mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-10-22 15:26:57 +00:00
5a560a060a
* add zero1 (#209) * add zero1 * add test zero1 * update zero stage 1 develop (#212) * Implement naive zero3 (#240) * naive zero3 works well * add zero3 param manager * add TODOs in comments * add gather full param ctx * fix sub module streams * add offload * fix bugs of hook and add unit tests * fix bugs of hook and add unit tests (#252) * add gather full param ctx * fix sub module streams * add offload * fix bugs of hook and add unit tests * polish code and add state dict hook * fix bug * update unit test * refactor reconstructed zero code * clip_grad support zero3 and add unit test * add unit test for Zero3ParameterManager * [WIP] initialize the shard param class * [WIP] Yet another sharded model implementation (#274) * [WIP] initialize the shard param class * [WIP] Yes another implementation of shardModel. Using a better hook method. * torch.concat -> torch.cat * fix test_zero_level_1.py::test_zero_level_1 unitest * remove deepspeed implementation and refactor for the reconstructed zero module * polish zero dp unittests Co-authored-by: ver217 <lhx0217@gmail.com> Co-authored-by: Frank Lee <somerlee.9@gmail.com>
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
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import contextlib
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import copy
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import functools
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import os
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import traceback
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from collections import OrderedDict
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from enum import Enum, auto
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from typing import (Any, Callable, Dict, Generator, List, NamedTuple, Optional,
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Set, Union)
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.logging import get_dist_logger
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from colossalai.utils import get_current_device
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from torch.distributed import ProcessGroup
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from colossalai.engine.ophooks import register_ophooks_recursively, BaseOpHook, ShardParamHook
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from colossalai.zero.shard_param import ShardParam
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class ShardedModelV2(nn.Module):
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def __init__(self,
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module: nn.Module,
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process_group: Optional[ProcessGroup] = None,
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reduce_scatter_process_group: Optional[ProcessGroup] = None
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):
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r"""
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A demo to reconfigure zero1 shared_model.
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Currently do not consider the Optimizer States.
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"""
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super().__init__()
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self.logger = get_dist_logger()
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self.process_group = process_group or gpc.get_group(ParallelMode.DATA)
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self.reduce_scatter_process_group = reduce_scatter_process_group or self.process_group
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self.world_size = dist.get_world_size(self.process_group)
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self.rank = dist.get_rank(self.process_group)
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# The module has to be placed on GPU
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self.module = module.cuda()
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# Shard the parameters at first
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for _, param in self.module.named_parameters():
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param.ca_attr = ShardParam(param)
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param.ca_attr.shard()
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# Register hooks
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register_ophooks_recursively(self.module, [ShardParamHook()])
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def forward(self, *args: Any, **kwargs: Any) -> torch.Tensor:
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outputs = self.module(*args, **kwargs)
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return outputs
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def backward(self, loss):
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if self.loss_scaler:
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self.loss_scaler.backward(loss)
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else:
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loss.backward()
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