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|
| import argparse |
| import torch |
| import glob |
| import math |
| import os |
| import re |
| from collections import OrderedDict |
| from dataclasses import dataclass |
|
|
| |
| |
| 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 |
|
|
| |
| 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") |
|
|
| |
| 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): |
| |
| 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) |
|
|
| |
| buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} |
| param_shapes = state_dict[PARAM_SHAPES] |
|
|
| |
| param_names = [] |
| for s in param_shapes: |
| for name in s.keys(): |
| param_names.append(name) |
|
|
| |
| 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()) |
|
|
| |
| 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) |
| |
| |
| 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] |
|
|
| |
| |
| |
|
|
| 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." |
| ) |
|
|
| |
| 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: |
| |
| |
| |
| |
| |
|
|
| 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 |
|
|
| |
| |
| |
|
|
| 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}") |
|
|
| |
| 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]) |
| |
| print(f"Have {avail_numel} numels to process.") |
| print(f"Need {wanted_numel} numels in {wanted_params} params.") |
|
|
| |
| |
| |
| 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 |
|
|
| |
| |
| |
| |
| 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}") |
|
|
| |
| 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 = 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) |
|
|
| |
| 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 |
| |
| |
|
|
| |
| 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()) |
| |
| 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.") |
|
|
| |
| |
| |
| offset = 0 |
| total_numel = 0 |
| total_params = 0 |
| for name, shape in param_shapes.items(): |
|
|
| 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}" |
| ) |
|
|
| |
| 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 |
|
|
| |
| 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 = 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) |
|
|
| |
| 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_file, 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_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin) |
| - ``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 |
| """ |
|
|
| state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters) |
| print(f"Saving fp32 state dict to {output_file}") |
| torch.save(state_dict, output_file) |
|
|
|
|
| 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_file", |
| type=str, |
| help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)") |
| 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_file, |
| tag=args.tag, |
| exclude_frozen_parameters=args.exclude_frozen_parameters) |
|
|