids_to_int16)[name = string("cast_50")]; + tensor greater_equal_0 = greater_equal(x = cast_45, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(8322)]; + tensor add_0 = add(x = cast_45, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_45, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_49")]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = m_char_embedder_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_55_perm_0 = const()[name = string("op_55_perm_0"), val = tensor([0, 2, 1])]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_48")]; + tensor var_55_cast_fp16 = transpose(perm = var_55_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_45")]; + tensor x_3_cast_fp16 = mul(x = var_55_cast_fp16, y = text_mask_to_fp16)[name = string("x_3_cast_fp16")]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = text_mask_to_fp16)[name = string("x_5_cast_fp16")]; + tensor input_3_begin_0 = const()[name = string("input_3_begin_0"), val = tensor([0, 0, 1])]; + tensor input_3_end_0 = const()[name = string("input_3_end_0"), val = tensor([1, 1, 128])]; + tensor input_3_end_mask_0 = const()[name = string("input_3_end_mask_0"), val = tensor([true, true, true])]; + tensor input_3_cast_fp16 = slice_by_index(begin = input_3_begin_0, end = input_3_end_0, end_mask = input_3_end_mask_0, x = text_mask_to_fp16)[name = string("input_3_cast_fp16")]; + tensor var_65_pad_0 = const()[name = string("op_65_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_65_mode_0 = const()[name = string("op_65_mode_0"), val = string("constant")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_65_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_65_mode_0, pad = var_65_pad_0, x = input_3_cast_fp16)[name = string("op_65_cast_fp16")]; + fp16 var_21_to_fp16 = const()[name = string("op_21_to_fp16"), val = fp16(0x1p+0)]; + tensor var_66_cast_fp16 = sub(x = var_21_to_fp16, y = var_65_cast_fp16)[name = string("op_66_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = text_mask_to_fp16, y = var_66_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_68_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_68_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_68_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_71_cast_fp16 = mul(x = x_5_cast_fp16, y = text_mask_to_fp16)[name = string("op_71_cast_fp16")]; + tensor var_72_cast_fp16 = sub(x = var_21_to_fp16, y = text_mask_to_fp16)[name = string("op_72_cast_fp16")]; + tensor var_73_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_73_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_71_cast_fp16, y = var_73_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_7_mode_0 = const()[name = string("input_7_mode_0"), val = string("replicate")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor input_7_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_7_mode_0, pad = input_7_pad_0, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")]; + int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(256)]; + tensor h_1_strides_0 = const()[name = string("h_1_strides_0"), val = tensor([1])]; + tensor h_1_pad_0 = const()[name = string("h_1_pad_0"), val = tensor([0, 0])]; + tensor h_1_dilations_0 = const()[name = string("h_1_dilations_0"), val = tensor([1])]; + tensor m_convnext_0_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4260992)))]; + tensor m_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4263616)))]; + tensor h_1_cast_fp16 = conv(bias = m_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = m_convnext_0_dwconv__conv_weight_to_fp16, x = input_7_cast_fp16)[name = string("h_1_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_1_cast_fp16, y = text_mask_to_fp16)[name = string("x_7_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor([-1])]; + tensor m_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4264192)))]; + tensor m_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4264768)))]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_7_cast_fp16)[name = string("transpose_44")]; + tensor var_91_cast_fp16 = layer_norm(axes = var_91_axes_0, beta = m_convnext_0_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_0_norm_norm_weight_to_fp16, x = input_9_cast_fp16)[name = string("op_91_cast_fp16")]; + tensor input_11_perm_0 = const()[name = string("input_11_perm_0"), val = tensor([0, 2, 1])]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(1)]; + tensor m_convnext_0_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4265344)))]; + tensor m_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4789696)))]; + tensor input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = var_91_cast_fp16)[name = string("transpose_43")]; + tensor h_3_cast_fp16 = conv(bias = m_convnext_0_pwconv1_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = m_convnext_0_pwconv1_weight_to_fp16, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = h_3_cast_fp16)[name = string("input_13_cast_fp16")]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor var_108_weight_0_to_fp16 = const()[name = string("op_108_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4791808)))]; + tensor var_108_bias_0_to_fp16 = const()[name = string("op_108_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5316160)))]; + tensor var_108_cast_fp16 = conv(bias = var_108_bias_0_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = var_108_weight_0_to_fp16, x = input_13_cast_fp16)[name = string("op_108_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_108_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = text_mask_to_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = text_mask_to_fp16)[name = string("x_11_cast_fp16")]; + tensor var_122_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_122_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_122_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_125_cast_fp16 = mul(x = x_11_cast_fp16, y = text_mask_to_fp16)[name = string("op_125_cast_fp16")]; + tensor var_127_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_127_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = var_125_cast_fp16, y = var_127_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("replicate")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_19_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_19_mode_0, pad = input_19_pad_0, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(256)]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + tensor m_convnext_1_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5316736)))]; + tensor m_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319360)))]; + tensor h_7_cast_fp16 = conv(bias = m_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = m_convnext_1_dwconv__conv_weight_to_fp16, x = input_19_cast_fp16)[name = string("h_7_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_7_cast_fp16, y = text_mask_to_fp16)[name = string("x_13_cast_fp16")]; + tensor input_21_perm_0 = const()[name = string("input_21_perm_0"), val = tensor([0, 2, 1])]; + tensor var_145_axes_0 = const()[name = string("op_145_axes_0"), val = tensor([-1])]; + tensor m_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319936)))]; + tensor m_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5320512)))]; + tensor input_21_cast_fp16 = transpose(perm = input_21_perm_0, x = x_13_cast_fp16)[name = string("transpose_42")]; + tensor var_145_cast_fp16 = layer_norm(axes = var_145_axes_0, beta = m_convnext_1_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_1_norm_norm_weight_to_fp16, x = input_21_cast_fp16)[name = string("op_145_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([1])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(1)]; + tensor m_convnext_1_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5321088)))]; + tensor m_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5845440)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = var_145_cast_fp16)[name = string("transpose_41")]; + tensor h_9_cast_fp16 = conv(bias = m_convnext_1_pwconv1_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = m_convnext_1_pwconv1_weight_to_fp16, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = h_9_cast_fp16)[name = string("input_25_cast_fp16")]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor var_162_weight_0_to_fp16 = const()[name = string("op_162_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5847552)))]; + tensor var_162_bias_0_to_fp16 = const()[name = string("op_162_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6371904)))]; + tensor var_162_cast_fp16 = conv(bias = var_162_bias_0_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = var_162_weight_0_to_fp16, x = input_25_cast_fp16)[name = string("op_162_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_162_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = text_mask_to_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = text_mask_to_fp16)[name = string("x_17_cast_fp16")]; + tensor var_176_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_176_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_176_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_179_cast_fp16 = mul(x = x_17_cast_fp16, y = text_mask_to_fp16)[name = string("op_179_cast_fp16")]; + tensor var_181_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_181_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = var_179_cast_fp16, y = var_181_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_31_mode_0 = const()[name = string("input_31_mode_0"), val = string("replicate")]; + fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)]; + tensor input_31_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_31_mode_0, pad = input_31_pad_0, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([2])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(256)]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_2_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6372480)))]; + tensor m_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6375104)))]; + tensor h_13_cast_fp16 = conv(bias = m_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = m_convnext_2_dwconv__conv_weight_to_fp16, x = input_31_cast_fp16)[name = string("h_13_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_13_cast_fp16, y = text_mask_to_fp16)[name = string("x_19_cast_fp16")]; + tensor input_33_perm_0 = const()[name = string("input_33_perm_0"), val = tensor([0, 2, 1])]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor m_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6375680)))]; + tensor m_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6376256)))]; + tensor input_33_cast_fp16 = transpose(perm = input_33_perm_0, x = x_19_cast_fp16)[name = string("transpose_40")]; + tensor var_199_cast_fp16 = layer_norm(axes = var_199_axes_0, beta = m_convnext_2_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_2_norm_norm_weight_to_fp16, x = input_33_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([1])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(1)]; + tensor m_convnext_2_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6376832)))]; + tensor m_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901184)))]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = var_199_cast_fp16)[name = string("transpose_39")]; + tensor h_15_cast_fp16 = conv(bias = m_convnext_2_pwconv1_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = m_convnext_2_pwconv1_weight_to_fp16, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + string input_37_mode_0 = const()[name = string("input_37_mode_0"), val = string("EXACT")]; + tensor input_37_cast_fp16 = gelu(mode = input_37_mode_0, x = h_15_cast_fp16)[name = string("input_37_cast_fp16")]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor var_216_weight_0_to_fp16 = const()[name = string("op_216_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6903296)))]; + tensor var_216_bias_0_to_fp16 = const()[name = string("op_216_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7427648)))]; + tensor var_216_cast_fp16 = conv(bias = var_216_bias_0_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = var_216_weight_0_to_fp16, x = input_37_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_216_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = text_mask_to_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = text_mask_to_fp16)[name = string("x_23_cast_fp16")]; + tensor var_230_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_230_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_230_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = x_23_cast_fp16, y = text_mask_to_fp16)[name = string("op_233_cast_fp16")]; + tensor var_235_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_235_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = var_233_cast_fp16, y = var_235_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_43_mode_0 = const()[name = string("input_43_mode_0"), val = string("replicate")]; + fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)]; + tensor input_43_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_43_mode_0, pad = input_43_pad_0, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([2])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(256)]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_3_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7428224)))]; + tensor m_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7430848)))]; + tensor h_19_cast_fp16 = conv(bias = m_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = m_convnext_3_dwconv__conv_weight_to_fp16, x = input_43_cast_fp16)[name = string("h_19_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_19_cast_fp16, y = text_mask_to_fp16)[name = string("x_25_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor([-1])]; + tensor m_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7431424)))]; + tensor m_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7432000)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_25_cast_fp16)[name = string("transpose_38")]; + tensor var_253_cast_fp16 = layer_norm(axes = var_253_axes_0, beta = m_convnext_3_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_3_norm_norm_weight_to_fp16, x = input_45_cast_fp16)[name = string("op_253_cast_fp16")]; + tensor input_47_perm_0 = const()[name = string("input_47_perm_0"), val = tensor([0, 2, 1])]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([1])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(1)]; + tensor m_convnext_3_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7432576)))]; + tensor m_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7956928)))]; + tensor input_47_cast_fp16 = transpose(perm = input_47_perm_0, x = var_253_cast_fp16)[name = string("transpose_37")]; + tensor h_21_cast_fp16 = conv(bias = m_convnext_3_pwconv1_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = m_convnext_3_pwconv1_weight_to_fp16, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = h_21_cast_fp16)[name = string("input_49_cast_fp16")]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor var_270_weight_0_to_fp16 = const()[name = string("op_270_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7959040)))]; + tensor var_270_bias_0_to_fp16 = const()[name = string("op_270_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8483392)))]; + tensor var_270_cast_fp16 = conv(bias = var_270_bias_0_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = var_270_weight_0_to_fp16, x = input_49_cast_fp16)[name = string("op_270_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_270_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = text_mask_to_fp16)[name = string("x_27_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = x_27_cast_fp16, y = text_mask_to_fp16)[name = string("x_29_cast_fp16")]; + tensor var_284_cast_fp16 = mul(x = x_29_cast_fp16, y = last_1_cast_fp16)[name = string("op_284_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_284_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_287_cast_fp16 = mul(x = x_29_cast_fp16, y = text_mask_to_fp16)[name = string("op_287_cast_fp16")]; + tensor var_289_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_289_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = var_287_cast_fp16, y = var_289_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("replicate")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_55_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([4])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(256)]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_4_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8483968)))]; + tensor m_convnext_4_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8486592)))]; + tensor h_25_cast_fp16 = conv(bias = m_convnext_4_dwconv__conv_bias_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = m_convnext_4_dwconv__conv_weight_to_fp16, x = input_55_cast_fp16)[name = string("h_25_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = h_25_cast_fp16, y = text_mask_to_fp16)[name = string("x_31_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([0, 2, 1])]; + tensor var_307_axes_0 = const()[name = string("op_307_axes_0"), val = tensor([-1])]; + tensor m_convnext_4_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_4_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8487168)))]; + tensor m_convnext_4_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_4_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8487744)))]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = x_31_cast_fp16)[name = string("transpose_36")]; + tensor var_307_cast_fp16 = layer_norm(axes = var_307_axes_0, beta = m_convnext_4_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_4_norm_norm_weight_to_fp16, x = input_57_cast_fp16)[name = string("op_307_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(1)]; + tensor m_convnext_4_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_4_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8488320)))]; + tensor m_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9012672)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = var_307_cast_fp16)[name = string("transpose_35")]; + tensor h_27_cast_fp16 = conv(bias = m_convnext_4_pwconv1_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = m_convnext_4_pwconv1_weight_to_fp16, x = input_59_cast_fp16)[name = string("h_27_cast_fp16")]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("EXACT")]; + tensor input_61_cast_fp16 = gelu(mode = input_61_mode_0, x = h_27_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor var_324_weight_0_to_fp16 = const()[name = string("op_324_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9014784)))]; + tensor var_324_bias_0_to_fp16 = const()[name = string("op_324_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9539136)))]; + tensor var_324_cast_fp16 = conv(bias = var_324_bias_0_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = var_324_weight_0_to_fp16, x = input_61_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_29_cast_fp16, y = var_324_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = out_9_cast_fp16, y = text_mask_to_fp16)[name = string("x_33_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = x_33_cast_fp16, y = text_mask_to_fp16)[name = string("x_35_cast_fp16")]; + tensor var_338_cast_fp16 = mul(x = x_35_cast_fp16, y = last_1_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_338_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_35_cast_fp16, y = text_mask_to_fp16)[name = string("op_341_cast_fp16")]; + tensor var_343_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_cast_fp16)[name = string("op_343_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_341_cast_fp16, y = var_343_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor input_67_pad_0 = const()[name = string("input_67_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("replicate")]; + fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_67_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_67_mode_0, pad = input_67_pad_0, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([4])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(256)]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_5_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9539712)))]; + tensor m_convnext_5_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9542336)))]; + tensor h_31_cast_fp16 = conv(bias = m_convnext_5_dwconv__conv_bias_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = m_convnext_5_dwconv__conv_weight_to_fp16, x = input_67_cast_fp16)[name = string("h_31_cast_fp16")]; + tensor x_37_cast_fp16 = mul(x = h_31_cast_fp16, y = text_mask_to_fp16)[name = string("x_37_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor m_convnext_5_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_5_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9542912)))]; + tensor m_convnext_5_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_5_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9543488)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_37_cast_fp16)[name = string("transpose_34")]; + tensor var_361_cast_fp16 = layer_norm(axes = var_361_axes_0, beta = m_convnext_5_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_5_norm_norm_weight_to_fp16, x = input_69_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(1)]; + tensor m_convnext_5_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_5_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9544064)))]; + tensor m_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10068416)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = var_361_cast_fp16)[name = string("transpose_33")]; + tensor h_33_cast_fp16 = conv(bias = m_convnext_5_pwconv1_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = m_convnext_5_pwconv1_weight_to_fp16, x = input_71_cast_fp16)[name = string("h_33_cast_fp16")]; + string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; + tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = h_33_cast_fp16)[name = string("input_73_cast_fp16")]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor var_378_weight_0_to_fp16 = const()[name = string("op_378_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10070528)))]; + tensor var_378_bias_0_to_fp16 = const()[name = string("op_378_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10594880)))]; + tensor var_378_cast_fp16 = conv(bias = var_378_bias_0_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = var_378_weight_0_to_fp16, x = input_73_cast_fp16)[name = string("op_378_cast_fp16")]; + tensor out_11_cast_fp16 = add(x = x_35_cast_fp16, y = var_378_cast_fp16)[name = string("out_11_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = out_11_cast_fp16, y = text_mask_to_fp16)[name = string("x_39_cast_fp16")]; + tensor x_41_cast_fp16 = mul(x = x_39_cast_fp16, y = text_mask_to_fp16)[name = string("x_41_cast_fp16")]; + string var_401_pad_type_0 = const()[name = string("op_401_pad_type_0"), val = string("valid")]; + tensor var_401_strides_0 = const()[name = string("op_401_strides_0"), val = tensor([1])]; + tensor var_401_pad_0 = const()[name = string("op_401_pad_0"), val = tensor([0, 0])]; + tensor var_401_dilations_0 = const()[name = string("op_401_dilations_0"), val = tensor([1])]; + int32 var_401_groups_0 = const()[name = string("op_401_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10595456)))]; + tensor m_attn_layers_0_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10726592)))]; + tensor var_401_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_q_bias_to_fp16, dilations = var_401_dilations_0, groups = var_401_groups_0, pad = var_401_pad_0, pad_type = var_401_pad_type_0, strides = var_401_strides_0, weight = m_attn_layers_0_attn_conv_q_weight_to_fp16, x = x_41_cast_fp16)[name = string("op_401_cast_fp16")]; + tensor var_402 = const()[name = string("op_402"), val = tensor([1, 4, 64, 128])]; + tensor var_403_cast_fp16 = reshape(shape = var_402, x = var_401_cast_fp16)[name = string("op_403_cast_fp16")]; + tensor q_1_perm_0 = const()[name = string("q_1_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_411_pad_type_0 = const()[name = string("op_411_pad_type_0"), val = string("valid")]; + tensor var_411_strides_0 = const()[name = string("op_411_strides_0"), val = tensor([1])]; + tensor var_411_pad_0 = const()[name = string("op_411_pad_0"), val = tensor([0, 0])]; + tensor var_411_dilations_0 = const()[name = string("op_411_dilations_0"), val = tensor([1])]; + int32 var_411_groups_0 = const()[name = string("op_411_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10727168)))]; + tensor m_attn_layers_0_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10858304)))]; + tensor var_411_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_k_bias_to_fp16, dilations = var_411_dilations_0, groups = var_411_groups_0, pad = var_411_pad_0, pad_type = var_411_pad_type_0, strides = var_411_strides_0, weight = m_attn_layers_0_attn_conv_k_weight_to_fp16, x = x_41_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, 4, 64, 128])]; + tensor var_413_cast_fp16 = reshape(shape = var_412, x = var_411_cast_fp16)[name = string("op_413_cast_fp16")]; + string var_421_pad_type_0 = const()[name = string("op_421_pad_type_0"), val = string("valid")]; + tensor var_421_strides_0 = const()[name = string("op_421_strides_0"), val = tensor([1])]; + tensor var_421_pad_0 = const()[name = string("op_421_pad_0"), val = tensor([0, 0])]; + tensor var_421_dilations_0 = const()[name = string("op_421_dilations_0"), val = tensor([1])]; + int32 var_421_groups_0 = const()[name = string("op_421_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10858880)))]; + tensor m_attn_layers_0_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10990016)))]; + tensor var_421_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_v_bias_to_fp16, dilations = var_421_dilations_0, groups = var_421_groups_0, pad = var_421_pad_0, pad_type = var_421_pad_type_0, strides = var_421_strides_0, weight = m_attn_layers_0_attn_conv_v_weight_to_fp16, x = x_41_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422 = const()[name = string("op_422"), val = tensor([1, 4, 64, 128])]; + tensor var_423_cast_fp16 = reshape(shape = var_422, x = var_421_cast_fp16)[name = string("op_423_cast_fp16")]; + fp16 var_425_to_fp16 = const()[name = string("op_425_to_fp16"), val = fp16(0x1p-3)]; + tensor q_1_cast_fp16 = transpose(perm = q_1_perm_0, x = var_403_cast_fp16)[name = string("transpose_32")]; + tensor var_426_cast_fp16 = mul(x = q_1_cast_fp16, y = var_425_to_fp16)[name = string("op_426_cast_fp16")]; + bool scores_1_transpose_x_0 = const()[name = string("scores_1_transpose_x_0"), val = bool(false)]; + bool scores_1_transpose_y_0 = const()[name = string("scores_1_transpose_y_0"), val = bool(false)]; + tensor scores_1_cast_fp16 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = var_426_cast_fp16, y = var_413_cast_fp16)[name = string("scores_1_cast_fp16")]; + bool x_43_transpose_x_0 = const()[name = string("x_43_transpose_x_0"), val = bool(false)]; + bool x_43_transpose_y_0 = const()[name = string("x_43_transpose_y_0"), val = bool(false)]; + tensor var_450_to_fp16 = const()[name = string("op_450_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10990592)))]; + tensor x_43_cast_fp16 = matmul(transpose_x = x_43_transpose_x_0, transpose_y = x_43_transpose_y_0, x = var_426_cast_fp16, y = var_450_to_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_45_mode_0 = const()[name = string("x_45_mode_0"), val = string("constant")]; + fp16 const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = fp16(0x0p+0)]; + tensor x_45_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = x_45_mode_0, pad = x_45_pad_0, x = x_43_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_462 = const()[name = string("op_462"), val = tensor([1, 4, 32768])]; + tensor input_77_cast_fp16 = reshape(shape = var_462, x = x_45_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_47_mode_0 = const()[name = string("x_47_mode_0"), val = string("constant")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor x_47_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = x_47_mode_0, pad = x_47_pad_0, x = input_77_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor var_477 = const()[name = string("op_477"), val = tensor([1, 4, 129, 255])]; + tensor x_49_cast_fp16 = reshape(shape = var_477, x = x_47_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_484_begin_0 = const()[name = string("op_484_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_484_end_0 = const()[name = string("op_484_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_484_end_mask_0 = const()[name = string("op_484_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_484_cast_fp16 = slice_by_index(begin = var_484_begin_0, end = var_484_end_0, end_mask = var_484_end_mask_0, x = x_49_cast_fp16)[name = string("op_484_cast_fp16")]; + tensor var_485_begin_0 = const()[name = string("op_485_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_485_end_0 = const()[name = string("op_485_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_485_end_mask_0 = const()[name = string("op_485_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_485_cast_fp16 = slice_by_index(begin = var_485_begin_0, end = var_485_end_0, end_mask = var_485_end_mask_0, x = var_484_cast_fp16)[name = string("op_485_cast_fp16")]; + tensor scores_3_cast_fp16 = add(x = scores_1_cast_fp16, y = var_485_cast_fp16)[name = string("scores_3_cast_fp16")]; + tensor var_487_axes_0 = const()[name = string("op_487_axes_0"), val = tensor([2])]; + tensor var_487_cast_fp16 = expand_dims(axes = var_487_axes_0, x = text_mask_to_fp16)[name = string("op_487_cast_fp16")]; + tensor var_488_cast_fp16 = sub(x = var_21_to_fp16, y = var_487_cast_fp16)[name = string("op_488_cast_fp16")]; + fp16 var_489_to_fp16 = const()[name = string("op_489_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_490_cast_fp16 = mul(x = var_488_cast_fp16, y = var_489_to_fp16)[name = string("op_490_cast_fp16")]; + tensor input_79_cast_fp16 = sub(x = scores_3_cast_fp16, y = var_490_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor p_attn_1_cast_fp16 = softmax(axis = var_29, x = input_79_cast_fp16)[name = string("p_attn_1_cast_fp16")]; + bool out_13_transpose_x_1 = const()[name = string("out_13_transpose_x_1"), val = bool(false)]; + bool out_13_transpose_y_1 = const()[name = string("out_13_transpose_y_1"), val = bool(true)]; + tensor out_13_cast_fp16 = matmul(transpose_x = out_13_transpose_x_1, transpose_y = out_13_transpose_y_1, x = p_attn_1_cast_fp16, y = var_423_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_51_mode_0 = const()[name = string("x_51_mode_0"), val = string("constant")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor x_51_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_51_mode_0, pad = x_51_pad_0, x = p_attn_1_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_527 = const()[name = string("op_527"), val = tensor([1, 4, 32640])]; + tensor input_83_cast_fp16 = reshape(shape = var_527, x = x_51_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")]; + fp16 const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = fp16(0x0p+0)]; + tensor x_53_cast_fp16 = pad(constant_val = const_25_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = input_83_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 4, 128, 256])]; + tensor x_55_cast_fp16 = reshape(shape = var_534, x = x_53_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor rel_weights_1_begin_0 = const()[name = string("rel_weights_1_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_1_end_0 = const()[name = string("rel_weights_1_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_1_end_mask_0 = const()[name = string("rel_weights_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_1_cast_fp16 = slice_by_index(begin = rel_weights_1_begin_0, end = rel_weights_1_end_0, end_mask = rel_weights_1_end_mask_0, x = x_55_cast_fp16)[name = string("rel_weights_1_cast_fp16")]; + bool var_541_transpose_x_0 = const()[name = string("op_541_transpose_x_0"), val = bool(false)]; + bool var_541_transpose_y_0 = const()[name = string("op_541_transpose_y_0"), val = bool(false)]; + tensor var_540_to_fp16 = const()[name = string("op_540_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11023296)))]; + tensor var_541_cast_fp16 = matmul(transpose_x = var_541_transpose_x_0, transpose_y = var_541_transpose_y_0, x = rel_weights_1_cast_fp16, y = var_540_to_fp16)[name = string("op_541_cast_fp16")]; + tensor out_15_cast_fp16 = add(x = out_13_cast_fp16, y = var_541_cast_fp16)[name = string("out_15_cast_fp16")]; + tensor var_543_perm_0 = const()[name = string("op_543_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_544 = const()[name = string("op_544"), val = tensor([1, 256, 128])]; + tensor var_543_cast_fp16 = transpose(perm = var_543_perm_0, x = out_15_cast_fp16)[name = string("transpose_31")]; + tensor input_85_cast_fp16 = reshape(shape = var_544, x = var_543_cast_fp16)[name = string("input_85_cast_fp16")]; + string y_1_pad_type_0 = const()[name = string("y_1_pad_type_0"), val = string("valid")]; + tensor y_1_strides_0 = const()[name = string("y_1_strides_0"), val = tensor([1])]; + tensor y_1_pad_0 = const()[name = string("y_1_pad_0"), val = tensor([0, 0])]; + tensor y_1_dilations_0 = const()[name = string("y_1_dilations_0"), val = tensor([1])]; + int32 y_1_groups_0 = const()[name = string("y_1_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11056000)))]; + tensor m_attn_layers_0_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11187136)))]; + tensor y_1_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_o_bias_to_fp16, dilations = y_1_dilations_0, groups = y_1_groups_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = m_attn_layers_0_attn_conv_o_weight_to_fp16, x = input_85_cast_fp16)[name = string("y_1_cast_fp16")]; + tensor x_57_cast_fp16 = add(x = x_41_cast_fp16, y = y_1_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_0_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11187712)))]; + tensor m_attn_layers_0_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11188288)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_57_cast_fp16)[name = string("transpose_30")]; + tensor var_559_cast_fp16 = layer_norm(axes = var_559_axes_0, beta = m_attn_layers_0_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_0_norm_1_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_559_cast_fp16")]; + tensor x_59_perm_0 = const()[name = string("x_59_perm_0"), val = tensor([0, 2, 1])]; + tensor x_59_cast_fp16 = transpose(perm = x_59_perm_0, x = var_559_cast_fp16)[name = string("transpose_29")]; + tensor input_89_cast_fp16 = mul(x = x_59_cast_fp16, y = text_mask_to_fp16)[name = string("input_89_cast_fp16")]; + string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("valid")]; + tensor input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor([1])]; + tensor input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor([0, 0])]; + tensor input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor([1])]; + int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11188864)))]; + tensor m_attn_layers_0_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11713216)))]; + tensor input_91_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_1_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = m_attn_layers_0_ffn_conv_1_weight_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor h_37_cast_fp16 = relu(x = input_91_cast_fp16)[name = string("h_37_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = h_37_cast_fp16, y = text_mask_to_fp16)[name = string("input_93_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11715328)))]; + tensor m_attn_layers_0_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12239680)))]; + tensor h_39_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_2_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = m_attn_layers_0_ffn_conv_2_weight_to_fp16, x = input_93_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor y_3_cast_fp16 = mul(x = h_39_cast_fp16, y = text_mask_to_fp16)[name = string("y_3_cast_fp16")]; + tensor x_61_cast_fp16 = add(x = x_59_cast_fp16, y = y_3_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_0_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12240256)))]; + tensor m_attn_layers_0_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12240832)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_61_cast_fp16)[name = string("transpose_28")]; + tensor var_587_cast_fp16 = layer_norm(axes = var_587_axes_0, beta = m_attn_layers_0_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_0_norm_2_norm_weight_to_fp16, x = input_95_cast_fp16)[name = string("op_587_cast_fp16")]; + tensor x_63_perm_0 = const()[name = string("x_63_perm_0"), val = tensor([0, 2, 1])]; + tensor x_63_cast_fp16 = transpose(perm = x_63_perm_0, x = var_587_cast_fp16)[name = string("transpose_27")]; + tensor x_65_cast_fp16 = mul(x = x_63_cast_fp16, y = text_mask_to_fp16)[name = string("x_65_cast_fp16")]; + string var_609_pad_type_0 = const()[name = string("op_609_pad_type_0"), val = string("valid")]; + tensor var_609_strides_0 = const()[name = string("op_609_strides_0"), val = tensor([1])]; + tensor var_609_pad_0 = const()[name = string("op_609_pad_0"), val = tensor([0, 0])]; + tensor var_609_dilations_0 = const()[name = string("op_609_dilations_0"), val = tensor([1])]; + int32 var_609_groups_0 = const()[name = string("op_609_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12241408)))]; + tensor m_attn_layers_1_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12372544)))]; + tensor var_609_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_q_bias_to_fp16, dilations = var_609_dilations_0, groups = var_609_groups_0, pad = var_609_pad_0, pad_type = var_609_pad_type_0, strides = var_609_strides_0, weight = m_attn_layers_1_attn_conv_q_weight_to_fp16, x = x_65_cast_fp16)[name = string("op_609_cast_fp16")]; + tensor var_610 = const()[name = string("op_610"), val = tensor([1, 4, 64, 128])]; + tensor var_611_cast_fp16 = reshape(shape = var_610, x = var_609_cast_fp16)[name = string("op_611_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_619_pad_type_0 = const()[name = string("op_619_pad_type_0"), val = string("valid")]; + tensor var_619_strides_0 = const()[name = string("op_619_strides_0"), val = tensor([1])]; + tensor var_619_pad_0 = const()[name = string("op_619_pad_0"), val = tensor([0, 0])]; + tensor var_619_dilations_0 = const()[name = string("op_619_dilations_0"), val = tensor([1])]; + int32 var_619_groups_0 = const()[name = string("op_619_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12373120)))]; + tensor m_attn_layers_1_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12504256)))]; + tensor var_619_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_k_bias_to_fp16, dilations = var_619_dilations_0, groups = var_619_groups_0, pad = var_619_pad_0, pad_type = var_619_pad_type_0, strides = var_619_strides_0, weight = m_attn_layers_1_attn_conv_k_weight_to_fp16, x = x_65_cast_fp16)[name = string("op_619_cast_fp16")]; + tensor var_620 = const()[name = string("op_620"), val = tensor([1, 4, 64, 128])]; + tensor var_621_cast_fp16 = reshape(shape = var_620, x = var_619_cast_fp16)[name = string("op_621_cast_fp16")]; + string var_629_pad_type_0 = const()[name = string("op_629_pad_type_0"), val = string("valid")]; + tensor var_629_strides_0 = const()[name = string("op_629_strides_0"), val = tensor([1])]; + tensor var_629_pad_0 = const()[name = string("op_629_pad_0"), val = tensor([0, 0])]; + tensor var_629_dilations_0 = const()[name = string("op_629_dilations_0"), val = tensor([1])]; + int32 var_629_groups_0 = const()[name = string("op_629_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12504832)))]; + tensor m_attn_layers_1_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12635968)))]; + tensor var_629_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_v_bias_to_fp16, dilations = var_629_dilations_0, groups = var_629_groups_0, pad = var_629_pad_0, pad_type = var_629_pad_type_0, strides = var_629_strides_0, weight = m_attn_layers_1_attn_conv_v_weight_to_fp16, x = x_65_cast_fp16)[name = string("op_629_cast_fp16")]; + tensor var_630 = const()[name = string("op_630"), val = tensor([1, 4, 64, 128])]; + tensor var_631_cast_fp16 = reshape(shape = var_630, x = var_629_cast_fp16)[name = string("op_631_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p-3)]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_611_cast_fp16)[name = string("transpose_26")]; + tensor var_634_cast_fp16 = mul(x = q_3_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + bool scores_5_transpose_x_0 = const()[name = string("scores_5_transpose_x_0"), val = bool(false)]; + bool scores_5_transpose_y_0 = const()[name = string("scores_5_transpose_y_0"), val = bool(false)]; + tensor scores_5_cast_fp16 = matmul(transpose_x = scores_5_transpose_x_0, transpose_y = scores_5_transpose_y_0, x = var_634_cast_fp16, y = var_621_cast_fp16)[name = string("scores_5_cast_fp16")]; + bool x_67_transpose_x_0 = const()[name = string("x_67_transpose_x_0"), val = bool(false)]; + bool x_67_transpose_y_0 = const()[name = string("x_67_transpose_y_0"), val = bool(false)]; + tensor var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12636544)))]; + tensor x_67_cast_fp16 = matmul(transpose_x = x_67_transpose_x_0, transpose_y = x_67_transpose_y_0, x = var_634_cast_fp16, y = var_658_to_fp16)[name = string("x_67_cast_fp16")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_69_mode_0 = const()[name = string("x_69_mode_0"), val = string("constant")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor x_69_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = x_69_mode_0, pad = x_69_pad_0, x = x_67_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_670 = const()[name = string("op_670"), val = tensor([1, 4, 32768])]; + tensor input_99_cast_fp16 = reshape(shape = var_670, x = x_69_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor x_71_pad_0 = const()[name = string("x_71_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_71_mode_0 = const()[name = string("x_71_mode_0"), val = string("constant")]; + fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)]; + tensor x_71_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = x_71_mode_0, pad = x_71_pad_0, x = input_99_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor var_685 = const()[name = string("op_685"), val = tensor([1, 4, 129, 255])]; + tensor x_73_cast_fp16 = reshape(shape = var_685, x = x_71_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_692_begin_0 = const()[name = string("op_692_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_692_end_0 = const()[name = string("op_692_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_692_end_mask_0 = const()[name = string("op_692_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_692_cast_fp16 = slice_by_index(begin = var_692_begin_0, end = var_692_end_0, end_mask = var_692_end_mask_0, x = x_73_cast_fp16)[name = string("op_692_cast_fp16")]; + tensor var_693_begin_0 = const()[name = string("op_693_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_693_end_0 = const()[name = string("op_693_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_693_end_mask_0 = const()[name = string("op_693_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_693_cast_fp16 = slice_by_index(begin = var_693_begin_0, end = var_693_end_0, end_mask = var_693_end_mask_0, x = var_692_cast_fp16)[name = string("op_693_cast_fp16")]; + tensor scores_7_cast_fp16 = add(x = scores_5_cast_fp16, y = var_693_cast_fp16)[name = string("scores_7_cast_fp16")]; + tensor input_101_cast_fp16 = sub(x = scores_7_cast_fp16, y = var_490_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor p_attn_3_cast_fp16 = softmax(axis = var_29, x = input_101_cast_fp16)[name = string("p_attn_3_cast_fp16")]; + bool out_17_transpose_x_1 = const()[name = string("out_17_transpose_x_1"), val = bool(false)]; + bool out_17_transpose_y_1 = const()[name = string("out_17_transpose_y_1"), val = bool(true)]; + tensor out_17_cast_fp16 = matmul(transpose_x = out_17_transpose_x_1, transpose_y = out_17_transpose_y_1, x = p_attn_3_cast_fp16, y = var_631_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_75_mode_0 = const()[name = string("x_75_mode_0"), val = string("constant")]; + fp16 const_38_to_fp16 = const()[name = string("const_38_to_fp16"), val = fp16(0x0p+0)]; + tensor x_75_cast_fp16 = pad(constant_val = const_38_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = p_attn_3_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_735 = const()[name = string("op_735"), val = tensor([1, 4, 32640])]; + tensor input_105_cast_fp16 = reshape(shape = var_735, x = x_75_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_77_mode_0 = const()[name = string("x_77_mode_0"), val = string("constant")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor x_77_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = x_77_mode_0, pad = x_77_pad_0, x = input_105_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, 4, 128, 256])]; + tensor x_79_cast_fp16 = reshape(shape = var_742, x = x_77_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor rel_weights_3_begin_0 = const()[name = string("rel_weights_3_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_3_end_0 = const()[name = string("rel_weights_3_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_3_end_mask_0 = const()[name = string("rel_weights_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_3_cast_fp16 = slice_by_index(begin = rel_weights_3_begin_0, end = rel_weights_3_end_0, end_mask = rel_weights_3_end_mask_0, x = x_79_cast_fp16)[name = string("rel_weights_3_cast_fp16")]; + bool var_749_transpose_x_0 = const()[name = string("op_749_transpose_x_0"), val = bool(false)]; + bool var_749_transpose_y_0 = const()[name = string("op_749_transpose_y_0"), val = bool(false)]; + tensor var_748_to_fp16 = const()[name = string("op_748_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12669248)))]; + tensor var_749_cast_fp16 = matmul(transpose_x = var_749_transpose_x_0, transpose_y = var_749_transpose_y_0, x = rel_weights_3_cast_fp16, y = var_748_to_fp16)[name = string("op_749_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = out_17_cast_fp16, y = var_749_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor var_751_perm_0 = const()[name = string("op_751_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_752 = const()[name = string("op_752"), val = tensor([1, 256, 128])]; + tensor var_751_cast_fp16 = transpose(perm = var_751_perm_0, x = out_19_cast_fp16)[name = string("transpose_25")]; + tensor input_107_cast_fp16 = reshape(shape = var_752, x = var_751_cast_fp16)[name = string("input_107_cast_fp16")]; + string y_5_pad_type_0 = const()[name = string("y_5_pad_type_0"), val = string("valid")]; + tensor y_5_strides_0 = const()[name = string("y_5_strides_0"), val = tensor([1])]; + tensor y_5_pad_0 = const()[name = string("y_5_pad_0"), val = tensor([0, 0])]; + tensor y_5_dilations_0 = const()[name = string("y_5_dilations_0"), val = tensor([1])]; + int32 y_5_groups_0 = const()[name = string("y_5_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12701952)))]; + tensor m_attn_layers_1_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12833088)))]; + tensor y_5_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_o_bias_to_fp16, dilations = y_5_dilations_0, groups = y_5_groups_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = m_attn_layers_1_attn_conv_o_weight_to_fp16, x = input_107_cast_fp16)[name = string("y_5_cast_fp16")]; + tensor x_81_cast_fp16 = add(x = x_65_cast_fp16, y = y_5_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_767_axes_0 = const()[name = string("op_767_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_1_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12833664)))]; + tensor m_attn_layers_1_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12834240)))]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_81_cast_fp16)[name = string("transpose_24")]; + tensor var_767_cast_fp16 = layer_norm(axes = var_767_axes_0, beta = m_attn_layers_1_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_1_norm_1_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor x_83_perm_0 = const()[name = string("x_83_perm_0"), val = tensor([0, 2, 1])]; + tensor x_83_cast_fp16 = transpose(perm = x_83_perm_0, x = var_767_cast_fp16)[name = string("transpose_23")]; + tensor input_111_cast_fp16 = mul(x = x_83_cast_fp16, y = text_mask_to_fp16)[name = string("input_111_cast_fp16")]; + string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("valid")]; + tensor input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor([1])]; + tensor input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor([0, 0])]; + tensor input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor([1])]; + int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12834816)))]; + tensor m_attn_layers_1_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13359168)))]; + tensor input_113_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_1_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = m_attn_layers_1_ffn_conv_1_weight_to_fp16, x = input_111_cast_fp16)[name = string("input_113_cast_fp16")]; + tensor h_41_cast_fp16 = relu(x = input_113_cast_fp16)[name = string("h_41_cast_fp16")]; + tensor input_115_cast_fp16 = mul(x = h_41_cast_fp16, y = text_mask_to_fp16)[name = string("input_115_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13361280)))]; + tensor m_attn_layers_1_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13885632)))]; + tensor h_43_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_2_bias_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = m_attn_layers_1_ffn_conv_2_weight_to_fp16, x = input_115_cast_fp16)[name = string("h_43_cast_fp16")]; + tensor y_7_cast_fp16 = mul(x = h_43_cast_fp16, y = text_mask_to_fp16)[name = string("y_7_cast_fp16")]; + tensor x_85_cast_fp16 = add(x = x_83_cast_fp16, y = y_7_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_117_perm_0 = const()[name = string("input_117_perm_0"), val = tensor([0, 2, 1])]; + tensor var_795_axes_0 = const()[name = string("op_795_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_1_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13886208)))]; + tensor m_attn_layers_1_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13886784)))]; + tensor input_117_cast_fp16 = transpose(perm = input_117_perm_0, x = x_85_cast_fp16)[name = string("transpose_22")]; + tensor var_795_cast_fp16 = layer_norm(axes = var_795_axes_0, beta = m_attn_layers_1_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_1_norm_2_norm_weight_to_fp16, x = input_117_cast_fp16)[name = string("op_795_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_795_cast_fp16)[name = string("transpose_21")]; + tensor x_89_cast_fp16 = mul(x = x_87_cast_fp16, y = text_mask_to_fp16)[name = string("x_89_cast_fp16")]; + string var_817_pad_type_0 = const()[name = string("op_817_pad_type_0"), val = string("valid")]; + tensor var_817_strides_0 = const()[name = string("op_817_strides_0"), val = tensor([1])]; + tensor var_817_pad_0 = const()[name = string("op_817_pad_0"), val = tensor([0, 0])]; + tensor var_817_dilations_0 = const()[name = string("op_817_dilations_0"), val = tensor([1])]; + int32 var_817_groups_0 = const()[name = string("op_817_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13887360)))]; + tensor m_attn_layers_2_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14018496)))]; + tensor var_817_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_q_bias_to_fp16, dilations = var_817_dilations_0, groups = var_817_groups_0, pad = var_817_pad_0, pad_type = var_817_pad_type_0, strides = var_817_strides_0, weight = m_attn_layers_2_attn_conv_q_weight_to_fp16, x = x_89_cast_fp16)[name = string("op_817_cast_fp16")]; + tensor var_818 = const()[name = string("op_818"), val = tensor([1, 4, 64, 128])]; + tensor var_819_cast_fp16 = reshape(shape = var_818, x = var_817_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor q_5_perm_0 = const()[name = string("q_5_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_827_pad_type_0 = const()[name = string("op_827_pad_type_0"), val = string("valid")]; + tensor var_827_strides_0 = const()[name = string("op_827_strides_0"), val = tensor([1])]; + tensor var_827_pad_0 = const()[name = string("op_827_pad_0"), val = tensor([0, 0])]; + tensor var_827_dilations_0 = const()[name = string("op_827_dilations_0"), val = tensor([1])]; + int32 var_827_groups_0 = const()[name = string("op_827_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14019072)))]; + tensor m_attn_layers_2_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14150208)))]; + tensor var_827_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_k_bias_to_fp16, dilations = var_827_dilations_0, groups = var_827_groups_0, pad = var_827_pad_0, pad_type = var_827_pad_type_0, strides = var_827_strides_0, weight = m_attn_layers_2_attn_conv_k_weight_to_fp16, x = x_89_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_828 = const()[name = string("op_828"), val = tensor([1, 4, 64, 128])]; + tensor var_829_cast_fp16 = reshape(shape = var_828, x = var_827_cast_fp16)[name = string("op_829_cast_fp16")]; + string var_837_pad_type_0 = const()[name = string("op_837_pad_type_0"), val = string("valid")]; + tensor var_837_strides_0 = const()[name = string("op_837_strides_0"), val = tensor([1])]; + tensor var_837_pad_0 = const()[name = string("op_837_pad_0"), val = tensor([0, 0])]; + tensor var_837_dilations_0 = const()[name = string("op_837_dilations_0"), val = tensor([1])]; + int32 var_837_groups_0 = const()[name = string("op_837_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14150784)))]; + tensor m_attn_layers_2_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14281920)))]; + tensor var_837_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_v_bias_to_fp16, dilations = var_837_dilations_0, groups = var_837_groups_0, pad = var_837_pad_0, pad_type = var_837_pad_type_0, strides = var_837_strides_0, weight = m_attn_layers_2_attn_conv_v_weight_to_fp16, x = x_89_cast_fp16)[name = string("op_837_cast_fp16")]; + tensor var_838 = const()[name = string("op_838"), val = tensor([1, 4, 64, 128])]; + tensor var_839_cast_fp16 = reshape(shape = var_838, x = var_837_cast_fp16)[name = string("op_839_cast_fp16")]; + fp16 var_841_to_fp16 = const()[name = string("op_841_to_fp16"), val = fp16(0x1p-3)]; + tensor q_5_cast_fp16 = transpose(perm = q_5_perm_0, x = var_819_cast_fp16)[name = string("transpose_20")]; + tensor var_842_cast_fp16 = mul(x = q_5_cast_fp16, y = var_841_to_fp16)[name = string("op_842_cast_fp16")]; + bool scores_9_transpose_x_0 = const()[name = string("scores_9_transpose_x_0"), val = bool(false)]; + bool scores_9_transpose_y_0 = const()[name = string("scores_9_transpose_y_0"), val = bool(false)]; + tensor scores_9_cast_fp16 = matmul(transpose_x = scores_9_transpose_x_0, transpose_y = scores_9_transpose_y_0, x = var_842_cast_fp16, y = var_829_cast_fp16)[name = string("scores_9_cast_fp16")]; + bool x_91_transpose_x_0 = const()[name = string("x_91_transpose_x_0"), val = bool(false)]; + bool x_91_transpose_y_0 = const()[name = string("x_91_transpose_y_0"), val = bool(false)]; + tensor var_866_to_fp16 = const()[name = string("op_866_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14282496)))]; + tensor x_91_cast_fp16 = matmul(transpose_x = x_91_transpose_x_0, transpose_y = x_91_transpose_y_0, x = var_842_cast_fp16, y = var_866_to_fp16)[name = string("x_91_cast_fp16")]; + tensor x_93_pad_0 = const()[name = string("x_93_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_93_mode_0 = const()[name = string("x_93_mode_0"), val = string("constant")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor x_93_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = x_93_mode_0, pad = x_93_pad_0, x = x_91_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor var_878 = const()[name = string("op_878"), val = tensor([1, 4, 32768])]; + tensor input_121_cast_fp16 = reshape(shape = var_878, x = x_93_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor x_95_pad_0 = const()[name = string("x_95_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("constant")]; + fp16 const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = fp16(0x0p+0)]; + tensor x_95_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = x_95_mode_0, pad = x_95_pad_0, x = input_121_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_893 = const()[name = string("op_893"), val = tensor([1, 4, 129, 255])]; + tensor x_97_cast_fp16 = reshape(shape = var_893, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_900_begin_0 = const()[name = string("op_900_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_900_end_0 = const()[name = string("op_900_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_900_end_mask_0 = const()[name = string("op_900_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_900_cast_fp16 = slice_by_index(begin = var_900_begin_0, end = var_900_end_0, end_mask = var_900_end_mask_0, x = x_97_cast_fp16)[name = string("op_900_cast_fp16")]; + tensor var_901_begin_0 = const()[name = string("op_901_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_901_end_0 = const()[name = string("op_901_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_901_end_mask_0 = const()[name = string("op_901_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_901_cast_fp16 = slice_by_index(begin = var_901_begin_0, end = var_901_end_0, end_mask = var_901_end_mask_0, x = var_900_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor scores_11_cast_fp16 = add(x = scores_9_cast_fp16, y = var_901_cast_fp16)[name = string("scores_11_cast_fp16")]; + tensor input_123_cast_fp16 = sub(x = scores_11_cast_fp16, y = var_490_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor p_attn_5_cast_fp16 = softmax(axis = var_29, x = input_123_cast_fp16)[name = string("p_attn_5_cast_fp16")]; + bool out_21_transpose_x_1 = const()[name = string("out_21_transpose_x_1"), val = bool(false)]; + bool out_21_transpose_y_1 = const()[name = string("out_21_transpose_y_1"), val = bool(true)]; + tensor out_21_cast_fp16 = matmul(transpose_x = out_21_transpose_x_1, transpose_y = out_21_transpose_y_1, x = p_attn_5_cast_fp16, y = var_839_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_99_pad_0 = const()[name = string("x_99_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_99_mode_0 = const()[name = string("x_99_mode_0"), val = string("constant")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor x_99_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = x_99_mode_0, pad = x_99_pad_0, x = p_attn_5_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor var_943 = const()[name = string("op_943"), val = tensor([1, 4, 32640])]; + tensor input_127_cast_fp16 = reshape(shape = var_943, x = x_99_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_101_mode_0 = const()[name = string("x_101_mode_0"), val = string("constant")]; + fp16 const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = fp16(0x0p+0)]; + tensor x_101_cast_fp16 = pad(constant_val = const_53_to_fp16, mode = x_101_mode_0, pad = x_101_pad_0, x = input_127_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_950 = const()[name = string("op_950"), val = tensor([1, 4, 128, 256])]; + tensor x_103_cast_fp16 = reshape(shape = var_950, x = x_101_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor rel_weights_5_begin_0 = const()[name = string("rel_weights_5_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_5_end_0 = const()[name = string("rel_weights_5_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_5_end_mask_0 = const()[name = string("rel_weights_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_5_cast_fp16 = slice_by_index(begin = rel_weights_5_begin_0, end = rel_weights_5_end_0, end_mask = rel_weights_5_end_mask_0, x = x_103_cast_fp16)[name = string("rel_weights_5_cast_fp16")]; + bool var_957_transpose_x_0 = const()[name = string("op_957_transpose_x_0"), val = bool(false)]; + bool var_957_transpose_y_0 = const()[name = string("op_957_transpose_y_0"), val = bool(false)]; + tensor var_956_to_fp16 = const()[name = string("op_956_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14315200)))]; + tensor var_957_cast_fp16 = matmul(transpose_x = var_957_transpose_x_0, transpose_y = var_957_transpose_y_0, x = rel_weights_5_cast_fp16, y = var_956_to_fp16)[name = string("op_957_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = out_21_cast_fp16, y = var_957_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor var_959_perm_0 = const()[name = string("op_959_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_960 = const()[name = string("op_960"), val = tensor([1, 256, 128])]; + tensor var_959_cast_fp16 = transpose(perm = var_959_perm_0, x = out_23_cast_fp16)[name = string("transpose_19")]; + tensor input_129_cast_fp16 = reshape(shape = var_960, x = var_959_cast_fp16)[name = string("input_129_cast_fp16")]; + string y_9_pad_type_0 = const()[name = string("y_9_pad_type_0"), val = string("valid")]; + tensor y_9_strides_0 = const()[name = string("y_9_strides_0"), val = tensor([1])]; + tensor y_9_pad_0 = const()[name = string("y_9_pad_0"), val = tensor([0, 0])]; + tensor y_9_dilations_0 = const()[name = string("y_9_dilations_0"), val = tensor([1])]; + int32 y_9_groups_0 = const()[name = string("y_9_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14347904)))]; + tensor m_attn_layers_2_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14479040)))]; + tensor y_9_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_o_bias_to_fp16, dilations = y_9_dilations_0, groups = y_9_groups_0, pad = y_9_pad_0, pad_type = y_9_pad_type_0, strides = y_9_strides_0, weight = m_attn_layers_2_attn_conv_o_weight_to_fp16, x = input_129_cast_fp16)[name = string("y_9_cast_fp16")]; + tensor x_105_cast_fp16 = add(x = x_89_cast_fp16, y = y_9_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor input_131_perm_0 = const()[name = string("input_131_perm_0"), val = tensor([0, 2, 1])]; + tensor var_975_axes_0 = const()[name = string("op_975_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_2_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_2_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14479616)))]; + tensor m_attn_layers_2_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_2_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14480192)))]; + tensor input_131_cast_fp16 = transpose(perm = input_131_perm_0, x = x_105_cast_fp16)[name = string("transpose_18")]; + tensor var_975_cast_fp16 = layer_norm(axes = var_975_axes_0, beta = m_attn_layers_2_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_2_norm_1_norm_weight_to_fp16, x = input_131_cast_fp16)[name = string("op_975_cast_fp16")]; + tensor x_107_perm_0 = const()[name = string("x_107_perm_0"), val = tensor([0, 2, 1])]; + tensor x_107_cast_fp16 = transpose(perm = x_107_perm_0, x = var_975_cast_fp16)[name = string("transpose_17")]; + tensor input_133_cast_fp16 = mul(x = x_107_cast_fp16, y = text_mask_to_fp16)[name = string("input_133_cast_fp16")]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14480768)))]; + tensor m_attn_layers_2_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15005120)))]; + tensor input_135_cast_fp16 = conv(bias = m_attn_layers_2_ffn_conv_1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = m_attn_layers_2_ffn_conv_1_weight_to_fp16, x = input_133_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor h_45_cast_fp16 = relu(x = input_135_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor input_137_cast_fp16 = mul(x = h_45_cast_fp16, y = text_mask_to_fp16)[name = string("input_137_cast_fp16")]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15007232)))]; + tensor m_attn_layers_2_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15531584)))]; + tensor h_47_cast_fp16 = conv(bias = m_attn_layers_2_ffn_conv_2_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = m_attn_layers_2_ffn_conv_2_weight_to_fp16, x = input_137_cast_fp16)[name = string("h_47_cast_fp16")]; + tensor y_11_cast_fp16 = mul(x = h_47_cast_fp16, y = text_mask_to_fp16)[name = string("y_11_cast_fp16")]; + tensor x_109_cast_fp16 = add(x = x_107_cast_fp16, y = y_11_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1003_axes_0 = const()[name = string("op_1003_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_2_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_2_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15532160)))]; + tensor m_attn_layers_2_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_2_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15532736)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_109_cast_fp16)[name = string("transpose_16")]; + tensor var_1003_cast_fp16 = layer_norm(axes = var_1003_axes_0, beta = m_attn_layers_2_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_2_norm_2_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_1003_cast_fp16")]; + tensor x_111_perm_0 = const()[name = string("x_111_perm_0"), val = tensor([0, 2, 1])]; + tensor x_111_cast_fp16 = transpose(perm = x_111_perm_0, x = var_1003_cast_fp16)[name = string("transpose_15")]; + tensor x_113_cast_fp16 = mul(x = x_111_cast_fp16, y = text_mask_to_fp16)[name = string("x_113_cast_fp16")]; + string var_1025_pad_type_0 = const()[name = string("op_1025_pad_type_0"), val = string("valid")]; + tensor var_1025_strides_0 = const()[name = string("op_1025_strides_0"), val = tensor([1])]; + tensor var_1025_pad_0 = const()[name = string("op_1025_pad_0"), val = tensor([0, 0])]; + tensor var_1025_dilations_0 = const()[name = string("op_1025_dilations_0"), val = tensor([1])]; + int32 var_1025_groups_0 = const()[name = string("op_1025_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15533312)))]; + tensor m_attn_layers_3_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15664448)))]; + tensor var_1025_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_q_bias_to_fp16, dilations = var_1025_dilations_0, groups = var_1025_groups_0, pad = var_1025_pad_0, pad_type = var_1025_pad_type_0, strides = var_1025_strides_0, weight = m_attn_layers_3_attn_conv_q_weight_to_fp16, x = x_113_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1026 = const()[name = string("op_1026"), val = tensor([1, 4, 64, 128])]; + tensor var_1027_cast_fp16 = reshape(shape = var_1026, x = var_1025_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_1035_pad_type_0 = const()[name = string("op_1035_pad_type_0"), val = string("valid")]; + tensor var_1035_strides_0 = const()[name = string("op_1035_strides_0"), val = tensor([1])]; + tensor var_1035_pad_0 = const()[name = string("op_1035_pad_0"), val = tensor([0, 0])]; + tensor var_1035_dilations_0 = const()[name = string("op_1035_dilations_0"), val = tensor([1])]; + int32 var_1035_groups_0 = const()[name = string("op_1035_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15665024)))]; + tensor m_attn_layers_3_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796160)))]; + tensor var_1035_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_k_bias_to_fp16, dilations = var_1035_dilations_0, groups = var_1035_groups_0, pad = var_1035_pad_0, pad_type = var_1035_pad_type_0, strides = var_1035_strides_0, weight = m_attn_layers_3_attn_conv_k_weight_to_fp16, x = x_113_cast_fp16)[name = string("op_1035_cast_fp16")]; + tensor var_1036 = const()[name = string("op_1036"), val = tensor([1, 4, 64, 128])]; + tensor var_1037_cast_fp16 = reshape(shape = var_1036, x = var_1035_cast_fp16)[name = string("op_1037_cast_fp16")]; + string var_1045_pad_type_0 = const()[name = string("op_1045_pad_type_0"), val = string("valid")]; + tensor var_1045_strides_0 = const()[name = string("op_1045_strides_0"), val = tensor([1])]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0])]; + tensor var_1045_dilations_0 = const()[name = string("op_1045_dilations_0"), val = tensor([1])]; + int32 var_1045_groups_0 = const()[name = string("op_1045_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796736)))]; + tensor m_attn_layers_3_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15927872)))]; + tensor var_1045_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_v_bias_to_fp16, dilations = var_1045_dilations_0, groups = var_1045_groups_0, pad = var_1045_pad_0, pad_type = var_1045_pad_type_0, strides = var_1045_strides_0, weight = m_attn_layers_3_attn_conv_v_weight_to_fp16, x = x_113_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor var_1046 = const()[name = string("op_1046"), val = tensor([1, 4, 64, 128])]; + tensor var_1047_cast_fp16 = reshape(shape = var_1046, x = var_1045_cast_fp16)[name = string("op_1047_cast_fp16")]; + fp16 var_1049_to_fp16 = const()[name = string("op_1049_to_fp16"), val = fp16(0x1p-3)]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1027_cast_fp16)[name = string("transpose_14")]; + tensor var_1050_cast_fp16 = mul(x = q_7_cast_fp16, y = var_1049_to_fp16)[name = string("op_1050_cast_fp16")]; + bool scores_13_transpose_x_0 = const()[name = string("scores_13_transpose_x_0"), val = bool(false)]; + bool scores_13_transpose_y_0 = const()[name = string("scores_13_transpose_y_0"), val = bool(false)]; + tensor scores_13_cast_fp16 = matmul(transpose_x = scores_13_transpose_x_0, transpose_y = scores_13_transpose_y_0, x = var_1050_cast_fp16, y = var_1037_cast_fp16)[name = string("scores_13_cast_fp16")]; + bool x_115_transpose_x_0 = const()[name = string("x_115_transpose_x_0"), val = bool(false)]; + bool x_115_transpose_y_0 = const()[name = string("x_115_transpose_y_0"), val = bool(false)]; + tensor var_1074_to_fp16 = const()[name = string("op_1074_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15928448)))]; + tensor x_115_cast_fp16 = matmul(transpose_x = x_115_transpose_x_0, transpose_y = x_115_transpose_y_0, x = var_1050_cast_fp16, y = var_1074_to_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("constant")]; + fp16 const_60_to_fp16 = const()[name = string("const_60_to_fp16"), val = fp16(0x0p+0)]; + tensor x_117_cast_fp16 = pad(constant_val = const_60_to_fp16, mode = x_117_mode_0, pad = x_117_pad_0, x = x_115_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1086 = const()[name = string("op_1086"), val = tensor([1, 4, 32768])]; + tensor input_143_cast_fp16 = reshape(shape = var_1086, x = x_117_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_119_mode_0 = const()[name = string("x_119_mode_0"), val = string("constant")]; + fp16 const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = fp16(0x0p+0)]; + tensor x_119_cast_fp16 = pad(constant_val = const_61_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = input_143_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor var_1101 = const()[name = string("op_1101"), val = tensor([1, 4, 129, 255])]; + tensor x_121_cast_fp16 = reshape(shape = var_1101, x = x_119_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor var_1108_begin_0 = const()[name = string("op_1108_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1108_end_0 = const()[name = string("op_1108_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_1108_end_mask_0 = const()[name = string("op_1108_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1108_cast_fp16 = slice_by_index(begin = var_1108_begin_0, end = var_1108_end_0, end_mask = var_1108_end_mask_0, x = x_121_cast_fp16)[name = string("op_1108_cast_fp16")]; + tensor var_1109_begin_0 = const()[name = string("op_1109_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_1109_end_0 = const()[name = string("op_1109_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_1109_end_mask_0 = const()[name = string("op_1109_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1109_cast_fp16 = slice_by_index(begin = var_1109_begin_0, end = var_1109_end_0, end_mask = var_1109_end_mask_0, x = var_1108_cast_fp16)[name = string("op_1109_cast_fp16")]; + tensor scores_cast_fp16 = add(x = scores_13_cast_fp16, y = var_1109_cast_fp16)[name = string("scores_cast_fp16")]; + tensor input_145_cast_fp16 = sub(x = scores_cast_fp16, y = var_490_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor p_attn_cast_fp16 = softmax(axis = var_29, x = input_145_cast_fp16)[name = string("p_attn_cast_fp16")]; + bool out_25_transpose_x_1 = const()[name = string("out_25_transpose_x_1"), val = bool(false)]; + bool out_25_transpose_y_1 = const()[name = string("out_25_transpose_y_1"), val = bool(true)]; + tensor out_25_cast_fp16 = matmul(transpose_x = out_25_transpose_x_1, transpose_y = out_25_transpose_y_1, x = p_attn_cast_fp16, y = var_1047_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_123_pad_0 = const()[name = string("x_123_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_123_mode_0 = const()[name = string("x_123_mode_0"), val = string("constant")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor x_123_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = x_123_mode_0, pad = x_123_pad_0, x = p_attn_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1151 = const()[name = string("op_1151"), val = tensor([1, 4, 32640])]; + tensor input_149_cast_fp16 = reshape(shape = var_1151, x = x_123_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor x_125_pad_0 = const()[name = string("x_125_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("constant")]; + fp16 const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = fp16(0x0p+0)]; + tensor x_125_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = x_125_mode_0, pad = x_125_pad_0, x = input_149_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor var_1158 = const()[name = string("op_1158"), val = tensor([1, 4, 128, 256])]; + tensor x_127_cast_fp16 = reshape(shape = var_1158, x = x_125_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor rel_weights_begin_0 = const()[name = string("rel_weights_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_end_0 = const()[name = string("rel_weights_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_end_mask_0 = const()[name = string("rel_weights_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_cast_fp16 = slice_by_index(begin = rel_weights_begin_0, end = rel_weights_end_0, end_mask = rel_weights_end_mask_0, x = x_127_cast_fp16)[name = string("rel_weights_cast_fp16")]; + bool var_1165_transpose_x_0 = const()[name = string("op_1165_transpose_x_0"), val = bool(false)]; + bool var_1165_transpose_y_0 = const()[name = string("op_1165_transpose_y_0"), val = bool(false)]; + tensor var_1164_to_fp16 = const()[name = string("op_1164_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15961152)))]; + tensor var_1165_cast_fp16 = matmul(transpose_x = var_1165_transpose_x_0, transpose_y = var_1165_transpose_y_0, x = rel_weights_cast_fp16, y = var_1164_to_fp16)[name = string("op_1165_cast_fp16")]; + tensor out_27_cast_fp16 = add(x = out_25_cast_fp16, y = var_1165_cast_fp16)[name = string("out_27_cast_fp16")]; + tensor var_1167_perm_0 = const()[name = string("op_1167_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_1168 = const()[name = string("op_1168"), val = tensor([1, 256, 128])]; + tensor var_1167_cast_fp16 = transpose(perm = var_1167_perm_0, x = out_27_cast_fp16)[name = string("transpose_13")]; + tensor input_151_cast_fp16 = reshape(shape = var_1168, x = var_1167_cast_fp16)[name = string("input_151_cast_fp16")]; + string y_13_pad_type_0 = const()[name = string("y_13_pad_type_0"), val = string("valid")]; + tensor y_13_strides_0 = const()[name = string("y_13_strides_0"), val = tensor([1])]; + tensor y_13_pad_0 = const()[name = string("y_13_pad_0"), val = tensor([0, 0])]; + tensor y_13_dilations_0 = const()[name = string("y_13_dilations_0"), val = tensor([1])]; + int32 y_13_groups_0 = const()[name = string("y_13_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15993856)))]; + tensor m_attn_layers_3_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16124992)))]; + tensor y_13_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_o_bias_to_fp16, dilations = y_13_dilations_0, groups = y_13_groups_0, pad = y_13_pad_0, pad_type = y_13_pad_type_0, strides = y_13_strides_0, weight = m_attn_layers_3_attn_conv_o_weight_to_fp16, x = input_151_cast_fp16)[name = string("y_13_cast_fp16")]; + tensor x_129_cast_fp16 = add(x = x_113_cast_fp16, y = y_13_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1183_axes_0 = const()[name = string("op_1183_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_3_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_3_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16125568)))]; + tensor m_attn_layers_3_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_3_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16126144)))]; + tensor input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_129_cast_fp16)[name = string("transpose_12")]; + tensor var_1183_cast_fp16 = layer_norm(axes = var_1183_axes_0, beta = m_attn_layers_3_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_3_norm_1_norm_weight_to_fp16, x = input_153_cast_fp16)[name = string("op_1183_cast_fp16")]; + tensor x_131_perm_0 = const()[name = string("x_131_perm_0"), val = tensor([0, 2, 1])]; + tensor x_131_cast_fp16 = transpose(perm = x_131_perm_0, x = var_1183_cast_fp16)[name = string("transpose_11")]; + tensor input_155_cast_fp16 = mul(x = x_131_cast_fp16, y = text_mask_to_fp16)[name = string("input_155_cast_fp16")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("valid")]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1])]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16126720)))]; + tensor m_attn_layers_3_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16651072)))]; + tensor input_157_cast_fp16 = conv(bias = m_attn_layers_3_ffn_conv_1_bias_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = m_attn_layers_3_ffn_conv_1_weight_to_fp16, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor h_49_cast_fp16 = relu(x = input_157_cast_fp16)[name = string("h_49_cast_fp16")]; + tensor input_159_cast_fp16 = mul(x = h_49_cast_fp16, y = text_mask_to_fp16)[name = string("input_159_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16653184)))]; + tensor m_attn_layers_3_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17177536)))]; + tensor h_cast_fp16 = conv(bias = m_attn_layers_3_ffn_conv_2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = m_attn_layers_3_ffn_conv_2_weight_to_fp16, x = input_159_cast_fp16)[name = string("h_cast_fp16")]; + tensor y_cast_fp16 = mul(x = h_cast_fp16, y = text_mask_to_fp16)[name = string("y_cast_fp16")]; + tensor x_133_cast_fp16 = add(x = x_131_cast_fp16, y = y_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1211_axes_0 = const()[name = string("op_1211_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_3_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_3_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17178112)))]; + tensor m_attn_layers_3_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_3_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17178688)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_133_cast_fp16)[name = string("transpose_10")]; + tensor var_1211_cast_fp16 = layer_norm(axes = var_1211_axes_0, beta = m_attn_layers_3_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_3_norm_2_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor x_135_perm_0 = const()[name = string("x_135_perm_0"), val = tensor([0, 2, 1])]; + tensor x_135_cast_fp16 = transpose(perm = x_135_perm_0, x = var_1211_cast_fp16)[name = string("transpose_9")]; + tensor x_137_cast_fp16 = mul(x = x_135_cast_fp16, y = text_mask_to_fp16)[name = string("x_137_cast_fp16")]; + tensor var_1214_cast_fp16 = add(x = x_137_cast_fp16, y = x_39_cast_fp16)[name = string("op_1214_cast_fp16")]; + tensor text_1_cast_fp16 = mul(x = var_1214_cast_fp16, y = text_mask_to_fp16)[name = string("text_1_cast_fp16")]; + tensor text_3_perm_0 = const()[name = string("text_3_perm_0"), val = tensor([0, 2, 1])]; + tensor m_speech_prompted_attention1_W_query_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17179264)))]; + tensor m_speech_prompted_attention1_W_query_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17310400)))]; + tensor text_3_cast_fp16 = transpose(perm = text_3_perm_0, x = text_1_cast_fp16)[name = string("transpose_8")]; + tensor linear_0_cast_fp16 = linear(bias = m_speech_prompted_attention1_W_query_linear_bias_to_fp16, weight = m_speech_prompted_attention1_W_query_linear_weight_to_fp16, x = text_3_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_1235 = const()[name = string("op_1235"), val = tensor([1, 128, 2, 128])]; + tensor var_1236_cast_fp16 = reshape(shape = var_1235, x = linear_0_cast_fp16)[name = string("op_1236_cast_fp16")]; + tensor q_9_perm_0 = const()[name = string("q_9_perm_0"), val = tensor([0, 2, 1, 3])]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor m_speech_prompted_attention1_W_value_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17310976)))]; + tensor m_speech_prompted_attention1_W_value_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17442112)))]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_47")]; + tensor linear_2_cast_fp16 = linear(bias = m_speech_prompted_attention1_W_value_linear_bias_to_fp16, weight = m_speech_prompted_attention1_W_value_linear_weight_to_fp16, x = style_ttl_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_1249 = const()[name = string("op_1249"), val = tensor([1, 50, 2, 128])]; + tensor var_1250_cast_fp16 = reshape(shape = var_1249, x = linear_2_cast_fp16)[name = string("op_1250_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_1254_transpose_x_0 = const()[name = string("op_1254_transpose_x_0"), val = bool(false)]; + bool var_1254_transpose_y_0 = const()[name = string("op_1254_transpose_y_0"), val = bool(false)]; + tensor var_1253_to_fp16 = const()[name = string("op_1253_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17442688)))]; + tensor q_9_cast_fp16 = transpose(perm = q_9_perm_0, x = var_1236_cast_fp16)[name = string("transpose_7")]; + tensor var_1254_cast_fp16 = matmul(transpose_x = var_1254_transpose_x_0, transpose_y = var_1254_transpose_y_0, x = q_9_cast_fp16, y = var_1253_to_fp16)[name = string("op_1254_cast_fp16")]; + fp16 _inversed_input_163_y_0_to_fp16 = const()[name = string("_inversed_input_163_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_163_cast_fp16 = mul(x = var_1254_cast_fp16, y = _inversed_input_163_y_0_to_fp16)[name = string("_inversed_input_163_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_29, x = _inversed_input_163_cast_fp16)[name = string("attn_1_cast_fp16")]; + tensor var_1258_perm_0 = const()[name = string("op_1258_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_q_1_axes_0 = const()[name = string("mask_q_1_axes_0"), val = tensor([1])]; + tensor var_1258_cast_fp16 = transpose(perm = var_1258_perm_0, x = text_mask_to_fp16)[name = string("transpose_5")]; + tensor mask_q_1_cast_fp16 = expand_dims(axes = mask_q_1_axes_0, x = var_1258_cast_fp16)[name = string("mask_q_1_cast_fp16")]; + tensor attn_3_cast_fp16 = mul(x = attn_1_cast_fp16, y = mask_q_1_cast_fp16)[name = string("attn_3_cast_fp16")]; + bool out_29_transpose_x_0 = const()[name = string("out_29_transpose_x_0"), val = bool(false)]; + bool out_29_transpose_y_0 = const()[name = string("out_29_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1250_cast_fp16)[name = string("transpose_6")]; + tensor out_29_cast_fp16 = matmul(transpose_x = out_29_transpose_x_0, transpose_y = out_29_transpose_y_0, x = attn_3_cast_fp16, y = v_9_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor var_1262_perm_0 = const()[name = string("op_1262_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1263 = const()[name = string("op_1263"), val = tensor([1, 128, 256])]; + tensor var_1262_cast_fp16 = transpose(perm = var_1262_perm_0, x = out_29_cast_fp16)[name = string("transpose_4")]; + tensor input_165_cast_fp16 = reshape(shape = var_1263, x = var_1262_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor m_speech_prompted_attention1_out_fc_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention1_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17468352)))]; + tensor m_speech_prompted_attention1_out_fc_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention1_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17599488)))]; + tensor linear_3_cast_fp16 = linear(bias = m_speech_prompted_attention1_out_fc_linear_bias_to_fp16, weight = m_speech_prompted_attention1_out_fc_linear_weight_to_fp16, x = input_165_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor attn1_out_cast_fp16 = mul(x = linear_3_cast_fp16, y = var_1258_cast_fp16)[name = string("attn1_out_cast_fp16")]; + tensor text_cast_fp16 = add(x = text_3_cast_fp16, y = attn1_out_cast_fp16)[name = string("text_cast_fp16")]; + tensor m_speech_prompted_attention2_W_query_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17600064)))]; + tensor m_speech_prompted_attention2_W_query_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17731200)))]; + tensor linear_4_cast_fp16 = linear(bias = m_speech_prompted_attention2_W_query_linear_bias_to_fp16, weight = m_speech_prompted_attention2_W_query_linear_weight_to_fp16, x = text_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_1283 = const()[name = string("op_1283"), val = tensor([1, 128, 2, 128])]; + tensor var_1284_cast_fp16 = reshape(shape = var_1283, x = linear_4_cast_fp16)[name = string("op_1284_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor m_speech_prompted_attention2_W_value_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17731776)))]; + tensor m_speech_prompted_attention2_W_value_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17862912)))]; + tensor linear_6_cast_fp16 = linear(bias = m_speech_prompted_attention2_W_value_linear_bias_to_fp16, weight = m_speech_prompted_attention2_W_value_linear_weight_to_fp16, x = style_ttl_to_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_1297 = const()[name = string("op_1297"), val = tensor([1, 50, 2, 128])]; + tensor var_1298_cast_fp16 = reshape(shape = var_1297, x = linear_6_cast_fp16)[name = string("op_1298_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_1302_transpose_x_0 = const()[name = string("op_1302_transpose_x_0"), val = bool(false)]; + bool var_1302_transpose_y_0 = const()[name = string("op_1302_transpose_y_0"), val = bool(false)]; + tensor var_1301_to_fp16 = const()[name = string("op_1301_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17863488)))]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_1284_cast_fp16)[name = string("transpose_3")]; + tensor var_1302_cast_fp16 = matmul(transpose_x = var_1302_transpose_x_0, transpose_y = var_1302_transpose_y_0, x = q_cast_fp16, y = var_1301_to_fp16)[name = string("op_1302_cast_fp16")]; + fp16 _inversed_input_167_y_0_to_fp16 = const()[name = string("_inversed_input_167_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_167_cast_fp16 = mul(x = var_1302_cast_fp16, y = _inversed_input_167_y_0_to_fp16)[name = string("_inversed_input_167_cast_fp16")]; + tensor attn_5_cast_fp16 = softmax(axis = var_29, x = _inversed_input_167_cast_fp16)[name = string("attn_5_cast_fp16")]; + tensor attn_7_cast_fp16 = mul(x = attn_5_cast_fp16, y = mask_q_1_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool out_33_transpose_x_0 = const()[name = string("out_33_transpose_x_0"), val = bool(false)]; + bool out_33_transpose_y_0 = const()[name = string("out_33_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_1298_cast_fp16)[name = string("transpose_2")]; + tensor out_33_cast_fp16 = matmul(transpose_x = out_33_transpose_x_0, transpose_y = out_33_transpose_y_0, x = attn_7_cast_fp16, y = v_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor var_1310_perm_0 = const()[name = string("op_1310_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1311 = const()[name = string("op_1311"), val = tensor([1, 128, 256])]; + tensor var_1310_cast_fp16 = transpose(perm = var_1310_perm_0, x = out_33_cast_fp16)[name = string("transpose_1")]; + tensor input_169_cast_fp16 = reshape(shape = var_1311, x = var_1310_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor m_speech_prompted_attention2_out_fc_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention2_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17889152)))]; + tensor m_speech_prompted_attention2_out_fc_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention2_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18020288)))]; + tensor linear_7_cast_fp16 = linear(bias = m_speech_prompted_attention2_out_fc_linear_bias_to_fp16, weight = m_speech_prompted_attention2_out_fc_linear_weight_to_fp16, x = input_169_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor attn2_out_cast_fp16 = mul(x = linear_7_cast_fp16, y = var_1258_cast_fp16)[name = string("attn2_out_cast_fp16")]; + tensor y_final_cast_fp16 = add(x = text_3_cast_fp16, y = attn2_out_cast_fp16)[name = string("y_final_cast_fp16")]; + tensor var_1326_axes_0 = const()[name = string("op_1326_axes_0"), val = tensor([-1])]; + tensor m_speech_prompted_norm_norm_weight_to_fp16 = const()[name = string("m_speech_prompted_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18020864)))]; + tensor m_speech_prompted_norm_norm_bias_to_fp16 = const()[name = string("m_speech_prompted_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18021440)))]; + tensor var_1326_cast_fp16 = layer_norm(axes = var_1326_axes_0, beta = m_speech_prompted_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_speech_prompted_norm_norm_weight_to_fp16, x = y_final_cast_fp16)[name = string("op_1326_cast_fp16")]; + tensor x_perm_0 = const()[name = string("x_perm_0"), val = tensor([0, 2, 1])]; + tensor x_cast_fp16 = transpose(perm = x_perm_0, x = var_1326_cast_fp16)[name = string("transpose_0")]; + tensor var_1328_cast_fp16 = mul(x = x_cast_fp16, y = text_mask_to_fp16)[name = string("op_1328_cast_fp16")]; + string var_1328_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1328_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor text_emb = cast(dtype = var_1328_cast_fp16_to_fp32_dtype_0, x = var_1328_cast_fp16)[name = string("cast_46")]; + } -> (text_emb); +} \ No newline at end of file