124 lines
5.0 KiB
Python
124 lines
5.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from: https://github.com/vllm-project/vllm/blob/main/vllm/distributed/device_communicators/cpu_communicator.py
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import os
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import torch
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from torch.distributed import ProcessGroup
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from fastvideo.platforms.interface import CpuArchEnum
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from .base_device_communicator import DeviceCommunicatorBase
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class CpuCommunicator(DeviceCommunicatorBase):
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def __init__(self,
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cpu_group: ProcessGroup,
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device: torch.device | None = None,
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device_group: ProcessGroup | None = None,
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unique_name: str = ""):
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super().__init__(cpu_group, device, device_group, unique_name)
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self.dist_module = torch.distributed
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from fastvideo.platforms import current_platform
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if (current_platform.get_cpu_architecture() == CpuArchEnum.X86) and hasattr(
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torch.ops._C, "init_shm_manager") and unique_name.startswith("tp"):
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self.dist_module = _CPUSHMDistributed(self)
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def all_reduce(self,
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input_: torch.Tensor,
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op: torch.distributed.ReduceOp | None = torch.distributed.ReduceOp.SUM) -> torch.Tensor:
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self.dist_module.all_reduce(input_, group=self.device_group, op=op)
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return input_
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def gather(self, input_: torch.Tensor, dst: int = 0, dim: int = -1) -> torch.Tensor | None:
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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(), (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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gather_list = [torch.empty_like(input_) for _ in range(world_size)] if self.rank_in_group == dst else None
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# Gather.
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self.dist_module.gather(input_, gather_list, dst=self.ranks[dst], group=self.device_group)
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output_tensor = torch.cat(gather_list, dim=dim) if self.rank_in_group == dst else None
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return output_tensor
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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, dtype=input_.dtype, device=input_.device)
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# All-gather.
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self.dist_module.all_gather_into_tensor(output_tensor, input_, 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] + (self.world_size * input_size[dim], ) +
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input_size[dim + 1:])
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return output_tensor
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class _CPUSHMDistributed:
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def __init__(self, communicator: CpuCommunicator):
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instance_identifier = os.environ["VLLM_DIST_IDENT"]
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unique_name = communicator.unique_name
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instance_identifier = f"{instance_identifier}-{unique_name}"
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self.communicator = communicator
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group_ranks = [str(rank) for rank in self.communicator.ranks]
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shm_group_identifier = f"[{'-'.join(group_ranks)}]"
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self.group_name = f"{instance_identifier}-{shm_group_identifier}-cpushm"
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self.handle = self._init_cpu_shm()
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def _init_cpu_shm(self) -> int:
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handle = torch.ops._C.init_shm_manager(
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self.group_name,
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self.communicator.world_size,
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self.communicator.rank,
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)
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torch.distributed.barrier(self.communicator.device_group)
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torch.ops._C.join_shm_manager(
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handle,
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self.group_name,
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)
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torch.distributed.barrier(self.communicator.device_group)
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return int(handle)
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def all_reduce(self, input: torch.Tensor, group: ProcessGroup | None = None) -> None:
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torch.ops._C.shm_allreduce(self.handle, input)
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def gather(self,
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input: torch.Tensor,
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gather_list: list[torch.Tensor] | None,
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dst: int = -1,
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group: ProcessGroup | None = None) -> None:
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# Note: different from the torch gather, here we use local dst rank.
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torch.ops._C.shm_gather(self.handle, input, gather_list, torch.distributed.get_group_rank(group, dst))
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def all_gather_into_tensor(self,
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output: torch.Tensor,
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input: torch.Tensor,
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group: ProcessGroup | None = None) -> None:
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torch.ops._C.shm_all_gather(self.handle, input, output)
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