Alibaba-NLP/Simulation_LLM_google_3B_V2

1#!/usr/bin/env python2 3# Copyright (c) Microsoft Corporation.4# SPDX-License-Identifier: Apache-2.05 6# DeepSpeed Team7 8# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets9# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in10# the future. Once extracted, the weights don't require DeepSpeed and can be used in any11# application.12#13# example: python zero_to_fp32.py . pytorch_model.bin14 15import argparse16import torch17import glob18import math19import os20import re21from collections import OrderedDict22from dataclasses import dataclass23 24# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with25# DeepSpeed data structures it has to be available in the current python environment.26from deepspeed.utils import logger27from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,28                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,29                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)30 31 32@dataclass33class zero_model_state:34    buffers: dict()35    param_shapes: dict()36    shared_params: list37    ds_version: int38    frozen_param_shapes: dict()39    frozen_param_fragments: dict()40 41 42debug = 043 44# load to cpu45device = torch.device('cpu')46 47 48def atoi(text):49    return int(text) if text.isdigit() else text50 51 52def natural_keys(text):53    '''54    alist.sort(key=natural_keys) sorts in human order55    http://nedbatchelder.com/blog/200712/human_sorting.html56    (See Toothy's implementation in the comments)57    '''58    return [atoi(c) for c in re.split(r'(\d+)', text)]59 60 61def get_model_state_file(checkpoint_dir, zero_stage):62    if not os.path.isdir(checkpoint_dir):63        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")64 65    # there should be only one file66    if zero_stage <= 2:67        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")68    elif zero_stage == 3:69        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")70 71    if not os.path.exists(file):72        raise FileNotFoundError(f"can't find model states file at '{file}'")73 74    return file75 76 77def get_checkpoint_files(checkpoint_dir, glob_pattern):78    # XXX: need to test that this simple glob rule works for multi-node setup too79    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)80 81    if len(ckpt_files) == 0:82        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")83 84    return ckpt_files85 86 87def get_optim_files(checkpoint_dir):88    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")89 90 91def get_model_state_files(checkpoint_dir):92    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")93 94 95def parse_model_states(files):96    zero_model_states = []97    for file in files:98        state_dict = torch.load(file, map_location=device)99 100        if BUFFER_NAMES not in state_dict:101            raise ValueError(f"{file} is not a model state checkpoint")102        buffer_names = state_dict[BUFFER_NAMES]103        if debug:104            print("Found buffers:", buffer_names)105 106        # recover just the buffers while restoring them to fp32 if they were saved in fp16107        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}108        param_shapes = state_dict[PARAM_SHAPES]109 110        # collect parameters that are included in param_shapes111        param_names = []112        for s in param_shapes:113            for name in s.keys():114                param_names.append(name)115 116        # update with frozen parameters117        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)118        if frozen_param_shapes is not None:119            if debug:120                print(f"Found frozen_param_shapes: {frozen_param_shapes}")121            param_names += list(frozen_param_shapes.keys())122 123        # handle shared params124        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]125 126        ds_version = state_dict.get(DS_VERSION, None)127 128        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)129 130        z_model_state = zero_model_state(buffers=buffers,131                                         param_shapes=param_shapes,132                                         shared_params=shared_params,133                                         ds_version=ds_version,134                                         frozen_param_shapes=frozen_param_shapes,135                                         frozen_param_fragments=frozen_param_fragments)136        zero_model_states.append(z_model_state)137 138    return zero_model_states139 140 141def parse_optim_states(files, ds_checkpoint_dir):142 143    total_files = len(files)144    state_dicts = []145    for f in files:146        state_dict = torch.load(f, map_location=device)147        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights148        # and also handle the case where it was already removed by another helper script149        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)150        state_dicts.append(state_dict)151 152    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:153        raise ValueError(f"{files[0]} is not a zero checkpoint")154    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]155    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]156 157    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert158    # parameters can be different from data parallelism for non-expert parameters. So we can just159    # use the max of the partition_count to get the dp world_size.160 161    if type(world_size) is list:162        world_size = max(world_size)163 164    if world_size != total_files:165        raise ValueError(166            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "167            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."168        )169 170    # the groups are named differently in each stage171    if zero_stage <= 2:172        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS173    elif zero_stage == 3:174        fp32_groups_key = FP32_FLAT_GROUPS175    else:176        raise ValueError(f"unknown zero stage {zero_stage}")177 178    if zero_stage <= 2:179        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]180    elif zero_stage == 3:181        # if there is more than one param group, there will be multiple flattened tensors - one182        # flattened tensor per group - for simplicity merge them into a single tensor183        #184        # XXX: could make the script more memory efficient for when there are multiple groups - it185        # will require matching the sub-lists of param_shapes for each param group flattened tensor186 187        fp32_flat_groups = [188            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))189        ]190 191    return zero_stage, world_size, fp32_flat_groups192 193 194def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):195    """196    Returns fp32 state_dict reconstructed from ds checkpoint197 198    Args:199        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)200 201    """202    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")203 204    optim_files = get_optim_files(ds_checkpoint_dir)205    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)206    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")207 208    model_files = get_model_state_files(ds_checkpoint_dir)209 210    zero_model_states = parse_model_states(model_files)211    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')212 213    if zero_stage <= 2:214        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,215                                                          exclude_frozen_parameters)216    elif zero_stage == 3:217        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,218                                                          exclude_frozen_parameters)219 220 221def _zero2_merge_frozen_params(state_dict, zero_model_states):222    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:223        return224 225    frozen_param_shapes = zero_model_states[0].frozen_param_shapes226    frozen_param_fragments = zero_model_states[0].frozen_param_fragments227 228    if debug:229        num_elem = sum(s.numel() for s in frozen_param_shapes.values())230        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')231 232        wanted_params = len(frozen_param_shapes)233        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())234        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])235        print(f'Frozen params: Have {avail_numel} numels to process.')236        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')237 238    total_params = 0239    total_numel = 0240    for name, shape in frozen_param_shapes.items():241        total_params += 1242        unpartitioned_numel = shape.numel()243        total_numel += unpartitioned_numel244 245        state_dict[name] = frozen_param_fragments[name]246 247        if debug:248            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")249 250    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")251 252 253def _has_callable(obj, fn):254    attr = getattr(obj, fn, None)255    return callable(attr)256 257 258def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):259    param_shapes = zero_model_states[0].param_shapes260 261    # Reconstruction protocol:262    #263    # XXX: document this264 265    if debug:266        for i in range(world_size):267            for j in range(len(fp32_flat_groups[0])):268                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")269 270    # XXX: memory usage doubles here (zero2)271    num_param_groups = len(fp32_flat_groups[0])272    merged_single_partition_of_fp32_groups = []273    for i in range(num_param_groups):274        merged_partitions = [sd[i] for sd in fp32_flat_groups]275        full_single_fp32_vector = torch.cat(merged_partitions, 0)276        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)277    avail_numel = sum(278        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])279 280    if debug:281        wanted_params = sum([len(shapes) for shapes in param_shapes])282        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])283        # not asserting if there is a mismatch due to possible padding284        print(f"Have {avail_numel} numels to process.")285        print(f"Need {wanted_numel} numels in {wanted_params} params.")286 287    # params288    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support289    # out-of-core computing solution290    total_numel = 0291    total_params = 0292    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):293        offset = 0294        avail_numel = full_single_fp32_vector.numel()295        for name, shape in shapes.items():296 297            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)298            total_numel += unpartitioned_numel299            total_params += 1300 301            if debug:302                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")303            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)304            offset += unpartitioned_numel305 306        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and307        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex308        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the309        # live optimizer object, so we are checking that the numbers are within the right range310        align_to = 2 * world_size311 312        def zero2_align(x):313            return align_to * math.ceil(x / align_to)314 315        if debug:316            print(f"original offset={offset}, avail_numel={avail_numel}")317 318        offset = zero2_align(offset)319        avail_numel = zero2_align(avail_numel)320 321        if debug:322            print(f"aligned  offset={offset}, avail_numel={avail_numel}")323 324        # Sanity check325        if offset != avail_numel:326            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")327 328    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")329 330 331def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,332                                               exclude_frozen_parameters):333    state_dict = OrderedDict()334 335    # buffers336    buffers = zero_model_states[0].buffers337    state_dict.update(buffers)338    if debug:339        print(f"added {len(buffers)} buffers")340 341    if not exclude_frozen_parameters:342        _zero2_merge_frozen_params(state_dict, zero_model_states)343 344    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)345 346    # recover shared parameters347    for pair in zero_model_states[0].shared_params:348        if pair[1] in state_dict:349            state_dict[pair[0]] = state_dict[pair[1]]350 351    return state_dict352 353 354def zero3_partitioned_param_info(unpartitioned_numel, world_size):355    remainder = unpartitioned_numel % world_size356    padding_numel = (world_size - remainder) if remainder else 0357    partitioned_numel = math.ceil(unpartitioned_numel / world_size)358    return partitioned_numel, padding_numel359 360 361def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):362    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:363        return364 365    if debug:366        for i in range(world_size):367            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())368            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')369 370        frozen_param_shapes = zero_model_states[0].frozen_param_shapes371        wanted_params = len(frozen_param_shapes)372        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())373        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size374        print(f'Frozen params: Have {avail_numel} numels to process.')375        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')376 377    total_params = 0378    total_numel = 0379    for name, shape in zero_model_states[0].frozen_param_shapes.items():380        total_params += 1381        unpartitioned_numel = shape.numel()382        total_numel += unpartitioned_numel383 384        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)385        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)386 387        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)388 389        if debug:390            print(391                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"392            )393 394    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")395 396 397def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):398    param_shapes = zero_model_states[0].param_shapes399    avail_numel = fp32_flat_groups[0].numel() * world_size400    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each401    # param, re-consolidating each param, while dealing with padding if any402 403    # merge list of dicts, preserving order404    param_shapes = {k: v for d in param_shapes for k, v in d.items()}405 406    if debug:407        for i in range(world_size):408            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")409 410        wanted_params = len(param_shapes)411        wanted_numel = sum(shape.numel() for shape in param_shapes.values())412        # not asserting if there is a mismatch due to possible padding413        avail_numel = fp32_flat_groups[0].numel() * world_size414        print(f"Trainable params: Have {avail_numel} numels to process.")415        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")416 417    # params418    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support419    # out-of-core computing solution420    offset = 0421    total_numel = 0422    total_params = 0423    for name, shape in param_shapes.items():424 425        unpartitioned_numel = shape.numel()426        total_numel += unpartitioned_numel427        total_params += 1428 429        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)430 431        if debug:432            print(433                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"434            )435 436        # XXX: memory usage doubles here437        state_dict[name] = torch.cat(438            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),439            0).narrow(0, 0, unpartitioned_numel).view(shape)440        offset += partitioned_numel441 442    offset *= world_size443 444    # Sanity check445    if offset != avail_numel:446        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")447 448    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")449 450 451def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,452                                               exclude_frozen_parameters):453    state_dict = OrderedDict()454 455    # buffers456    buffers = zero_model_states[0].buffers457    state_dict.update(buffers)458    if debug:459        print(f"added {len(buffers)} buffers")460 461    if not exclude_frozen_parameters:462        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)463 464    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)465 466    # recover shared parameters467    for pair in zero_model_states[0].shared_params:468        if pair[1] in state_dict:469            state_dict[pair[0]] = state_dict[pair[1]]470 471    return state_dict472 473 474def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False):475    """476    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with477    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example478    via a model hub.479 480    Args:481        - ``checkpoint_dir``: path to the desired checkpoint folder482        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``483        - ``exclude_frozen_parameters``: exclude frozen parameters484 485    Returns:486        - pytorch ``state_dict``487 488    Note: this approach may not work if your application doesn't have sufficient free CPU memory and489    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with490    the checkpoint.491 492    A typical usage might be ::493 494        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint495        # do the training and checkpoint saving496        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu497        model = model.cpu() # move to cpu498        model.load_state_dict(state_dict)499        # submit to model hub or save the model to share with others500 501    In this example the ``model`` will no longer be usable in the deepspeed context of the same502    application. i.e. you will need to re-initialize the deepspeed engine, since503    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.504 505    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.506 507    """508    if tag is None:509        latest_path = os.path.join(checkpoint_dir, 'latest')510        if os.path.isfile(latest_path):511            with open(latest_path, 'r') as fd:512                tag = fd.read().strip()513        else:514            raise ValueError(f"Unable to find 'latest' file at {latest_path}")515 516    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)517 518    if not os.path.isdir(ds_checkpoint_dir):519        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")520 521    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)522 523 524def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None, exclude_frozen_parameters=False):525    """526    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be527    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.528 529    Args:530        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)531        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)532        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``533        - ``exclude_frozen_parameters``: exclude frozen parameters534    """535 536    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)537    print(f"Saving fp32 state dict to {output_file}")538    torch.save(state_dict, output_file)539 540 541def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):542    """543    1. Put the provided model to cpu544    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``545    3. Load it into the provided model546 547    Args:548        - ``model``: the model object to update549        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)550        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``551 552    Returns:553        - ``model`: modified model554 555    Make sure you have plenty of CPU memory available before you call this function. If you don't556    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it557    conveniently placed for you in the checkpoint folder.558 559    A typical usage might be ::560 561        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint562        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)563        # submit to model hub or save the model to share with others564 565    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context566    of the same application. i.e. you will need to re-initialize the deepspeed engine, since567    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.568 569    """570    logger.info(f"Extracting fp32 weights")571    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)572 573    logger.info(f"Overwriting model with fp32 weights")574    model = model.cpu()575    model.load_state_dict(state_dict, strict=False)576 577    return model578 579 580if __name__ == "__main__":581 582    parser = argparse.ArgumentParser()583    parser.add_argument("checkpoint_dir",584                        type=str,585                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")586    parser.add_argument(587        "output_file",588        type=str,589        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")590    parser.add_argument("-t",591                        "--tag",592                        type=str,593                        default=None,594                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")595    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")596    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")597    args = parser.parse_args()598 599    debug = args.debug600 601    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,602                                               args.output_file,603                                               tag=args.tag,604                                               exclude_frozen_parameters=args.exclude_frozen_parameters)605