twitter-algorithm-ml/ml_logging/torch_logging.py

62 lines
2 KiB
Python

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