Making KFAC use pjit instead of pmap

PiperOrigin-RevId: 526595657

examples/datasets.py

@@ -35,6 +35,14 @@
 _IMAGENET_STDDEV_RGB = (0.229, 0.224, 0.225)
 
 
+def sharded_iterator(
+    dataset: tf.data.Dataset,
+    sharding: jax.sharding.NamedSharding,
+) -> Iterator[Batch]:
+  for batch in iter(tensorflow_datasets.as_numpy(dataset)):
+    yield jax.device_put(batch, sharding)
+
+
 def mnist_dataset(
     split: str,
     has_labels: bool,
@@ -43,6 +51,7 @@ def mnist_dataset(
     repeat: bool,
     shuffle: bool,
     drop_remainder: bool,
+    sharding: jax.sharding.NamedSharding,
     seed: Optional[int] = None,
     multi_device: bool = True,
     reshuffle_each_iteration: bool = True,
@@ -59,13 +68,13 @@ def mnist_dataset(
     shuffle: Whether to shuffle the dataset.
     drop_remainder: Whether to drop the remainder of the dataset if the number
       of data points is not divisible by the total batch size.
+    sharding: Sharding spec for each batch.
     seed: Any seed to use for random pre-processing.
     multi_device: If the returned batch should take into account the number of
       devices present, in which case it will return an array with shape
       `(num_device, device_batch_size, ...)`.
     reshuffle_each_iteration: Whether to reshuffle the dataset in a new order
       after each iteration.
-    dtype: The returned data type of the images.
 
   Returns:
     The MNIST dataset as a tensorflow dataset.
@@ -74,14 +83,7 @@ def mnist_dataset(
   # Set for multi devices vs single device
   num_devices = jax.device_count() if multi_device else 1
   num_local_devices = jax.local_device_count() if multi_device else 1
-
-  if multi_device:
-    host_batch_shape = [num_local_devices, device_batch_size]
-  else:
-    host_batch_shape = [device_batch_size]
-
   host_batch_size = num_local_devices * device_batch_size
-
   num_examples = tfds.builder("mnist").info.splits[split].num_examples
 
   if num_examples % num_devices != 0:
@@ -95,8 +97,7 @@ def preprocess_batch(
     """Standard reshaping of the images to (28, 28)."""
     images = tf.image.convert_image_dtype(images, dtype)
     single_example_shape = [784] if flatten_images else [28, 28]
-    images = tf.reshape(images, host_batch_shape + single_example_shape)
-    labels = tf.reshape(labels, host_batch_shape)
+    images = tf.reshape(images, [host_batch_size] + single_example_shape)
     if has_labels:
       return dict(images=images, labels=labels)
     else:
@@ -123,7 +124,7 @@ def preprocess_batch(
 
   ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
 
-  return iter(tensorflow_datasets.as_numpy(ds))
+  return sharded_iterator(ds, sharding)
 
 
 def imagenet_num_examples_and_split(

examples/optimizers.py

@@ -85,6 +85,10 @@ def __init__(
         axis_name=self.pmap_axis_name,
     )
 
+  @property
+  def state_sharding(self) -> jax.sharding.NamedSharding:
+    raise NotImplementedError()
+
   def init(
       self,
       params: Params,
@@ -438,7 +442,6 @@ def create_optimizer(
         value_func_has_aux=has_aux,
         value_func_has_state=has_func_state,
         value_func_has_rng=has_rng,
-        multi_device=True,
         **kwargs,
     )
   elif name == "sgd":