From b2eb6e36186f76153a7b6a4d5790bd76ef4d3816 Mon Sep 17 00:00:00 2001
From: Igor Tsvetkov <igorts@google.com>
Date: Wed, 11 Mar 2026 16:51:16 -0700
Subject: [PATCH] Add unit test for prompt filtering

---
 tests/post_training/unit/train_rl_test.py | 75 +++++++++++++++++++++++
 1 file changed, 75 insertions(+)

diff --git a/tests/post_training/unit/train_rl_test.py b/tests/post_training/unit/train_rl_test.py
index af66d52a98..e5a4aa8ec2 100644
--- a/tests/post_training/unit/train_rl_test.py
+++ b/tests/post_training/unit/train_rl_test.py
@@ -16,6 +16,7 @@
 
 import unittest
 from unittest import mock
+import grain
 import pytest
 from types import SimpleNamespace
 import jax
@@ -288,6 +289,80 @@ def test_get_rollout_kwargs_errors(self):
     with self.assertRaisesRegex(ValueError, r"!= len\(sampler_devices\)"):
       train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 8)
 
+  @pytest.mark.cpu_only
+  def test_prompt_filtering(self):
+    """Test that prompts longer than max_prefill_predict_length are filtered out."""
+    # Setup mocks
+    mock_tokenizer = mock.MagicMock()
+
+    # Define tokenizer side effect
+    def tokenize_side_effect(text):
+      if text == "short":
+        return [0] * 5
+      else:
+        return [0] * 15
+
+    mock_tokenizer.tokenize.side_effect = tokenize_side_effect
+
+    # Define dataset mock data
+    train_data = [{"prompts": "short"}, {"prompts": "long"}, {"prompts": "short"}, {"prompts": "long"}]
+    test_data = [{"prompts": "short"}, {"prompts": "long"}]
+    train_map_ds = grain.MapDataset.source(train_data)
+    test_map_ds = grain.MapDataset.source(test_data)
+
+    def get_dataset_side_effect(model_tokenizer, config, data_dir, split, data_files=None, dataset_name=None):
+      if split == "train":
+        return train_map_ds
+      else:
+        return test_map_ds
+
+    # Configs
+    trainer_config = SimpleNamespace(
+        debug=SimpleNamespace(rl=False),
+        tokenizer_path="dummy_path",
+        dataset_name="dummy_dataset",
+        train_split="train",
+        eval_split="eval",
+        hf_train_files=None,
+        hf_eval_files=None,
+        max_prefill_predict_length=10,
+        batch_size=2,
+        num_batches=2,
+        train_fraction=1.0,
+        num_epoch=1,
+        num_test_batches=1,
+    )
+
+    # Patch everything!
+    with (
+        mock.patch("maxtext.trainers.post_train.rl.train_rl.get_dataset", side_effect=get_dataset_side_effect),
+        mock.patch("maxtext.trainers.post_train.rl.train_rl.os.makedirs"),
+        mock.patch("maxtext.trainers.post_train.rl.train_rl.os.path.exists", return_value=True),
+    ):
+      train_dataset, test_dataset = train_rl.prepare_datasets(trainer_config, mock_tokenizer)
+
+      # Check filtered train dataset
+      elements = list(train_dataset)
+      # dataset_size = 4. Indices [0,1,2,3] are [short, long, short, long].
+      # Filtered results: [short, short].
+      # batch(2) will return 1 batch of 2 elements.
+      self.assertEqual(len(elements), 1)
+      batch = elements[0]
+      self.assertEqual(len(batch["prompts"]), 2)
+      for prompt in batch["prompts"]:
+        self.assertEqual(prompt, "short")
+
+      # Check filtered test dataset
+      test_elements = list(test_dataset)
+      # test_data indices [0,1] are [short, long].
+      # num_test_batches=1, batch_size=2 -> test dataset_size = 2.
+      # Filtering results: [short].
+      # batch(2) will return 1 batch of 1 element.
+      self.assertEqual(len(test_elements), 1)
+      test_batch = test_elements[0]
+      self.assertEqual(len(test_batch["prompts"]), 1)
+      self.assertEqual(test_batch["prompts"][0], "short")
+
 
 if __name__ == "__main__":
   unittest.main()