feat: add ORPO trainer support

tests/sft/dpo/dpo_trainer_test.py

@@ -270,10 +270,14 @@ def test_dpo_loss_fn(self):
     with mock.patch.object(
         common, "get_per_token_logps", return_value=jnp.array(per_token_logps)
     ):
-      loss, _ = dpo_lib.dpo_loss_fn(model, train_example, 0.1, 0)
+      loss, _ = dpo_lib.dpo_loss_fn(
+          model, train_example, beta=0.1, label_smoothing=0
+      )
       np.testing.assert_allclose(loss, 0.753059, atol=1e-5)
 
-      loss, _ = dpo_lib.dpo_loss_fn(model, train_example, 0.1, 0.3)
+      loss, _ = dpo_lib.dpo_loss_fn(
+          model, train_example, beta=0.1, label_smoothing=0.3
+      )
       np.testing.assert_allclose(loss, 0.925447, atol=1e-5)
 
   def test_dpo_prepare_inputs_for_strings(self):

tests/sft/dpo/orpo_trainer_test.py

@@ -0,0 +1,361 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from unittest import mock
+from absl.testing import absltest
+from absl.testing import parameterized
+from flax import nnx
+from grain import python as grain
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+from tunix.rl import common
+from tunix.sft.dpo import dpo_trainer as orpo_lib
+from tunix.tests import test_common as tc
+
+jax.config.update("jax_threefry_partitionable", False)
+# jax.config.update("jax_debug_nans", True) # useful for debugging NaN
+
+
+class MySource(grain.RandomAccessDataSource):
+
+  def __init__(self, data):
+    self._data = data
+
+  def __getitem__(self, idx):
+    return self._data[idx]
+
+  def __len__(self):
+    return len(self._data)
+
+
+def _dummy_dataset(
+    source: MySource,
+    prompt_ids: np.ndarray,
+    prompt_mask: np.ndarray,
+    chosen_ids: np.ndarray,
+    chosen_mask: np.ndarray,
+    rejected_ids: np.ndarray,
+    rejected_mask: np.ndarray,
+):
+  return grain.MapDataset.source(source).map(
+      lambda x: orpo_lib.TrainingInput(
+          prompt_ids=prompt_ids,
+          prompt_mask=prompt_mask,
+          chosen_ids=chosen_ids,
+          chosen_mask=chosen_mask,
+          rejected_ids=rejected_ids,
+          rejected_mask=rejected_mask,
+      )
+  )
+
+
+def _dummy_string_dataset(
+    source: MySource,
+    prompts: np.ndarray,
+    chosen_responses: np.ndarray,
+    rejected_responses: np.ndarray,
+    return_dict=False,
+):
+  ds = grain.MapDataset.source(source)
+  if return_dict:
+    return ds.map(
+        lambda x: {
+            "prompts": prompts,
+            "chosen_responses": chosen_responses,
+            "rejected_responses": rejected_responses,
+        }
+    )
+  else:
+    return ds.map(
+        lambda x: orpo_lib.DataInput(
+            prompts=prompts,
+            chosen_responses=chosen_responses,
+            rejected_responses=rejected_responses,
+        )
+    )
+
+
+class ORPOTrainerTest(parameterized.TestCase):
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="basic_training",
+          prompt_ids=np.arange(0, 10).reshape(2, 5),
+          prompt_mask=np.ones((2, 5)),
+          chosen_ids=np.arange(10, 20).reshape(2, 5),
+          chosen_mask=np.ones((2, 5)),
+          rejected_ids=np.arange(20, 30).reshape(2, 5),
+          rejected_mask=np.ones((2, 5)),
+      ),
+  )
+  def test_orpo_trainer(
+      self,
+      prompt_ids,
+      prompt_mask,
+      chosen_ids,
+      chosen_mask,
+      rejected_ids,
+      rejected_mask,
+  ):
+    model = tc.ToyTransformer(config=tc.ModelConfig(), rngs=nnx.Rngs(0))
+    original_variables = jax.tree.map(jnp.copy, nnx.state(model, nnx.Param))
+    orpo_config = orpo_lib.ORPOTrainingConfig(
+        algorithm="orpo",
+        eval_every_n_steps=5,
+        max_steps=10,
+    )
+    orpo_trainer = orpo_lib.ORPOTrainer(
+        model=model,
+        ref_model=None,
+        optimizer=optax.sgd(1e-3),
+        training_config=orpo_config,
+    )
+    train_ds = _dummy_dataset(
+        MySource(np.arange(10)),
+        prompt_ids,
+        prompt_mask,
+        chosen_ids,
+        chosen_mask,
+        rejected_ids,
+        rejected_mask,
+    )
+    eval_ds = _dummy_dataset(
+        MySource(np.arange(2)),
+        prompt_ids,
+        prompt_mask,
+        chosen_ids,
+        chosen_mask,
+        rejected_ids,
+        rejected_mask,
+    )
+    orpo_trainer.train(train_ds, eval_ds=eval_ds)
+
+    variables = nnx.state(model, nnx.Param)
+    jax.tree.map_with_path(tc.assert_not_equal, original_variables, variables)
+
+    for metric_name in [
+        "rewards/chosen",
+        "rewards/rejected",
+        "rewards/margin",
+        "rewards/accuracy",
+        "log_probs/chosen",
+        "log_probs/rejected",
+        "odds_ratio",
+    ]:
+      self.assertLen(
+          orpo_trainer.metrics_logger.get_metric_history(
+              "", metric_name, "train"
+          ),
+          orpo_trainer._train_steps,
+      )
+      self.assertLen(
+          orpo_trainer.metrics_logger.get_metric_history("", metric_name, "eval"),
+          3,
+      )
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="dataclass_inputs",
+          train_ds=_dummy_string_dataset(
+              MySource(np.arange(10)),
+              prompts=["Tunix", "Parallax"],
+              chosen_responses=["PT", "distributed training"],
+              rejected_responses=["optimizer library", "quantization"],
+          ),
+      ),
+      dict(
+          testcase_name="dict_inputs",
+          train_ds=_dummy_string_dataset(
+              MySource(np.arange(10)),
+              prompts=["Tunix", "Parallax"],
+              chosen_responses=["PT", "distributed training"],
+              rejected_responses=["optimizer library", "quantization"],
+              return_dict=True,
+          ),
+      ),
+  )
+  def test_orpo_trainer_with_string_inputs(self, train_ds):
+    tokenizer = tc.MockVocab()
+    model = tc.ToyTransformer(
+        config=tc.ModelConfig(vocab_size=tokenizer.GetPieceSize()),
+        rngs=nnx.Rngs(0),
+    )
+    original_variables = jax.tree.map(jnp.copy, nnx.state(model, nnx.Param))
+    orpo_config = orpo_lib.ORPOTrainingConfig(
+        algorithm="orpo",
+        eval_every_n_steps=10,
+        max_steps=10,
+        max_prompt_length=3,
+        max_response_length=3,
+    )
+    orpo_trainer = orpo_lib.ORPOTrainer(
+        model=model,
+        ref_model=None,
+        optimizer=optax.sgd(1e-3),
+        training_config=orpo_config,
+        tokenizer=tokenizer,
+    )
+    orpo_trainer.train(train_ds, None)
+
+    variables = nnx.state(model, nnx.Param)
+    jax.tree.map_with_path(tc.assert_not_equal, original_variables, variables)
+
+    for metric_name in [
+        "rewards/chosen",
+        "rewards/rejected",
+        "rewards/margin",
+        "rewards/accuracy",
+    ]:
+      self.assertLen(
+          orpo_trainer.metrics_logger.get_metric_history(
+              "", metric_name, "train"
+          ),
+          orpo_trainer._train_steps,
+      )
+
+  def test_orpo_loss_fn(self):
+    """Test ORPO loss function directly with mocked logps."""
+    np.random.seed(0)
+    model = tc.ToyTransformer(config=tc.ModelConfig(), rngs=nnx.Rngs(0))
+    # Use negative log probs (as they should be in reality)
+    per_token_logps = -np.abs(np.random.normal(2, 1, size=(8, 4)))
+    train_example = orpo_lib.TrainExample(
+        input_ids=jnp.arange(0, 32).reshape(8, 4),
+        positions=jnp.ones((8, 4)),
+        attention_mask=jnp.ones((8, 4, 4)),
+        ref_chosen_logps=None,
+        ref_rejected_logps=None,
+        completion_mask=jnp.ones((8, 4)),
+        logits_to_keep=4,
+    )
+
+    with mock.patch.object(
+        common,
+        "get_per_token_logps",
+        return_value=jnp.array(per_token_logps),
+    ):
+      loss, aux = orpo_lib.dpo_loss_fn(
+          model,
+          train_example,
+          algorithm="orpo",
+          lambda_orpo=0.1,
+          label_smoothing=0,
+      )
+      # Loss should be a scalar and finite
+      self.assertEqual(loss.shape, ())
+      self.assertTrue(jnp.isfinite(loss))
+
+      # Check that aux metrics exist
+      self.assertIn("rewards/chosen", aux)
+      self.assertIn("rewards/rejected", aux)
+      self.assertIn("rewards/margin", aux)
+      self.assertIn("rewards/accuracy", aux)
+      self.assertIn("log_probs/chosen", aux)
+      self.assertIn("log_probs/rejected", aux)
+      self.assertIn("odds_ratio", aux)
+
+      # Check that accuracy is between 0 and 1
+      self.assertGreaterEqual(aux["rewards/accuracy"], 0.0)
+      self.assertLessEqual(aux["rewards/accuracy"], 1.0)
+
+  def test_orpo_prepare_inputs_for_strings(self):
+    tokenizer = tc.MockVocab()
+
+    model = tc.ToyTransformer(
+        config=tc.ModelConfig(vocab_size=tokenizer.GetPieceSize()),
+        rngs=nnx.Rngs(0),
+    )
+    orpo_trainer = orpo_lib.ORPOTrainer(
+        model=model,
+        ref_model=None,
+        optimizer=optax.sgd(1e-3),
+        training_config=orpo_lib.ORPOTrainingConfig(
+            algorithm="orpo",
+            eval_every_n_steps=10,
+            max_steps=10,
+            max_prompt_length=3,
+            max_response_length=3,
+        ),
+        tokenizer=tokenizer,
+    )
+
+    # These are random strings, they hold no meaning.
+    training_input = orpo_lib.DataInput(
+        prompts=["Tunix", "Parallax"],
+        chosen_responses=["PT", "distributed training"],
+        rejected_responses=["optimizer library", "quantization"],
+    )
+    out = orpo_trainer._prepare_inputs(training_input)
+
+    expected_input_ids = np.array([
+        [0, 1, 14, 1, 16, 0],
+        [0, 1, 15, 1, 18, 19],
+        [0, 1, 14, 1, 20, 17],
+        [0, 1, 15, 1, 21, 0],
+    ])
+    np.testing.assert_array_equal(out.input_ids, expected_input_ids)
+    self.assertEqual(np.sum(out.attention_mask[0]), 14)
+    self.assertEqual(np.sum(out.attention_mask[1]), 15)
+    self.assertEqual(np.sum(out.attention_mask[2]), 15)
+    self.assertEqual(np.sum(out.attention_mask[3]), 14)
+    expected_completion_mask = np.array(
+        [[1, 1, 0], [1, 1, 1], [1, 1, 1], [1, 1, 0]]
+    )
+    np.testing.assert_array_equal(out.completion_mask, expected_completion_mask)
+    self.assertEqual(out.logits_to_keep, 3)
+
+  def test_orpo_prepare_inputs(self):
+    model = tc.ToyTransformer(config=tc.ModelConfig(), rngs=nnx.Rngs(0))
+    orpo_trainer = orpo_lib.ORPOTrainer(
+        model=model,
+        ref_model=None,
+        optimizer=optax.sgd(1e-3),
+        training_config=orpo_lib.ORPOTrainingConfig(
+            algorithm="orpo",
+            eval_every_n_steps=10,
+            max_steps=10,
+        ),
+    )
+
+    training_input = orpo_lib.TrainingInput(
+        prompt_ids=np.array([[1, 2, 3, 4, 5], [0, 0, 1, 2, 3]]),
+        prompt_mask=np.array([[1, 1, 1, 1, 1], [0, 0, 1, 1, 1]]),
+        chosen_ids=np.array([[10, 11, 12, 0], [13, 14, 15, 16]]),
+        chosen_mask=np.array([[1, 1, 1, 0], [1, 1, 1, 1]]),
+        rejected_ids=np.array([[20, 21, 22, 0], [23, 0, 0, 0]]),
+        rejected_mask=np.array([[1, 1, 1, 0], [1, 0, 0, 0]]),
+    )
+    out = orpo_trainer._prepare_inputs(training_input)
+    expected_input_ids = np.array([
+        [1, 2, 3, 4, 5, 10, 11, 12, 0],
+        [0, 0, 1, 2, 3, 13, 14, 15, 16],
+        [1, 2, 3, 4, 5, 20, 21, 22, 0],
+        [0, 0, 1, 2, 3, 23, 0, 0, 0],
+    ])
+    np.testing.assert_array_equal(out.input_ids, expected_input_ids)
+    self.assertEqual(np.sum(out.attention_mask[0]), 44)
+    self.assertEqual(np.sum(out.attention_mask[1]), 28)
+    self.assertEqual(np.sum(out.attention_mask[2]), 44)
+    self.assertEqual(np.sum(out.attention_mask[3]), 22)
+    expected_completion_mask = np.array(
+        [[1, 1, 1, 0], [1, 1, 1, 1], [1, 1, 1, 0], [1, 0, 0, 0]]
+    )
+    np.testing.assert_array_equal(out.completion_mask, expected_completion_mask)
+    self.assertEqual(out.logits_to_keep, 4)
+
+
+if __name__ == "__main__":
+  absltest.main()

tunix/__init__.py

@@ -54,6 +54,10 @@
 from tunix.sft.dpo.dpo_trainer import DpoTrainer
 from tunix.sft.dpo.dpo_trainer import DPOTrainingConfig
 from tunix.sft.dpo.dpo_trainer import DpoTrainingConfig
+from tunix.sft.dpo.dpo_trainer import ORPOTrainer
+from tunix.sft.dpo.dpo_trainer import OrpoTrainer
+from tunix.sft.dpo.dpo_trainer import ORPOTrainingConfig
+from tunix.sft.dpo.dpo_trainer import OrpoTrainingConfig
 from tunix.sft.metrics_logger import MetricsLogger
 from tunix.sft.metrics_logger import MetricsLoggerOptions
 from tunix.sft.peft_trainer import PeftTrainer