[Orbit] The global_step variable is required and cannot be None.

We use kwargs for all arguments and enforce them should be kwargs.
Reorder args.

PiperOrigin-RevId: 346908658

official/core/train_lib.py

@@ -100,7 +100,7 @@ def run_experiment(distribution_strategy: tf.distribute.Strategy,
     checkpoint_manager = None
 
   controller = orbit.Controller(
-      distribution_strategy,
+      strategy=distribution_strategy,
       trainer=trainer if 'train' in mode else None,
       evaluator=trainer,
       global_step=trainer.global_step,

official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py

@@ -161,9 +161,9 @@ def run(flags_obj):
       checkpoint_interval=checkpoint_interval)
 
   resnet_controller = orbit.Controller(
-      strategy,
-      runnable,
-      runnable if not flags_obj.skip_eval else None,
+      strategy=strategy,
+      trainer=runnable,
+      evaluator=runnable if not flags_obj.skip_eval else None,
       global_step=runnable.global_step,
       steps_per_loop=steps_per_loop,
       checkpoint_manager=checkpoint_manager,

orbit/controller.py

@@ -74,10 +74,11 @@ class Controller:
 
   def __init__(
       self,
-      strategy: Optional[tf.distribute.Strategy] = None,
+      *,  # Makes all args keyword only.
+      global_step: tf.Variable,
       trainer: Optional[runner.AbstractTrainer] = None,
       evaluator: Optional[runner.AbstractEvaluator] = None,
-      global_step: Optional[tf.Variable] = None,
+      strategy: Optional[tf.distribute.Strategy] = None,
       # Train related
       steps_per_loop: Optional[int] = None,
       checkpoint_manager: Optional[tf.train.CheckpointManager] = None,
@@ -93,13 +94,6 @@ def __init__(
     recent checkpoint during this `__init__` method.
 
     Args:
-      strategy: An instance of `tf.distribute.Strategy`. If not provided, the
-        strategy will be initialized from the current in-scope strategy using
-        `tf.distribute.get_strategy()`.
-      trainer: An instance of `orbit.AbstractTrainer`, which implements the
-        inner training loop.
-      evaluator: An instance of `orbit.AbstractEvaluator`, which implements
-        evaluation.
       global_step: An integer `tf.Variable` storing the global training step
         number. Usually this can be obtained from the `iterations` property of
         the model's optimizer (e.g. `trainer.optimizer.iterations`). In cases
@@ -109,6 +103,13 @@ def __init__(
         recommended to create the `tf.Variable` inside the distribution strategy
         scope, with `aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA` (see
         also `orbit.utils.create_global_step()`).
+      trainer: An instance of `orbit.AbstractTrainer`, which implements the
+        inner training loop.
+      evaluator: An instance of `orbit.AbstractEvaluator`, which implements
+        evaluation.
+      strategy: An instance of `tf.distribute.Strategy`. If not provided, the
+        strategy will be initialized from the current in-scope strategy using
+        `tf.distribute.get_strategy()`.
       steps_per_loop: The number of steps to run in each inner loop of training
         (passed as the `num_steps` parameter of `trainer.train`).
       checkpoint_manager: An instance of `tf.train.CheckpointManager`. If
@@ -137,7 +138,6 @@ def __init__(
     """
     if trainer is None and evaluator is None:
       raise ValueError("`trainer` and `evaluator` should not both be `None`.")
-
     if trainer is not None:
       if steps_per_loop is None:
         raise ValueError(
@@ -155,9 +155,7 @@ def __init__(
               f"`summary interval` ({summary_interval}) must be a multiple "
               f"of `steps_per_loop` ({steps_per_loop}).")
 
-    if global_step is None:
-      raise ValueError("`global_step` is required.")
-    elif not isinstance(global_step, tf.Variable):
+    if not isinstance(global_step, tf.Variable):
       raise ValueError("`global_step` must be a `tf.Variable`.")
 
     self.trainer = trainer
@@ -185,8 +183,7 @@ def __init__(
         self.eval_summary_manager = utils.SummaryManager(
             eval_summary_dir, tf.summary.scalar, global_step=self.global_step)
 
-    if self.global_step is not None:
-      tf.summary.experimental.set_step(self.global_step)
+    tf.summary.experimental.set_step(self.global_step)
 
     # Restores the model if needed.
     if self.checkpoint_manager is not None: