mirror of https://github.com/hpcaitech/ColossalAI
96 lines
3.9 KiB
Python
96 lines
3.9 KiB
Python
from typing import Optional
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import torch
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import torch.distributed as dist
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from colossalai.registry import OPHOOKS
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from colossalai.utils import get_current_device
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from colossalai.utils.memory_tracer.memstats_collector import MemStatsCollector
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from colossalai.zero.shard_utils import BaseShardStrategy
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from colossalai.zero.sharded_param.tensorful_state import TensorState
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from ._base_ophook import BaseOpHook
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from colossalai.utils.memory_utils.utils import \
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colo_model_data_tensor_move_inline
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@OPHOOKS.register_module
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class ZeroHook(BaseOpHook):
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"""
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A hook to process sharded param for ZeRO method.
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"""
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def __init__(self,
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shard_strategy: BaseShardStrategy,
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memstarts_collector: Optional[MemStatsCollector],
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process_group: Optional[dist.ProcessGroup] = None):
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super().__init__()
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self.shard_strategy = shard_strategy
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self.process_group = process_group
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# NOTE(jiaruifang) Now the computing device of FWD and BWD is always on GPU
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self.computing_device = torch.device(f'cuda:{get_current_device()}')
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self._memstarts_collector = memstarts_collector
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def pre_fwd_exec(self, module: torch.nn.Module, *args):
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tensor_list = []
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for param in module.parameters(recurse=False):
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assert hasattr(param, 'col_attr')
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tensor_list.append(param.col_attr.sharded_data_tensor)
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self.shard_strategy.gather(tensor_list, self.process_group)
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for param in module.parameters(recurse=False):
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colo_model_data_tensor_move_inline(param.col_attr.sharded_data_tensor, self.computing_device)
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param.data = param.col_attr.sharded_data_tensor.payload
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if self._memstarts_collector:
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self._memstarts_collector.sample_memstats()
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for param in module.parameters(recurse=False):
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param.col_attr.sharded_data_tensor.trans_state(TensorState.COMPUTE)
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def post_fwd_exec(self, module: torch.nn.Module, *args):
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for param in module.parameters(recurse=False):
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param.col_attr.sharded_data_tensor.trans_state(TensorState.HOLD_AFTER_FWD)
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tensor_list = []
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for param in module.parameters(recurse=False):
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assert hasattr(param, 'col_attr')
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tensor_list.append(param.col_attr.sharded_data_tensor)
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self.shard_strategy.shard(tensor_list, self.process_group)
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for param in module.parameters(recurse=False):
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param.col_attr.remove_torch_payload()
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def pre_bwd_exec(self, module: torch.nn.Module, input, output):
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tensor_list = []
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for param in module.parameters(recurse=False):
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assert hasattr(param, 'col_attr')
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tensor_list.append(param.col_attr.sharded_data_tensor)
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self.shard_strategy.gather(tensor_list, self.process_group)
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for param in module.parameters(recurse=False):
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colo_model_data_tensor_move_inline(param.col_attr.sharded_data_tensor, self.computing_device)
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param.data = param.col_attr.sharded_data_tensor.payload
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if self._memstarts_collector:
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self._memstarts_collector.sample_memstats()
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for param in module.parameters(recurse=False):
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param.col_attr.sharded_data_tensor.trans_state(TensorState.COMPUTE)
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def post_bwd_exec(self, module: torch.nn.Module, input):
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for param in module.parameters(recurse=False):
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param.col_attr.sharded_data_tensor.trans_state(TensorState.HOLD_AFTER_BWD)
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tensor_list = []
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for param in module.parameters(recurse=False):
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assert hasattr(param, 'col_attr')
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tensor_list.append(param.col_attr.sharded_data_tensor)
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self.shard_strategy.shard(tensor_list, self.process_group)
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for param in module.parameters(recurse=False):
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param.col_attr.remove_torch_payload()
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def pre_iter(self):
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pass
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def post_iter(self):
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pass
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