120 lines
4.8 KiB
Python
120 lines
4.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/distributed/device_communicators/base_device_communicator.py
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from typing import Optional
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import torch
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import torch.distributed as dist
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from torch.distributed import ProcessGroup
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class DeviceCommunicatorBase:
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"""
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Base class for device-specific communicator.
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It can use the `cpu_group` to initialize the communicator.
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If the device has PyTorch integration (PyTorch can recognize its
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communication backend), the `device_group` will also be given.
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"""
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def __init__(self,
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cpu_group: ProcessGroup,
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device: Optional[torch.device] = None,
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device_group: Optional[ProcessGroup] = None,
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unique_name: str = ""):
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self.device = device or torch.device("cpu")
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self.cpu_group = cpu_group
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self.device_group = device_group
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self.unique_name = unique_name
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self.rank = dist.get_rank(cpu_group)
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self.world_size = dist.get_world_size(cpu_group)
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self.ranks = dist.get_process_group_ranks(cpu_group)
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self.global_rank = dist.get_rank()
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self.global_world_size = dist.get_world_size()
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self.rank_in_group = dist.get_group_rank(self.cpu_group,
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self.global_rank)
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def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
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dist.all_reduce(input_, group=self.device_group)
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return input_
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def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
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if dim < 0:
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# Convert negative dim to positive.
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dim += input_.dim()
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input_size = input_.size()
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# NOTE: we have to use concat-style all-gather here,
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# stack-style all-gather has compatibility issues with
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# torch.compile . see https://github.com/pytorch/pytorch/issues/138795
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output_size = (input_size[0] * self.world_size, ) + input_size[1:]
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# Allocate output tensor.
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output_tensor = torch.empty(output_size,
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dtype=input_.dtype,
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device=input_.device)
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# All-gather.
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dist.all_gather_into_tensor(output_tensor,
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input_,
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group=self.device_group)
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# Reshape
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output_tensor = output_tensor.reshape((self.world_size, ) + input_size)
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output_tensor = output_tensor.movedim(0, dim)
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output_tensor = output_tensor.reshape(input_size[:dim] +
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(self.world_size *
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input_size[dim], ) +
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input_size[dim + 1:])
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return output_tensor
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def gather(self,
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input_: torch.Tensor,
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dst: int = 0,
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dim: int = -1) -> Optional[torch.Tensor]:
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"""
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NOTE: We assume that the input tensor is on the same device across
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all the ranks.
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NOTE: `dst` is the local rank of the destination rank.
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"""
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world_size = self.world_size
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assert -input_.dim() <= dim < input_.dim(), (
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f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
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if dim < 0:
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# Convert negative dim to positive.
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dim += input_.dim()
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# Allocate output tensor.
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if self.rank_in_group == dst:
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gather_list = [torch.empty_like(input_) for _ in range(world_size)]
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else:
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gather_list = None
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# Gather.
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torch.distributed.gather(input_,
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gather_list,
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dst=self.ranks[dst],
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group=self.device_group)
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if self.rank_in_group == dst:
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output_tensor = torch.cat(gather_list, dim=dim)
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else:
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output_tensor = None
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return output_tensor
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def send(self, tensor: torch.Tensor, dst: Optional[int] = None) -> None:
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"""Sends a tensor to the destination rank in a non-blocking way"""
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"""NOTE: `dst` is the local rank of the destination rank."""
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if dst is None:
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dst = (self.rank_in_group + 1) % self.world_size
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torch.distributed.send(tensor, self.ranks[dst], self.device_group)
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def recv(self,
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size: torch.Size,
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dtype: torch.dtype,
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src: Optional[int] = None) -> torch.Tensor:
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"""Receives a tensor from the source rank."""
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"""NOTE: `src` is the local rank of the source rank."""
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if src is None:
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src = (self.rank_in_group - 1) % self.world_size
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tensor = torch.empty(size, dtype=dtype, device=self.device)
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torch.distributed.recv(tensor, self.ranks[src], self.device_group)
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return tensor
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def destroy(self):
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pass
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