Log LearningRateMonitor values to Trainer.callback_metrics for `E…

…arlyStopping` (#17626)

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

src/lightning/pytorch/CHANGELOG.md

@@ -54,6 +54,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added non-layer param count to the model summary ([#17005](https://github.com/Lightning-AI/lightning/pull/17005))
 
 
+- Updated `LearningRateMonitor` to log monitored values to `trainer.callback_metrics` ([#17626](https://github.com/Lightning-AI/lightning/pull/17626))
+
+
 ### Changed
 
 - Removed the limitation to call `self.trainer.model.parameters()` in `LightningModule.configure_optimizers()` ([#17309](https://github.com/Lightning-AI/lightning/pull/17309))

src/lightning/pytorch/callbacks/lr_monitor.py

@@ -23,6 +23,7 @@
 from collections import defaultdict
 from typing import Any, DefaultDict, Dict, List, Optional, Set, Tuple, Type
 
+import torch
 from torch.optim.optimizer import Optimizer
 
 import lightning.pytorch as pl
@@ -193,6 +194,10 @@ def _extract_stats(self, trainer: "pl.Trainer", interval: str) -> Dict[str, floa
             current_stat = self._get_lr_momentum_stat(opt, names)
             latest_stat.update(current_stat)
 
+        trainer.callback_metrics.update(
+            {name: torch.tensor(value, device=trainer.strategy.root_device) for name, value in latest_stat.items()}
+        )
+
         return latest_stat
 
     def _get_lr_momentum_stat(self, optimizer: Optimizer, names: List[str]) -> Dict[str, float]:

tests/tests_pytorch/callbacks/test_lr_monitor.py

@@ -16,7 +16,7 @@
 from torch import optim
 
 from lightning.pytorch import Trainer
-from lightning.pytorch.callbacks import LearningRateMonitor
+from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
 from lightning.pytorch.callbacks.callback import Callback
 from lightning.pytorch.callbacks.finetuning import BackboneFinetuning
 from lightning.pytorch.demos.boring_classes import BoringModel
@@ -626,3 +626,40 @@ def configure_optimizers(self):
     assert list(lr_monitor.last_momentum_values) == ["lr-Adam/pg1-momentum", "lr-Adam/pg2-momentum"]
     assert all(val == momentum for val in lr_monitor.last_momentum_values.values())
     assert all(all(val == lr for val in lr_monitor.lrs[lr_key]) for lr_key in lr_monitor.lrs)
+
+
+def test_lr_monitor_update_callback_metrics(tmpdir):
+    """Test that the `LearningRateMonitor` callback updates trainer.callback_metrics."""
+
+    class TestModel(BoringModel):
+        def configure_optimizers(self):
+            optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
+            lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.5)
+            return [optimizer], [lr_scheduler]
+
+    monitor_key = "lr-SGD"
+    stop_threshold = 0.02
+    expected_stop_epoch = 3
+
+    lr_monitor = LearningRateMonitor()
+    lr_es = EarlyStopping(
+        monitor=monitor_key, mode="min", stopping_threshold=stop_threshold, check_on_train_epoch_end=True
+    )
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        callbacks=[lr_monitor, lr_es],
+        max_epochs=5,
+        limit_val_batches=0,
+        limit_train_batches=2,
+        logger=CSVLogger(tmpdir),
+    )
+    model = TestModel()
+    trainer.fit(model)
+
+    assert monitor_key in trainer.callback_metrics
+    assert lr_monitor.lrs[monitor_key] == [0.1, 0.05, 0.025, 0.0125]
+    assert min(lr_monitor.lrs[monitor_key][:expected_stop_epoch]) > stop_threshold
+    assert len(lr_monitor.lrs[monitor_key][expected_stop_epoch:]) == 1
+    assert min(lr_monitor.lrs[monitor_key][expected_stop_epoch:]) < stop_threshold
+    assert trainer.current_epoch - 1 == expected_stop_epoch
+    assert lr_es.stopped_epoch == expected_stop_epoch