ludwig-ai · justinxzhao · Sep 26, 2023 · Sep 22, 2023 · Sep 22, 2023 · Sep 25, 2023
@@ -63,6 +63,10 @@ def next_batch(self):
         return sub_batch
 
     def last_batch(self):
+        """Returns whether we've exhausted all batches for this epoch.
+
+        If False, then there is at least 1 more batch available with next_batch().
+        """
         # If our current index in the dataset exceeds the size of the dataset,
         # we've finished the epoch and can indicate that this is the last batch
         if self.index >= self.total_size:
@@ -71,8 +75,9 @@ def last_batch(self):
         # For e.g., batch size = 128 but the dataset only has 100 rows.
         elif self.ignore_last and self.step:
             # index += batch_size after each epoch. So, if our current index in total dataset is 1 less than the total
-            # dataset size, then the last batch will only have 1 row. Drop it if this happens.
-            if self.index - self.total_size == -1:
+            # dataset size, then the last batch will only have 1 row.
+            # If this happens, we drop the last batch, unless batch_size is 1.
+            if self.batch_size > 1 and self.index - self.total_size == -1:
                 logger.info("Last batch in epoch only has 1 sample and will be dropped.")
                 return True
         return False

@@ -589,6 +589,34 @@ def test_api_callbacks_fixed_train_steps(tmpdir, csv_filename):
     assert mock_callback.on_epoch_start.call_count == 10
 
 
+def test_api_callbacks_batch_size_1(tmpdir, csv_filename):
+    epochs = 2
+    batch_size = 1
+    num_examples = 80
+    mock_callback = mock.Mock(wraps=Callback())
+
+    input_features = [sequence_feature(encoder={"reduce_output": "sum"})]
+    output_features = [category_feature(decoder={"vocab_size": 5}, reduce_input="sum")]
+    config = {
+        "input_features": input_features,
+        "output_features": output_features,
+        "combiner": {"type": "concat", "output_size": 14},
+        TRAINER: {"epochs": epochs, "batch_size": batch_size},
+    }
+    model = LudwigModel(config, callbacks=[mock_callback])
+    model.train(
+        training_set=generate_data(
+            input_features, output_features, os.path.join(tmpdir, csv_filename), num_examples=num_examples
+        )
+    )
+
+    # There are exactly 2 epoch starts, even with batch_size = 1.
+    assert mock_callback.on_epoch_start.call_count == 2
+    assert mock_callback.on_epoch_end.call_count == 2
+    assert mock_callback.on_batch_start.call_count == 160
+    assert mock_callback.on_batch_end.call_count == 160
+
+
 def test_api_callbacks_fixed_train_steps_less_than_one_epoch(tmpdir, csv_filename):
     # If train_steps is set manually, epochs is ignored.
     train_steps = total_batches = 6