mirror of https://github.com/hpcaitech/ColossalAI
167 lines
6.1 KiB
Python
167 lines
6.1 KiB
Python
import functools
|
|
from time import time
|
|
from typing import Dict, List, Optional, Tuple
|
|
|
|
import torch
|
|
|
|
from .chunk import Chunk, ChunkManager
|
|
from .memory_tracer import ChunkMemStatsCollector, MemStats
|
|
from .placement_policy import PlacementPolicyFactory
|
|
|
|
|
|
class GeminiManager:
|
|
"""
|
|
Stateful Tensor Manager, inspired from PatrickStar
|
|
|
|
PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
|
|
https://arxiv.org/abs/2108.05818
|
|
|
|
Args:
|
|
placement_policy (str): Which device to place *held* tensors. It can be 'static' and 'auto'.
|
|
If it's 'auto', they are moving dynamically based on CPU and CUDA memory usage. It will utilize heterogeneous memory space evenly and well.
|
|
Note that 'auto' policy can only work well when no other processes use CUDA during your training.
|
|
chunk_manager (ChunkManager): A ``ChunkManager`` instance.
|
|
memstats (MemStats, optional): a mem stats collected by a runtime mem tracer. if None then GeminiManager will collect it during a warmup iteration.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
placement_policy: str,
|
|
chunk_manager: ChunkManager,
|
|
memstats: Optional[MemStats] = None,
|
|
**placement_kwargs,
|
|
) -> None:
|
|
assert placement_policy in PlacementPolicyFactory.get_policy_names()
|
|
self.policy_name = placement_policy
|
|
policy_cls = PlacementPolicyFactory.create(placement_policy)
|
|
self._chunk_manager = chunk_manager
|
|
|
|
self._premade_memstats_ = memstats is not None
|
|
self._memstats = memstats
|
|
self._mem_stats_collector = (
|
|
ChunkMemStatsCollector(chunk_manager, self._memstats) if policy_cls.need_mem_stats else None
|
|
)
|
|
self._placement_policy = policy_cls(chunk_manager, self._mem_stats_collector, **placement_kwargs)
|
|
self._compute_list: List[Tuple[Chunk, ...]] = []
|
|
self._compute_idx: int = -1
|
|
|
|
self._h2d_volume = 0
|
|
self._d2h_volume = 0
|
|
self._layout_time = 0
|
|
self._evict_time = 0
|
|
self._warmup = True
|
|
self._comp_cuda_demand_time = 0
|
|
|
|
def reset_attributes(self):
|
|
self._compute_idx = -1
|
|
self._h2d_volume = 0
|
|
self._d2h_volume = 0
|
|
self._layout_time = 0
|
|
self._evict_time = 0
|
|
self._comp_cuda_demand_time = 0
|
|
|
|
@property
|
|
def need_warmup(self) -> bool:
|
|
return self.policy_name in ("auto", "const")
|
|
|
|
def is_warmup(self):
|
|
return self._warmup
|
|
|
|
def memstats(self):
|
|
"""memstats
|
|
|
|
get the memory statistics during training.
|
|
The stats could be collected by a runtime memory tracer, or collected by the GeminiManager.
|
|
Note, for the latter, you can not access the memstats before warmup iteration finishes.
|
|
"""
|
|
if self._premade_memstats_:
|
|
return self._memstats
|
|
else:
|
|
assert not self._warmup, "Gemini Manager has memstats after warm up! Now is during warmup."
|
|
return self._mem_stats_collector._memstats
|
|
|
|
def pre_iter(self, *args):
|
|
if self._mem_stats_collector and self._warmup:
|
|
self._mem_stats_collector.start_collection()
|
|
|
|
def post_iter(self):
|
|
"""This function must be called when each iteration finishes"""
|
|
if self._mem_stats_collector and self._warmup:
|
|
self._mem_stats_collector.finish_collection()
|
|
self._warmup = False
|
|
self.reset_attributes()
|
|
|
|
def adjust_layout(self, chunks: Tuple[Chunk, ...]) -> None:
|
|
"""Adjust the layout of stateful tensors according to the information provided
|
|
by mem_stats_collector, which should belongs to a Sharded Model.
|
|
"""
|
|
# find stateful tensor in state COMPUTE
|
|
start = time()
|
|
self._record_chunks_order(chunks)
|
|
cuda_demand, hold_cuda_tensor_list = self._get_layout_info(self._compute_idx, self._warmup, chunks)
|
|
self._layout_time += time() - start
|
|
|
|
vol, evict_time = self._placement_policy.evict_tensors(
|
|
can_evict_chunks=hold_cuda_tensor_list,
|
|
cuda_demand=cuda_demand,
|
|
warmup=self._warmup,
|
|
compute_list=self._compute_list,
|
|
compute_idx=self._compute_idx,
|
|
)
|
|
|
|
self._d2h_volume += vol
|
|
self._evict_time += evict_time
|
|
# move COMPUTE tensors to CUDA
|
|
self._h2d_volume += cuda_demand
|
|
|
|
@functools.lru_cache(maxsize=None)
|
|
def _get_layout_info(self, compute_idx: int, warmup: bool, chunks: Tuple[Chunk, ...]):
|
|
start = time()
|
|
cuda_demand = 0
|
|
for chunk in chunks:
|
|
if chunk.device_type == "cuda" or chunk.device_type == "npu":
|
|
if chunk.is_gathered:
|
|
pass
|
|
else:
|
|
cuda_demand += chunk.chunk_mem - chunk.shard_mem
|
|
elif chunk.device_type == "cpu":
|
|
cuda_demand += chunk.chunk_mem
|
|
else:
|
|
raise RuntimeError
|
|
self._comp_cuda_demand_time += time() - start
|
|
|
|
can_evict_chunks = self._chunk_manager.get_cuda_movable_chunks()
|
|
return cuda_demand, can_evict_chunks
|
|
|
|
def _record_chunks_order(self, chunks: Tuple[Chunk, ...]) -> None:
|
|
self._compute_idx += 1
|
|
if self._warmup and self._placement_policy.need_mem_stats:
|
|
self._compute_list.append(chunks)
|
|
|
|
def sample_overall_data(self):
|
|
if self._mem_stats_collector:
|
|
self._mem_stats_collector.sample_overall_data()
|
|
|
|
def record_model_data_volume(self):
|
|
if self._mem_stats_collector:
|
|
self._mem_stats_collector.record_model_data_volume()
|
|
|
|
@property
|
|
def chunk_manager(self):
|
|
return self._chunk_manager
|
|
|
|
@property
|
|
def cuda_margin_mem(self) -> Optional[float]:
|
|
if self._mem_stats_collector:
|
|
return self._mem_stats_collector.cuda_margin_mem
|
|
return None
|
|
|
|
@property
|
|
def is_cuda_margin_mem_avail(self) -> bool:
|
|
return self._placement_policy.need_mem_stats
|
|
|
|
def setup_grads_device(
|
|
self, params: List[torch.Tensor], grads_device_map: Dict[torch.Tensor, torch.device]
|
|
) -> None:
|
|
self._placement_policy.setup_grads_device(params, grads_device_map)
|