Add unit tests for train_rl.get_rollout_kwargs_for_parallelism

tests/unit/train_rl_test.py

@@ -93,6 +93,116 @@ def test_setup_configs_and_devices_pathways_fractional_split(self):
       self.assertEqual(trainer_devices, mock_devices[:2])
       self.assertEqual(sampler_devices, mock_devices[2:])
 
+  @pytest.mark.cpu_only
+  def test_get_rollout_kwargs_no_dp(self):
+    """Test case 1: sampler_config.rollout_data_parallelism=-1 -> verify result is calculated."""
+    # num_sampler_devices=16, tp=2, ep=4 -> dp should be 16 // (2 * 4) = 2
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=-1,
+        rollout_tensor_parallelism=2,
+        rollout_expert_parallelism=4,
+    )
+    expected_result = {
+        "data_parallel_size": 2,
+        "tensor_parallel_size": 2,
+        "expert_parallel_size": 4,
+    }
+    self.assertEqual(train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 16), expected_result)
+
+  @pytest.mark.cpu_only
+  def test_get_rollout_kwargs_auto_tp(self):
+    """Test case 2: dp=2, tp=-1, num_sampler_devices=4."""
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=2,
+        rollout_tensor_parallelism=-1,
+        rollout_expert_parallelism=1,
+    )
+    expected_result = {
+        "data_parallel_size": 2,
+        "tensor_parallel_size": 2,
+        "expert_parallel_size": 1,
+    }
+    self.assertEqual(train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 4), expected_result)
+
+  @pytest.mark.cpu_only
+  def test_get_rollout_kwargs_fixed_tp_dp(self):
+    """Test case 3: dp=2, tp=2, num_sampler_devices=4."""
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=2,
+        rollout_tensor_parallelism=2,
+        rollout_expert_parallelism=1,
+    )
+    expected_result = {
+        "data_parallel_size": 2,
+        "tensor_parallel_size": 2,
+        "expert_parallel_size": 1,
+    }
+    self.assertEqual(train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 4), expected_result)
+
+  @pytest.mark.cpu_only
+  def test_get_rollout_kwargs_auto_ep(self):
+    """Test case 4: ep=-1 -> verify result is calculated."""
+    # num_sampler_devices=8, tp=2, dp=2 -> ep should be 8 // (2 * 2) = 2
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=2,
+        rollout_tensor_parallelism=2,
+        rollout_expert_parallelism=-1,
+    )
+    expected_result = {
+        "data_parallel_size": 2,
+        "tensor_parallel_size": 2,
+        "expert_parallel_size": 2,
+    }
+    self.assertEqual(train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 8), expected_result)
+
+  @pytest.mark.cpu_only
+  def test_get_rollout_kwargs_errors(self):
+    """Test various error cases for get_rollout_kwargs_for_parallelism."""
+    # More than one -1
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=-1,
+        rollout_tensor_parallelism=-1,
+        rollout_expert_parallelism=1,
+    )
+    with self.assertRaisesRegex(ValueError, "At most one of .* can be -1"):
+      train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 4)
+
+    # num_devices % (tp * ep) != 0 when dp == -1
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=-1,
+        rollout_tensor_parallelism=3,
+        rollout_expert_parallelism=1,
+    )
+    with self.assertRaisesRegex(ValueError, "must be divisible by"):
+      train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 4)
+
+    # num_devices % (tp * dp) != 0 when ep == -1
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=2,
+        rollout_tensor_parallelism=3,
+        rollout_expert_parallelism=-1,
+    )
+    with self.assertRaisesRegex(ValueError, "must be divisible by"):
+      train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 8)
+
+    # num_devices % (dp * ep) != 0 when tp == -1
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=3,
+        rollout_tensor_parallelism=-1,
+        rollout_expert_parallelism=2,
+    )
+    with self.assertRaisesRegex(ValueError, "must be divisible by"):
+      train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 8)
+
+    # tp * dp * ep != num_sampler_devices when all are positive
+    sampler_config = SimpleNamespace(
+        rollout_data_parallelism=2,
+        rollout_tensor_parallelism=2,
+        rollout_expert_parallelism=1,
+    )
+    with self.assertRaisesRegex(ValueError, r"!= len\(sampler_devices\)"):
+      train_rl.get_rollout_kwargs_for_parallelism(sampler_config, 8)
+
 
 if __name__ == "__main__":
   unittest.main()