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PiperOrigin-RevId: 653631904

checkpoint/CHANGELOG.md

@@ -22,6 +22,8 @@ entries will not return information about array properties).
 ### Changed
 - Allow one directory creation request per item rather than 1 per item per host.
 - Make atomicity logic configurable, and encapsulate it within a class.
+- Move all work for `_write_metadata_file` into a background thread to avoid
+O(n) computation in building metadata.
 
 ### Fixed
 - Refactor ts.Context usage to be per-operation (save/restore) rather than a

checkpoint/orbax/checkpoint/array_checkpoint_handler.py

@@ -88,13 +88,14 @@ async def async_save(
           )
       ]
 
+    type_handler = type_handlers.get_type_handler(type(item))
     info = type_handlers.ParamInfo(
         name=self._checkpoint_name,
         path=directory / self._checkpoint_name,
         parent_dir=directory,
         is_ocdbt_checkpoint=False,
+        value_typestr=type_handler.typestr(),
     )
-    type_handler = type_handlers.get_type_handler(type(item))
     futures = await type_handler.serialize([item], [info], args=[save_args])
     return list(futures)
 

checkpoint/orbax/checkpoint/base_pytree_checkpoint_handler.py

@@ -20,9 +20,9 @@
 """
 
 import asyncio
+from concurrent import futures
 import dataclasses
 import json
-import os
 import time
 from typing import Any, List, Optional, Tuple, Union
 
@@ -273,6 +273,8 @@ def __init__(
         '/jax/orbax/pytree_checkpoint_handler/init/ocdbt'
     )
 
+    self._thread_pool = futures.ThreadPoolExecutor(max_workers=1)
+
   def get_param_names(self, item: PyTree) -> PyTree:
     """Gets parameter names for PyTree elements."""
     return get_param_names(item)
@@ -304,8 +306,6 @@ def _get_param_infos(
     Returns:
       A PyTree matching `item` of ParamInfo.
     """
-    if not item:
-      raise ValueError('Found empty item')
     if use_zarr3 is None:
       use_zarr3 = self._use_zarr3
     names = self.get_param_names(item)
@@ -325,6 +325,9 @@ def _param_info(name, value):
           ocdbt_target_data_file_size=ocdbt_target_data_file_size,
           byte_limiter=byte_limiter,
           ts_context=ts_context,
+          value_typestr=type_handlers.get_param_typestr(
+              value, self._type_handler_registry
+          ),
       )
 
     return jax.tree.map(
@@ -373,6 +376,8 @@ async def async_save(
       the data from its source will be awaited in this function.
     """
     item = args.item
+    if not item:
+      raise ValueError('Found empty item.')
     save_args = args.save_args
     ocdbt_target_data_file_size = args.ocdbt_target_data_file_size
     if ocdbt_target_data_file_size is not None and not self._use_zarr3:
@@ -425,8 +430,8 @@ async def async_save(
 
     if multihost.is_primary_host(self._primary_host):
       metadata_write_start_time = time.time()
-      metadata_future = await self._write_metadata_file(
-          directory, item, save_args, self._use_zarr3
+      metadata_future = self._write_metadata_file(
+          directory, param_infos, save_args, self._use_zarr3
       )
       commit_futures += [metadata_future]
       jax.monitoring.record_event_duration_secs(
@@ -609,6 +614,8 @@ class TrainState:
         if use_zarr3_metadata is not None
         else self._use_zarr3
     )
+    if not metadata:
+      raise ValueError('Found empty metadata.')
     param_infos = self._get_param_infos(
         metadata,
         directory,
@@ -633,33 +640,25 @@ class TrainState:
 
     return restored_item
 
-  async def _write_metadata_file(
+  def _write_metadata_file(
       self,
       directory: epath.Path,
-      item: PyTree,
+      param_infos: PyTree,
       save_args: PyTree,
       use_zarr3: bool = False,
   ) -> future.Future:
-    tspec = type_handlers._get_tensorstore_spec(  # pylint: disable=protected-access
-        os.fspath(directory), name=METADATA_FILE, use_ocdbt=False
-    )['kvstore']
-    txn = ts.Transaction()
-    metadata_ts_context = type_handlers.get_ts_context()
-    t = await ts.KvStore.open(
-        tspec, context=metadata_ts_context
-    )
-    metadata_content = tree_metadata.TreeMetadata.build(
-        item,
-        save_args=save_args,
-        type_handler_registry=self._type_handler_registry,
-        use_zarr3=use_zarr3,
-    )
-    write_future = t.with_transaction(txn).write(
-        '', json.dumps(metadata_content.to_json())
-    )
-    await write_future
-    commit_future = txn.commit_async()
-    return commit_future
+    def _save_fn():
+      if utils.is_primary_host(self._primary_host):
+        path = directory / METADATA_FILE
+        metadata_content = tree_metadata.TreeMetadata.build(
+            param_infos,
+            save_args=save_args,
+            use_zarr3=use_zarr3,
+        )
+        path.write_text(json.dumps(metadata_content.to_json()))
+      return 0
+
+    return self._thread_pool.submit(_save_fn)
 
   def _read_metadata_file(
       self, directory: epath.Path

checkpoint/orbax/checkpoint/metadata/tree.py

@@ -150,32 +150,18 @@ def from_json(cls, json_dict: Dict[str, Any]) -> 'ValueMetadataEntry':
   @classmethod
   def build(
       cls,
-      value: Any,
+      info: type_handlers.ParamInfo,
       save_arg: type_handlers.SaveArgs,
-      registry: Optional[type_handlers.TypeHandlerRegistry],
   ) -> 'ValueMetadataEntry':
     """Builds a ValueMetadataEntry."""
-    if type_handlers.is_supported_empty_aggregation_type(value):
-      typestr = type_handlers.get_empty_value_typestr(value)
-      skip_deserialize = True
-    elif registry is None:
-      return ValueMetadataEntry(
-          value_type=type_handlers.RESTORE_TYPE_UNKNOWN, skip_deserialize=False
+    del save_arg
+    if info.value_typestr is None:
+      raise AssertionError(
+          'Must set `value_typestr` in `ParamInfo` when saving.'
       )
-    else:
-      try:
-        handler = registry.get(type(value))
-        typestr = handler.typestr()
-        skip_deserialize = save_arg.aggregate
-      except ValueError:
-        # Not an error because users' training states often have a bunch of
-        # random unserializable objects in them (empty states, optimizer
-        # objects, etc.). An error occurring due to a missing TypeHandler
-        # will be surfaced elsewhere.
-        typestr = type_handlers.RESTORE_TYPE_NONE
-        skip_deserialize = True
+    skip_deserialize = type_handlers.is_empty_typestr(info.value_typestr)
     return ValueMetadataEntry(
-        value_type=typestr, skip_deserialize=skip_deserialize
+        value_type=info.value_typestr, skip_deserialize=skip_deserialize
     )
 
 
@@ -208,15 +194,12 @@ def from_json(
   def build(
       cls,
       keypath: KeyPath,
-      value: Any,
+      info: type_handlers.ParamInfo,
       save_arg: type_handlers.SaveArgs,
-      type_handler_registry: Optional[type_handlers.TypeHandlerRegistry],
   ) -> 'TreeMetadataEntry':
     """Builds a TreeMetadataEntry."""
     key_metadata_entry = KeyMetadataEntry.build(keypath)
-    value_metadata_entry = ValueMetadataEntry.build(
-        value, save_arg, type_handler_registry
-    )
+    value_metadata_entry = ValueMetadataEntry.build(info, save_arg)
     return TreeMetadataEntry(
         str(tuple([str(tree_utils.get_key_name(k)) for k in keypath])),
         key_metadata_entry,
@@ -242,33 +225,30 @@ class TreeMetadata:
   @classmethod
   def build(
       cls,
-      tree: PyTree,
+      param_infos: PyTree,
       *,
-      type_handler_registry: Optional[type_handlers.TypeHandlerRegistry] = None,
       save_args: Optional[PyTree] = None,
       use_zarr3: bool = False,
   ) -> 'TreeMetadata':
     """Builds the tree metadata."""
     if save_args is None:
       save_args = jax.tree.map(
           lambda _: type_handlers.SaveArgs(),
-          tree,
+          param_infos,
           is_leaf=tree_utils.is_empty_or_leaf,
       )
     flat_with_keys, _ = jax.tree_util.tree_flatten_with_path(
-        tree, is_leaf=tree_utils.is_empty_or_leaf
+        param_infos, is_leaf=tree_utils.is_empty_or_leaf
     )
     flat_save_args_with_keys, _ = jax.tree_util.tree_flatten_with_path(
         save_args, is_leaf=tree_utils.is_empty_or_leaf
     )
     tree_metadata_entries = []
-    for (keypath, value), (_, save_arg) in zip(
+    for (keypath, info), (_, save_arg) in zip(
         flat_with_keys, flat_save_args_with_keys
     ):
       tree_metadata_entries.append(
-          TreeMetadataEntry.build(
-              keypath, value, save_arg, type_handler_registry
-          )
+          TreeMetadataEntry.build(keypath, info, save_arg)
       )
     return TreeMetadata(tree_metadata_entries, use_zarr3)
 

checkpoint/orbax/checkpoint/metadata/tree_test.py

@@ -16,6 +16,7 @@
 import jax
 from jax import numpy as jnp
 import numpy as np
+from orbax.checkpoint import tree as tree_utils
 from orbax.checkpoint import type_handlers
 from orbax.checkpoint.metadata import tree as tree_metadata
 
@@ -26,7 +27,15 @@ def setUp(self):
     super().setUp()
     arr = jnp.arange(8)
     assert isinstance(arr, jax.Array)
-    self.tree = {'a': 1, 'b': {'c': 'hi', 'd': 3.4}, 'e': [np.arange(8), arr]}
+    self.tree = {
+        'a': 1,
+        'b': {'c': 'hi', 'd': 3.4},
+        'e': [np.arange(8), arr],
+        'f': None,
+        'g': {},
+        'h': [],
+        'i': tuple([]),
+    }
     self.tree_json = {
         'tree_metadata': {
             "('a',)": {
@@ -76,36 +85,53 @@ def setUp(self):
                     'skip_deserialize': False,
                 },
             },
+            "('f',)": {
+                'key_metadata': ({'key': 'f', 'key_type': 2},),
+                'value_metadata': {
+                    'value_type': 'None',
+                    'skip_deserialize': True,
+                },
+            },
+            "('g',)": {
+                'key_metadata': ({'key': 'g', 'key_type': 2},),
+                'value_metadata': {
+                    'value_type': 'Dict',
+                    'skip_deserialize': True,
+                },
+            },
+            "('h',)": {
+                'key_metadata': ({'key': 'h', 'key_type': 2},),
+                'value_metadata': {
+                    'value_type': 'List',
+                    'skip_deserialize': True,
+                },
+            },
+            "('i',)": {
+                'key_metadata': ({'key': 'i', 'key_type': 2},),
+                'value_metadata': {
+                    'value_type': 'None',
+                    'skip_deserialize': True,
+                },
+            },
         },
         'use_zarr3': True,
     }
-
-  def test_json_conversion(self):
-    metadata = tree_metadata.TreeMetadata.build(
+    self.param_infos = jax.tree.map(
+        # Other properties are not relevant.
+        lambda x: type_handlers.ParamInfo(
+            value_typestr=type_handlers.get_param_typestr(
+                x, type_handlers.GLOBAL_TYPE_HANDLER_REGISTRY
+            )
+        ),
         self.tree,
-        type_handler_registry=type_handlers.GLOBAL_TYPE_HANDLER_REGISTRY,
-        use_zarr3=True,
-    )
-    self.assertDictEqual(self.tree_json, metadata.to_json())
-    self.assertEqual(
-        metadata, tree_metadata.TreeMetadata.from_json(self.tree_json)
+        is_leaf=tree_utils.is_empty_or_leaf,
     )
 
-  def test_aggregate_option(self):
-    save_args = jax.tree.map(
-        lambda x: type_handlers.SaveArgs(), self.tree
-    )
-    save_args['a'] = type_handlers.SaveArgs(aggregate=True)
+  def test_json_conversion(self):
     metadata = tree_metadata.TreeMetadata.build(
-        self.tree,
-        type_handler_registry=type_handlers.GLOBAL_TYPE_HANDLER_REGISTRY,
-        save_args=save_args,
+        self.param_infos,
         use_zarr3=True,
     )
-    tree_json = dict(**self.tree_json)
-    tree_json['tree_metadata']["('a',)"]['value_metadata'][
-        'skip_deserialize'
-    ] = True
     self.assertDictEqual(self.tree_json, metadata.to_json())
     self.assertEqual(
         metadata, tree_metadata.TreeMetadata.from_json(self.tree_json)

checkpoint/orbax/checkpoint/type_handlers.py

@@ -182,6 +182,8 @@ class ParamInfo:
     Specifies the target size (in bytes) of each OCDBT data file.
   ts_context:
     Tensorstore context to use for reading/writing.
+  value_typestr: stores the original value's typestr (from TypeHandler).
+    Only required when saving.
   """
 
   name: Optional[str] = None
@@ -193,6 +195,7 @@ class ParamInfo:
   use_zarr3: Optional[bool] = False
   ocdbt_target_data_file_size: Optional[int] = None
   ts_context: Optional[ts.Context] = None
+  value_typestr: Optional[str] = None
 
 
 @dataclasses.dataclass
@@ -2056,3 +2059,20 @@ def default_restore_type(args: RestoreArgs) -> Any:
     return np.ndarray
   else:
     raise ValueError(f'Unsupported restore_args type: {type(args)}')
+
+
+def get_param_typestr(value: Any, registry: TypeHandlerRegistry) -> str:
+  """Retrieves the typestr for a given value."""
+  if is_supported_empty_aggregation_type(value):
+    typestr = get_empty_value_typestr(value)
+  else:
+    try:
+      handler = registry.get(type(value))
+      typestr = handler.typestr()
+    except ValueError:
+      # Not an error because users' training states often have a bunch of
+      # random unserializable objects in them (empty states, optimizer
+      # objects, etc.). An error occurring due to a missing TypeHandler
+      # will be surfaced elsewhere.
+      typestr = RESTORE_TYPE_NONE
+  return typestr