202 lines
7.3 KiB
Python
202 lines
7.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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from typing import Any, Optional, Union
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import torch
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from torch.distributed import ProcessGroup
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from vllm.distributed.utils import pickle
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from vllm.platforms import current_platform
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from vllm.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: Optional[torch.device] = None,
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device_group: Optional[ProcessGroup] = 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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if (current_platform.get_cpu_architecture()
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== CpuArchEnum.X86) and hasattr(
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torch.ops._C,
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"init_shm_manager") and (unique_name.startswith("tp")
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or unique_name.startswith("pp")):
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self.dist_module = _CPUSHMDistributed(self)
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def all_reduce(self, input_):
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self.dist_module.all_reduce(input_, group=self.device_group)
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return input_
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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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self.dist_module.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 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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self.dist_module.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 send_tensor_dict(
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self,
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tensor_dict: dict[str, Union[torch.Tensor, Any]],
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dst: int,
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) -> None:
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return self.dist_module.send_tensor_dict(tensor_dict, dst)
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def recv_tensor_dict(
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self,
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src: int,
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) -> dict[str, Union[torch.Tensor, Any]]:
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return self.dist_module.recv_tensor_dict(src)
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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 handle
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def all_reduce(self,
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input: torch.Tensor,
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group: Optional[ProcessGroup] = 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: Optional[list[torch.Tensor]],
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dst: int = -1,
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group: Optional[ProcessGroup] = 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,
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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: Optional[ProcessGroup] = None) -> None:
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torch.ops._C.shm_all_gather(self.handle, input, output)
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def send_tensor_dict(
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self,
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tensor_dict: dict[str, Union[torch.Tensor, Any]],
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dst: int,
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) -> None:
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key_list = list(tensor_dict.keys())
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value_list = list(tensor_dict.values())
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size_list = []
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for v in value_list:
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if not isinstance(v, torch.Tensor):
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raise RuntimeError(
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"CpuCommunicator only supports sending tensors.")
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size_list.append(v.size())
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key_size_tensor = torch.frombuffer(pickle.dumps([key_list, size_list]),
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dtype=torch.uint8)
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value_list.append(key_size_tensor)
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torch.ops._C.shm_send_tensor_list(self.handle, value_list, dst)
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return None
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def recv_tensor_dict(
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self,
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src: int,
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) -> dict[str, Union[torch.Tensor, Any]]:
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tensor_list = torch.ops._C.shm_recv_tensor_list(self.handle, src)
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value_list: list[torch.Tensor] = tensor_list[:-1]
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key_size_tensor = tensor_list[-1]
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key_size = pickle.loads(key_size_tensor.numpy().tobytes())
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key_list = key_size[0]
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size_list = key_size[1]
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assert len(key_list) == len(size_list)
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assert len(key_list) == len(value_list)
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tensor_dict: dict[str, torch.Tensor] = {}
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for key, size, t in zip(key_list, size_list, value_list):
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tensor_dict[key] = t.view(size)
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return tensor_dict
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