Add support for extra args to multi transform

PiperOrigin-RevId: 541573248

optax/_src/combine.py

@@ -75,7 +75,7 @@ class MultiTransformState(NamedTuple):
 def multi_transform(
     transforms: Mapping[Hashable, base.GradientTransformation],
     param_labels: Union[base.PyTree, Callable[[base.PyTree], base.PyTree]]
-) -> base.GradientTransformation:
+) -> base.GradientTransformationExtraArgs:
   """Partitions params and applies a different transformation to each subset.
 
   Below is an example where we apply Adam to the weights and SGD to the biases
@@ -130,6 +130,12 @@ def map_fn(nested_dict):
   Returns:
     An ``optax.GradientTransformation``.
   """
+
+  transforms = {
+      k: base.with_extra_args_support(v)
+      for k, v in transforms.items()
+  }
+
   def make_mask(labels, group):
     return jax.tree_util.tree_map(lambda label: label == group, labels)
 
@@ -148,13 +154,13 @@ def init_fn(params):
     }
     return MultiTransformState(inner_states)
 
-  def update_fn(updates, state, params=None):
+  def update_fn(updates, state, params=None, **extra_args):
     labels = param_labels(updates) if callable(param_labels) else param_labels
     new_inner_state = {}
     for group, tx in transforms.items():
       masked_tx = wrappers.masked(tx, make_mask(labels, group))
       updates, new_inner_state[group] = masked_tx.update(
-          updates, state.inner_states[group], params)
+          updates, state.inner_states[group], params, **extra_args)
     return updates, MultiTransformState(new_inner_state)
 
-  return base.GradientTransformation(init_fn, update_fn)
+  return base.GradientTransformationExtraArgs(init_fn, update_fn)

optax/_src/combine_test.py

@@ -179,6 +179,44 @@ def test_multi_transform(self, use_fn):
     updates, state = update_fn(updates, state)
     chex.assert_trees_all_close(updates, correct_updates)
 
+  def test_extra_args(self):
+
+    class ArgNotEqual1Error(ValueError):
+      """Raised when argument not set as expected."""
+
+    def init(params):
+      return {'mu': params}
+
+    def update_with_arg(updates, state, params=None, *, arg, **extra_args):
+      del params, extra_args
+      if arg != 1:
+        raise ArgNotEqual1Error()
+      return updates, state
+
+    def update_without_arg(updates, state, params=None):
+      del params
+      return updates, state
+
+    opt_no_arg = base.GradientTransformation(init, update_without_arg)
+    opt_extra_arg = base.GradientTransformationExtraArgs(init, update_with_arg)
+
+    opt = combine.multi_transform(
+        {
+            'a': opt_no_arg,
+            'b': opt_extra_arg,
+        },
+        ('a', 'b'),
+    )
+
+    fake_params = ({'u': jnp.array([1])}, {'v': jnp.array([1])})
+    state = opt.init(fake_params)
+
+    with self.assertRaises(TypeError):
+      opt.update(fake_params, state)
+    with self.assertRaises(ArgNotEqual1Error):
+      opt.update(fake_params, state, arg=2, ignored_kwarg='hi')
+    opt.update(fake_params, state, arg=1, ignored_kwarg='hi')
+
   @parameterized.parameters(list, tuple, dict)
   def test_empty(self, container):
     init_fn, update_fn = combine.multi_transform(