chore(mlx-lm): fix the number of validation batches configuration. (#752)

* chore: fix number of validation batches

* clean up

* address comment

llms/mlx_lm/tuner/trainer.py

@@ -137,8 +137,11 @@ def evaluate(
 ):
     all_losses = []
     ntokens = 0
-    for it, batch in zip(
+
-        range(num_batches),
+    index_iterator = iter(range(num_batches)) if num_batches != -1 else iter(int, 1)
+
+    for _, batch in zip(
+        index_iterator,
         iterate_batches(
             dataset=dataset,
             tokenizer=tokenizer,

llms/tests/test_lora.py

@@ -11,6 +11,7 @@ import mlx.optimizers as opt
 from mlx.utils import tree_flatten
 from mlx_lm import lora, tuner
 from mlx_lm.tuner.lora import LoRALinear
+from mlx_lm.tuner.trainer import evaluate
 from mlx_lm.tuner.utils import build_schedule
 
 
@@ -169,6 +170,85 @@ class TestScheduleConfig(unittest.TestCase):
         config = {"cosine_decay": None}
         self.assertRaises(KeyError, build_schedule, config)
 
+    def test_evaluate_calls(self):
+        mock_model = MagicMock()
+        mock_dataset = MagicMock()
+        mock_tokenizer = MagicMock()
+        mock_default_loss = MagicMock()
+        mock_iterate_batches = MagicMock()
+
+        mock_iterate_batches.return_value = [
+            (MagicMock(), MagicMock()),
+            (MagicMock(), MagicMock()),
+            (MagicMock(), MagicMock()),
+            (MagicMock(), MagicMock()),
+            (MagicMock(), MagicMock()),
+        ]
+
+        mock_default_loss.side_effect = [
+            (MagicMock(return_value=0.5), MagicMock(return_value=100)),
+            (MagicMock(return_value=0.3), MagicMock(return_value=200)),
+            (MagicMock(return_value=0.2), MagicMock(return_value=150)),
+            (MagicMock(return_value=0.4), MagicMock(return_value=180)),
+            (MagicMock(return_value=0.6), MagicMock(return_value=120)),
+        ]
+        evaluate(
+            model=mock_model,
+            dataset=mock_dataset,
+            tokenizer=mock_tokenizer,
+            batch_size=2,
+            num_batches=2,
+            max_seq_length=2048,
+            loss=mock_default_loss,
+            iterate_batches=mock_iterate_batches,
+        )
+
+        mock_iterate_batches.assert_called_once_with(
+            dataset=mock_dataset,
+            tokenizer=mock_tokenizer,
+            batch_size=2,
+            max_seq_length=2048,
+        )
+        self.assertEqual(mock_default_loss.call_count, 2)
+
+    def test_evaluate_infinite_batches(self):
+        mock_model = MagicMock()
+        mock_dataset = MagicMock()
+        mock_tokenizer = MagicMock()
+        mock_default_loss = MagicMock()
+        mock_iterate_batches = MagicMock()
+
+        mock_iterate_batches.return_value = [
+            (MagicMock(), MagicMock()),
+            (MagicMock(), MagicMock()),
+            (MagicMock(), MagicMock()),
+        ]
+
+        mock_default_loss.side_effect = [
+            (MagicMock(return_value=0.5), MagicMock(return_value=100)),
+            (MagicMock(return_value=0.3), MagicMock(return_value=200)),
+            (MagicMock(return_value=0.2), MagicMock(return_value=150)),
+        ]
+
+        evaluate(
+            model=mock_model,
+            dataset=mock_dataset,
+            tokenizer=mock_tokenizer,
+            batch_size=2,
+            num_batches=-1,
+            max_seq_length=2048,
+            loss=mock_default_loss,
+            iterate_batches=mock_iterate_batches,
+        )
+
+        mock_iterate_batches.assert_called_once_with(
+            dataset=mock_dataset,
+            tokenizer=mock_tokenizer,
+            batch_size=2,
+            max_seq_length=2048,
+        )
+        self.assertEqual(mock_default_loss.call_count, 3)
+
 
 if __name__ == "__main__":
     unittest.main()