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62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
"""Overrides absl logger to be rank-aware for distributed pytorch usage.
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>>> # in-bazel import
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>>> from twitter.ml.logging.torch_logging import logging
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>>> # out-bazel import
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>>> from ml.logging.torch_logging import logging
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>>> logging.info(f"This only prints on rank 0 if distributed, otherwise prints normally.")
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>>> logging.info(f"This prints on all ranks if distributed, otherwise prints normally.", rank=-1)
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"""
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import functools
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from typing import Optional
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from tml.ml_logging.absl_logging import logging as logging
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from absl import logging as absl_logging
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import torch.distributed as dist
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def rank_specific(logger):
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"""Ensures that we only override a given logger once."""
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if hasattr(logger, "_ALREADY_OVERWRITTEN_TO_BE_RANK_SPECIFIC"):
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return logger
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def _if_rank(logger_method, limit: Optional[int] = None):
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if limit:
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# If we are limiting redundant logs, wrap logging call with a cache
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# to not execute if already cached.
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def _wrap(_call):
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@functools.lru_cache(limit)
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def _logger_method(*args, **kwargs):
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_call(*args, **kwargs)
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return _logger_method
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logger_method = _wrap(logger_method)
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def _inner(msg, *args, rank: int = 0, **kwargs):
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if not dist.is_initialized():
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logger_method(msg, *args, **kwargs)
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elif dist.get_rank() == rank:
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logger_method(msg, *args, **kwargs)
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elif rank < 0:
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logger_method(f"Rank{dist.get_rank()}: {msg}", *args, **kwargs)
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# Register this stack frame with absl logging so that it doesn't trample logging lines.
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absl_logging.ABSLLogger.register_frame_to_skip(__file__, _inner.__name__)
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return _inner
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logger.fatal = _if_rank(logger.fatal)
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logger.error = _if_rank(logger.error)
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logger.warning = _if_rank(logger.warning, limit=1)
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logger.info = _if_rank(logger.info)
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logger.debug = _if_rank(logger.debug)
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logger.exception = _if_rank(logger.exception)
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logger._ALREADY_OVERWRITTEN_TO_BE_RANK_SPECIFIC = True
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rank_specific(logging)
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