[distributed] add SingletonLogger to avoid duplicate logging, with custom formatting options including file name: line_number

dist_run.py

@@ -15,7 +15,8 @@
 import torch.distributed as dist
 from torch.distributed.pipelining import PipelineStage, ScheduleGPipe
 
-from distributed.logging_utils import setup_logging
+
+from distributed.logging_utils import SingletonLogger
 
 # TODO - these are not distributed specific, consider moving to new package
 from distributed.safetensor_utils import (
@@ -41,7 +42,7 @@
     SentencePieceProcessor = None
 
 
-logger = setup_logging(__name__)
+logger = SingletonLogger.get_logger()
 
 MODEL_NAME = "Transformer-2-7b-chat-hf"
 NAME_TO_HF_MODEL_ID_AND_DTYPE = {

distributed/__init__.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 from distributed.checkpoint import load_checkpoints_to_model
-from distributed.logging_utils import setup_logging
+from distributed.logging_utils import SingletonLogger
 from distributed.parallel_config import ParallelDims
 from distributed.parallelize_llama import parallelize_llama
 from distributed.utils import init_distributed

distributed/config_manager.py

@@ -12,9 +12,9 @@
 
 import torch
 
-from distributed.logging_utils import setup_logging
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
 
-logger = setup_logging(__name__)
 
 try:
     import tomllib

distributed/dtensor_utils.py

@@ -1,10 +1,12 @@
 import torch
 from torch.distributed._tensor import DTensor, Shard, Replicate
 
-from distributed.logging_utils import setup_logging
+
 from collections import defaultdict
 
-logger = setup_logging(__name__)
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
+
 
 
 def is_dtensor(tensor):

distributed/logging_utils.py

@@ -7,25 +7,103 @@
 import logging
 import os
 from datetime import datetime
+from typing import Optional
 
-def millisecond_timestamp(*args):
-    return datetime.now().strftime('%m-%d %H:%M:%S.%f')[:-3]
 
-def setup_logging(name=None, log_level=logging.INFO):
-    logger = logging.getLogger(name)
-    logger.setLevel(log_level)
+def millisecond_timestamp(include_year: bool = False) -> str:
+    format_string = "%Y-%m-%d %H:%M:%S.%f" if include_year else "%m-%d %H:%M:%S.%f"
+    return datetime.now().strftime(format_string)[:-3]
 
-    if not logger.handlers:
-        console_handler = logging.StreamHandler()
-        console_handler.setLevel(log_level)
 
-        formatter = logging.Formatter('%(asctime)s - %(name)s:%(lineno)d - %(levelname)s - %(message)s')
-        formatter.formatTime = millisecond_timestamp
+class CompactFormatter(logging.Formatter):
+    def __init__(
+        self,
+        fmt: Optional[str] = None,
+        datefmt: Optional[str] = None,
+        style: str = "%",
+        validate: bool = True,
+        *,
+        defaults: Optional[dict] = None,
+        show_lower_levels: bool = True,
+    ):
+        super().__init__(fmt, datefmt, style, validate, defaults=defaults)
+        self.show_lower_levels = show_lower_levels
+        self.original_fmt = fmt
 
-        console_handler.setFormatter(formatter)
-        logger.addHandler(console_handler)
+    def format(self, record: logging.LogRecord) -> str:
+        # Remove .py extension from filename
+        record.filename = os.path.splitext(record.filename)[0]
 
-        # suppress verbose torch.profiler logging
-        os.environ["KINETO_LOG_LEVEL"] = "5"
+        if self.show_lower_levels or record.levelno > logging.INFO:
+            return super().format(record)
+        else:
+            # Create a copy of the record and modify it
+            new_record = logging.makeLogRecord(record.__dict__)
+            new_record.levelname = ""
+            # Temporarily change the format string
+            temp_fmt = self.original_fmt.replace(" - %(levelname)s", "")
+            self._style._fmt = temp_fmt
+            formatted_message = super().format(new_record)
+            # Restore the original format string
+            self._style._fmt = self.original_fmt
+            return formatted_message
 
-    return logger
+
+class SingletonLogger:
+    """Singleton (global) logger to avoid logging duplication"""
+
+    _instance = None
+
+    @classmethod
+    def get_logger(
+        cls,
+        name: str = "global_logger",
+        level: int = logging.INFO,
+        include_year: bool = False,
+        show_lower_levels: bool = False,
+    ) -> logging.Logger:
+        """
+        Get or create a singleton logger instance.
+
+        :param name: Name of the logger
+        :param level: Logging level
+        :param include_year: Whether to include the year in timestamps
+        :param show_lower_levels: Whether to show level names for INFO and DEBUG messages
+        :return: Logger instance
+        """
+        if cls._instance is None:
+            cls._instance = cls._setup_logger(
+                name, level, include_year, show_lower_levels
+            )
+        return cls._instance
+
+    @staticmethod
+    def _setup_logger(
+        name: str,
+        level: int,
+        include_year: bool = False,
+        show_lower_levels: bool = False,
+    ) -> logging.Logger:
+        logger = logging.getLogger(name)
+
+        if not logger.handlers:
+            logger.setLevel(level)
+
+            console_handler = logging.StreamHandler()
+            console_handler.setLevel(level)
+
+            formatter = CompactFormatter(
+                "%(asctime)s - %(filename)s:%(lineno)d - %(levelname)s - %(message)s",
+                show_lower_levels=show_lower_levels,
+            )
+            formatter.formatTime = lambda record, datefmt=None: millisecond_timestamp(
+                include_year
+            )
+            console_handler.setFormatter(formatter)
+            logger.addHandler(console_handler)
+
+            # Suppress verbose torch.profiler logging
+            os.environ["KINETO_LOG_LEVEL"] = "5"
+
+        logger.propagate = False
+        return logger

distributed/parallel_config.py

@@ -8,8 +8,8 @@
 
 from torch.distributed.device_mesh import init_device_mesh
 
-from distributed.logging_utils import setup_logging
-logger = setup_logging(__name__)
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
 
 @dataclass
 class ParallelDims:

distributed/parallelize_llama.py

@@ -10,10 +10,11 @@
                                                RowwiseParallel,
                                                parallelize_module)
 
-from distributed.logging_utils import setup_logging
+
 from distributed.parallel_config import ParallelDims
 
-logger = setup_logging(__name__)
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
 
 
 def apply_tp(

distributed/safetensor_utils.py

@@ -13,14 +13,17 @@
 from torch.nn import Module
 from typing import Dict, Tuple, Set, Optional
 
-from distributed.logging_utils import setup_logging
+
 from distributed.dtensor_utils import is_dtensor, load_into_dtensor
 
 
 _DEFAULT_SAFETENSOR_FILE_NAME = "model.safetensors.index.json"
 _CONFIG_NAME = "config.json"
 
-logger = setup_logging(__name__)
+
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
+
 
 
 def compare_and_reverse(tensor1: torch.Tensor, tensor2: torch.Tensor) -> torch.Tensor:

distributed/utils.py

@@ -13,9 +13,9 @@
 
 import torch
 
-from distributed.logging_utils import setup_logging
 
-logger = setup_logging(__name__)
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
 
 
 def _warn_overwrite_env(env, val):

distributed/verification_utils.py

@@ -6,9 +6,10 @@
 import numpy as np
 from distributed.dtensor_utils import is_dtensor
 from typing import Dict, List, Tuple
-from distributed.logging_utils import setup_logging
 
-logger = setup_logging(__name__)
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
+
 
 
 def record_module_dtypes(module):

distributed/world_maker.py

@@ -9,13 +9,16 @@
 
 from torch.distributed.device_mesh import DeviceMesh
 
-from distributed.logging_utils import setup_logging
+
 from distributed.parallel_config import ParallelDims
 from distributed.utils import init_distributed
 
 from .config_manager import InferenceConfig
 
-logger = setup_logging(__name__)
+
+from distributed.logging_utils import SingletonLogger
+logger = SingletonLogger.get_logger()
+
 
 def launch_distributed(
     toml_config: str,