mirror of https://github.com/hpcaitech/ColossalAI
60 lines
2.6 KiB
Python
60 lines
2.6 KiB
Python
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from typing import List, Optional
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import torch
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import torch.distributed as dist
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from colossalai.utils import get_current_device
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from colossalai.zero.legacy.gemini.tensor_utils import colo_model_data_tensor_move_inline
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from colossalai.zero.legacy.shard_utils import BaseShardStrategy
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from colossalai.zero.legacy.shard_utils.commons import get_shard
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from colossalai.zero.legacy.sharded_param.sharded_tensor import ShardedTensor
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class TensorShardStrategy(BaseShardStrategy):
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"""
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A naive implementation which shard each tensor evenly over all ranks
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"""
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def shard(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None):
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for t in tensor_list:
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self._shard_tensor(t, process_group)
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def gather(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None):
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for t in tensor_list:
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self._gather_tensor(t, process_group)
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def _shard_tensor(self, t: ShardedTensor, process_group: Optional[dist.ProcessGroup] = None):
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""" Shard tensor among processes.
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Args:
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t (ShardedTensor): a tensor to be sharded.
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process_group (Optional[dist.ProcessGroup], optional): the process group among which tensor shards.
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Defaults to None.
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"""
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if t.is_sharded:
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return
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if t.payload.device.type == 'cuda':
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assert t.payload.device == get_current_device(), f"shard tensor on cuda device index {t.payload.device.index},"\
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f" but current cuda device is {get_current_device()}"
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sharded_payload, _ = get_shard(t.payload, dist.get_rank(process_group), dist.get_world_size(process_group))
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t.payload_reset(sharded_payload)
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t.is_sharded = True
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def _gather_tensor(self, t: ShardedTensor, process_group: Optional[dist.ProcessGroup] = None):
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if not t.is_sharded:
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return
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target_device = t.device
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payload_numel = t.payload.numel()
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world_size = dist.get_world_size(process_group)
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rank = dist.get_rank(process_group)
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buffer = torch.empty(payload_numel * world_size, dtype=t.payload.dtype, device=get_current_device())
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buffer_list = list(torch.chunk(buffer, chunks=world_size, dim=0))
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buffer_list[rank].copy_(t.payload)
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dist.all_gather(buffer_list, buffer_list[rank], group=process_group, async_op=False)
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gathered_payload = torch.narrow(buffer, 0, 0, t.origin_numel).reshape(t.origin_shape)
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t.payload_reset(gathered_payload)
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colo_model_data_tensor_move_inline(t, target_device)
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t.is_sharded = False
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