opt-350m-email-generation / zero_to_fp32.py

adding model files

4dfb7b5 almost 2 years ago

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	#!/usr/bin/env python

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

	import argparse
	import torch
	import glob
	import math
	import os
	import re
	from collections import OrderedDict

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

	debug = 0

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


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


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


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

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

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

	return file


	def get_optim_files(checkpoint_dir):
	# XXX: need to test that this simple glob rule works for multi-node setup too
	optim_files = sorted(glob.glob(os.path.join(checkpoint_dir,
	"*_optim_states.pt")),
	key=natural_keys)

	if len(optim_files) == 0:
	raise FileNotFoundError(
	f"can't find '*_optim_states.pt' files in directory '{checkpoint_dir}'")

	return optim_files


	def parse_model_state(file):
	state_dict = torch.load(file, map_location=device)

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

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

	ds_version = state_dict.get(DS_VERSION, None)

	return buffers, param_shapes, ds_version


	def parse_optim_states(files, ds_checkpoint_dir):

	total_files = len(files)
	state_dicts = []
	for f in files:
	state_dicts.append(torch.load(f, map_location=device))

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

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

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

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

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

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

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

	return zero_stage, world_size, fp32_flat_groups


	def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
	"""
	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_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
	buffers, param_shapes, ds_version = parse_model_state(model_file)
	print(f'Parsing checkpoint created by deepspeed=={ds_version}')

	if zero_stage == 2:
	return _get_fp32_state_dict_from_zero2_checkpoint(world_size,
	param_shapes,
	fp32_flat_groups,
	buffers)
	elif zero_stage == 3:
	return _get_fp32_state_dict_from_zero3_checkpoint(world_size,
	param_shapes,
	fp32_flat_groups,
	buffers)


	def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
	param_shapes,
	fp32_flat_groups,
	buffers):

	# Reconstruction protocol:
	#
	# XXX: document this

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

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

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

	state_dict = OrderedDict()

	# buffers
	state_dict.update(buffers)
	if debug:
	print(f"added {len(buffers)} buffers")

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

	unpartitioned_numel = shape.numel()
	total_numel += unpartitioned_numel
	total_params += 1

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

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

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

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

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

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

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

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

	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 _get_fp32_state_dict_from_zero3_checkpoint(world_size,
	param_shapes,
	fp32_flat_groups,
	buffers):

	# 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

	avail_numel = fp32_flat_groups[0].numel() * world_size
	# 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
	print(f"Have {avail_numel} numels to process.")
	print(f"Need {wanted_numel} numels in {wanted_params} params.")

	state_dict = OrderedDict()

	# buffers
	state_dict.update(buffers)
	if debug:
	print(f"added {len(buffers)} buffers")

	# params
	# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
	# out-of-core computing solution
	offset = 0
	total_numel = 0
	total_params = 0
	for name, shape in 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"{total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
	)

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

	offset *= world_size

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

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

	return state_dict


	def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
	"""
	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``

	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)


	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``)
	- ``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``
	"""

	state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
	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("-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)