chore: import upstream snapshot with attribution
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# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# coding: utf-8
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import os
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import torch
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import torch.distributed as dist
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def get_global_rank() -> int:
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"""
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Get the global rank, the global index of the GPU.
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"""
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return int(os.environ.get("RANK", "0"))
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def get_local_rank() -> int:
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"""
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Get the local rank, the local index of the GPU.
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"""
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return int(os.environ.get("LOCAL_RANK", "0"))
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def get_world_size() -> int:
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"""
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Get the world size, the total amount of GPUs.
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"""
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return int(os.environ.get("WORLD_SIZE", "1"))
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def is_master():
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"""
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Check if the current process is the master process (rank 0).
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"""
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if not dist.is_available() or not dist.is_initialized():
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return True
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return dist.get_rank() == 0
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def get_device() -> torch.device:
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"""
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Get current rank device.
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"""
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return torch.device("cuda", get_local_rank())
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def barrier_if_distributed(*args, **kwargs):
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"""
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Synchronizes all processes if under distributed context.
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"""
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if dist.is_initialized():
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return dist.barrier(*args, **kwargs)
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