Fix calculate training time by summing all elapsed times instead the last one

src/lightning/pytorch/callbacks/throughput_monitor.py

@@ -109,7 +109,9 @@ def _start(self, trainer: "Trainer") -> None:
         stage = trainer.state.stage
         assert stage is not None
 
-        if stage not in self._samples:
+        reset_needed = trainer.state.fn == TrainerFn.FITTING or stage not in self._samples
+
+        if reset_needed:
             self._throughputs[stage].reset()
             self._lengths[stage] = 0
             self._samples[stage] = 0
@@ -202,10 +204,17 @@ def on_validation_batch_end(
     def on_validation_end(self, trainer: "Trainer", *_: Any) -> None:
         if trainer.sanity_checking or trainer.state.fn != TrainerFn.FITTING:
             return
+
+        train_times = self._throughputs[RunningStage.TRAINING]._time
+        val_times = self._throughputs[RunningStage.VALIDATING]._time
+
+        train_elapsed = train_times[-1] if train_times else 0.0
+        val_elapsed = val_times[-1] if val_times else 0.0
+
         # add the validation time to the training time before continuing to avoid sinking the training throughput
-        training_finished = self._t0s[RunningStage.TRAINING] + sum(self._throughputs[RunningStage.TRAINING]._time)
+        training_finished = self._t0s[RunningStage.TRAINING] + train_elapsed
         time_between_train_and_val = self._t0s[RunningStage.VALIDATING] - training_finished
-        val_time = sum(self._throughputs[RunningStage.VALIDATING]._time)
+        val_time = val_elapsed
         self._t0s[RunningStage.TRAINING] += time_between_train_and_val + val_time
 
     @override

tests/tests_pytorch/callbacks/test_throughput_monitor.py

@@ -420,3 +420,26 @@ def variable_batch_size_fn(batch):
             train_samples.append(metrics["train/samples"])
         elif "train|samples" in metrics:
             train_samples.append(metrics["train|samples"])
+
+
+def test_throughput_monitor_validation_sum_overflow_real(tmp_path):
+    logger_mock = Mock()
+    logger_mock.save_dir = tmp_path
+    monitor = ThroughputMonitor(batch_size_fn=lambda x: 1)
+    model = BoringModel()
+    model.flops_per_batch = 10
+
+    trainer = Trainer(
+        devices=1,
+        logger=logger_mock,
+        callbacks=[monitor],
+        max_epochs=100,
+        enable_checkpointing=False,
+        enable_model_summary=False,
+        enable_progress_bar=False,
+    )
+
+    try:
+        trainer.fit(model)
+    except Exception as e:
+        pytest.fail(f"ThroughputMonitor raised an unexpected exception: {e}")