mirror of https://github.com/hpcaitech/ColossalAI
71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
import torch
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import torch.distributed as dist
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.utils import get_current_device
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def send_tensor_meta(tensor, need_meta=True, next_rank=None):
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"""Sends tensor meta information before sending a specific tensor.
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Since the recipient must know the shape of the tensor in p2p communications,
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meta information of the tensor should be sent before communications. This function
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synchronizes with :func:`recv_tensor_meta`.
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:param tensor: Tensor to be sent
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:param need_meta: If False, meta information won't be sent
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:param next_rank: The rank of the next member in pipeline parallel group
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:type tensor: Tensor
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:type need_meta: bool, optional
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:type next_rank: int
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:return: False
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:rtype: bool
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"""
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if need_meta:
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if next_rank is None:
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next_rank = gpc.get_next_global_rank(ParallelMode.PIPELINE)
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tensor_kwargs = {'dtype': torch.long, 'device': get_current_device()}
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send_shape = torch.tensor(tensor.size(), **tensor_kwargs)
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send_ndims = torch.tensor(len(tensor.size()), **tensor_kwargs)
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ops = [
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dist.P2POp(dist.isend, send_ndims, next_rank),
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dist.P2POp(dist.isend, send_shape, next_rank)
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]
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reqs = dist.batch_isend_irecv(ops)
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for req in reqs:
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req.wait()
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torch.cuda.synchronize()
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return False
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def recv_tensor_meta(tensor_shape, prev_rank=None):
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"""Recieves tensor meta information before recieving a specific tensor.
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Since the recipient must know the shape of the tensor in p2p communications,
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meta information of the tensor should be recieved before communications. This function
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synchronizes with :func:`send_tensor_meta`.
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:param tensor_shape: The shape of the tensor to be recieved
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:param prev_rank: The rank of the source of the tensor
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:type tensor_shape: torch.Size
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:type prev_rank: int, optional
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:return: The shape of the tensor to be recieved
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:rtype: torch.Size
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"""
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if tensor_shape is None:
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if prev_rank is None:
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prev_rank = gpc.get_prev_global_rank(ParallelMode.PIPELINE)
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tensor_kwargs = {'dtype': torch.long, 'device': get_current_device()}
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recv_ndims = torch.empty((), **tensor_kwargs)
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dist.recv(recv_ndims, prev_rank)
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recv_shape = torch.empty(recv_ndims, **tensor_kwargs)
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dist.recv(recv_shape, prev_rank)
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tensor_shape = torch.Size(recv_shape)
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return tensor_shape
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