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#!/usr/bin/env python
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# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
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# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
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# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
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# application.
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#
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# example:
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# python zero_to_fp32.py . output_dir/
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# or
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# python zero_to_fp32.py . output_dir/ --safe_serialization
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import argparse
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import torch
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import glob
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import math
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import os
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import re
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import gc
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import json
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import numpy as np
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from tqdm import tqdm
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from collections import OrderedDict
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from dataclasses import dataclass
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# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
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# DeepSpeed data structures it has to be available in the current python environment.
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from deepspeed.utils import logger
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from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
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FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
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FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
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@dataclass
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class zero_model_state:
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buffers: dict()
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param_shapes: dict()
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shared_params: list
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ds_version: int
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frozen_param_shapes: dict()
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frozen_param_fragments: dict()
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debug = 0
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# load to cpu
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device = torch.device('cpu')
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def atoi(text):
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return int(text) if text.isdigit() else text
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def natural_keys(text):
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'''
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alist.sort(key=natural_keys) sorts in human order
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http://nedbatchelder.com/blog/200712/human_sorting.html
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(See Toothy's implementation in the comments)
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'''
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return [atoi(c) for c in re.split(r'(\d+)', text)]
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def get_model_state_file(checkpoint_dir, zero_stage):
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if not os.path.isdir(checkpoint_dir):
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raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
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# there should be only one file
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if zero_stage <= 2:
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file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
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elif zero_stage == 3:
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file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
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if not os.path.exists(file):
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raise FileNotFoundError(f"can't find model states file at '{file}'")
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return file
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def get_checkpoint_files(checkpoint_dir, glob_pattern):
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# XXX: need to test that this simple glob rule works for multi-node setup too
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ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
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if len(ckpt_files) == 0:
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raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
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return ckpt_files
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def get_optim_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
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def get_model_state_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
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def parse_model_states(files):
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zero_model_states = []
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for file in files:
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state_dict = torch.load(file, map_location=device, weights_only=False)
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if BUFFER_NAMES not in state_dict:
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raise ValueError(f"{file} is not a model state checkpoint")
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buffer_names = state_dict[BUFFER_NAMES]
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if debug:
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print("Found buffers:", buffer_names)
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# recover just the buffers while restoring them to fp32 if they were saved in fp16
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buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
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param_shapes = state_dict[PARAM_SHAPES]
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# collect parameters that are included in param_shapes
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param_names = []
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for s in param_shapes:
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for name in s.keys():
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param_names.append(name)
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# update with frozen parameters
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frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
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if frozen_param_shapes is not None:
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if debug:
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print(f"Found frozen_param_shapes: {frozen_param_shapes}")
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param_names += list(frozen_param_shapes.keys())
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# handle shared params
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shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
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ds_version = state_dict.get(DS_VERSION, None)
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frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
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z_model_state = zero_model_state(buffers=buffers,
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param_shapes=param_shapes,
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shared_params=shared_params,
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ds_version=ds_version,
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frozen_param_shapes=frozen_param_shapes,
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frozen_param_fragments=frozen_param_fragments)
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zero_model_states.append(z_model_state)
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return zero_model_states
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def parse_optim_states(files, ds_checkpoint_dir):
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total_files = len(files)
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state_dicts = []
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for f in tqdm(files, desc='Loading checkpoint shards'):
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state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
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# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
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# and also handle the case where it was already removed by another helper script
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state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
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state_dicts.append(state_dict)
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if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
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raise ValueError(f"{files[0]} is not a zero checkpoint")
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zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
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world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
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# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
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# parameters can be different from data parallelism for non-expert parameters. So we can just
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# use the max of the partition_count to get the dp world_size.
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if type(world_size) is list:
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world_size = max(world_size)
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if world_size != total_files:
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raise ValueError(
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f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
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"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
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)
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# the groups are named differently in each stage
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if zero_stage <= 2:
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fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
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elif zero_stage == 3:
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fp32_groups_key = FP32_FLAT_GROUPS
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else:
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raise ValueError(f"unknown zero stage {zero_stage}")
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fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
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return zero_stage, world_size, fp32_flat_groups
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def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
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"""
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Returns fp32 state_dict reconstructed from ds checkpoint
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Args:
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- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
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"""
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print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
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optim_files = get_optim_files(ds_checkpoint_dir)
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zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
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print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
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model_files = get_model_state_files(ds_checkpoint_dir)
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zero_model_states = parse_model_states(model_files)
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print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
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if zero_stage <= 2:
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return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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exclude_frozen_parameters)
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elif zero_stage == 3:
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return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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exclude_frozen_parameters)
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def _zero2_merge_frozen_params(state_dict, zero_model_states):
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if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
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return
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frozen_param_shapes = zero_model_states[0].frozen_param_shapes
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frozen_param_fragments = zero_model_states[0].frozen_param_fragments
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if debug:
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num_elem = sum(s.numel() for s in frozen_param_shapes.values())
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print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
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wanted_params = len(frozen_param_shapes)
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wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
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avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
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print(f'Frozen params: Have {avail_numel} numels to process.')
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print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
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total_params = 0
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total_numel = 0
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for name, shape in frozen_param_shapes.items():
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total_params += 1
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unpartitioned_numel = shape.numel()
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total_numel += unpartitioned_numel
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state_dict[name] = frozen_param_fragments[name]
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if debug:
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print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
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def _has_callable(obj, fn):
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attr = getattr(obj, fn, None)
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return callable(attr)
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def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
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param_shapes = zero_model_states[0].param_shapes
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# Reconstruction protocol:
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#
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# XXX: document this
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if debug:
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for i in range(world_size):
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for j in range(len(fp32_flat_groups[0])):
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print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
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# XXX: memory usage doubles here (zero2)
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num_param_groups = len(fp32_flat_groups[0])
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merged_single_partition_of_fp32_groups = []
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for i in range(num_param_groups):
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merged_partitions = [sd[i] for sd in fp32_flat_groups]
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full_single_fp32_vector = torch.cat(merged_partitions, 0)
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merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
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avail_numel = sum(
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[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
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if debug:
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wanted_params = sum([len(shapes) for shapes in param_shapes])
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wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
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# not asserting if there is a mismatch due to possible padding
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print(f"Have {avail_numel} numels to process.")
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print(f"Need {wanted_numel} numels in {wanted_params} params.")
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# params
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# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
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# out-of-core computing solution
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total_numel = 0
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total_params = 0
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for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
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offset = 0
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avail_numel = full_single_fp32_vector.numel()
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for name, shape in shapes.items():
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unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
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total_numel += unpartitioned_numel
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total_params += 1
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if debug:
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print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
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offset += unpartitioned_numel
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# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
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|
|
|
# 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")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class GatheredTensor:
|
|
|
|
|
"""
|
|
|
|
|
A pseudo tensor that collects partitioned weights.
|
|
|
|
|
It is more memory efficient when there are multiple groups.
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
|
|
|
|
|
self.flat_groups = flat_groups
|
|
|
|
|
self.flat_groups_offset = flat_groups_offset
|
|
|
|
|
self.offset = offset
|
|
|
|
|
self.partitioned_numel = partitioned_numel
|
|
|
|
|
self.shape = shape
|
|
|
|
|
self.dtype = self.flat_groups[0][0].dtype
|
|
|
|
|
|
|
|
|
|
def contiguous(self):
|
|
|
|
|
"""
|
|
|
|
|
Merge partitioned weights from flat_groups into a single tensor.
|
|
|
|
|
"""
|
|
|
|
|
end_idx = self.offset + self.partitioned_numel
|
|
|
|
|
world_size = len(self.flat_groups)
|
|
|
|
|
pad_flat_param_chunks = []
|
|
|
|
|
|
|
|
|
|
for rank_i in range(world_size):
|
|
|
|
|
# for each rank, we need to collect weights from related group/groups
|
|
|
|
|
flat_groups_at_rank_i = self.flat_groups[rank_i]
|
|
|
|
|
start_group_id = None
|
|
|
|
|
end_group_id = None
|
|
|
|
|
for group_id in range(len(self.flat_groups_offset)):
|
|
|
|
|
if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
|
|
|
|
|
start_group_id = group_id
|
|
|
|
|
if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
|
|
|
|
|
end_group_id = group_id
|
|
|
|
|
break
|
|
|
|
|
# collect weights from related group/groups
|
|
|
|
|
for group_id in range(start_group_id, end_group_id + 1):
|
|
|
|
|
flat_tensor = flat_groups_at_rank_i[group_id]
|
|
|
|
|
start_offset = self.offset - self.flat_groups_offset[group_id]
|
|
|
|
|
end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
|
|
|
|
|
pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
|
|
|
|
|
|
|
|
|
|
# collect weights from all ranks
|
|
|
|
|
pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
|
|
|
|
|
param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
|
|
|
|
|
return param
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
|
|
|
|
|
flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# memory efficient tensor
|
|
|
|
|
tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
|
|
|
|
|
state_dict[name] = tensor
|
|
|
|
|
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 to_torch_tensor(state_dict, return_empty_tensor=False):
|
|
|
|
|
"""
|
|
|
|
|
Convert state_dict of GatheredTensor to torch tensor
|
|
|
|
|
"""
|
|
|
|
|
torch_state_dict = {}
|
|
|
|
|
converted_tensors = {}
|
|
|
|
|
for name, tensor in state_dict.items():
|
|
|
|
|
tensor_id = id(tensor)
|
|
|
|
|
if tensor_id in converted_tensors: # shared tensors
|
|
|
|
|
shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
|
|
|
|
|
torch_state_dict[name] = shared_tensor
|
|
|
|
|
else:
|
|
|
|
|
converted_tensors[tensor_id] = name
|
|
|
|
|
if return_empty_tensor:
|
|
|
|
|
torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
|
|
|
|
|
else:
|
|
|
|
|
torch_state_dict[name] = tensor.contiguous()
|
|
|
|
|
return torch_state_dict
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
|
|
|
|
tag=None,
|
|
|
|
|
exclude_frozen_parameters=False,
|
|
|
|
|
lazy_mode=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
|
|
|
|
|
- ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
|
|
|
|
|
Convert the pesduo tensor to torch tensor by ``.contiguous()``
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
- pytorch ``state_dict``
|
|
|
|
|
|
|
|
|
|
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.
|
|
|
|
|
|
|
|
|
|
Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
|
|
|
|
|
You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
|
|
|
|
the checkpoint. Or you can load state_dict in lazy mode ::
|
|
|
|
|
|
|
|
|
|
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
|
|
|
|
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
|
|
|
|
|
for name, lazy_tensor in state_dict.item():
|
|
|
|
|
tensor = lazy_tensor.contiguous() # to cpu
|
|
|
|
|
print(name, tensor)
|
|
|
|
|
# del tensor to release memory if it no longer in use
|
|
|
|
|
"""
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
|
state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
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if lazy_mode:
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return state_dict
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else:
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return to_torch_tensor(state_dict)
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def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
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output_dir,
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max_shard_size="5GB",
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safe_serialization=False,
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tag=None,
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exclude_frozen_parameters=False):
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"""
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Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
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loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
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Args:
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- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
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- ``output_dir``: directory to the pytorch fp32 state_dict output files
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- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
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- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
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- ``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``
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- ``exclude_frozen_parameters``: exclude frozen parameters
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"""
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# Dependency pre-check
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if safe_serialization:
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try:
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from safetensors.torch import save_file
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except ImportError:
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print('If you want to use `safe_serialization`, please `pip install safetensors`')
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raise
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if max_shard_size is not None:
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try:
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from huggingface_hub import split_torch_state_dict_into_shards
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except ImportError:
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print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
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raise
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# Convert zero checkpoint to state_dict
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state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
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tag,
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exclude_frozen_parameters,
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lazy_mode=True)
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# Shard the model if it is too big.
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weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
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if max_shard_size is not None:
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filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
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# an memory-efficient approach for sharding
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empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
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state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
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filename_pattern=filename_pattern,
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max_shard_size=max_shard_size)
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else:
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from collections import namedtuple
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StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
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state_dict_split = StateDictSplit(is_sharded=False,
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filename_to_tensors={weights_name: list(state_dict.keys())})
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# Save the model by shard
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os.makedirs(output_dir, exist_ok=True)
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filename_to_tensors = state_dict_split.filename_to_tensors.items()
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for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
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shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
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shard_state_dict = to_torch_tensor(shard_state_dict)
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output_path = os.path.join(output_dir, shard_file)
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if safe_serialization:
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save_file(shard_state_dict, output_path, metadata={"format": "pt"})
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else:
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torch.save(shard_state_dict, output_path)
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# release the memory of current shard
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for tensor_name in list(shard_state_dict.keys()):
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del state_dict[tensor_name]
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del shard_state_dict[tensor_name]
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del shard_state_dict
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gc.collect()
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# Save index if sharded
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if state_dict_split.is_sharded:
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index = {
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"metadata": state_dict_split.metadata,
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"weight_map": state_dict_split.tensor_to_filename,
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}
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save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
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save_index_file = os.path.join(output_dir, save_index_file)
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with open(save_index_file, "w", encoding="utf-8") as f:
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content = json.dumps(index, indent=2, sort_keys=True) + "\n"
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f.write(content)
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def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
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|
"""
|
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|
|
|
1. Put the provided model to cpu
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|
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
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|
|
3. Load it into the provided model
|
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|
|
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|
Args:
|
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|
- ``model``: the model object to update
|
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|
|
|
- ``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:
|
|
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|
|
- ``model`: modified model
|
|
|
|
|
|
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|
|
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
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|
|
|
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
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|
|
|
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)
|
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