Refactored the distillation input pipeline and checkpoint manager to support state restoration via maybe_restore.

Author: gagika

src/maxtext/trainers/post_train/distillation/distillation_utils.py

@@ -44,25 +44,25 @@ class DistillationForwardOutput:
   """Dataclass to carry MaxText-specific output fields."""
 
   #: logits
-  logits: jax.Array = None
+  logits: jax.Array
   #: out_projection_activations
-  out_projection_activations: jax.Array = None
+  out_projection_activations: jax.Array | None = None
 
 
 @flax.struct.dataclass(frozen=True)
 class MaxTextTrainingInput(peft_trainer.TrainingInput):
   """Extended TrainingInput dataclass to carry MaxText-specific fields."""
 
   #: Position indices for the tokens (for RoPE).
-  positions: jax.Array = None
+  positions: jax.Array | None = None
   #: Segment IDs for packed sequences (0=padding, 1+=examples).
-  decoder_segment_ids: jax.Array = None
+  decoder_segment_ids: jax.Array | None = None
   #: Ground truth target tokens (used for loss calculation and logging).
-  targets: jax.Array = None
+  targets: jax.Array | None = None
   #: Position indices for the target tokens.
-  targets_position: jax.Array = None
+  targets_position: jax.Array | None = None
   #: Segment IDs for packed target tokens.
-  targets_segmentation: jax.Array = None
+  targets_segmentation: jax.Array | None = None
 
 
 # -----------------------------------------------------------------------------
@@ -222,7 +222,7 @@ def compute_loss(
     # 3. Combine losses
     base_logit_loss = (self.alpha * soft_loss) + ((1.0 - self.alpha) * hard_loss)
 
-    feature_loss = 0.0
+    feature_loss = jnp.array(0.0)
     if self.beta_feature > 0.0:
 
       if self.layer_indices is not None:
@@ -364,6 +364,69 @@ def save(self, step, model, optimizer=None, save_only_lora_params=False, force=F
         force=force,
     )
 
+  def maybe_restore(
+      self,
+      model: Any,
+      optimizer: Any = None,
+      restore_only_lora_params: bool = False,
+  ) -> tuple[int, dict[str, Any]]:
+    """Restores model and optimizer state if a checkpoint exists, using correct sharding specs."""
+    if self._checkpoint_manager is None:
+      return 0, {}
+
+    step = self._checkpoint_manager.latest_step()
+    if step is None:
+      return 0, {}
+
+    max_logging.log(f"Restoring from checkpoint step {step}...")
+
+    # Extract student model safely
+    target_model = getattr(model, "student_model", model)
+
+    if restore_only_lora_params:
+      params = nnx.state(target_model, nnx.LoRAParam)
+    else:
+      params = nnx.state(target_model)
+
+    def map_to_pspec(data):
+      if hasattr(data, "sharding"):
+        return checkpoint.type_handlers.ArrayRestoreArgs(sharding=data.sharding)
+      return None
+
+    restore_args = jax.tree.map(map_to_pspec, params)
+
+    cp_restore_args = {
+        "model_params": checkpoint.args.PyTreeRestore(
+            item=params,
+            restore_args=restore_args,
+        )
+    }
+
+    if optimizer is not None:
+      optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)
+      opt_restore_args = jax.tree.map(map_to_pspec, optimizer_state)
+      cp_restore_args["optimizer_state"] = checkpoint.args.PyTreeRestore(
+          item=optimizer_state,
+          restore_args=opt_restore_args,
+      )
+
+    restored = self._checkpoint_manager.restore(
+        step,
+        args=checkpoint.args.Composite(**cp_restore_args),
+    )
+
+    nnx.update(target_model, restored.model_params)
+    if optimizer is not None:
+      nnx.update(optimizer, restored.optimizer_state)
+
+    metadata = self._checkpoint_manager.metadata(step)
+    if metadata and hasattr(metadata, "custom_metadata") and metadata.custom_metadata is not None:
+      custom_metadata = metadata.custom_metadata
+    else:
+      custom_metadata = {}
+
+    return step, dict(custom_metadata)
+
   def restore_iterator(self):
     """Restores the iterator using MaxText's logic."""
     if self._checkpoint_manager is None or self._iterator is None: