let tunix see lr hyperparams for logging

src/maxtext/trainers/post_train/rl/utils_rl.py

@@ -460,32 +460,40 @@ def extract_hash_answer(text: str) -> str | None:
 
 def get_optimizer(tmvp_config, max_train_steps):
   """Function to obtain an optax optimizer, currently we use adamw."""
-  optimizer = optax.adamw(
-      learning_rate=optax.schedules.warmup_cosine_decay_schedule(
-          init_value=0.0,
-          peak_value=tmvp_config.learning_rate,
-          # Linearly increase learning rate from 0. to learning_rate in the first
-          # warmup_steps_fraction training steps, and then gradually decrease the
-          # learning rate to 0 using cosine scheduler.
-          warmup_steps=int(tmvp_config.warmup_steps_fraction * max_train_steps),
-          decay_steps=max_train_steps,
-          end_value=0.0,
-      ),
-      b1=tmvp_config.adam_b1,
-      b2=tmvp_config.adam_b2,
-      weight_decay=tmvp_config.adam_weight_decay,
+  schedule = optax.schedules.warmup_cosine_decay_schedule(
+      init_value=0.0,
+      peak_value=tmvp_config.learning_rate,
+      # Linearly increase learning rate from 0. to learning_rate in the first
+      # warmup_steps_fraction training steps, and then gradually decrease the
+      # learning rate to 0 using cosine scheduler.
+      warmup_steps=int(tmvp_config.warmup_steps_fraction * max_train_steps),
+      decay_steps=max_train_steps,
+      end_value=0.0,
   )
 
   # TODO: @mazumdera: try optimizer offloading with adamw
   # Add gradient clipping if specified
   # Grad clipping to prevent large gradients. We find this
   # important to keep KL divergence in check.
-  if tmvp_config.gradient_clipping_threshold > 0:
-    optimizer = optax.chain(
-        optax.clip_by_global_norm(max_norm=tmvp_config.gradient_clipping_threshold),
-        optimizer,
+  def make_optimizer(learning_rate):
+    transforms = []
+    if tmvp_config.gradient_clipping_threshold > 0:
+      transforms.append(optax.clip_by_global_norm(max_norm=tmvp_config.gradient_clipping_threshold))
+    transforms.append(
+        optax.adamw(
+            learning_rate=learning_rate,
+            b1=tmvp_config.adam_b1,
+            b2=tmvp_config.adam_b2,
+            weight_decay=tmvp_config.adam_weight_decay,
+        )
     )
-  return optimizer
+    return optax.chain(*transforms)
+
+  # Wrap the entire optimizer (including gradient clipping) with
+  # inject_hyperparams so opt_state.hyperparams['learning_rate'] is at the
+  # top level of the state tree. This is required for tunix's peft_trainer to
+  # automatically read and log the per-step learning rate.
+  return optax.inject_hyperparams(make_optimizer)(learning_rate=schedule)
 
 
 def process_data(dataset_name, model_tokenizer, template_config, tmvp_config, x):

tests/post_training/unit/rl_utils_test.py

@@ -332,5 +332,42 @@ def test_without_hash(self):
     self.assertIsNone(utils_rl.extract_hash_answer(""))
 
 
+class TestGetOptimizer(unittest.TestCase):
+  """Tests for utils_rl.get_optimizer."""
+
+  def _make_optimizer_config(self, gradient_clipping_threshold=0.0):
+    return SimpleNamespace(
+        learning_rate=1e-4,
+        warmup_steps_fraction=0.1,
+        gradient_clipping_threshold=gradient_clipping_threshold,
+        adam_b1=0.9,
+        adam_b2=0.999,
+        adam_weight_decay=0.01,
+    )
+
+  @pytest.mark.cpu_only
+  def test_returns_optimizer_without_clipping(self):
+    """get_optimizer returns an optax optimizer when gradient clipping is disabled."""
+    import jax.numpy as jnp  # pylint: disable=import-outside-toplevel
+
+    config = self._make_optimizer_config(gradient_clipping_threshold=0.0)
+    opt = utils_rl.get_optimizer(config, max_train_steps=100)
+    # Should be usable: init on a simple param tree
+    params = {"w": jnp.ones(3)}
+    state = opt.init(params)
+    self.assertIn("learning_rate", state.hyperparams)
+
+  @pytest.mark.cpu_only
+  def test_returns_optimizer_with_clipping(self):
+    """get_optimizer includes gradient clipping when threshold > 0."""
+    import jax.numpy as jnp  # pylint: disable=import-outside-toplevel
+
+    config = self._make_optimizer_config(gradient_clipping_threshold=1.0)
+    opt = utils_rl.get_optimizer(config, max_train_steps=100)
+    params = {"w": jnp.ones(3)}
+    state = opt.init(params)
+    self.assertIn("learning_rate", state.hyperparams)
+
+
 if __name__ == "__main__":
   unittest.main()