[Tunix] Replace legacy TrainExample with RLTrainerPayload in experimental orchestrator and workers.

tests/experimental/orchestrator/batch_assembly_test.py

@@ -19,7 +19,6 @@
 import numpy as np
 from tunix.experimental.common import datatypes
 from tunix.experimental.orchestrator import batch_assembly
-from tunix.rl import common as rl_common
 
 
 class HelperFunctionsTest(absltest.TestCase):
@@ -104,8 +103,8 @@ def test_completion_aligned_slices_full_sequence(self):
 
 class WithRefPerTokenLogpsTest(absltest.TestCase):
 
-  def _make_train_example(self, b=2, p=3, c=4):
-    return rl_common.TrainExample(
+  def _make_payload(self, b=2, p=3, c=4):
+    return datatypes.RLTrainerPayload(
         prompt_ids=np.ones((b, p), dtype=np.int32),
         prompt_mask=np.ones((b, p), dtype=np.float32),
         completion_ids=np.ones((b, c), dtype=np.int32),
@@ -116,31 +115,33 @@ def _make_train_example(self, b=2, p=3, c=4):
     )
 
   def test_success_with_ndarray(self):
-    batch = self._make_train_example(b=2, p=3, c=4)
+    batch = self._make_payload(b=2, p=3, c=4)
     ref_logps = np.full((2, 4), -0.5, dtype=np.float32)
     updated = batch_assembly.with_ref_per_token_logps(batch, ref_logps)
 
+    self.assertIsInstance(updated, datatypes.RLTrainerPayload)
     self.assertIsNotNone(updated.ref_per_token_logps)
     self.assertEqual(updated.ref_per_token_logps.shape, (2, 4))
     np.testing.assert_allclose(updated.ref_per_token_logps, ref_logps)
     np.testing.assert_array_equal(updated.prompt_ids, batch.prompt_ids)
     np.testing.assert_array_equal(updated.completion_ids, batch.completion_ids)
 
   def test_success_with_logprobs_response(self):
-    batch = self._make_train_example(b=2, p=3, c=4)
+    batch = self._make_payload(b=2, p=3, c=4)
     resp = datatypes.LogprobsResponse(
         per_token_logps=np.full((2, 4), -0.8, dtype=np.float32)
     )
     updated = batch_assembly.with_ref_per_token_logps(batch, resp)
 
+    self.assertIsInstance(updated, datatypes.RLTrainerPayload)
     self.assertIsNotNone(updated.ref_per_token_logps)
     self.assertEqual(updated.ref_per_token_logps.shape, (2, 4))
     np.testing.assert_allclose(
         updated.ref_per_token_logps, resp.per_token_logps
     )
 
   def test_error_in_logprobs_response_raises_runtime_error(self):
-    batch = self._make_train_example(b=2, p=3, c=4)
+    batch = self._make_payload(b=2, p=3, c=4)
     resp = datatypes.LogprobsResponse(
         per_token_logps=None,
         error=datatypes.ErrorInfo(
@@ -150,18 +151,14 @@ def test_error_in_logprobs_response_raises_runtime_error(self):
     with self.assertRaisesRegex(RuntimeError, "inference worker failed"):
       batch_assembly.with_ref_per_token_logps(batch, resp)
 
-  def test_rejects_non_train_example(self):
-    payload = datatypes.RLTrainerPayload(
-        token_ids=np.array([1, 2], dtype=np.int32),
-        token_mask=np.array([1, 1], dtype=np.float32),
-        loss_mask=np.array([0, 1], dtype=np.float32),
-        advantages=np.array([1.0, 1.0], dtype=np.float32),
-    )
-    with self.assertRaisesRegex(TypeError, "expects a padded TrainExample"):
-      batch_assembly.with_ref_per_token_logps(payload, np.zeros((2, 2)))
+  def test_rejects_unsupported_type(self):
+    with self.assertRaisesRegex(TypeError, "expects a padded RLTrainerPayload"):
+      batch_assembly.with_ref_per_token_logps(
+          {"raw": "batch"}, np.zeros((2, 2))
+      )
 
   def test_mismatched_shape_raises_value_error(self):
-    batch = self._make_train_example(b=2, p=3, c=4)
+    batch = self._make_payload(b=2, p=3, c=4)
     bad_shape_logps = np.zeros((2, 3), dtype=np.float32)
     with self.assertRaisesRegex(
         ValueError,
@@ -277,121 +274,6 @@ def test_sequence_packed_assembler_multiple_bins(self):
     self.assertEqual(payloads[1].token_ids.shape, (1, 12))
 
 
-class GRPOTrainExampleAssemblerTest(absltest.TestCase):
-
-  def test_rejects_non_positive_batch_size(self):
-    with self.assertRaisesRegex(ValueError, "batch size must be positive"):
-      batch_assembly.GRPOTrainExampleAssembler(
-          batch_size=0,
-          max_prompt_length=4,
-          max_response_length=5,
-          pad_id=0,
-      )
-
-  def test_empty_input_returns_empty_list(self):
-    assembler = batch_assembly.GRPOTrainExampleAssembler(
-        batch_size=2,
-        max_prompt_length=4,
-        max_response_length=5,
-        pad_id=0,
-    )
-    self.assertEmpty(assembler.pack([]))
-
-  def test_grpo_train_example_assembler_basic(self):
-    payload = datatypes.RLTrainerPayload(
-        token_ids=np.array([10, 11, 20, 21, 22], dtype=np.int32),
-        token_mask=np.ones(5, dtype=np.float32),
-        loss_mask=np.array([0, 0, 1, 1, 0], dtype=np.float32),
-        action_mask=np.array([0, 0, 1, 1, 0], dtype=np.float32),
-        advantages=np.array([0, 0, 2, 2, 2], dtype=np.float32),
-        prompt_ids=np.array([10, 11], dtype=np.int32),
-        prompt_mask=np.ones(2, dtype=np.float32),
-        completion_ids=np.array([20, 21, 22], dtype=np.int32),
-        completion_mask=np.array([1, 1, 0], dtype=np.float32),
-    )
-
-    assembler = batch_assembly.GRPOTrainExampleAssembler(
-        batch_size=2,
-        max_prompt_length=4,
-        max_response_length=5,
-        pad_id=0,
-    )
-    train_example = assembler.pack([payload])[0]
-
-    self.assertEqual(train_example.prompt_ids.shape, (2, 4))
-    self.assertEqual(train_example.completion_ids.shape, (2, 5))
-    np.testing.assert_array_equal(
-        train_example.prompt_ids[0], np.array([0, 0, 10, 11])
-    )
-    np.testing.assert_array_equal(
-        train_example.completion_ids[0], np.array([20, 21, 22, 0, 0])
-    )
-    np.testing.assert_array_equal(
-        train_example.completion_mask[0], np.array([1, 1, 0, 0, 0])
-    )
-    np.testing.assert_array_equal(
-        train_example.advantages[0], np.array([2, 2, 2, 0, 0])
-    )
-
-  def test_grpo_assembler_optional_fields_propagation(self):
-    payload = datatypes.RLTrainerPayload(
-        token_ids=np.array([10, 11, 20, 21, 22], dtype=np.int32),
-        token_mask=np.ones(5, dtype=np.float32),
-        loss_mask=np.array([0, 0, 1, 1, 1], dtype=np.float32),
-        action_mask=np.array([0, 0, 1, 1, 1], dtype=np.float32),
-        advantages=np.array([2, 2, 2], dtype=np.float32),
-        prompt_ids=np.array([10, 11], dtype=np.int32),
-        prompt_mask=np.ones(2, dtype=np.float32),
-        completion_ids=np.array([20, 21, 22], dtype=np.int32),
-        completion_mask=np.ones(3, dtype=np.float32),
-        ref_per_token_logps=np.array([-0.3, -0.4, -0.5], dtype=np.float32),
-        old_per_token_logps=np.array([-0.1, -0.2, -0.3], dtype=np.float32),
-    )
-
-    assembler = batch_assembly.GRPOTrainExampleAssembler(
-        batch_size=2,
-        max_prompt_length=4,
-        max_response_length=5,
-        pad_id=0,
-    )
-    train_example = assembler.pack([payload])[0]
-
-    self.assertIsNotNone(train_example.ref_per_token_logps)
-    self.assertIsNotNone(train_example.old_per_token_logps)
-
-    self.assertEqual(train_example.ref_per_token_logps.shape, (2, 5))
-    self.assertEqual(train_example.old_per_token_logps.shape, (2, 5))
-
-    np.testing.assert_allclose(
-        train_example.ref_per_token_logps[0], [-0.3, -0.4, -0.5, 0.0, 0.0]
-    )
-    np.testing.assert_allclose(
-        train_example.old_per_token_logps[0], [-0.1, -0.2, -0.3, 0.0, 0.0]
-    )
-
-  def test_grpo_assembler_chunks_multiple_microbatches(self):
-    payload = datatypes.RLTrainerPayload(
-        token_ids=np.array([1, 2, 3], dtype=np.int32),
-        token_mask=np.ones(3, dtype=np.float32),
-        loss_mask=np.array([0, 1, 1], dtype=np.float32),
-        advantages=np.array([1.0, 1.0], dtype=np.float32),
-        prompt_ids=np.array([1], dtype=np.int32),
-        completion_ids=np.array([2, 3], dtype=np.int32),
-    )
-
-    assembler = batch_assembly.GRPOTrainExampleAssembler(
-        batch_size=2,
-        max_prompt_length=3,
-        max_response_length=4,
-        pad_id=0,
-    )
-    train_examples = assembler.pack([payload, payload, payload])
-
-    self.assertLen(train_examples, 2)
-    self.assertEqual(train_examples[0].prompt_ids.shape, (2, 3))
-    self.assertEqual(train_examples[1].prompt_ids.shape, (2, 3))
-
-
 def _make_payload(
     prompt_len: int,
     completion_len: int,
@@ -585,8 +467,9 @@ def test_scalar_advantage_broadcasts_over_completion(self):
     np.testing.assert_allclose(payload.advantages[0], [2.5, 2.5, 2.5, 0, 0])
 
   def test_sequence_aligned_advantage_is_sliced_to_completion(self):
-    item = _make_payload(2, 3)
-    item.advantages = np.array([0, 0, 2, 2, 2], dtype=np.float32)
+    item = _make_payload(
+        2, 3, advantage=np.array([0, 0, 2, 2, 2], dtype=np.float32)
+    )
     payload = self._assembler().pack([item])[0]
 
     np.testing.assert_allclose(payload.advantages[0], [2, 2, 2, 0, 0])

tests/experimental/orchestrator/rl_program_test.py

@@ -28,7 +28,6 @@
 from tunix.experimental.orchestrator import distributed_rl_engine
 from tunix.experimental.orchestrator import rl_program
 from tunix.experimental.worker import remote_execution
-from tunix.rl import common as rl_common
 from tunix.sft import metrics_logger as metrics_logger_lib
 from tunix.sft import utils as sft_utils
 
@@ -726,7 +725,7 @@ async def _run():
   def test_reference_kl_logprobs_scoring_in_train_stage(self):
     async def _run():
       self.mock_algo.requires_reference_kl = True
-      mock_train_example = rl_common.TrainExample(
+      mock_payload = datatypes.RLTrainerPayload(
           prompt_ids=np.array([[1, 2]], dtype=np.int32),
           prompt_mask=np.ones((1, 2), dtype=np.float32),
           completion_ids=np.array([[3, 4]], dtype=np.int32),
@@ -735,7 +734,7 @@ async def _run():
           ref_per_token_logps=None,
           old_per_token_logps=None,
       )
-      self.assembler.pack = mock.MagicMock(return_value=[mock_train_example])
+      self.assembler.pack = mock.MagicMock(return_value=[mock_payload])
       self.mock_engine.per_token_logps = mock.AsyncMock(
           return_value=np.array([[-0.1, -0.2]], dtype=np.float32)
       )
@@ -746,7 +745,7 @@ async def _run():
       await program.run_async(self.mock_engine)
 
       self.mock_engine.per_token_logps.assert_called_once_with(
-          datatypes.Role.REFERENCE, items=mock_train_example
+          datatypes.Role.REFERENCE, items=mock_payload
       )
       self.assertEqual(program.step, 1)
 
@@ -755,7 +754,7 @@ async def _run():
   def test_reference_kl_raises_type_error_for_invalid_microbatch(self):
     async def _run():
       self.mock_algo.requires_reference_kl = True
-      # Returning a raw dict instead of TrainExample
+      # Returning a raw dict instead of RLTrainerPayload
       self.assembler.pack = mock.MagicMock(return_value=[{"raw": "batch"}])
       _set_mock_poll_batches(self.mock_engine, _make_trajectory_group())
       program = self._create_program(dataset=["prompt_0"])

tests/experimental/worker/inference_worker_test.py

@@ -21,7 +21,6 @@
 from tunix.experimental.common import datatypes
 from tunix.experimental.common import rpc_utils
 from tunix.experimental.worker import inference_worker as inference_lib
-from tunix.rl import common as rl_common
 
 WorkerState = datatypes.WorkerState
 
@@ -107,7 +106,7 @@ def test_chunking_matches_single_pass(self):
 
   def test_per_token_logps_uses_padded_batch_without_repadding(self):
     core = _StubCore()
-    batch = rl_common.TrainExample(
+    batch = datatypes.RLTrainerPayload(
         prompt_ids=np.array([[0, 0, 5, 6], [0, 7, 8, 9]], dtype=np.int32),
         prompt_mask=np.array([[0, 0, 1, 1], [0, 1, 1, 1]], dtype=np.float32),
         completion_ids=np.array([[10, 11, 0], [12, 0, 0]], dtype=np.int32),

tunix/experimental/common/datatypes.py

@@ -23,6 +23,7 @@
 import enum
 import time
 from typing import Any, Dict
+import flax
 import uuid
 from jax.typing import ArrayLike  # pylint: disable=g-importing-member
 import numpy as np
@@ -519,7 +520,7 @@ def __post_init__(self):
 ##### Training DTOs #####
 
 
-@dataclasses.dataclass(kw_only=True)
+@flax.struct.dataclass(frozen=True, kw_only=True)
 class TrainerPayload:
   """Base class for generic trainer payloads.
 
@@ -540,7 +541,7 @@ class TrainerPayload:
   segment_positions: ArrayLike | None = None
 
 
-@dataclasses.dataclass(kw_only=True)
+@flax.struct.dataclass(frozen=True, kw_only=True)
 class SFTTrainerPayload(TrainerPayload):
   """Supervised Fine-Tuning (SFT) trainer payload.
 
@@ -560,7 +561,7 @@ class SFTTrainerPayload(TrainerPayload):
 
 # TODO(tunix-dev): Introduce PPOTrainerPayload to replace generic
 # RLTrainerPayload when PPO specific fields are needed.
-@dataclasses.dataclass(kw_only=True)
+@flax.struct.dataclass(frozen=True, kw_only=True)
 class RLTrainerPayload(TrainerPayload):
   """RL training payload.
 
@@ -582,8 +583,8 @@ class RLTrainerPayload(TrainerPayload):
     metadata: Extra payload metadata dictionary.
   """
 
-  advantages: ArrayLike
-  loss_mask: ArrayLike
+  advantages: ArrayLike | None = None
+  loss_mask: ArrayLike | None = None
   action_mask: ArrayLike | None = None
   # TODO(tunix-dev): make prompt_ids/mask and completion_ids/mask required after
   # SequencePackedBatchAssembler refactor is done.
@@ -596,7 +597,9 @@ class RLTrainerPayload(TrainerPayload):
   sampler_is_weights: ArrayLike | None = None
   returns: ArrayLike | None = None
   old_values: ArrayLike | None = None
-  metadata: dict[str, Any] = dataclasses.field(default_factory=dict)
+  metadata: dict[str, Any] = flax.struct.field(
+      default_factory=dict, pytree_node=False
+  )
   # TODO(tunix-dev): add ppo specific fields in PPORLTrainerPayload.
 
 

tunix/experimental/examples/math_gsm8k_dist/run_gsm8k_dist_grpo.py

@@ -263,7 +263,7 @@ def _grpo_model_input(
     pad_id: int,
     eos_id: int,
 ) -> dict[str, Any]:
-  """Maps a TrainExample microbatch to algo_core.grpo_loss_fn kwargs."""
+  """Maps an RLTrainerPayload microbatch to algo_core.grpo_loss_fn kwargs."""
   return {
       "train_example": train_example,
       "algo_config": algo_config,
@@ -592,7 +592,7 @@ def accept_worker(hostname: str, _: int, metadata: bytes) -> None:
       dataset=_iter_prompt_items(args),
       max_steps=args.max_steps,
       reward_fns=reward_fns,
-      assembler=batch_assembly.GRPOTrainExampleAssembler(
+      assembler=batch_assembly.PaddedBatchAssembler(
           batch_size=args.train_micro_batch_size,
           max_prompt_length=args.max_prompt_length,
           max_response_length=args.max_response_length,