finetune/work_dirs/assistTuner/zero_to_fp32.py

#!/usr/bin/env python

# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
# application.
#
# example:
#   python zero_to_fp32.py . output_dir/
#   or
#   python zero_to_fp32.py . output_dir/ --safe_serialization

import argparse
import torch
import glob
import math
import os
import re
import json
from tqdm import tqdm
from collections import OrderedDict
from dataclasses import dataclass

# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
# DeepSpeed data structures it has to be available in the current python environment.
from deepspeed.utils import logger
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)


@dataclass
class zero_model_state:
    buffers: dict()
    param_shapes: dict()
    shared_params: list
    ds_version: int
    frozen_param_shapes: dict()
    frozen_param_fragments: dict()


debug = 0

# load to cpu
device = torch.device('cpu')


def atoi(text):
    return int(text) if text.isdigit() else text


def natural_keys(text):
    '''
    alist.sort(key=natural_keys) sorts in human order
    http://nedbatchelder.com/blog/200712/human_sorting.html
    (See Toothy's implementation in the comments)
    '''
    return [atoi(c) for c in re.split(r'(\d+)', text)]


def get_model_state_file(checkpoint_dir, zero_stage):
    if not os.path.isdir(checkpoint_dir):
        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")

    # there should be only one file
    if zero_stage <= 2:
        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
    elif zero_stage == 3:
        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")

    if not os.path.exists(file):
        raise FileNotFoundError(f"can't find model states file at '{file}'")

    return file


def get_checkpoint_files(checkpoint_dir, glob_pattern):
    # XXX: need to test that this simple glob rule works for multi-node setup too
    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)

    if len(ckpt_files) == 0:
        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")

    return ckpt_files


def get_optim_files(checkpoint_dir):
    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")


def get_model_state_files(checkpoint_dir):
    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")


def parse_model_states(files):
    zero_model_states = []
    for file in files:
        state_dict = torch.load(file, map_location=device)

        if BUFFER_NAMES not in state_dict:
            raise ValueError(f"{file} is not a model state checkpoint")
        buffer_names = state_dict[BUFFER_NAMES]
        if debug:
            print("Found buffers:", buffer_names)

        # recover just the buffers while restoring them to fp32 if they were saved in fp16
        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
        param_shapes = state_dict[PARAM_SHAPES]

        # collect parameters that are included in param_shapes
        param_names = []
        for s in param_shapes:
            for name in s.keys():
                param_names.append(name)

        # update with frozen parameters
        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
        if frozen_param_shapes is not None:
            if debug:
                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
            param_names += list(frozen_param_shapes.keys())

        # handle shared params
        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]

        ds_version = state_dict.get(DS_VERSION, None)

        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)

        z_model_state = zero_model_state(buffers=buffers,
                                         param_shapes=param_shapes,
                                         shared_params=shared_params,
                                         ds_version=ds_version,
                                         frozen_param_shapes=frozen_param_shapes,
                                         frozen_param_fragments=frozen_param_fragments)
        zero_model_states.append(z_model_state)

    return zero_model_states


def parse_optim_states(files, ds_checkpoint_dir):
    total_files = len(files)
    state_dicts = []
    for f in files:
        state_dict = torch.load(f, map_location=device)
        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
        # and also handle the case where it was already removed by another helper script
        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
        state_dicts.append(state_dict)

    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
        raise ValueError(f"{files[0]} is not a zero checkpoint")
    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]

    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
    # parameters can be different from data parallelism for non-expert parameters. So we can just
    # use the max of the partition_count to get the dp world_size.

    if type(world_size) is list:
        world_size = max(world_size)

    if world_size != total_files:
        raise ValueError(
            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
        )

    # the groups are named differently in each stage
    if zero_stage <= 2:
        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
    elif zero_stage == 3:
        fp32_groups_key = FP32_FLAT_GROUPS
    else:
        raise ValueError(f"unknown zero stage {zero_stage}")

    if zero_stage <= 2:
        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
    elif zero_stage == 3:
        # if there is more than one param group, there will be multiple flattened tensors - one
        # flattened tensor per group - for simplicity merge them into a single tensor
        #
        # XXX: could make the script more memory efficient for when there are multiple groups - it
        # will require matching the sub-lists of param_shapes for each param group flattened tensor

        fp32_flat_groups = [
            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
        ]

    return zero_stage, world_size, fp32_flat_groups


def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
    """
    Returns fp32 state_dict reconstructed from ds checkpoint

    Args:
        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)

    """
    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")

    optim_files = get_optim_files(ds_checkpoint_dir)
    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")

    model_files = get_model_state_files(ds_checkpoint_dir)

    zero_model_states = parse_model_states(model_files)
    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')

    if zero_stage <= 2:
        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
                                                          exclude_frozen_parameters)
    elif zero_stage == 3:
        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
                                                          exclude_frozen_parameters)


def _zero2_merge_frozen_params(state_dict, zero_model_states):
    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
        return

    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
    frozen_param_fragments = zero_model_states[0].frozen_param_fragments

    if debug:
        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')

        wanted_params = len(frozen_param_shapes)
        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
        print(f'Frozen params: Have {avail_numel} numels to process.')
        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')

    total_params = 0
    total_numel = 0
    for name, shape in frozen_param_shapes.items():
        total_params += 1
        unpartitioned_numel = shape.numel()
        total_numel += unpartitioned_numel

        state_dict[name] = frozen_param_fragments[name]

        if debug:
            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")

    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")


def _has_callable(obj, fn):
    attr = getattr(obj, fn, None)
    return callable(attr)


def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
    param_shapes = zero_model_states[0].param_shapes

    # Reconstruction protocol:
    #
    # XXX: document this

    if debug:
        for i in range(world_size):
            for j in range(len(fp32_flat_groups[0])):
                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")

    # XXX: memory usage doubles here (zero2)
    num_param_groups = len(fp32_flat_groups[0])
    merged_single_partition_of_fp32_groups = []
    for i in range(num_param_groups):
        merged_partitions = [sd[i] for sd in fp32_flat_groups]
        full_single_fp32_vector = torch.cat(merged_partitions, 0)
        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
    avail_numel = sum(
        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])

    if debug:
        wanted_params = sum([len(shapes) for shapes in param_shapes])
        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
        # not asserting if there is a mismatch due to possible padding
        print(f"Have {avail_numel} numels to process.")
        print(f"Need {wanted_numel} numels in {wanted_params} params.")

    # params
    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
    # out-of-core computing solution
    total_numel = 0
    total_params = 0
    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
        offset = 0
        avail_numel = full_single_fp32_vector.numel()
        for name, shape in shapes.items():

            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
            total_numel += unpartitioned_numel
            total_params += 1

            if debug:
                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
            offset += unpartitioned_numel

        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
        # live optimizer object, so we are checking that the numbers are within the right range
        align_to = 2 * world_size

        def zero2_align(x):
            return align_to * math.ceil(x / align_to)

        if debug:
            print(f"original offset={offset}, avail_numel={avail_numel}")

        offset = zero2_align(offset)
        avail_numel = zero2_align(avail_numel)

        if debug:
            print(f"aligned  offset={offset}, avail_numel={avail_numel}")

        # Sanity check
        if offset != avail_numel:
            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")

    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")


def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
                                               exclude_frozen_parameters):
    state_dict = OrderedDict()

    # buffers
    buffers = zero_model_states[0].buffers
    state_dict.update(buffers)
    if debug:
        print(f"added {len(buffers)} buffers")

    if not exclude_frozen_parameters:
        _zero2_merge_frozen_params(state_dict, zero_model_states)

    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)

    # recover shared parameters
    for pair in zero_model_states[0].shared_params:
        if pair[1] in state_dict:
            state_dict[pair[0]] = state_dict[pair[1]]

    return state_dict


def zero3_partitioned_param_info(unpartitioned_numel, world_size):
    remainder = unpartitioned_numel % world_size
    padding_numel = (world_size - remainder) if remainder else 0
    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
    return partitioned_numel, padding_numel


def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
        return

    if debug:
        for i in range(world_size):
            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')

        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
        wanted_params = len(frozen_param_shapes)
        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
        print(f'Frozen params: Have {avail_numel} numels to process.')
        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')

    total_params = 0
    total_numel = 0
    for name, shape in zero_model_states[0].frozen_param_shapes.items():
        total_params += 1
        unpartitioned_numel = shape.numel()
        total_numel += unpartitioned_numel

        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)

        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)

        if debug:
            print(
                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
            )

    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")


def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
    param_shapes = zero_model_states[0].param_shapes
    avail_numel = fp32_flat_groups[0].numel() * world_size
    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
    # param, re-consolidating each param, while dealing with padding if any

    # merge list of dicts, preserving order
    param_shapes = {k: v for d in param_shapes for k, v in d.items()}

    if debug:
        for i in range(world_size):
            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")

        wanted_params = len(param_shapes)
        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
        # not asserting if there is a mismatch due to possible padding
        avail_numel = fp32_flat_groups[0].numel() * world_size
        print(f"Trainable params: Have {avail_numel} numels to process.")
        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")

    # params
    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
    # out-of-core computing solution
    offset = 0
    total_numel = 0
    total_params = 0
    for name, shape in tqdm(param_shapes.items(), desc='Gathering Sharded Weights'):
        unpartitioned_numel = shape.numel()
        total_numel += unpartitioned_numel
        total_params += 1
        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)

        if debug:
            print(
                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
            )

        # XXX: memory usage doubles here
        state_dict[name] = torch.cat(
            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
            0).narrow(0, 0, unpartitioned_numel).view(shape)
        offset += partitioned_numel

    offset *= world_size

    # Sanity check
    if offset != avail_numel:
        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")

    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")


def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
                                               exclude_frozen_parameters):
    state_dict = OrderedDict()

    # buffers
    buffers = zero_model_states[0].buffers
    state_dict.update(buffers)
    if debug:
        print(f"added {len(buffers)} buffers")

    if not exclude_frozen_parameters:
        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)

    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)

    # recover shared parameters
    for pair in zero_model_states[0].shared_params:
        if pair[1] in state_dict:
            state_dict[pair[0]] = state_dict[pair[1]]

    return state_dict


def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False):
    """
    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
    via a model hub.

    Args:
        - ``checkpoint_dir``: path to the desired checkpoint folder
        - ``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``
        - ``exclude_frozen_parameters``: exclude frozen parameters

    Returns:
        - pytorch ``state_dict``

    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
    the checkpoint.

    A typical usage might be ::

        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
        # do the training and checkpoint saving
        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
        model = model.cpu() # move to cpu
        model.load_state_dict(state_dict)
        # submit to model hub or save the model to share with others

    In this example the ``model`` will no longer be usable in the deepspeed context of the same
    application. i.e. you will need to re-initialize the deepspeed engine, since
    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.

    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.

    """
    if tag is None:
        latest_path = os.path.join(checkpoint_dir, 'latest')
        if os.path.isfile(latest_path):
            with open(latest_path, 'r') as fd:
                tag = fd.read().strip()
        else:
            raise ValueError(f"Unable to find 'latest' file at {latest_path}")

    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)

    if not os.path.isdir(ds_checkpoint_dir):
        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")

    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)


def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
                                               output_dir,
                                               max_shard_size="5GB",
                                               safe_serialization=False,
                                               tag=None,
                                               exclude_frozen_parameters=False):
    """
    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.

    Args:
        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
        - ``output_dir``: directory to the pytorch fp32 state_dict output files
        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
        - ``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``
        - ``exclude_frozen_parameters``: exclude frozen parameters
    """
    # Dependency pre-check
    if safe_serialization:
        try:
            from safetensors.torch import save_file
        except ImportError:
            print('If you want to use `safe_serialization`, please `pip install safetensors`')
            raise
    if max_shard_size is not None:
        try:
            from huggingface_hub import split_torch_state_dict_into_shards
        except ImportError:
            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
            raise

    # Convert zero checkpoint to state_dict
    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)

    # Shard the model if it is too big.
    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
    if max_shard_size is not None:
        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
        state_dict_split = split_torch_state_dict_into_shards(state_dict,
                                                              filename_pattern=filename_pattern,
                                                              max_shard_size=max_shard_size)
    else:
        from collections import namedtuple
        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
        state_dict_split = StateDictSplit(is_sharded=False,
                                          filename_to_tensors={weights_name: list(state_dict.keys())})

    # Save the model
    filename_to_tensors = state_dict_split.filename_to_tensors.items()
    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
        shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
        output_path = os.path.join(output_dir, shard_file)
        if safe_serialization:
            save_file(shard, output_path, metadata={"format": "pt"})
        else:
            torch.save(shard, output_path)

    # Save index if sharded
    if state_dict_split.is_sharded:
        index = {
            "metadata": state_dict_split.metadata,
            "weight_map": state_dict_split.tensor_to_filename,
        }
        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
        save_index_file = os.path.join(output_dir, save_index_file)
        with open(save_index_file, "w", encoding="utf-8") as f:
            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
            f.write(content)


def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
    """
    1. Put the provided model to cpu
    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
    3. Load it into the provided model

    Args:
        - ``model``: the model object to update
        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
        - ``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``

    Returns:
        - ``model`: modified model

    Make sure you have plenty of CPU memory available before you call this function. If you don't
    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
    conveniently placed for you in the checkpoint folder.

    A typical usage might be ::

        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
        # submit to model hub or save the model to share with others

    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.

    """
    logger.info(f"Extracting fp32 weights")
    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)

    logger.info(f"Overwriting model with fp32 weights")
    model = model.cpu()
    model.load_state_dict(state_dict, strict=False)

    return model


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("checkpoint_dir",
                        type=str,
                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
    parser.add_argument("output_dir",
                        type=str,
                        help="directory to the pytorch fp32 state_dict output files"
                        "(e.g. path/checkpoint-12-output/)")
    parser.add_argument(
        "--max_shard_size",
        type=str,
        default="5GB",
        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
        "without CPU OOM issues.")
    parser.add_argument(
        "--safe_serialization",
        default=False,
        action='store_true',
        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
    parser.add_argument("-t",
                        "--tag",
                        type=str,
                        default=None,
                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
    args = parser.parse_args()

    debug = args.debug

    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
                                               args.output_dir,
                                               max_shard_size=args.max_shard_size,
                                               safe_serialization=args.safe_serialization,
                                               tag=args.tag,
                                               exclude_frozen_parameters=args.exclude_frozen_parameters)