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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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from collections import OrderedDict |
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from dataclasses import dataclass |
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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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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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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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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) |
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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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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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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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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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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 files: |
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state_dict = torch.load(f, map_location=device) |
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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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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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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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if zero_stage <= 2: |
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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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elif zero_stage == 3: |
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fp32_flat_groups = [ |
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torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts)) |
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] |
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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): |
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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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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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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 _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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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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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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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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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() |
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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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align_to = 2 * world_size |
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def zero2_align(x): |
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return align_to * math.ceil(x / align_to) |
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if debug: |
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print(f"original offset={offset}, avail_numel={avail_numel}") |
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offset = zero2_align(offset) |
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avail_numel = zero2_align(avail_numel) |
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if debug: |
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print(f"aligned offset={offset}, avail_numel={avail_numel}") |
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if offset != avail_numel: |
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raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") |
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print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") |
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def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states): |
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state_dict = OrderedDict() |
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buffers = zero_model_states[0].buffers |
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state_dict.update(buffers) |
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if debug: |
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print(f"added {len(buffers)} buffers") |
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_zero2_merge_frozen_params(state_dict, zero_model_states) |
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_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) |
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for pair in zero_model_states[0].shared_params: |
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if pair[1] in state_dict: |
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state_dict[pair[0]] = state_dict[pair[1]] |
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return state_dict |
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def zero3_partitioned_param_info(unpartitioned_numel, world_size): |
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remainder = unpartitioned_numel % world_size |
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padding_numel = (world_size - remainder) if remainder else 0 |
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partitioned_numel = math.ceil(unpartitioned_numel / world_size) |
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return partitioned_numel, padding_numel |
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def _zero3_merge_frozen_params(state_dict, world_size, 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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if debug: |
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for i in range(world_size): |
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num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) |
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print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') |
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frozen_param_shapes = zero_model_states[0].frozen_param_shapes |
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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 zero_model_states[0].frozen_param_fragments.values()]) * world_size |
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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 zero_model_states[0].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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param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) |
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state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) |
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partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) |
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if debug: |
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print( |
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f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" |
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) |
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print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") |
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def _zero3_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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avail_numel = fp32_flat_groups[0].numel() * world_size |
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param_shapes = {k: v for d in param_shapes for k, v in d.items()} |
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if debug: |
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for i in range(world_size): |
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print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") |
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wanted_params = len(param_shapes) |
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wanted_numel = sum(shape.numel() for shape in param_shapes.values()) |
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avail_numel = fp32_flat_groups[0].numel() * world_size |
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print(f"Trainable params: Have {avail_numel} numels to process.") |
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print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") |
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offset = 0 |
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total_numel = 0 |
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total_params = 0 |
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for name, shape in param_shapes.items(): |
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unpartitioned_numel = shape.numel() |
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total_numel += unpartitioned_numel |
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total_params += 1 |
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partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) |
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if debug: |
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print( |
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f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" |
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) |
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state_dict[name] = torch.cat( |
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tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)), |
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0).narrow(0, 0, unpartitioned_numel).view(shape) |
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offset += partitioned_numel |
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offset *= world_size |
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|
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if offset != avail_numel: |
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raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") |
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|
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print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") |
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|
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def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states): |
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state_dict = OrderedDict() |
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buffers = zero_model_states[0].buffers |
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state_dict.update(buffers) |
|
if debug: |
|
print(f"added {len(buffers)} buffers") |
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|
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_zero3_merge_frozen_params(state_dict, world_size, zero_model_states) |
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|
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_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) |
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for pair in zero_model_states[0].shared_params: |
|
if pair[1] in state_dict: |
|
state_dict[pair[0]] = state_dict[pair[1]] |
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return state_dict |
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|
|
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None): |
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""" |
|
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. |
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|
|
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`` |
|
|
|
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 :: |
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|
|
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 |
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model.load_state_dict(state_dict) |
|
# submit to model hub or save the model to share with others |
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|
|
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. |
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|
|
""" |
|
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) |
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|
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|
|
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None): |
|
""" |
|
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``) |
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- ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin) |
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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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""" |
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|
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state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) |
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print(f"Saving fp32 state dict to {output_file}") |
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torch.save(state_dict, output_file) |
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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``) |
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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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|
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Returns: |
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- ``model`: modified model |
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|
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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. |
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|
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A typical usage might be :: |
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|
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from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint |
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model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) |
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# submit to model hub or save the model to share with others |
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|
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Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context |
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of the same application. i.e. you will need to re-initialize the deepspeed engine, since |
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``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. |
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|
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""" |
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logger.info(f"Extracting fp32 weights") |
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state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) |
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|
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logger.info(f"Overwriting model with fp32 weights") |
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model = model.cpu() |
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model.load_state_dict(state_dict, strict=False) |
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return model |
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|
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if __name__ == "__main__": |
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|
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parser = argparse.ArgumentParser() |
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parser.add_argument("checkpoint_dir", |
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type=str, |
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help="path to the desired checkpoint folder, e.g., path/checkpoint-12") |
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parser.add_argument( |
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"output_file", |
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type=str, |
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help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)") |
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parser.add_argument("-d", "--debug", action='store_true', help="enable debug") |
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args = parser.parse_args() |
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|
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debug = args.debug |
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|
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convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file) |
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