mirror of https://github.com/hpcaitech/ColossalAI
102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
import functools
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import types
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from time import time
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from typing import List
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from colossalai.utils.device import get_current_device
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from .stateful_tensor import StatefulTensor, TensorState
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from .tensor_placement_policy import TensorPlacementPolicy
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from .tensor_utils import colo_model_data_tensor_move_inline
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class StatefulTensorMgr(object):
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"""
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Stateful Tensor Manager, inspired from PatrickStar
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PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
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https://arxiv.org/abs/2108.05818
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"""
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def __init__(self, tensor_placement_policy: TensorPlacementPolicy) -> None:
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self._tensor_placement_policy: TensorPlacementPolicy = tensor_placement_policy
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self._stateful_tensor_list: List[StatefulTensor] = []
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self._compute_list: List[StatefulTensor] = []
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self._compute_idx: int = -1
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self._cpu_gpu_move_volume = 0
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self._layout_time = 0
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self._evict_time = 0
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self._warmup = True
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def register_stateful_tensor_list(self, tensor_list: List[StatefulTensor]) -> None:
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assert self._stateful_tensor_list == [], "Can't register stateful tensors for manager twice"
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self._stateful_tensor_list = tensor_list
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for t in self._stateful_tensor_list:
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assert isinstance(t, StatefulTensor)
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t.trans_state = types.MethodType(functools.partial(self._trans_state, t.trans_state), t)
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def start_iter(self):
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pass
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def finish_iter(self):
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"""This function must be called when each iteration finishes"""
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self._warmup = False
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self._compute_idx = -1
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self._cpu_gpu_move_volume = 0
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self._layout_time = 0
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self._evict_time = 0
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def adjust_layout(self) -> None:
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"""Adjust the layout of stateful tensor according to the information provided
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by mem_stats_collector, which should belongs to a Sharded Model.
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"""
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# find stateful tensor in state COMPUTE
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cuda_demand = StatefulTensor.GST_MGR.state_mem["cpu"][TensorState.COMPUTE]
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start = time()
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move_to_cuda_tensor_list, hold_cuda_tensor_list = self._get_layout_info(self._compute_idx, self._warmup)
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self._layout_time += time() - start
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vol, evict_time = self._tensor_placement_policy.evict_tensors(
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hold_cuda_tensor_list,
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cuda_demand=cuda_demand,
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warmup=self._warmup,
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compute_list=self._compute_list,
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compute_idx=self._compute_idx,
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)
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self._cpu_gpu_move_volume += vol
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self._evict_time += evict_time
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# move COMPUTE tensors to CUDA
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self._cpu_gpu_move_volume += cuda_demand
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for t in move_to_cuda_tensor_list:
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colo_model_data_tensor_move_inline(t, get_current_device())
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@property
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def cpu_gpu_move_volume(self):
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return self._cpu_gpu_move_volume
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def _trans_state(self, trans_state_func, stateful_tensor, state):
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trans_state_func(state)
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if state == TensorState.COMPUTE:
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self._compute_idx += 1
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if self._warmup:
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self._compute_list.append(stateful_tensor)
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@functools.lru_cache(maxsize=None)
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def _get_layout_info(self, compute_idx: int, warmup: bool):
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move_to_cuda_tensor_list = []
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hold_cuda_tensor_list = []
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for tensor in self._stateful_tensor_list:
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if tensor.state == TensorState.FREE:
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continue
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if tensor.device.type == "cuda":
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if tensor.state in [TensorState.HOLD, TensorState.HOLD_AFTER_BWD, TensorState.HOLD_AFTER_FWD]:
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hold_cuda_tensor_list.append(tensor)
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elif tensor.device.type == "cpu":
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if tensor.state == TensorState.COMPUTE:
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move_to_cuda_tensor_list.append(tensor)
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else:
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raise RuntimeError
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return move_to_cuda_tensor_list, hold_cuda_tensor_list
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