Fix for #20425

The issue was caused by the fact that the iterator was not fully consumed and `on_epoch_end` was not called.

Added an exception to catch this situation in the future.

Added a unit test to test `model.fit()` with all the combinations of data adapters.

keras/src/trainers/data_adapters/py_dataset_adapter.py

@@ -193,6 +193,7 @@ def __init__(
         self.enqueuer = None
         self.shuffle = shuffle
         self._output_signature = None
+        self._within_epoch = False
 
         workers = self.py_dataset.workers
         use_multiprocessing = self.py_dataset.use_multiprocessing
@@ -314,6 +315,12 @@ def get_torch_dataloader(self):
         return data_adapter_utils.get_torch_dataloader(self._get_iterator())
 
     def on_epoch_begin(self):
+        if self._within_epoch:
+            raise ValueError(
+                "`on_epoch_begin` was called twice without `on_epoch_end` "
+                "having been called."
+            )
+        self._within_epoch = True
         if self.enqueuer:
             self.enqueuer.start()
         self.py_dataset.on_epoch_begin()
@@ -322,6 +329,7 @@ def on_epoch_end(self):
         if self.enqueuer:
             self.enqueuer.stop()
         self.py_dataset.on_epoch_end()
+        self._within_epoch = False
 
     @property
     def num_batches(self):
@@ -460,7 +468,7 @@ def start(self):
                 return
             self.running = True
             self.run_thread = threading.Thread(target=self._run)
-            self.run_thread.name = f"Worker_{self.uid}"  # TODO remove
+            self.run_thread.name = f"Worker_{self.uid}"
             self.run_thread.daemon = True
             self.run_thread.start()
 
@@ -644,11 +652,7 @@ def get(self):
                 if inputs is not None:
                     yield inputs
             except queue.Empty:
-                warnings.warn(
-                    "Generator ran out of batches before reaching `num_batches`"
-                )
-                self.stop()
-                return
+                pass
             except Exception as e:
                 self.stop(drain_queue_and_join=True)
                 raise e

keras/src/trainers/epoch_iterator.py

@@ -91,6 +91,7 @@ def reset(self):
         self._num_batches = self.data_adapter.num_batches
         self._steps_seen = 0
         self._epoch_iterator = None
+        self.data_adapter.on_epoch_end()
 
     def _enumerate_iterator(self):
         self.data_adapter.on_epoch_begin()

keras/src/trainers/trainer_test.py

@@ -18,6 +18,7 @@
 from keras.src.callbacks.callback import Callback
 from keras.src.optimizers.rmsprop import RMSprop
 from keras.src.testing.test_utils import named_product
+from keras.src.trainers.data_adapters import py_dataset_adapter
 
 if backend.backend() == "jax":
     from keras.src.backend.jax.trainer import JAXTrainer as Trainer
@@ -141,6 +142,76 @@ def call(self, x, training=False):
         return x * 0
 
 
+class TestPyDataset(py_dataset_adapter.PyDataset):
+    def __init__(self, infinite=False, **kwargs):
+        super().__init__(**kwargs)
+        self.infinite = infinite
+
+    @property
+    def num_batches(self):
+        return None if self.infinite else 20
+
+    def __getitem__(self, idx):
+        return ops.ones((5, 4)), ops.zeros((5, 3))
+
+
+def create_dataset(dataset_type, dataset_kwargs):
+    if dataset_type == "np_array":
+        return np.ones((100, 4)), np.zeros((100, 3))
+    elif dataset_type == "native_array":
+        return ops.ones((100, 4)), ops.zeros((100, 3))
+    elif dataset_type == "py_dataset":
+        return TestPyDataset(**dataset_kwargs), None
+    elif dataset_type == "tf_dataset":
+        import tensorflow as tf
+
+        dataset = tf.data.Dataset.from_tensor_slices(
+            (tf.ones((100, 4)), tf.zeros((100, 3)))
+        ).batch(5)
+        if dataset_kwargs.get("infinite", False):
+            dataset = dataset.repeat()
+        return dataset, None
+    elif dataset_type == "torch_dataloader":
+        import torch
+
+        class TestIterableDataset(torch.utils.data.IterableDataset):
+            def __iter__(self):
+                for _ in range(20):
+                    yield torch.ones((5, 4)), torch.zeros((5, 3))
+
+        class TestIterableDatasetWithLen(TestIterableDataset):
+            def __len__(self):
+                return 20
+
+        if dataset_kwargs.get("iterable", False):
+            if dataset_kwargs.get("has_len", False):
+                dataset = TestIterableDatasetWithLen()
+            else:
+                dataset = TestIterableDataset()
+            return torch.utils.data.DataLoader(dataset), None
+        else:
+            dataset = torch.utils.data.TensorDataset(
+                torch.ones((100, 4)), torch.zeros((100, 3))
+            )
+            return torch.utils.data.DataLoader(dataset, batch_size=5), None
+    elif dataset_type == "generator":
+
+        def generate_finite():
+            for _ in range(20):
+                yield ops.ones((5, 4)), ops.zeros((5, 3))
+
+        def generate_infinite():
+            while True:
+                yield ops.ones((5, 4)), ops.zeros((5, 3))
+
+        if dataset_kwargs.get("infinite", False):
+            return generate_infinite(), None
+        else:
+            return generate_finite(), None
+    else:
+        raise ValueError(f"Invalid dataset type {dataset_type}")
+
+
 def sparse_generator(generator_type):
     if generator_type == "scipy":
         import scipy
@@ -397,6 +468,105 @@ def test_fit_flow(self, run_eagerly, jit_compile, use_steps_per_epoch):
             atol=1.0,  # TODO: results vary across backends
         )
 
+    @parameterized.named_parameters(
+        [
+            {
+                "testcase_name": "np_array",
+                "dataset_type": "np_array",
+                "fit_kwargs": {"batch_size": 5},
+            },
+            {
+                "testcase_name": "native_array",
+                "dataset_type": "native_array",
+                "fit_kwargs": {"batch_size": 5},
+            },
+            {
+                "testcase_name": "py_dataset",
+                "dataset_type": "py_dataset",
+            },
+            {
+                "testcase_name": "py_dataset_infinite",
+                "dataset_type": "py_dataset",
+                "dataset_kwargs": {"infinite": True},
+                "fit_kwargs": {"steps_per_epoch": 20},
+            },
+            {
+                "testcase_name": "py_dataset_multithreading",
+                "dataset_type": "py_dataset",
+                "dataset_kwargs": {"workers": 2},
+            },
+            {
+                "testcase_name": "py_dataset_multithreading_infinite",
+                "dataset_type": "py_dataset",
+                "dataset_kwargs": {"infinite": True, "workers": 2},
+                "fit_kwargs": {"steps_per_epoch": 20},
+            },
+            {
+                "testcase_name": "py_dataset_multiprocessing",
+                "dataset_type": "py_dataset",
+                "dataset_kwargs": {"workers": 2, "use_multiprocessing": True},
+            },
+            {
+                "testcase_name": "py_dataset_multiprocessing_infinite",
+                "dataset_type": "py_dataset",
+                "dataset_kwargs": {
+                    "infinite": True,
+                    "workers": 2,
+                    "use_multiprocessing": True,
+                },
+                "fit_kwargs": {"steps_per_epoch": 20},
+            },
+            {
+                "testcase_name": "tf_dataset",
+                "dataset_type": "tf_dataset",
+            },
+            {
+                "testcase_name": "tf_dataset_infinite",
+                "dataset_type": "tf_dataset",
+                "dataset_kwargs": {"infinite": True},
+                "fit_kwargs": {"steps_per_epoch": 20},
+            },
+            {
+                "testcase_name": "torch_dataloader_tensor",
+                "dataset_type": "torch_dataloader",
+            },
+            {
+                "testcase_name": "torch_dataloader_iterable",
+                "dataset_type": "torch_dataloader",
+                "dataset_kwargs": {"iterable": True, "has_len": False},
+            },
+            {
+                "testcase_name": "torch_dataloader_iterable_with_len",
+                "dataset_type": "torch_dataloader",
+                "dataset_kwargs": {"iterable": True, "has_len": True},
+            },
+            {
+                "testcase_name": "generator",
+                "dataset_type": "generator",
+            },
+            {
+                "testcase_name": "generator_infinite",
+                "dataset_type": "generator",
+                "dataset_kwargs": {"infinite": True},
+                "fit_kwargs": {"steps_per_epoch": 20},
+            },
+        ]
+    )
+    @pytest.mark.requires_trainable_backend
+    def test_fit_with_data_adapter(
+        self, dataset_type, dataset_kwargs={}, fit_kwargs={}
+    ):
+        model = ExampleModel(units=3)
+        optimizer = optimizers.Adagrad()
+        model.compile(
+            optimizer=optimizer,
+            loss=losses.MeanSquaredError(),
+            metrics=[metrics.MeanSquaredError()],
+            jit_compile=True,
+        )
+        x, y = create_dataset(dataset_type, dataset_kwargs)
+        model.fit(x, y, epochs=3, **fit_kwargs)
+
     @parameterized.named_parameters(
         [
             ("eager", True, False, False),