mirror of
https://github.com/vllm-project/vllm.git
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26a7a33b88
Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com> Signed-off-by: simon-mo <simon.mo@hey.com>
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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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import vllm.envs as envs
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from vllm.logger import init_logger
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from .base_device_communicator import DeviceCommunicatorBase
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logger = init_logger(__name__)
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class XpuCommunicator(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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if self.use_all2all:
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all2all_backend = envs.VLLM_ALL2ALL_BACKEND
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if all2all_backend != "naive":
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logger.warning(
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"`%s` all2all manager is not supported on XPU."
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"Falling back to `naive` all2all manager for XPU.",
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all2all_backend)
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all2all_backend = "naive"
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if all2all_backend == "naive":
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from .all2all import NaiveAll2AllManager
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self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
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logger.info("Using naive all2all manager.")
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def all_reduce(self, input_) -> 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 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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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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# For xpu path, gather doesn't work properly together with ray
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# cluster so we use all_gather instead for now.
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input_size = input_.size()
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# Allocate output tensor.
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output_tensor = torch.empty((self.world_size, ) + input_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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if self.rank_in_group == dst:
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# Reshape
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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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else:
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output_tensor = None
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return output_tensor
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def broadcast(self, input_: torch.Tensor, src: int = 0) -> None:
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dist.broadcast(input_, src=src, group=self.device_group)
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def dispatch(
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self,
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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is_sequence_parallel: bool = False
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) -> tuple[torch.Tensor, torch.Tensor]:
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assert self.all2all_manager is not None
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hidden_states, router_logits = self.all2all_manager.dispatch(
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hidden_states, router_logits, is_sequence_parallel)
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return hidden_states, router_logits
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def combine(self,
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hidden_states: torch.Tensor,
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is_sequence_parallel: bool = False) -> torch.Tensor:
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assert self.all2all_manager is not None
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hidden_states = self.all2all_manager.combine(hidden_states,
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is_sequence_parallel)
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return hidden_states
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