Add anomaly detection support to TensorboardLogger (#854)

Summary:
Pull Request resolved: #854

### This Stack

Based on [this RFC](https://docs.google.com/document/d/1K1KQ886dynMRejR0ySH1fctOjS7gxaCS8AB1L_PHxU4/edit?usp=sharing), we are adding a new logger that warns about anomalous values in metrics, and optionally executes a callback function with potential side effects. This could be useful for users to realize sooner that something has gone wrong during training.

### This Diff

To start leveraging the AnomalyLogger as easily as possible, let's make it the base class for the Tensorboard logger instead of MetricLogger. This will have no effect unless users specify the `tracked_metrics` attribute, which is optional. However, if they do want to use it, they have to make very little changes.

Next diff will do the same for the AIXLogger

Reviewed By: JKSenthil

Differential Revision: D58593222

fbshipit-source-id: bff900f9e3ce15640628f22a976166f6db59293c

Author: diego-urgell

tests/utils/loggers/test_tensorboard.py

@@ -11,20 +11,53 @@
 
 import tempfile
 import unittest
-from unittest.mock import Mock, patch
+from unittest.mock import MagicMock, Mock, patch
 
 from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+from torchtnt.utils.anomaly_evaluation import ThresholdEvaluator
+from torchtnt.utils.loggers.anomaly_logger import TrackedMetric
 
 from torchtnt.utils.loggers.tensorboard import TensorBoardLogger
 
 
 class TensorBoardLoggerTest(unittest.TestCase):
-    def test_log(self: TensorBoardLoggerTest) -> None:
+
+    @patch(
+        "torchtnt.utils.loggers.anomaly_logger.AnomalyLogger.on_anomaly_detected",
+    )
+    def test_log(
+        self: TensorBoardLoggerTest, mock_on_anomaly_detected: MagicMock
+    ) -> None:
         with tempfile.TemporaryDirectory() as log_dir:
-            logger = TensorBoardLogger(path=log_dir)
-            for i in range(5):
-                logger.log("test_log", float(i) ** 2, i)
-            logger.close()
+            logger = TensorBoardLogger(
+                path=log_dir,
+                tracked_metrics=[
+                    TrackedMetric(
+                        name="test_log",
+                        anomaly_evaluators=[
+                            ThresholdEvaluator(min_val=25),
+                        ],
+                        evaluate_every_n_steps=2,
+                        warmup_steps=2,
+                    )
+                ],
+            )
+            warning_container = []
+            with patch(
+                "torchtnt.utils.loggers.anomaly_logger.logging.Logger.warning",
+                side_effect=warning_container.append,
+            ):
+                for i in range(5):
+                    logger.log("test_log", float(i) ** 2, i)
+                logger.close()
+
+            self.assertEqual(
+                warning_container,
+                [
+                    "Found anomaly in metric: test_log, with value: 16.0, using evaluator: ThresholdEvaluator"
+                ],
+            )
+            mock_on_anomaly_detected.assert_called_with("test_log", 16.0, 4)
 
             acc = EventAccumulator(log_dir)
             acc.Reload()

torchtnt/utils/loggers/tensorboard.py

@@ -11,23 +11,28 @@
 
 import atexit
 import logging
-from typing import Any, Dict, Mapping, Optional, Union
+from typing import Any, Dict, List, Mapping, Optional, Union
 
 from torch.utils.tensorboard import SummaryWriter
 from torchtnt.utils.distributed import get_global_rank
-from torchtnt.utils.loggers.logger import MetricLogger, Scalar
+from torchtnt.utils.loggers.anomaly_logger import AnomalyLogger, TrackedMetric
+from torchtnt.utils.loggers.logger import Scalar
 
 logger: logging.Logger = logging.getLogger(__name__)
 
 
-class TensorBoardLogger(MetricLogger):
+class TensorBoardLogger(AnomalyLogger):
     """
     Simple logger for TensorBoard.
 
     On construction, the logger creates a new events file that logs
     will be written to.  If the environment variable `RANK` is defined,
     logger will only log if RANK = 0.
 
+    Metrics may be tracked for anomaly detection if they are configured in the
+    optional `tracked_metrics` argument. See :class:`torchtnt.utils.loggers.AnomalyLogger`
+    for more details.
+
     Note:
         If using this logger with distributed training:
 
@@ -38,6 +43,7 @@ class TensorBoardLogger(MetricLogger):
 
     Args:
         path (str): path to write logs to
+        tracked_metrics: Optional list of TrackedMetric objects to track for anomaly detection.
         *args: Extra positional arguments to pass to SummaryWriter
         **kwargs: Extra keyword arguments to pass to SummaryWriter
 
@@ -49,7 +55,14 @@ class TensorBoardLogger(MetricLogger):
         logger.close()
     """
 
-    def __init__(self: TensorBoardLogger, path: str, *args: Any, **kwargs: Any) -> None:
+    def __init__(
+        self: TensorBoardLogger,
+        path: str,
+        tracked_metrics: Optional[List[TrackedMetric]] = None,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(tracked_metrics)
         self._writer: Optional[SummaryWriter] = None
         self._path: str = path
         self._rank: int = get_global_rank()
@@ -100,6 +113,8 @@ def log(self: TensorBoardLogger, name: str, data: Scalar, step: int) -> None:
         if self._writer:
             self._writer.add_scalar(name, data, global_step=step, new_style=True)
 
+        super().log(name, data, step)
+
     def log_text(self: TensorBoardLogger, name: str, data: str, step: int) -> None:
         """Add text data to summary.