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>
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Dict, Optional, Union
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import torch
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import torch.distributed
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from .parallel_state import get_tp_group
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def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:
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"""All-reduce the input tensor across model parallel group."""
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return get_tp_group().all_reduce(input_)
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def tensor_model_parallel_all_gather(input_: torch.Tensor,
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dim: int = -1) -> torch.Tensor:
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"""All-gather the input tensor across model parallel group."""
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return get_tp_group().all_gather(input_, dim)
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def tensor_model_parallel_gather(input_: torch.Tensor,
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dst: int = 0,
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dim: int = -1) -> Optional[torch.Tensor]:
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"""Gather the input tensor across model parallel group."""
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return get_tp_group().gather(input_, dst, dim)
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def broadcast_tensor_dict(tensor_dict: Optional[Dict[Any, Union[torch.Tensor,
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Any]]] = None,
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src: int = 0):
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if not torch.distributed.is_initialized():
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return tensor_dict
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return get_tp_group().broadcast_tensor_dict(tensor_dict, src)
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