e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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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from vllm.platforms import current_platform
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if current_platform.is_hpu():
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import habana_frameworks.torch as htorch # noqa: F401
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class HpuCommunicator:
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def __init__(self, group: ProcessGroup):
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if not current_platform.is_hpu():
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self.disabled = True
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return
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self.disabled = False
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self.group = group
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self.world_size = dist.get_world_size(self.group)
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def all_reduce(self, x: torch.Tensor) -> torch.Tensor:
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# FIXME(kzawora): this is a workaround for a bug in Habana PT bridge
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# occurring when PT_HPU_ENABLE_LAZY_COLLECTIVES=true env var is used
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# (which is required for tensor parallel HPUGraph inference)
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htorch.core.mark_step()
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dist.all_reduce(x, group=self.group)
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return x
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def all_gather(self, x: torch.Tensor, dim: int = -1) -> torch.Tensor:
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world_size = self.world_size
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if dim < 0:
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# Convert negative dim to positive.
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dim += x.dim()
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input_size = x.size()
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# Allocate output tensor.
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output_tensor = torch.empty((world_size, ) + input_size,
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dtype=x.dtype,
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device=x.device)
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# All-gather.
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htorch.core.mark_step()
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dist.all_gather_into_tensor(output_tensor, x, group=self.group)
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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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(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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