Add compatibility for new fiddle version.

Reverts changelist 793734230

PiperOrigin-RevId: 808305742

Author: Hilly12

recml/core/data/tf_dataset_factory.py

@@ -24,6 +24,7 @@
 import re
 from typing import Any, Protocol
 
+from absl import flags
 from absl import logging
 import jax
 from recml.core.utils import types
@@ -162,12 +163,12 @@ class TFDatasetFactory(types.Factory[tf.data.Dataset]):
       Defaults to False.
     seed: An optional seed to use for deterministic shuffling / preprocessing.
       Defaults to None.
-    tf_data_service_address: An optional URI of a tf.data service to offload
-      preprocessing onto during training. The URI should be in the format
-      "protocol://address", e.g. "grpc://tf-data-service:5050". If `None` no
-      data service will be applied.
+    enable_tf_data_service: Whether to apply tf.data service for this dataset.
+      If True, flag `tf_data_service_address` must be set.
     tf_data_service_policy: Sharding policy to use for tf.data service when it
       is enabled.
+    tf_data_service_job_name: Job name to use for tf.data service. If None, the
+      default job name will be used.
     feature_spec: A mapping of feature keys to `FixedLenFeature`,
       `VarLenFeature`, `SparseFeature`, or `RaggedFeature` values. This will be
       used to parse the TF examples, or as context_features spec to parse TF
@@ -208,7 +209,7 @@ class TFDatasetFactory(types.Factory[tf.data.Dataset]):
       tensorflow.
     debug: An optional boolean indicating whether to debug input boundedness. If
       `True`, the dataset will consist of a single batch that's cached and
-      infinitely repeated
+      infinitely repeated.
   """
 
   cache_reading: bool = False
@@ -231,7 +232,8 @@ class TFDatasetFactory(types.Factory[tf.data.Dataset]):
   readahead: str | None = None
   group_uris_by_dir: bool = False
   seed: int | None = None
-  tf_data_service_address: str | None = None
+  enable_tf_data_service: bool = False
+  tf_data_service_job_name: str | None = None
   tf_data_service_policy: tf.data.experimental.service.ShardingPolicy = (
       tf.data.experimental.service.ShardingPolicy.OFF
   )
@@ -249,7 +251,12 @@ class TFDatasetFactory(types.Factory[tf.data.Dataset]):
   debug: bool = False
 
   def __post_init__(self):
-    if self.tf_data_service_address is not None:
+    if self.enable_tf_data_service:
+      if flags.FLAGS.tf_data_service_address is None:
+        raise ValueError(
+            "Flag `tf_data_service_address` must be set when"
+            " `enable_tf_data_service` is True."
+        )
       if self.seed is not None:
         raise ValueError("`seed` must be None for data service.")
       if self.sharding:
@@ -533,23 +540,26 @@ def _maybe_apply_tf_data_service(
       self, dataset: tf.data.Dataset
   ) -> tf.data.Dataset:
     """Applies the tf.data service to the dataset."""
-    if self.tf_data_service_address is None:
+    if not self.enable_tf_data_service:
       return dataset
 
+    tf_data_service_address = flags.FLAGS.tf_data_service_address
+
     per_proc_batch_size = self.sharding_info.per_process_batch_size(
         self.global_batch_size
     )
     logging.info(
         "Applying tf.data service with address %s and per replica batch"
         " size %s",
-        self.tf_data_service_address,
+        tf_data_service_address,
         per_proc_batch_size,
     )
     return dataset.apply(
         tf.data.experimental.service.distribute(
             processing_mode=self.tf_data_service_policy,
-            service=self.tf_data_service_address,
-            job_name=f"bs_{per_proc_batch_size}",
+            service=tf_data_service_address,
+            job_name=self.tf_data_service_job_name
+            or "tf_data_service_shared_job_name",
         )
     )
 

recml/core/ops/hstu_ops.py

@@ -125,9 +125,9 @@ def _apply_mask(
   masks = []
   if mask_ref is not None:
     if k_in_lanes:
-      mask = pl.load(mask_ref, (slice(None), k_slice))
+      mask = mask_ref[:, k_slice]
     else:
-      mask = pl.load(mask_ref, (k_slice, slice(None)))
+      mask = mask_ref[k_slice, :]
 
     snm = jnp.where(should_not_mask, 1, 0)
     masks.append(jnp.bitwise_or(mask, jnp.broadcast_to(snm, mask.shape)) != 0)
@@ -156,7 +156,7 @@ def _apply_mask(
       k_sequence = k_offset + jax.lax.broadcasted_iota(
           jnp.int32, (k_slice.size, bq), 0
       )
-      q_sequence = pl.load(q_sequence_ref, (pl.ds(1), slice(None)))  # [1, bq]
+      q_sequence = q_sequence_ref[:1, :]  # [1, bq]
       q_sequence = jnp.broadcast_to(q_sequence, (k_slice.size, bq))
 
     assert q_sequence.shape == k_sequence.shape
@@ -170,7 +170,7 @@ def _apply_mask(
 
   if q_segment_ids_ref is not None:
     if k_in_lanes:
-      kv_ids = pl.load(kv_segment_ids_ref, (pl.ds(1), k_slice))  # [1, k_slice]
+      kv_ids = kv_segment_ids_ref[:1, k_slice]  # [1, k_slice]
       repeats, rem = divmod(kv_ids.shape[1], NUM_LANES)
       if rem:
         raise NotImplementedError(f"block_kv must be a multiple of {NUM_LANES}")
@@ -181,9 +181,9 @@ def _apply_mask(
       if rem:
         raise NotImplementedError(f"block_q must be a multiple of {NUM_LANES}")
       kv_ids = pltpu.repeat(
-          pl.load(kv_segment_ids_ref, (k_slice, slice(None))), repeats, axis=1
+          kv_segment_ids_ref[k_slice, :], repeats, axis=1
       )  # [k_slice, bq]
-      q_ids = pl.load(q_segment_ids_ref, (pl.ds(1), slice(None)))  # [1, bq]
+      q_ids = q_segment_ids_ref[:1, :]  # [1, bq]
     masks.append(q_ids == kv_ids)
 
   if masks:
@@ -228,7 +228,7 @@ def body(kv_compute_index, _):
     slice_k = pl.ds(kv_compute_index * bkv_compute, bkv_compute)
 
     q = q_ref[...]
-    k = pl.load(k_ref, (slice_k, slice(None)))
+    k = k_ref[slice_k, :]
     qk = jax.lax.dot_general(
         q, k, NT_DIM_NUMBERS, preferred_element_type=jnp.float32
     )
@@ -256,7 +256,7 @@ def body(kv_compute_index, _):
     )
 
     sv_dims = NN_DIM_NUMBERS
-    v = pl.load(v_ref, (slice_k, slice(None)))
+    v = v_ref[slice_k, :]
 
     to_float32 = lambda x: x.astype(jnp.float32)
     v = to_float32(v)

recml/core/training/keras_trainer.py

@@ -18,6 +18,7 @@
 import abc
 from collections.abc import Mapping
 import dataclasses
+import functools
 import gc
 import os
 import time
@@ -96,7 +97,6 @@ def export_model(self, model: keras.Model, model_dir: str):
       model: The Keras model constructed by `create_model`.
       model_dir: The model directory passed to the trainer.
     """
-    model.save(os.path.join(model_dir, core.KERAS_MODEL_SAVEFILE))
 
 
 class KerasTrainer(core.Trainer[KerasTask]):
@@ -118,6 +118,7 @@ def __init__(
       max_checkpoints_to_keep: int = 5,
       checkpoint_save_interval_epochs: int = 1,
       rng_seed: int = core.DEFAULT_RNG_SEED,
+      legacy_checkpoint_format: bool = True,
   ):
     """Initializes the instance."""
 
@@ -143,60 +144,77 @@ def __init__(
     self._steps_per_eval = steps_per_eval
     self._continuous_eval_timeout = continuous_eval_timeout
     self._steps_per_loop = steps_per_loop
-    self._checkpoint_manager = None
     self._marker_path = os.path.join(
         model_dir, core.TRAINING_COMPLETE_MARKER_FILE
     )
     self._checkpoint_dir = os.path.join(model_dir, core.CHECKPOINT_DIR)
+    self._max_checkpoints_to_keep = max_checkpoints_to_keep
+    self._checkpoint_save_interval_epochs = checkpoint_save_interval_epochs
+    self._legacy_checkpoint_format = legacy_checkpoint_format
 
+  @functools.cached_property
+  def train_callbacks(self) -> list[keras.callbacks.Callback]:
+    """Returns the training callbacks."""
     if keras.backend.backend() == "jax":
-      self._checkpoint_manager = keras_utils.KerasOrbaxCheckpointManager(
-          checkpoint_dir=self._checkpoint_dir,
-          max_to_keep=max_checkpoints_to_keep,
-          save_interval_epochs=checkpoint_save_interval_epochs,
-      )
-      self._train_callbacks = [
+      if self._legacy_checkpoint_format:
+        checkpoint_manager = keras_utils.KerasOrbaxCheckpointManager(
+            checkpoint_dir=self._checkpoint_dir,
+            max_to_keep=self._max_checkpoints_to_keep,
+            save_interval_epochs=self._checkpoint_save_interval_epochs,
+        )
+      else:
+        checkpoint_manager = keras_utils.KerasOrbaxCheckpointManagerV2(
+            checkpoint_dir=self._checkpoint_dir,
+            max_to_keep=self._max_checkpoints_to_keep,
+            save_interval_epochs=self._checkpoint_save_interval_epochs,
+        )
+      return [
           keras_utils.EpochSummaryCallback(
-              log_dir=os.path.join(model_dir, core.LOG_DIR),
-              steps_per_epoch=steps_per_loop,
+              log_dir=os.path.join(self._model_dir, core.LOG_DIR),
+              steps_per_epoch=self._steps_per_loop,
               write_steps_per_second=True,
           ),
           keras_utils.EpochOrbaxCheckpointAndRestoreCallback(
-              checkpoint_manager=self._checkpoint_manager,
+              checkpoint_manager=checkpoint_manager,
               marker_path=self._marker_path,
           ),
       ]
-      self._eval_callbacks = [
+    return [
+        keras.callbacks.TensorBoard(
+            log_dir=os.path.join(self._model_dir, core.LOG_DIR),
+            write_steps_per_second=True,
+        ),
+        keras.callbacks.BackupAndRestore(
+            backup_dir=os.path.join(self._model_dir, core.BACKUP_DIR),
+        ),
+        keras.callbacks.ModelCheckpoint(
+            filepath=os.path.join(
+                self._model_dir,
+                core.CHECKPOINT_DIR,
+                "ckpt-{epoch:d}.weights.h5",
+            ),
+            save_weights_only=True,
+            verbose=1,
+        ),
+    ]
+
+  @functools.cached_property
+  def eval_callbacks(self) -> list[keras.callbacks.Callback]:
+    """Returns the evaluation callbacks."""
+    if keras.backend.backend() == "jax":
+      return [
           keras_utils.EpochSummaryCallback(
-              log_dir=os.path.join(model_dir, core.LOG_DIR),
-              steps_per_epoch=steps_per_loop,
+              log_dir=os.path.join(self._model_dir, core.LOG_DIR),
+              steps_per_epoch=self._steps_per_loop,
               write_steps_per_second=False,
           ),
       ]
-    else:
-      self._checkpoint_manager = None
-      self._train_callbacks = [
-          keras.callbacks.TensorBoard(
-              log_dir=os.path.join(model_dir, core.LOG_DIR),
-              write_steps_per_second=True,
-          ),
-          keras.callbacks.BackupAndRestore(
-              backup_dir=os.path.join(model_dir, core.BACKUP_DIR),
-          ),
-          keras.callbacks.ModelCheckpoint(
-              filepath=os.path.join(
-                  model_dir, core.CHECKPOINT_DIR, "ckpt-{epoch:d}.weights.h5"
-              ),
-              save_weights_only=True,
-              verbose=1,
-          ),
-      ]
-      self._eval_callbacks = [
-          keras.callbacks.TensorBoard(
-              log_dir=os.path.join(model_dir, core.LOG_DIR),
-              write_steps_per_second=True,
-          ),
-      ]
+    return [
+        keras.callbacks.TensorBoard(
+            log_dir=os.path.join(self._model_dir, core.LOG_DIR),
+            write_steps_per_second=True,
+        ),
+    ]
 
   def _maybe_get_model_kws(
       self, task: KerasTask, dataset: tf.data.Dataset
@@ -218,7 +236,7 @@ def train(self, task: KerasTask) -> core.Logs:
         dataset,
         epochs=self._train_epochs,
         steps_per_epoch=self._steps_per_loop,
-        callbacks=self._train_callbacks,
+        callbacks=self.train_callbacks,
     )
     model.summary(print_fn=logging.info)
 
@@ -237,14 +255,14 @@ def evaluate(self, task: KerasTask) -> core.Logs:
     if keras.backend.backend() == "jax":
       [tb_cbk] = [
           cbk
-          for cbk in self._eval_callbacks
+          for cbk in self.eval_callbacks
           if isinstance(cbk, keras_utils.EpochSummaryCallback)
       ]
       epoch_start_time = time.time()
       history = model.evaluate(
           dataset,
           steps=self._steps_per_eval,
-          callbacks=self._eval_callbacks,
+          callbacks=self.eval_callbacks,
           return_dict=True,
       )
       epoch_dt = time.time() - epoch_start_time
@@ -257,7 +275,7 @@ def evaluate(self, task: KerasTask) -> core.Logs:
     return model.evaluate(
         dataset,
         steps=self._steps_per_eval,
-        callbacks=self._eval_callbacks,
+        callbacks=self.eval_callbacks,
     )
 
   def train_and_evaluate(self, task: KerasTask) -> core.Logs:
@@ -277,7 +295,7 @@ def train_and_evaluate(self, task: KerasTask) -> core.Logs:
         steps_per_epoch=self._steps_per_loop,
         # Explicitly set to None for deterministic evaluation.
         validation_steps=None,
-        callbacks=self._train_callbacks,
+        callbacks=self.train_callbacks,
     )
     model.summary(print_fn=logging.info)
 
@@ -308,7 +326,10 @@ def timeout_fn() -> bool:
     else:
       steps_msg = "running complete evaluation..."
 
+    use_legacy_checkpoint_format = self._legacy_checkpoint_format
+
     class _RestoreCallback(keras.callbacks.Callback):
+      """Callback for restoring the model from the latest checkpoint."""
 
       def __init__(
           self,
@@ -319,9 +340,14 @@ def __init__(
         self._epoch = epoch
 
       def on_test_begin(self, logs: Mapping[str, Any] | None = None):
-        keras_utils.restore_keras_model(
-            model, self._checkpoint_dir, step=self._epoch
-        )
+        if use_legacy_checkpoint_format:
+          keras_utils.restore_keras_model(
+              model, self._checkpoint_dir, step=self._epoch
+          )
+        else:
+          keras_utils.restore_keras_checkpoint(
+              self._checkpoint_dir, model=model, epoch=self._epoch
+          )
 
     history = None
     for epoch in ocp.checkpoint_utils.checkpoints_iterator(
@@ -332,7 +358,7 @@ def on_test_begin(self, logs: Mapping[str, Any] | None = None):
       restore_callback = _RestoreCallback(self._checkpoint_dir, epoch)
       [tb_cbk] = [
           cbk
-          for cbk in self._eval_callbacks
+          for cbk in self.eval_callbacks
           if isinstance(cbk, keras_utils.EpochSummaryCallback)
       ]
       try:
@@ -346,7 +372,7 @@ def on_test_begin(self, logs: Mapping[str, Any] | None = None):
         history = model.evaluate(
             eval_dataset,
             steps=self._steps_per_eval,
-            callbacks=[restore_callback] + self._eval_callbacks,
+            callbacks=[restore_callback] + self.eval_callbacks,
             return_dict=True,
         )