mirror of https://github.com/hpcaitech/ColossalAI
120 lines
4.6 KiB
Python
120 lines
4.6 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.logging import get_dist_logger
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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.zero.shard_utils import BaseShardStrategy
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from colossalai.engine.ophooks import BaseOpHook
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from colossalai.gemini.stateful_tensor_mgr import StatefulTensorMgr
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from colossalai.gemini.memory_tracer import MemStatsCollector
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from colossalai.gemini.stateful_tensor import TensorState
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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] = None,
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stateful_tensor_mgr: Optional[StatefulTensorMgr] = None,
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process_group: Optional[dist.ProcessGroup] = None):
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super().__init__()
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self.logger = get_dist_logger("ZeROHook")
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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 = get_current_device()
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self._memstarts_collector = memstarts_collector
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self._stateful_tensor_mgr = stateful_tensor_mgr
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def gather_parameters(self, module: torch.nn.Module):
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# gather sharded parameters
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if module.param_is_sharded:
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tensor_list = []
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for param in module.parameters(recurse=False):
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assert hasattr(param, 'colo_attr')
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tensor_list.append(param.colo_attr.sharded_data_tensor)
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self.shard_strategy.gather(tensor_list, self.process_group)
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def shard_parameters(self, module: torch.nn.Module):
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# shard gathered parameters
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if module.param_is_sharded:
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tensor_list = []
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for param in module.parameters(recurse=False):
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assert hasattr(param, 'colo_attr')
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tensor_list.append(param.colo_attr.sharded_data_tensor)
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self.shard_strategy.shard(tensor_list, self.process_group)
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def adjust_module_data(self, module: torch.nn.Module):
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# record overall data statistics
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if self._memstarts_collector:
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self._memstarts_collector.sample_overall_data()
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for param in module.parameters(recurse=False):
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param.colo_attr.sharded_data_tensor.trans_state(TensorState.COMPUTE)
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# adjust stateful tensor to get enough CUDA memory
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self._stateful_tensor_mgr.adjust_layout()
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# record model data statistics
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if self._memstarts_collector:
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self._memstarts_collector.sample_model_data()
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def pre_fwd_exec(self, module: torch.nn.Module, *args):
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self.adjust_module_data(module)
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self.gather_parameters(module)
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for param in module.parameters(recurse=False):
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param.data = param.colo_attr.data_payload
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assert param.data.device.type == 'cuda', f"PRE FWD param.data must be on CUDA"
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def post_fwd_exec(self, module: torch.nn.Module, *args):
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# change tensor state to HOLD_AFTER_FWD
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for param in module.parameters(recurse=False):
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param.colo_attr.sharded_data_tensor.trans_state(TensorState.HOLD_AFTER_FWD)
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self.shard_parameters(module)
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# remove torch payload
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for param in module.parameters(recurse=False):
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param.colo_attr.set_data_none()
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def pre_bwd_exec(self, module: torch.nn.Module, input, output):
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self.adjust_module_data(module)
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self.gather_parameters(module)
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for param in module.parameters(recurse=False):
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param.data = param.colo_attr.data_payload
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assert param.data.device.type == 'cuda', f"PRE BWD param.data must be on CUDA"
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def post_bwd_exec(self, module: torch.nn.Module, input):
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# change tensor state to HOLD_AFTER_BWD
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for param in module.parameters(recurse=False):
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param.colo_attr.sharded_data_tensor.trans_state(TensorState.HOLD_AFTER_BWD)
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self.shard_parameters(module)
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# remove torch payload
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for param in module.parameters(recurse=False):
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param.colo_attr.set_data_none()
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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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if self._stateful_tensor_mgr:
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self.logger.info(
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f"CPU-GPU data moving this iteration {self._stateful_tensor_mgr.cpu_gpu_move_volume/1e9} GB, get layout info time: {self._stateful_tensor_mgr._layout_time}, evict cpu time: {self._stateful_tensor_mgr._evict_time}",
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ranks=[0])
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self._stateful_tensor_mgr.finish_iter()
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