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[booster] gemini plugin support shard checkpoint (#3610)
* gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint --------- Co-authored-by: luchen <luchen@luchendeMBP.lan> Co-authored-by: luchen <luchen@luchendeMacBook-Pro.local>
This commit is contained in:
jiangmingyan
GitHub
@@ -86,7 +86,7 @@ class CheckpointIO(ABC):
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# the existence of index file means it is a sharded checkpoint
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ckpt_path = Path(checkpoint)
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index_file_exists, index_file_path = has_index_file(checkpoint)
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# return the origin model instead of the unwrapped model
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origin_model = model
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@@ -1,12 +1,12 @@
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from pathlib import Path
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from functools import reduce
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import torch.nn as nn
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from torch.optim import Optimizer
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import logging
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import os
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import json
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import gc
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from typing import Optional
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from typing import Optional, Iterator, OrderedDict, Tuple
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from .checkpoint_io_base import CheckpointIO
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from .index_file import CheckpointIndexFile
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@@ -18,10 +18,9 @@ from .utils import (
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shard_checkpoint,
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load_shard_state_dict,
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load_state_dict_into_model,
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add_variant
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get_shard_filename,
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get_base_filenames
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)
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from .utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME, WEIGHTS_INDEX_NAME
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__all__ = ['GeneralCheckpointIO']
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@@ -85,30 +84,32 @@ class GeneralCheckpointIO(CheckpointIO):
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# shard checkpoint
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state_dict = model.state_dict()
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weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME
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weights_name = add_variant(weights_name, variant)
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shards, index = shard_checkpoint(state_dict, max_shard_size=max_shard_size, weights_name=weights_name)
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state_dict_shard = shard_checkpoint(state_dict, max_shard_size=max_shard_size)
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# Save the model
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for shard_file, shard in shards.items():
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weights_name, save_index_file = get_base_filenames(variant, use_safetensors)
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total_size = 0
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index_file = CheckpointIndexFile(checkpoint_path)
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for idx, shard_pair in enumerate(state_dict_shard):
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shard = shard_pair[0]
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shard_file = get_shard_filename(weights_name, idx)
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total_size = total_size + shard_pair[1]
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for key in shard.keys():
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index_file.append_weight_map(key, shard_file)
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checkpoint_file_path = os.path.join(checkpoint_path, shard_file)
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save_state_dict(shard, checkpoint_file_path, use_safetensors)
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# save index file
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save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME
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save_index_file = os.path.join(checkpoint_path, add_variant(save_index_file, variant))
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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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index_file.append_meta_data("total_size", total_size)
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index_file.write_index_file(save_index_file)
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logging.info(
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f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
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f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
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f"The model is going to be split to checkpoint shards. "
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f"You can find where each parameters has been saved in the "
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f"index located at {save_index_file}."
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)
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def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, strict: bool = False, use_safetensors: bool = False):
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def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, strict: bool = False,
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use_safetensors: bool = False, load_sub_module: bool = True):
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"""
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load shard model, load model from multiple files
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"""
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@@ -122,17 +123,21 @@ class GeneralCheckpointIO(CheckpointIO):
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# read checkpoint index file
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ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file)
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checkpoint_files, _ = ckpt_index_file.get_checkpoint_fileanames()
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missing_keys = ckpt_index_file.get_all_param_names()
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missing_keys = []
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for shard_file in checkpoint_files:
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state_dict = load_shard_state_dict(Path(shard_file), use_safetensors)
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load_state_dict_into_model(model, state_dict, missing_keys, strict)
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load_state_dict_into_model(model, state_dict, missing_keys, strict, load_sub_module)
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del state_dict
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gc.collect()
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if strict and len(missing_keys) > 0:
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error_msgs = 'Missing key(s) in state_dict: {}. '.format(
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', '.join('"{}"'.format(k) for k in missing_keys))
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raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
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self.__class__.__name__, "\n\t".join(error_msgs)))
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if strict:
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remain_keys = reduce(lambda a, b: a & b, map(set, missing_keys))
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if len(remain_keys) > 0:
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error_msgs = 'Missing key(s) in state_dict: {}. '.format(
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', '.join('"{}"'.format(k) for k in missing_keys))
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raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
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self.__class__.__name__, "\n\t".join(error_msgs)))
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@@ -1,6 +1,8 @@
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import json
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from pathlib import Path
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from typing import Any, List, Union
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import os
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import json
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from .utils import is_dtensor_checkpoint
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@@ -18,8 +20,8 @@ class CheckpointIndexFile:
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>>> index.export('new_index.json')
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"""
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def __init__(self) -> None:
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self.root_path = None
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def __init__(self, root_path=None) -> None:
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self.root_path = root_path
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self.metadata: dict = dict()
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self.weight_map: dict = dict()
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@@ -154,3 +156,13 @@ class CheckpointIndexFile:
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Get all the weight keys.
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"""
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return list(self.weight_map.keys())
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def write_index_file(self, save_index_file):
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"""
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Wriete index file.
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"""
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save_index_file = os.path.join(self.root_path, save_index_file)
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index = {"metadata": self.metadata, "weight_map": self.weight_map}
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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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@@ -2,7 +2,7 @@
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from pathlib import Path
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import torch
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import torch.nn as nn
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from typing import List, Dict, Mapping, OrderedDict, Optional, Tuple
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from typing import List, Mapping, OrderedDict, Optional, Tuple, Iterator
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from colossalai.tensor.d_tensor.d_tensor import DTensor
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import re
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@@ -77,55 +77,35 @@ def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool:
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# ======================================
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# Helper functions for saving shard file
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# ======================================
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def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024, weights_name: str = WEIGHTS_NAME):
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def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]:
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"""
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Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a
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given size.
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"""
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sharded_state_dicts = []
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current_block = {}
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current_block_size = 0
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total_size = 0
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for key, weight in state_dict.items():
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ret_block = None
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ret_block_size = 0
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if type(weight) != DTensor:
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weight_size = calculate_tensor_size(weight)
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# If this weight is going to tip up over the maximal size, we split.
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if current_block_size + weight_size > max_shard_size:
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sharded_state_dicts.append(current_block)
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ret_block = current_block
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ret_block_size = current_block_size
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current_block = {}
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current_block_size = 0
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current_block[key] = weight
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current_block_size += weight_size
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total_size += weight_size
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if ret_block != None:
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yield ret_block, ret_block_size
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# Add the last block
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sharded_state_dicts.append(current_block)
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yield current_block, current_block_size
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# If we only have one shard, we return it
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if len(sharded_state_dicts) == 1:
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return {weights_name: sharded_state_dicts[0]}, None
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# Otherwise, let's build the index
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weight_map = {}
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shards = {}
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for idx, shard in enumerate(sharded_state_dicts):
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shard_file = weights_name.replace(".bin", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.bin")
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shard_file = shard_file.replace(
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".safetensors", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.safetensors"
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)
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shards[shard_file] = shard
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for key in shard.keys():
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weight_map[key] = shard_file
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# Add the metadata
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metadata = {"total_size": total_size}
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index = {"metadata": metadata, "weight_map": weight_map}
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return shards, index
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def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool =False):
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"""
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@@ -146,7 +126,7 @@ def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool =False):
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else:
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return torch.load(checkpoint_file)
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def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missing_keys: List, strict: bool = False):
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def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missing_keys: List, strict: bool = False, load_sub_module: bool = True):
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r"""Copies parameters and buffers from :attr:`state_dict` into
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this module and its descendants.
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@@ -167,29 +147,22 @@ def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missi
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if metadata is not None:
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state_dict._metadata = metadata
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def load(module: nn.Module, state_dict, prefix=""):
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def load(module: nn.Module, state_dict, prefix="", load_sub_module: bool = True):
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local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
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args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
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args = (state_dict, prefix, local_metadata, True, sub_missing_keys, [], error_msgs)
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# Parameters of module and children will start with prefix. We can exit early if there are none in this
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# state_dict
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if len([key for key in state_dict if key.startswith(prefix)]) > 0:
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module._load_from_state_dict(*args)
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if load_sub_module:
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for name, child in module._modules.items():
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if child is not None:
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load(child, state_dict, prefix + name + ".")
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for name, child in module._modules.items():
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if child is not None:
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load(child, state_dict, prefix + name + ".")
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load(model, state_dict, "")
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load(model, state_dict, "", load_sub_module)
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del load
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# deal with missing key
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if len(missing_keys) > 0:
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deleted_keys = []
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for key in missing_keys:
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if key not in sub_missing_keys:
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deleted_keys.append(key)
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for key in deleted_keys:
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missing_keys.remove(key)
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missing_keys = missing_keys.append(sub_missing_keys)
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if strict:
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if len(unexpected_keys) > 0:
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@@ -417,3 +390,24 @@ def add_variant(weights_name: str, variant: Optional[str] = None) -> str:
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weights_name = ".".join(splits)
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return weights_name
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def get_base_filenames(variant: str=None, use_safetensors: bool=False):
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"""
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generate base weight filenames
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"""
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weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME
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weights_name = add_variant(weights_name, variant)
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save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME
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save_index_file = add_variant(save_index_file, variant)
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return weights_name, save_index_file
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def get_shard_filename(weights_name: str, idx: int):
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"""
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get shard file name
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"""
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shard_file = weights_name.replace(".bin", f"-{idx+1:05d}.bin")
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shard_file = shard_file.replace(".safetensors", f"-{idx + 1:05d}.safetensors")
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return shard_file
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