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ColossalAI/colossalai/zero/sharded_param/sharded_tensor.py

68 lines
2.0 KiB

import torch
import torch.distributed as dist
from colossalai.zero.sharded_model._zero3_utils import get_shard
from typing import Optional
class ShardedTensor(object):
def __init__(self, tensor: torch.Tensor, process_group: Optional[dist.ProcessGroup] = None) -> None:
r"""
A tensor sharded in multiple processes.
"""
self._payload = tensor
self.process_group = process_group
self.world_size = dist.get_world_size(self.process_group)
self.local_rank = dist.get_rank(self.process_group)
self._is_sharded = False
self._payload = tensor
self._origin_shape = tensor.shape
self._origin_numel = tensor.numel()
self._origin_dtype = tensor.dtype
@property
def is_sharded(self):
return self._is_sharded
@property
def payload(self):
return self._payload
@payload.setter
def payload(self, tensor):
self._payload.copy_(tensor)
@property
def dtype(self):
return self._origin_dtype
@property
def shape(self):
return self._payload.shape
def shard(self):
if self._is_sharded:
return
self._payload, _ = get_shard(self._payload, self.local_rank, self.world_size)
self._is_sharded = True
def gather(self):
if not self._is_sharded:
return
buffer_list = []
payload_numel = self._payload.numel()
for i in range(self.world_size):
if i == self.local_rank:
buffer_list.append(self._payload.cuda())
else:
buffer_list.append(torch.zeros(payload_numel).cuda())
torch.distributed.all_gather(buffer_list,
buffer_list[self.local_rank],
group=self.process_group,
async_op=False)
self._payload = torch.narrow(torch.cat(buffer_list), 0, 0, self._origin_numel).view(self._origin_shape)
self._is_sharded = False