mirror of https://github.com/hpcaitech/ColossalAI
133 lines
5.3 KiB
Python
133 lines
5.3 KiB
Python
import torch
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import functools
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from .memory_tracer.memstats_collector import MemStatsCollectorV2
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from typing import List, Optional, Tuple
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from time import time
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from colossalai.gemini.chunk import Chunk, ChunkManager
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from .placement_policy import PlacementPolicyFactory
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class GeminiManager:
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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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Args:
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placement_policy (str): Which device to place *held* tensors. It can be 'cpu', 'cuda' and 'auto'.
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If it's 'cpu', parameters, gradients and optimizer states will be offloaded to CPU, which means min CUDA memory will be used.
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If it's 'cuda', they won't be offloaded, which means max CUDA memory will be used.
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If it's 'auto', they are moving dynamically based on CPU and CUDA memory usage. It will utilize heterogeneous memory space evenly and well.
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Note that 'auto' policy can only work well when no other processes use CUDA during your training.
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chunk_manager (ChunkManager): A ``ChunkManager`` instance.
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"""
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def __init__(self, placement_policy: str, chunk_manager: ChunkManager) -> None:
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assert placement_policy in PlacementPolicyFactory.get_polocy_names()
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policy_cls = PlacementPolicyFactory.create(placement_policy)
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self._chunk_manager = chunk_manager
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self._mem_stats_collector = MemStatsCollectorV2(chunk_manager) if policy_cls.need_mem_stats else None
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self._placement_policy = policy_cls(chunk_manager, self._mem_stats_collector)
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self._compute_list: List[Tuple[Chunk, ...]] = []
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self._compute_idx: int = -1
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self._h2d_volume = 0
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self._d2h_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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self._comp_cuda_demand_time = 0
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def pre_iter(self):
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if self._mem_stats_collector and self._warmup:
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self._mem_stats_collector.start_collection()
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def post_iter(self):
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"""This function must be called when each iteration finishes
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"""
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if self._mem_stats_collector and self._warmup:
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self._mem_stats_collector.finish_collection()
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self._warmup = False
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self._compute_idx = -1
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self._h2d_volume = 0
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self._d2h_volume = 0
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self._layout_time = 0
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self._evict_time = 0
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self._comp_cuda_demand_time = 0
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def adjust_layout(self, chunks: Tuple[Chunk, ...]) -> None:
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""" Adjust the layout of statefuil 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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start = time()
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self._record_chunks_order(chunks)
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cuda_demand, hold_cuda_tensor_list = self._get_layout_info(self._compute_idx, self._warmup, chunks)
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self._layout_time += time() - start
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vol, evict_time = self._placement_policy.evict_tensors(can_evict_chunks=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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self._d2h_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._h2d_volume += cuda_demand
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@functools.lru_cache(maxsize=None)
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def _get_layout_info(self, compute_idx: int, warmup: bool, chunks: Tuple[Chunk, ...]):
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start = time()
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cuda_demand = 0
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for chunk in chunks:
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if chunk.device_type == 'cuda':
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if chunk.is_gathered:
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pass
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else:
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cuda_demand += chunk.chunk_mem - chunk.shard_mem
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elif chunk.device_type == 'cpu':
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cuda_demand += chunk.chunk_mem
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else:
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raise RuntimeError
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self._comp_cuda_demand_time += time() - start
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can_evict_chunks = self._chunk_manager.get_cuda_movable_chunks()
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return cuda_demand, can_evict_chunks
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def _record_chunks_order(self, chunks: Tuple[Chunk, ...]) -> None:
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self._compute_idx += 1
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if self._warmup and self._placement_policy.need_mem_stats:
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self._compute_list.append(chunks)
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@property
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def default_device(self):
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return self._placement_policy.get_default_device()
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def sample_overall_data(self):
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if self._mem_stats_collector:
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self._mem_stats_collector.sample_overall_data()
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def sample_model_data(self):
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if self._mem_stats_collector:
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self._mem_stats_collector.sample_model_data()
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@property
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def chunk_manager(self):
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return self._chunk_manager
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@property
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def cuda_margin_mem(self) -> Optional[float]:
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if self._mem_stats_collector:
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return self._mem_stats_collector.cuda_margin_mem
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return None
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@property
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def is_cuda_margin_mem_avail(self) -> bool:
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return self._placement_policy.need_mem_stats
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@staticmethod
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def get_default_device(policy_name: str) -> torch.device:
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return PlacementPolicyFactory.get_default_device(policy_name)
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