ColossalAI/colossalai/zero/shard_utils/tensor_shard_strategy.py

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from typing import List, Optional
import torch
import torch.distributed as dist
from colossalai.zero.shard_utils import BaseShardStrategy
from colossalai.zero.sharded_model._zero3_utils import get_shard
from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
class TensorShardStrategy(BaseShardStrategy):
def __init__(self, process_group: Optional[dist.ProcessGroup] = None) -> None:
super().__init__(process_group)
def shard(self, tensor_list: List[ShardedTensor]):
for t in tensor_list:
self._shard_tensor(t)
def gather(self, tensor_list: List[ShardedTensor]):
for t in tensor_list:
self._gather_tensor(t)
def _shard_tensor(self, t: ShardedTensor):
if t.is_sharded:
return
sharded_payload, _ = get_shard(t.payload, self.local_rank, self.world_size)
t.reset_payload(sharded_payload)
t.is_sharded = True
def _gather_tensor(self, t: ShardedTensor):
if not t.is_sharded:
return
target_device = t.device
buffer_list = []
payload_numel = t.payload.numel()
for i in range(self.world_size):
if i == self.local_rank:
buffer_list.append(t.payload.cuda())
else:
buffer_list.append(torch.zeros(payload_numel, dtype=t.dtype).cuda())
torch.distributed.all_gather(buffer_list,
buffer_list[self.local_rank],
group=self.process_group,
async_op=False)
gathered_payload = torch.narrow(torch.cat(buffer_list), 0, 0, t.origin_numel).reshape(t.origin_shape)
t.reset_payload(gathered_payload)
t.to(target_device)
t.is_sharded = False