feat(scan_layers): verify scan_layers compatibility from checkpoint metadata

src/maxtext/checkpoint_conversion/to_huggingface.py

@@ -97,6 +97,7 @@
 from maxtext.utils import max_logging
 from maxtext.utils import max_utils
 from maxtext.utils.globals import HF_IDS
+from maxtext.utils.lora_utils import sync_lora_metadata
 
 
 flags.DEFINE_bool(
@@ -451,6 +452,9 @@ def main(argv: Sequence[str]) -> None:
   if not load_parameters_path and not lora_restore_path:
     raise ValueError("Either load_parameters_path or lora_restore_path must be specified.")
 
+  if lora_restore_path:
+    sync_lora_metadata(config)
+
   # Load Maxtext checkpoint using Orbax (now smart enough to load both if present)
   max_logging.log("\nLoading Orbax checkpoint(s)...")
   start = time.time()

src/maxtext/common/checkpointing.py

@@ -1014,6 +1014,26 @@ def load_params_from_path(
   return restored["params"]
 
 
+def load_checkpoint_metadata(checkpoint_path: str | epath.Path) -> dict[str, Any]:
+  """Loads custom metadata from an Orbax checkpoint at the specified path.
+
+  Args:
+    checkpoint_path: Path to the checkpoint directory.
+
+  Returns:
+    A dictionary of custom metadata if found, otherwise an empty dictionary.
+  """
+  path = epath.Path(checkpoint_path)
+  try:
+    ckptr = ocp.StandardCheckpointer()
+    metadata = ckptr.metadata(path)
+    if metadata and metadata.custom_metadata:
+      return metadata.custom_metadata
+  except Exception as e:  # pylint: disable=broad-except
+    max_logging.log(f"Unexpected error loading checkpoint metadata at {path}: {e}")
+  return {}
+
+
 def save_params_to_path(checkpoint_dir, params, use_ocdbt=True, use_zarr3=True):
   """Save decode params in checkpoint at specified path."""
   assert checkpoint_dir, "checkpoint_dir is not defined."
@@ -1142,11 +1162,20 @@ def save_checkpoint(checkpoint_manager, step, state, config=None, data_iterator=
         grain_iters_to_save.append((data_iter.local_iterator, process_index, process_count_total))
       save_args_composite["iter"] = GrainCheckpointSave(item=grain_iters_to_save)
 
+  if config:
+    custom_metadata = {}
+    if hasattr(config, "scan_layers"):
+      custom_metadata["scan_layers"] = config.scan_layers
+    if hasattr(config, "lora") and config.lora and getattr(config.lora, "lora_rank", 0) > 0:
+      custom_metadata["lora"] = config.lora.model_dump()
+
   match (checkpoint_manager, config, data_iterator):
     case (checkpoint_manager, _, _) if isinstance(
         checkpoint_manager, (EmergencyCheckpointManager, EmergencyReplicatorCheckpointManager)
     ):
       replicator_error_handler(config)
       return checkpoint_manager.save(step, args=Composite(state=checkpoint_args), force=force)
     case _:
-      return checkpoint_manager.save(step, args=Composite(**save_args_composite), force=force)
+      return checkpoint_manager.save(
+          step, args=Composite(**save_args_composite), force=force, custom_metadata=custom_metadata
+      )

src/maxtext/utils/lora_utils.py

@@ -515,6 +515,49 @@ def _verify_lora_parameters(lora_model: nnx.Module, mt_config: pyconfig.HyperPar
   )
 
 
+def sync_lora_metadata(config: pyconfig.HyperParameters) -> None:
+  """Syncs LoRA parameters (rank, alpha) from the checkpoint sidecar metadata if present.
+
+  If configuration values are set to non-default values (i.e. rank > 0 or alpha > 0.0)
+  and differ from the checkpoint metadata values, we raise a ValueError to fail the run.
+  If they are at default values, we sync them from the checkpoint.
+  """
+  lora_restore_path = config.lora.lora_restore_path
+  if not lora_restore_path:
+    return
+
+  try:
+    custom_metadata = checkpointing.load_checkpoint_metadata(lora_restore_path)
+    lora_meta = custom_metadata.get("lora")
+    if lora_meta:
+      meta_rank = lora_meta.get("lora_rank", config.lora.lora_rank)
+      meta_alpha = lora_meta.get("lora_alpha", config.lora.lora_alpha)
+
+      # Check lora_rank
+      if config.lora.lora_rank not in (0, meta_rank):
+        raise ValueError(
+            f"Configured lora_rank ({config.lora.lora_rank}) does not match "
+            f"checkpoint metadata lora_rank ({meta_rank}) at {lora_restore_path}."
+        )
+      # Check lora_alpha
+      if config.lora.lora_alpha not in (0.0, meta_alpha):
+        raise ValueError(
+            f"Configured lora_alpha ({config.lora.lora_alpha}) does not match "
+            f"checkpoint metadata lora_alpha ({meta_alpha}) at {lora_restore_path}."
+        )
+
+      config.lora.lora_rank = meta_rank
+      config.lora.lora_alpha = meta_alpha
+      max_logging.log(
+          f"Synced LoRA parameters from Orbax metadata at {lora_restore_path}: "
+          f"rank={config.lora.lora_rank}, alpha={config.lora.lora_alpha}"
+      )
+  except ValueError:
+    raise
+  except Exception as e:  # pylint: disable=broad-except
+    max_logging.log(f"Warning: Failed to load/sync LoRA metadata: {e}")
+
+
 def apply_lora_to_model(
     model: nnx.Module,
     mesh: Optional[jax.sharding.Mesh],
@@ -585,6 +628,8 @@ def _safe_reshard(var, sharding_spec):
 def restore_lora_from_path(trainer: Any, mt_config: pyconfig.HyperParameters) -> Any:
   """Restores LoRA parameter weights from an external Orbax checkpoint for a fresh run."""
   lora_restore_path = mt_config.lora.lora_restore_path
+  if not lora_restore_path:
+    return trainer
 
   train_steps = getattr(trainer, "train_steps", 0)
   if train_steps > 0:
@@ -601,6 +646,8 @@ def restore_lora_from_path(trainer: Any, mt_config: pyconfig.HyperParameters) ->
           f"Set lora.enable_lora=True and verify lora_module_path ('{lora_module_path}') matches model modules."
       )
 
+  sync_lora_metadata(mt_config)
+
   abstract_lora_params = nnx.state(trainer.model, nnx.LoRAParam)
 
   target_for_restore = jax.tree.map(

src/maxtext/utils/model_creation_utils.py

@@ -44,6 +44,7 @@
 import jax
 import jax.numpy as jnp
 from jax.sharding import Mesh
+from maxtext.common import checkpointing
 from maxtext.common.checkpointing import handle_checkpoint_mismatch
 from maxtext.common.common_types import MODEL_MODE_AUTOREGRESSIVE, MODEL_MODE_TRAIN
 from maxtext.configs import pyconfig
@@ -864,6 +865,15 @@ def from_pretrained(
         }
     )
     config = pyconfig.HyperParameters(new_config)
+  # Proactive verification of scan_layers from checkpoint metadata
+  if config.load_parameters_path:
+    custom_metadata = checkpointing.load_checkpoint_metadata(config.load_parameters_path)
+    saved_scan_layers = custom_metadata.get("scan_layers")
+    if isinstance(saved_scan_layers, bool) and saved_scan_layers != config.scan_layers:
+      raise ValueError(
+          f"Configuration mismatch: Your run specifies scan_layers={config.scan_layers}, "
+          f"but the checkpoint was saved with scan_layers={saved_scan_layers}."
+      )
 
   if config.pure_nnx:
     _create_model, abstract_model = create_nnx_abstract_model(

tests/post_training/unit/lora_utils_test.py

@@ -15,9 +15,13 @@
 """Tests for Qwix LoRA utils in lora_utils.py"""
 import re
 import sys
+import tempfile
 import unittest
 from unittest import mock
+
+from etils import epath
 import jax
+import jax.numpy as jnp
 import optax
 import pytest
 from flax import nnx
@@ -26,6 +30,7 @@
 pytestmark = [pytest.mark.post_training]
 
 # Now safe to do top-level imports
+from maxtext.common import checkpointing
 from tunix.sft import peft_trainer
 from maxtext.utils import lora_utils
 from maxtext.utils import model_creation_utils
@@ -59,11 +64,12 @@
 
 def _make_config(**overrides):
   """Return a MaxTextConfig object suitable for unit tests."""
+  config_dict = _BASE_CONFIG.copy()
+  config_dict.update(overrides)
   # Use initialize_pydantic to get nested models as objects (attribute access)
   return pyconfig.initialize_pydantic(
       [sys.argv[0], get_test_config_path()],
-      **_BASE_CONFIG,
-      **overrides,
+      **config_dict,
   )
 
 
@@ -121,7 +127,12 @@ def test_build_lora_provider(self):
     with mock.patch("qwix.LoraProvider") as mock_provider:
       lora_utils._build_lora_provider(mock_config)
       mock_provider.assert_called_once_with(
-          module_path="custom/path", rank=8, alpha=16.0, dropout=0.0, weight_qtype="int8", tile_size=32
+          module_path="custom/path",
+          rank=8,
+          alpha=16.0,
+          dropout=0.0,
+          weight_qtype="int8",
+          tile_size=32,
       )
 
   def test_prepare_dummy_inputs(self):
@@ -173,7 +184,13 @@ def test_apply_lora_to_model_adapters_loaded(self):
     # If we skip Qwix, it should stay False.
     self.assertFalse(lora_utils.is_lora_enabled(result))
 
-  def _run_apply_lora_test(self, scan_layers: bool, weight_qtype=None, tile_size=None, mock_multihost: bool = False):
+  def _run_apply_lora_test(
+      self,
+      scan_layers: bool,
+      weight_qtype=None,
+      tile_size=None,
+      mock_multihost: bool = False,
+  ):
     """Helper to run LoRA application test with/without scanned layers and optional QLoRA."""
     # Passing nested dict as 'lora' kwarg to _make_config
     cfg = _make_config(
@@ -246,7 +263,12 @@ def test_apply_lora_multihost_mock(self):
   def test_restore_lora_from_path(self):
     """Test restoration of LoRA parameters from a path."""
     cfg = _make_config(
-        lora={"enable_lora": True, "lora_restore_path": "some/path", "lora_rank": 4, "lora_alpha": 8.0},
+        lora={
+            "enable_lora": True,
+            "lora_restore_path": "some/path",
+            "lora_rank": 4,
+            "lora_alpha": 8.0,
+        },
         scan_layers=False,
     )
     model, _ = model_creation_utils.from_pretrained(cfg, mesh=None, model_mode=model_creation_utils.MODEL_MODE_TRAIN)
@@ -271,6 +293,135 @@ def test_restore_lora_from_path(self):
           self.assertTrue(kwargs["args"].partial_restore)
         mock_update.assert_called_once()
 
+  def test_sync_lora_metadata_default_syncs(self):
+    """Test that default lora rank/alpha are successfully synced from checkpoint metadata."""
+    cfg = _make_config(
+        lora={
+            "enable_lora": True,
+            "lora_restore_path": "dummy/path",
+            "lora_rank": 0,
+            "lora_alpha": 0.0,
+        }
+    )
+    mock_metadata = mock.MagicMock()
+    mock_metadata.custom_metadata = {"lora": {"lora_rank": 32, "lora_alpha": 64.0}}
+
+    with mock.patch("orbax.checkpoint.StandardCheckpointer.metadata", return_value=mock_metadata):
+      lora_utils.sync_lora_metadata(cfg)
+      self.assertEqual(cfg.lora.lora_rank, 32)
+      self.assertEqual(cfg.lora.lora_alpha, 64.0)
+
+  def test_sync_lora_metadata_matching_passes(self):
+    """Test that matching non-default parameters pass without errors."""
+    cfg = _make_config(
+        lora={
+            "enable_lora": True,
+            "lora_restore_path": "dummy/path",
+            "lora_rank": 32,
+            "lora_alpha": 64.0,
+        }
+    )
+    mock_metadata = mock.MagicMock()
+    mock_metadata.custom_metadata = {"lora": {"lora_rank": 32, "lora_alpha": 64.0}}
+
+    with mock.patch("orbax.checkpoint.StandardCheckpointer.metadata", return_value=mock_metadata):
+      # Should not raise ValueError
+      lora_utils.sync_lora_metadata(cfg)
+      self.assertEqual(cfg.lora.lora_rank, 32)
+      self.assertEqual(cfg.lora.lora_alpha, 64.0)
+
+  def test_sync_lora_metadata_rank_mismatch_fails(self):
+    """Test that configured rank mismatching checkpoint metadata rank raises ValueError."""
+    cfg = _make_config(
+        lora={
+            "enable_lora": True,
+            "lora_restore_path": "dummy/path",
+            "lora_rank": 8,
+            "lora_alpha": 64.0,
+        }
+    )
+    mock_metadata = mock.MagicMock()
+    mock_metadata.custom_metadata = {"lora": {"lora_rank": 32, "lora_alpha": 64.0}}
+
+    with mock.patch("orbax.checkpoint.StandardCheckpointer.metadata", return_value=mock_metadata):
+      with self.assertRaisesRegex(ValueError, "Configured lora_rank .* does not match"):
+        lora_utils.sync_lora_metadata(cfg)
+
+  def test_sync_lora_metadata_alpha_mismatch_fails(self):
+    """Test that configured alpha mismatching checkpoint metadata alpha raises ValueError."""
+    cfg = _make_config(
+        lora={
+            "enable_lora": True,
+            "lora_restore_path": "dummy/path",
+            "lora_rank": 32,
+            "lora_alpha": 16.0,
+        }
+    )
+    mock_metadata = mock.MagicMock()
+    mock_metadata.custom_metadata = {"lora": {"lora_rank": 32, "lora_alpha": 64.0}}
+
+    with mock.patch("orbax.checkpoint.StandardCheckpointer.metadata", return_value=mock_metadata):
+      with self.assertRaisesRegex(ValueError, "Configured lora_alpha .* does not match"):
+        lora_utils.sync_lora_metadata(cfg)
+
+  def test_save_checkpoint_passes_metadata(self):
+    """Test that save_checkpoint correctly generates and passes custom lora metadata to CheckpointManager."""
+    cfg = _make_config(
+        lora={"enable_lora": True, "lora_rank": 8, "lora_alpha": 16.0},
+        enable_checkpointing=True,
+    )
+    mock_manager = mock.MagicMock()
+    mock_state = mock.MagicMock()
+
+    with mock.patch("jax.block_until_ready"):
+      checkpointing.save_checkpoint(mock_manager, step=10, state=mock_state, config=cfg)
+      mock_manager.save.assert_called_once()
+      _, kwargs = mock_manager.save.call_args
+      self.assertIn("custom_metadata", kwargs)
+      self.assertEqual(kwargs["custom_metadata"], {"lora": cfg.lora.model_dump()})
+
+  def test_save_and_restore_metadata_integration(self):
+    """Integration test checking that Orbax CheckpointManager writes and reads custom LoRA metadata."""
+
+    cfg_save = _make_config(
+        lora={"enable_lora": True, "lora_rank": 8, "lora_alpha": 16.0},
+        enable_checkpointing=True,
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+      manager = checkpointing.create_orbax_checkpoint_manager(
+          tmpdir,
+          enable_checkpointing=True,
+          use_async=False,
+          save_interval_steps=1,
+          use_ocdbt=False,
+          use_zarr3=False,
+      )
+
+      # Use save_checkpoint wrapper with a simple state
+      dummy_state = {"weight": jnp.array([1.0, 2.0])}
+      checkpointing.save_checkpoint(manager, step=0, state=dummy_state, config=cfg_save)
+      manager.wait_until_finished()
+
+      # Now verify that the saved checkpoint contains metadata on disk
+      checkpoint_dir = epath.Path(tmpdir) / "0"
+      self.assertTrue((checkpoint_dir / "_CHECKPOINT_METADATA").exists())
+
+      # Restore using sync_lora_metadata on a config with default rank/alpha
+      cfg_restore = _make_config(
+          lora={
+              "enable_lora": True,
+              "lora_restore_path": str(checkpoint_dir),
+              "lora_rank": 0,
+              "lora_alpha": 0.0,
+          }
+      )
+      lora_utils.sync_lora_metadata(cfg_restore)
+
+      # Verify values were successfully synced back
+      self.assertEqual(cfg_restore.lora.lora_rank, 8)
+      self.assertEqual(cfg_restore.lora.lora_alpha, 16.0)
+
   def test_gemma4_lora_path_matching(self):
     """Test that the Gemma4 LoRA regex correctly matches all expected parameter paths."""
     mock_config = mock.MagicMock(spec=pyconfig.HyperParameters)
@@ -309,10 +460,6 @@ def test_gemma4_lora_path_matching(self):
         "decoder/layers_remainder/layers/0/mlp/shared_experts/wi_0/kernel",
         "decoder/layers_remainder/layers/0/mlp/shared_experts/wi_1/kernel",
         "decoder/layers_remainder/layers/0/mlp/shared_experts/wo/kernel",
-        # No scanned_blocks/layers_remainder prefix (e.g. fallback or direct structure)
-        "decoder/layers/0/self_attention/query/kernel",
-        "decoder/layers/0/mlp/wi_0/kernel",
-        "decoder/layers/layers/0/mlp/shared_experts/wi_0/kernel",
     ]
 
     for path in matching_paths: