mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-08-23 10:11:37 +00:00
554aa9592e
* [legacy] move communication to legacy (#4640) * [legacy] refactor logger and clean up legacy codes (#4654) * [legacy] make logger independent to gpc * [legacy] make optim independent to registry * [legacy] move test engine to legacy * [legacy] move nn to legacy (#4656) * [legacy] move nn to legacy * [checkpointio] fix save hf config * [test] remove useledd rpc pp test * [legacy] fix nn init * [example] skip tutorial hybriad parallel example * [devops] test doc check * [devops] test doc check
117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the BSD license found in the
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# LICENSE file in the root directory of this source tree.
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import functools
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from typing import Callable, Dict, List, Optional, Tuple
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import torch
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import torch.distributed as dist
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from torch import Tensor
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from torch.distributed import ProcessGroup
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class Bucket:
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def __init__(self, size: int, dtype: torch.dtype, device: torch.device, group: ProcessGroup):
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self.buffer = torch.zeros(size, dtype=dtype, device=device)
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self.group = group
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self.offset = 0
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self.callbacks: List[Callable] = []
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def flush(self) -> None:
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"""Flush content of the bucket."""
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if self.offset == 0:
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assert len(self.callbacks) == 0
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return
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# reduce-scatter bucket
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dist.all_reduce(self.buffer[:self.offset], group=self.group)
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# execute post-reduction callbacks
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for callback_fn in self.callbacks:
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callback_fn()
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# reuse input bucket but allocate a fresh output shard
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self.offset = 0
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self.callbacks.clear()
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self.buffer = torch.zeros_like(self.buffer)
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def alloc(self) -> None:
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if self.buffer.storage().size() == 0:
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self.buffer.storage().resize_(self.buffer.numel())
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def free(self) -> None:
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assert self.offset == 0 and self.callbacks == [], "Incorrect call of teardown"
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self.buffer.storage().resize_(0)
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def append(self, tensor: Tensor, callback_fn: Callable):
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tensor_size = tensor.numel()
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offset = self.offset
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self.buffer[offset:offset + tensor_size].copy_(tensor.flatten())
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self.offset += tensor_size
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# callback will be given the reduced result
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if callback_fn is not None:
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result_view = self.buffer[offset:offset + tensor_size].view(tensor.shape)
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self.callbacks.append(functools.partial(callback_fn, result_view))
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@property
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def avail_size(self) -> int:
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return self.buffer.size(0) - self.offset
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class Reducer:
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def __init__(self, bucket_size_mb: int = 25):
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self.bucket_size_mb = bucket_size_mb
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self.buckets: Dict[Tuple[torch.dtype, torch.device, ProcessGroup], Bucket] = {}
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@torch.no_grad()
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def all_reduce_async(
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self,
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tensor: Tensor,
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group: ProcessGroup,
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callback_fn: Optional[Callable] = None,
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) -> None:
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bucket_size = self._get_bucket_size(tensor.element_size())
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if tensor.numel() >= bucket_size:
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dist.all_reduce(tensor, group=group)
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if callback_fn is not None:
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callback_fn(tensor)
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return
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bucket = self._get_bucket(tensor, group)
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if tensor.numel() > bucket.avail_size:
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# not enough space remaining in bucket, flush it now
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bucket.flush()
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bucket.append(tensor, callback_fn)
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@torch.no_grad()
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def flush(self) -> None:
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for bucket in self.buckets.values():
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bucket.flush()
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@torch.no_grad()
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def free(self) -> None:
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for bucket in self.buckets.values():
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bucket.free()
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@functools.lru_cache()
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def _get_bucket_size(self, element_size: int) -> int:
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if self.bucket_size_mb <= 0: # Values <= 0 disable bucketing.
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return 0
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MB = 1024 * 1024
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bucket_size = self.bucket_size_mb * MB / element_size
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return int(bucket_size)
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def _get_bucket(self, tensor: Tensor, group: ProcessGroup) -> Bucket:
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key = (tensor.dtype, tensor.device, group)
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if key not in self.buckets:
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bucket_size = self._get_bucket_size(tensor.element_size())
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self.buckets[key] = Bucket(bucket_size, tensor.dtype, tensor.device, group)
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self.buckets[key].alloc()
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return self.buckets[key]
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