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[V0 deprecation] Remove V0 HPU backend (#21131)

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This commit is contained in:
Woosuk Kwon
2025-07-17 16:37:36 -07:00
committed by GitHub
parent ac9fb732a5
commit 4de7146351
27 changed files with 10 additions and 3926 deletions
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46
vllm/distributed/device_communicators/hpu_communicator.py
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@@ -1,46 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import torch
import torch.distributed as dist
from vllm.platforms import current_platform
from .base_device_communicator import DeviceCommunicatorBase
if current_platform.is_hpu():
import habana_frameworks.torch as htorch # noqa: F401
class HpuCommunicator(DeviceCommunicatorBase):
def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
# FIXME(kzawora): this is a workaround for a bug in Habana PT bridge
# occurring when PT_HPU_ENABLE_LAZY_COLLECTIVES=true env var is used
# (which is required for tensor parallel HPUGraph inference)
htorch.core.mark_step()
dist.all_reduce(input_, group=self.device_group)
return input_
def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
world_size = self.world_size
if dim < 0:
# Convert negative dim to positive.
dim += input_.dim()
input_size = input_.size()
# Allocate output tensor.
output_tensor = torch.empty((world_size, ) + input_size,
dtype=input_.dtype,
device=input_.device)
# All-gather.
htorch.core.mark_step()
dist.all_gather_into_tensor(output_tensor,
input_,
group=self.device_group)
# Reshape
output_tensor = output_tensor.movedim(0, dim)
output_tensor = output_tensor.reshape(input_size[:dim] +
(world_size *
input_size[dim], ) +
input_size[dim + 1:])
return output_tensor