mirror of https://github.com/hpcaitech/ColossalAI
387 lines
19 KiB
Python
387 lines
19 KiB
Python
import gc
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import logging
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import os
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from pathlib import Path
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from typing import Callable, Iterator, List, Optional, Tuple
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import torch
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import torch.nn as nn
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils.data import DataLoader
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from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO, GeneralCheckpointIO
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from colossalai.checkpoint_io.utils import (
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get_model_base_filenames,
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get_optimizer_base_filenames,
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load_shard_state_dict,
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save_state_dict,
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save_state_dict_shards,
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)
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from colossalai.cluster import DistCoordinator
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from colossalai.interface import ModelWrapper, OptimizerWrapper
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from colossalai.utils import get_current_device
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from colossalai.zero import GeminiDDP, GeminiOptimizer
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from colossalai.zero.gemini.memory_tracer import MemStats
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from .dp_plugin_base import DPPluginBase
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__all__ = ['GeminiPlugin']
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SUPPORTED_PRECISION = ['fp16', 'bf16']
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PRECISION_STR_TO_DTYPE = {'fp16': torch.half, 'bf16': torch.bfloat16}
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class GeminiCheckpointIO(GeneralCheckpointIO):
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def __init__(self) -> None:
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super().__init__()
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self.coordinator = DistCoordinator()
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def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
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"""
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Save sharded model to checkpoint but only on master process.
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The model should be unwrapped in self.load_model via ModelWrapper.unwrap.
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As there is communication when getting state dict, model.state_dict() must be called on all processes.
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"""
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state_dict = model.state_dict(only_rank_0=True)
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if self.coordinator.is_master():
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save_state_dict(state_dict, checkpoint, use_safetensors)
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def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True):
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"""
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Load model from checkpoint with automatic unwrapping.
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The model should be unwrapped in self.load_model via ModelWrapper.unwrap.
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"""
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super().load_unsharded_model(model, checkpoint, strict=strict)
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def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
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"""
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Save unsharded optimizer state dict to checkpoint.
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After calling optimizer.state_dict(), the complete optimizer states will be collected on master rank.
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As there is communication when getting state dict, optimizer.state_dict() must be called on all processes.
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The saving process will only be executed by master rank.
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"""
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state_dict = optimizer.state_dict()
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if self.coordinator.is_master():
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save_state_dict(state_dict, checkpoint, use_safetensors=False)
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def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str):
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"""
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Loading unsharded optimizer from checkpoint file.
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For each process, only loading optimizer states of parameters it controls.
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"""
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super().load_unsharded_optimizer(optimizer, checkpoint)
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def save_sharded_model(self,
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model: GeminiDDP,
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checkpoint_path: str,
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gather_dtensor: bool = False,
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prefix: Optional[str] = None,
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max_shard_size: int = 1024,
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use_safetensors: bool = False):
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"""
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Save sharded model.
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As there is communication when getting state dict, model.state_dict() must be called on all processes.
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"""
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if os.path.isfile(checkpoint_path):
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logging.error(f"Provided path ({checkpoint_path}) should be a directory, not a file")
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return
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Path(checkpoint_path).mkdir(parents=True, exist_ok=True)
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state_dict_shard = model.state_dict_shard(max_shard_size=max_shard_size, only_rank_0=True, dtype=torch.float32)
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weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors)
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index_file = CheckpointIndexFile(checkpoint_path)
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# Save shards of optimizer states.
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is_master = self.coordinator.is_master()
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total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
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checkpoint=checkpoint_path,
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index_file=index_file,
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base_filename=weights_name,
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is_master=is_master,
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use_safetensors=use_safetensors)
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# only save the index file on the master rank
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if self.coordinator.is_master():
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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(f"The model is split into 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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def load_sharded_model(self,
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model: GeminiDDP,
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checkpoint_index_file: Path,
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strict: bool = False,
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use_safetensors: bool = False):
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"""
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Load shard model, load model from multiple files.
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"""
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return super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module=False)
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def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, gather_dtensor: bool, prefix: str,
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size_per_shard: int):
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"""
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Save sharded optimizer state dict to checkpoint folder.
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As there is communication when getting state dict, this must be called on all processes.
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"""
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assert isinstance(optimizer, GeminiOptimizer)
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if os.path.isfile(checkpoint):
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logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
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return
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Path(checkpoint).mkdir(parents=True, exist_ok=True)
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# Preparing file paths and index file.
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states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix)
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index_file = CheckpointIndexFile(checkpoint)
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# Store the information of param groups to param_group_file.
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index_file.append_meta_data("param_groups", param_group_file)
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group_file_path = os.path.join(checkpoint, param_group_file)
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param_groups = optimizer.get_param_groups_for_saving()
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torch.save(param_groups, group_file_path)
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# States are broken into shards within max_shard_size.
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state_dict_shard = optimizer.state_shard(prefix=prefix, max_shard_size=size_per_shard, only_rank_0=True)
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# Save shards of optimizer states.
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is_master = self.coordinator.is_master()
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total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
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checkpoint=checkpoint,
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index_file=index_file,
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base_filename=states_name,
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is_master=is_master,
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use_safetensors=False)
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# Wrap up index file. Only save it on master rank.
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if self.coordinator.is_master():
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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(f"The optimizer 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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def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint_index_file: Path, prefix: str):
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"""
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Loading sharded optimizer from checkpoint folder, with index file given.
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For each process, only loading optimizer states of parameters it controls.
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"""
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if not os.path.isfile(checkpoint_index_file):
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logging.error(f"Provided path ({checkpoint_index_file}) should be a file")
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assert isinstance(optimizer, GeminiOptimizer)
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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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# Load param_groups.
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param_group_path = ckpt_index_file.get_param_group_filename()
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if param_group_path is None:
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raise RuntimeError(f'Invalid index file path {checkpoint_index_file} for an optimizer. \
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Lacking param group file under current directory.')
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saved_param_groups = torch.load(param_group_path)
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optimizer.load_param_groups(saved_param_groups)
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checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames()
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# Load optimizer states from shard files under checkpoint path.
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# For each file, only load the states managed by current process.
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for shard_file in checkpoint_files:
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state_dict_shard = load_shard_state_dict(Path(shard_file), use_safetensors=False)
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optimizer.load_param_states(state_dict_shard)
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del state_dict_shard
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gc.collect()
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optimizer.optimizer_loading_epilogue()
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def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
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"""
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Save model to checkpoint but only on master process.
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"""
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if self.coordinator.is_master():
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super().save_lr_scheduler(lr_scheduler, checkpoint)
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class GeminiPlugin(DPPluginBase):
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"""
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Plugin for Gemini.
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Example:
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>>> from colossalai.booster import Booster
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>>> from colossalai.booster.plugin import GeminiPlugin
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>>>
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>>> model, train_dataset, optimizer, criterion = ...
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>>> plugin = GeminiPlugin()
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>>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
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>>> booster = Booster(plugin=plugin)
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>>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
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Args:
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chunk_config_dict (dict, optional): chunk configuration dictionary.
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chunk_init_device (torch.device, optional): device to initialize the chunk.
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placement_policy (str, optional): "static" and "auto". Defaults to "static".
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shard_param_frac (float, optional): fraction of parameters to be sharded. Only for "static" placement.
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If `shard_param_frac` is 1.0, it's equal to zero-3. If `shard_param_frac` is 0.0, it's equal to zero-2. Defaults to 1.0.
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offload_optim_frac (float, optional): fraction of optimizer states to be offloaded. Only for "static" placement.
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If `shard_param_frac` is 1.0 and `offload_optim_frac` is 0.0, it's equal to old "cuda" placement. Defaults to 0.0.
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offload_param_frac (float, optional): fraction of parameters to be offloaded. Only for "static" placement.
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For efficiency, this argument is useful only when `shard_param_frac` is 1.0 and `offload_optim_frac` is 1.0.
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If `shard_param_frac` is 1.0, `offload_optim_frac` is 1.0 and `offload_param_frac` is 1.0, it's equal to old "cpu" placement.
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When using static placement, we recommend users to tune `shard_param_frac` first and then `offload_optim_frac`.
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Defaults to 0.0.
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warmup_non_model_data_ratio (float, optional): ratio of expected non-model data memory during warmup. Only for "auto" placement. Defaults to 0.8.
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steady_cuda_cap_ratio (float, optional): ratio of allowed cuda capacity for model data during steady state. Only for "auto" placement. Defaults to 0.9.
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precision (str, optional): precision. Support 'fp16' and 'bf16'. Defaults to 'fp16'.
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pin_memory (bool, optional): use pin memory on CPU. Defaults to False.
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force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False.
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strict_ddp_mode (bool, optional): use strict ddp mode (only use dp without other parallelism). Defaults to False.
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search_range_m (int, optional): chunk size searching range divided by 2^20. Defaults to 32.
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hidden_dim (int, optional): the hidden dimension of DNN.
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Users can provide this argument to speed up searching.
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If users do not know this argument before training, it is ok. We will use a default value 1024.
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min_chunk_size_m (float, optional): the minimum chunk size divided by 2^20.
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If the aggregate size of parameters is still smaller than the minimum chunk size,
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all parameters will be compacted into one small chunk.
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memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer.
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gpu_margin_mem_ratio (float, optional): The ratio of GPU remaining memory (after the first forward-backward)
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which will be used when using hybrid CPU optimizer.
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This argument is meaningless when `placement_policy` of `GeminiManager` is not "auto".
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Defaults to 0.0.
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initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**16.
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min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1.
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growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2.
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backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5.
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growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000.
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hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2.
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max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32.
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max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do
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clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm.
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norm_type (float, optional): norm_type used for `clip_grad_norm`.
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verbose (bool, optional): verbose mode. Debug info including chunk search result will be printed. Defaults to False.
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"""
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def __init__(
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self,
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chunk_config_dict: Optional[dict] = None,
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chunk_init_device: Optional[torch.device] = None,
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placement_policy: str = "static",
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shard_param_frac: float = 1.0, # only for static placement
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offload_optim_frac: float = 0.0, # only for static placement
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offload_param_frac: float = 0.0, # only for static placement
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warmup_non_model_data_ratio: float = 0.8, # only for auto placement
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steady_cuda_cap_ratio: float = 0.9, # only for auto placement
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precision: str = "fp16",
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pin_memory: bool = False,
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force_outputs_fp32: bool = False,
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strict_ddp_mode: bool = False,
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search_range_m: int = 32,
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hidden_dim: Optional[int] = None,
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min_chunk_size_m: float = 32,
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memstats: Optional[MemStats] = None,
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gpu_margin_mem_ratio: float = 0.0,
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initial_scale: float = 2**16,
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min_scale: float = 1,
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growth_factor: float = 2,
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backoff_factor: float = 0.5,
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growth_interval: int = 1000,
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hysteresis: int = 2,
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max_scale: float = 2**32,
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max_norm: float = 0.0,
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norm_type: float = 2.0,
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verbose: bool = False,
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) -> None:
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super().__init__()
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assert precision in SUPPORTED_PRECISION, f'precision {precision} is not supported'
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self.gemini_config = dict(
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chunk_config_dict=chunk_config_dict,
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chunk_init_device=(chunk_init_device or get_current_device()),
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placement_policy=placement_policy,
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shard_param_frac=shard_param_frac,
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offload_optim_frac=offload_optim_frac,
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offload_param_frac=offload_param_frac,
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warmup_non_model_data_ratio=warmup_non_model_data_ratio,
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steady_cuda_cap_ratio=steady_cuda_cap_ratio,
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pin_memory=pin_memory,
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force_outputs_fp32=force_outputs_fp32,
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strict_ddp_mode=strict_ddp_mode,
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search_range_m=search_range_m,
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hidden_dim=hidden_dim,
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min_chunk_size_m=min_chunk_size_m,
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memstats=memstats,
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mixed_precision=PRECISION_STR_TO_DTYPE[precision],
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)
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self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio,)
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self.optim_kwargs = dict(initial_scale=initial_scale,
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growth_factor=growth_factor,
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backoff_factor=backoff_factor,
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growth_interval=growth_interval,
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hysteresis=hysteresis,
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min_scale=min_scale,
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max_scale=max_scale,
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max_norm=max_norm,
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norm_type=norm_type)
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self.verbose = verbose
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def support_no_sync(self) -> bool:
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return False
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def control_precision(self) -> bool:
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return True
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def supported_precisions(self) -> List[str]:
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return SUPPORTED_PRECISION
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def control_device(self) -> bool:
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return True
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def supported_devices(self) -> List[str]:
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return ['cuda']
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def configure(
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self,
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model: nn.Module,
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optimizer: Optional[Optimizer] = None,
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criterion: Optional[Callable] = None,
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dataloader: Optional[DataLoader] = None,
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lr_scheduler: Optional[LRScheduler] = None,
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) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
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if not isinstance(model, ModelWrapper):
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# convert model to sync bn
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# FIXME(ver217): gemini does not support sync bn
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# In torch/nn/modules/_functions.py, line 22, ``mean, invstd = torch.batch_norm_stats(input, eps)`` will get fp32 mean and invstd even though the input is fp16.
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# This inconsistency of dtype will cause the error.
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# We have two possible solutions:
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# 1. keep batch norm always in fp32. This is hard for gemini, as it use chunks.
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# 2. patch sync bn or write a new on. This is relatively easy, but we need to test it.
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# model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None)
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# wrap the model with Gemini
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model = GeminiDDP(model, **self.gemini_config, verbose=self.verbose)
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if optimizer is not None and \
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not isinstance(optimizer, OptimizerWrapper):
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optimizer = GeminiOptimizer(optimizer,
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model.unwrap(),
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**self.zero_optim_config,
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**self.optim_kwargs,
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verbose=self.verbose)
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return model, optimizer, criterion, dataloader, lr_scheduler
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def control_checkpoint_io(self) -> bool:
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return True
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def get_checkpoint_io(self) -> CheckpointIO:
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return GeminiCheckpointIO()
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def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
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raise NotImplementedError
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