[core] Only use runtime_options for training.

PiperOrigin-RevId: 363782489

official/core/base_trainer.py

@@ -163,7 +163,7 @@ def get_runtime_options(config: ExperimentConfig):
   xla_options = {}
   if config.runtime.tpu_enable_xla_dynamic_padder is not None:
     xla_options["enable_xla_dynamic_padder"] = (
-        config.runtime.enable_xla_dynamic_padder)
+        config.runtime.tpu_enable_xla_dynamic_padder)
   return tf.distribute.RunOptions(
       experimental_xla_options=tf.tpu.XLAOptions(**xla_options))
 
@@ -205,6 +205,8 @@ class Trainer(_AsyncTrainer):
     self._optimizer = optimizer
     self._checkpoint_exporter = checkpoint_exporter
     self._recovery = None
+    # Runtime options are only applied to train_step.
+    # We use default for eval_step.
     self._runtime_options = get_runtime_options(config)
 
     # Creates a shadow copy of the weights to store weights moving average.
@@ -407,8 +409,7 @@ class Trainer(_AsyncTrainer):
         self._validation_loss.update_state(logs[self.task.loss])
       return logs
 
-    distributed_outputs = self.strategy.run(
-        step_fn, args=(next(iterator),), options=self._runtime_options)
+    distributed_outputs = self.strategy.run(step_fn, args=(next(iterator),))
     return tf.nest.map_structure(self.strategy.experimental_local_results,
                                  distributed_outputs)
 

official/core/config_definitions.py

@@ -140,7 +140,11 @@ class RuntimeConfig(base_config.Config):
   run_eagerly: bool = False
   batchnorm_spatial_persistent: bool = False
 
-  # XLA runtime
+  # XLA runtime params.
+  # XLA params are only applied to the train_step.
+  # These augments can improve training speed. They can also improve eval, but
+  # may reduce usability and users would need to make changes to code.
+
   # Whether to enable XLA dynamic padder
   # infrastructure to handle dynamic shapes inputs inside XLA. True by
   # default. Disabling this may cause correctness issues with dynamic shapes