103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
# Copyright (c) 2026 SandAI. All Rights Reserved.
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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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import logging
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import os
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from typing import Callable
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import torch
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import torch.distributed as dist
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class GlobalLogger:
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_logger = None
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_rank = 0 # default rank=0 (single-node scenario)
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@classmethod
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def _init_rank(cls):
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"""Initialize rank information (distributed/single-node)."""
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if dist.is_available() and dist.is_initialized():
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cls._rank = dist.get_rank()
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else:
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cls._rank = int(os.getenv("RANK", 0))
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@classmethod
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def get_logger(cls, name=__name__, level=logging.INFO):
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if cls._logger is None:
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cls._init_rank()
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cls._logger = logging.getLogger("infra_logger")
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cls._logger.setLevel(level)
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cls._logger.propagate = False
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cls._logger.handlers.clear()
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formatter = logging.Formatter("[%(asctime)s - %(levelname)s] [Rank %(rank)s] %(message)s")
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class RankInjectHandler(logging.StreamHandler):
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def emit(self, record):
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record.rank = cls._rank
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super().emit(record)
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handler = RankInjectHandler()
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handler.setFormatter(formatter)
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cls._logger.addHandler(handler)
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return cls._logger
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infra_logger = GlobalLogger.get_logger()
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def print_per_rank(message, *args, **kwargs):
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infra_logger.info(message, *args, **kwargs)
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def print_rank_0(message, *args, **kwargs):
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if torch.distributed.is_initialized():
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if torch.distributed.get_rank() == 0:
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infra_logger.info(message, *args, **kwargs)
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else:
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infra_logger.info(message, *args, **kwargs)
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def print_rank_last(message, *args, **kwargs):
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if torch.distributed.is_initialized():
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if torch.distributed.get_rank() == torch.distributed.get_world_size() - 1:
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infra_logger.info(message, *args, **kwargs)
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else:
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infra_logger.info(message, *args, **kwargs)
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def print_mem_info_rank_0(prefix: str = ""):
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"Print the allocated and reserved GPU memory on device 0."
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allocated = torch.cuda.memory_allocated()
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max_allocated = torch.cuda.max_memory_allocated()
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reserved = torch.cuda.memory_reserved()
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max_reserved = torch.cuda.max_memory_reserved()
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allocated = round(allocated / 1024 / 1024 / 1024, 2)
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reserved = round(reserved / 1024 / 1024 / 1024, 2)
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max_allocated = round(max_allocated / 1024 / 1024 / 1024, 2)
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max_reserved = round(max_reserved / 1024 / 1024 / 1024, 2)
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print_rank_0(
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prefix
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+ f" GPU 0 memory allocated: {allocated} GB, max_allocated: {max_allocated} GB, reserved: {reserved} GB, max_reserved: {max_reserved} GB"
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)
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def print_model_size(model: torch.nn.Module, prefix: str = "", print_func: Callable[[str], None] = print):
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model_size_gb = sum([p.nelement() * p.element_size() for p in model.parameters()]) / (1024**3)
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parameter_count = sum([p.nelement() for p in model.parameters()])
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print_func(f"{prefix} Model size: {model_size_gb:.2f} GB, parameter count: {parameter_count}")
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