Fix error handling in learning rate finder

src/pytorch_lightning/CHANGELOG.md

@@ -376,6 +376,16 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed main progress bar counter when `val_check_interval=int` and `check_val_every_n_epoch=None` ([#12832](https://github.com/Lightning-AI/lightning/pull/12832)
 
 
+- Used `global_step` while restoring logging step for old checkpoints ([#13645](https://github.com/Lightning-AI/lightning/pull/13645))
+
+
+- Fixed error handling in learning rate finder when not enough data points are available to give a good suggestion ([#13845](https://github.com/Lightning-AI/lightning/pull/13845))
+
+
+- Fixed an issue that caused the learning rate finder to set the model's learning rate to None when no suggestion was possible ([#13845](https://github.com/Lightning-AI/lightning/pull/13845))
+
+
+
 ## [1.6.5] - 2022-07-13
 
 ### Fixed
@@ -386,9 +396,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed the restoration of log step during restart ([#13467](https://github.com/PyTorchLightning/pytorch-lightning/pull/13467))
 
 
-- Used `global_step` while restoring logging step for old checkpoints ([#13645](https://github.com/PyTorchLightning/pytorch-lightning/pull/13645))
-
-
 ## [1.6.4] - 2022-06-01
 
 ### Added

src/pytorch_lightning/tuner/lr_finder.py

@@ -174,24 +174,33 @@ def plot(self, suggest: bool = False, show: bool = False) -> Optional["plt.Figur
         return fig
 
     def suggestion(self, skip_begin: int = 10, skip_end: int = 1) -> Optional[float]:
-        """This will propose a suggestion for choice of initial learning rate as the point with the steepest
+        """This will propose a suggestion for an initial learning rate based on the point with the steepest
         negative gradient.
 
+        Args:
+            skip_begin: how many samples to skip in the beginning; helps to avoid too naive estimates
+            skip_end: how many samples to skip in the end; helps to avoid too optimistic estimates
+
         Returns:
-            lr: suggested initial learning rate to use
-            skip_begin: how many samples to skip in the beginning. Prevent too naive estimates
-            skip_end: how many samples to skip in the end. Prevent too optimistic estimates
+            The suggested initial learning rate to use, or `None` if a suggestion is not possible due to too few
+            loss samples.
         """
-        try:
-            loss = np.array(self.results["loss"][skip_begin:-skip_end])
-            loss = loss[np.isfinite(loss)]
-            min_grad = np.gradient(loss).argmin()
-            self._optimal_idx = min_grad + skip_begin
-            return self.results["lr"][self._optimal_idx]
-        # todo: specify the possible exception
-        except Exception:
-            log.exception("Failed to compute suggesting for `lr`. There might not be enough points.")
+        losses = np.array(self.results["loss"][skip_begin:-skip_end])
+        losses = losses[np.isfinite(losses)]
+        if len(losses) < 2:
+            # computing np.gradient requires at least 2 points
+            log.error(
+                "Failed to compute suggestion for learning rate because there are not enough points. Increase the loop"
+                " iteration limits or the size of your dataset/dataloader."
+            )
             self._optimal_idx = None
+            return None
+
+        # TODO: When computing the argmin here, and some losses are non-finite, the expected indices could be
+        #   incorrectly shifted by an offset
+        min_grad = np.gradient(losses).argmin()
+        self._optimal_idx = min_grad + skip_begin
+        return self.results["lr"][self._optimal_idx]
 
 
 def lr_find(
@@ -252,8 +261,9 @@ def lr_find(
         lr = lr_finder.suggestion()
 
         # TODO: log lr.results to self.logger
-        lightning_setattr(model, lr_attr_name, lr)
-        log.info(f"Learning rate set to {lr}")
+        if lr is not None:
+            lightning_setattr(model, lr_attr_name, lr)
+            log.info(f"Learning rate set to {lr}")
 
     # Restore initial state of model
     trainer._checkpoint_connector.restore(ckpt_path)

tests/tests_pytorch/tuner/test_lr_finder.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import logging
 import os
 from copy import deepcopy
 
@@ -19,6 +20,7 @@
 
 from pytorch_lightning import seed_everything, Trainer
 from pytorch_lightning.demos.boring_classes import BoringModel
+from pytorch_lightning.tuner.lr_finder import _LRFinder
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests_pytorch.helpers.datamodules import ClassifDataModule
 from tests_pytorch.helpers.simple_models import ClassificationModel
@@ -359,3 +361,55 @@ def test_multiple_lr_find_calls_gives_same_results(tmpdir):
         for curr_lr_finder in all_res[1:]
         for k in all_res[0].keys()
     )
+
+
+@pytest.mark.parametrize(
+    "skip_begin,skip_end,losses,expected_error",
+    [
+        (0, 0, [], True),
+        (10, 1, [], True),
+        (0, 2, [0, 1, 2], True),
+        (0, 1, [0, 1, 2], False),
+        (1, 1, [0, 1, 2], True),
+        (1, 1, [0, 1, 2, 3], False),
+        (0, 1, [float("nan"), float("nan"), 0, float("inf"), 1, 2, 3, float("inf"), 2, float("nan"), 1], False),
+        (4, 1, [float("nan"), float("nan"), 0, float("inf"), 1, 2, 3, float("inf"), 2, float("nan"), 1], False),
+    ],
+)
+def test_suggestion_not_enough_finite_points(losses, skip_begin, skip_end, expected_error, caplog):
+    """Tests the error handling when not enough finite points are available to make a suggestion."""
+    caplog.clear()
+    lr_finder = _LRFinder(
+        mode="exponential",
+        lr_min=1e-8,
+        lr_max=1,
+        num_training=100,
+    )
+    lrs = list(torch.arange(len(losses)))
+    lr_finder.results = {
+        "lr": lrs,
+        "loss": losses,
+    }
+    with caplog.at_level(logging.ERROR, logger="root.tuner.lr_finder"):
+        lr = lr_finder.suggestion(skip_begin=skip_begin, skip_end=skip_end)
+
+        if expected_error:
+            assert lr is None
+            assert "Failed to compute suggestion for learning rate" in caplog.text
+        else:
+            assert lr is not None
+
+
+def test_lr_attribute_when_suggestion_invalid(tmpdir):
+    """Tests learning rate finder ends before `num_training` steps."""
+
+    class TestModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.learning_rate = 0.123
+
+    model = TestModel()
+    trainer = Trainer(default_root_dir=tmpdir)
+    lr_finder = trainer.tuner.lr_find(model=model, update_attr=True, num_training=1)  # force insufficient data points
+    assert lr_finder.suggestion() is None
+    assert model.learning_rate == 0.123  # must remain unchanged because suggestion is not possible