fix transform_metadata

flax/nnx/extract.py

@@ -517,7 +517,14 @@ def updates_and_snapshot(args: A) -> tuple[A, A]:
   for leaf in leaves:
     if isinstance(leaf, variablelib.Variable):
       updates_leaves.append(leaf)
-      snapshot_leaves.append(leaf.copy())
+      # don't snapshot hijax or ref Variables as their updates are automatically
+      # masked out in mask_variable_updates. However, the leaf is kept in the
+      # updates to check for aliasing. This avoids a copy operation which has
+      # significance for ref Variables.
+      if leaf.hijax or leaf.ref:
+        snapshot_leaves.append(Mask())
+      else:
+        snapshot_leaves.append(leaf.copy())
     else:
       updates_leaves.append(Mask())
       snapshot_leaves.append(Mask())
@@ -597,6 +604,10 @@ def mask_variable_updates(
     keep_fn = lambda _, _pfx, cur, snap: variable_changed(cur, snap)
 
   def _mask_updates(path, prefix_leaf, current, snapshot):
+    if current is None:
+      # None leaves should remain None, they only appear here because
+      # is_leaf catches None values for the prefix
+      return None
     if isinstance(current, variablelib.Variable):
       if current.hijax or current.ref:
         return Mask()
@@ -610,7 +621,7 @@ def _mask_updates(path, prefix_leaf, current, snapshot):
         current_tree, snapshot_tree, is_leaf=is_leaf,
     )
   return broadcast_prefix_map(
-      _mask_updates, prefix, current_tree, snapshot_tree, is_leaf=is_leaf,
+      _mask_updates, prefix, current_tree, snapshot_tree, is_leaf=is_leaf
   )
 
 

flax/nnx/transforms/iteration.py

@@ -69,21 +69,19 @@ def _apply_axis_fn(
     axis_fn: tp.Callable[..., tp.Any],
 ) -> None:
   is_leaf = lambda x: x is None or isinstance(x, variablelib.Variable)
-  _, per_leaf_axes = extract.broadcast_prefix2(axes, tree, is_leaf=is_leaf)
-  leaves = jax.tree_util.tree_leaves(tree, is_leaf=is_leaf)
-  for leaf, axis in zip(leaves, per_leaf_axes):
-    if (axis is None or isinstance(axis, int)) and isinstance(
-        leaf, variablelib.Variable
-    ):
+  def apply_fn(path, axis, leaf):
+    if isinstance(axis, int) and isinstance(leaf, variablelib.Variable):
       axis_fn(leaf, axis, metadata)
 
+  extract.broadcast_prefix_map(apply_fn, axes, tree, is_leaf=is_leaf)
+
 
 @tp.overload
 def transform_metadata(
     *,
     in_axes: tp.Any = 0,
     out_axes: tp.Any = 0,
-    partition: str,
+    partition: str | None,
     graph: bool | None = None,
 ) -> tp.Callable[[F], F]:
   ...
@@ -96,7 +94,7 @@ def transform_metadata(
     in_axes: tp.Any = 0,
     out_axes: tp.Any = 0,
     graph: bool | None = None,
-    partition: str,
+    partition: str | None,
 ) -> F:
   ...
 
@@ -106,8 +104,8 @@ def transform_metadata(
     *,
     in_axes: tp.Any = 0,
     out_axes: tp.Any = 0,
+    partition: str | None,
     graph: bool | None = None,
-    partition: str,
 ) -> F | tp.Callable[[F], F]:
   if f is Missing:
     return functools.partial(

tests/nnx/spmd_test.py

@@ -219,6 +219,30 @@ def f(v):
     self.assertEqual(v2[...], 10)
 
 
+  def test_transform_metadata_decorator_none_partition(self):
+    v = nnx.Param(
+      jnp.array(1),
+      out_sharding=(None, 'dout'),
+      eager_sharding=False,
+    )
+
+    @nnx.transform_metadata(in_axes=0, out_axes=1, partition=None)
+    def f(v):
+      v[...] += 1
+      self.assertEqual(v.out_sharding, ('dout',))
+      v2 = nnx.Param(
+        jnp.array(10),
+        out_sharding=('dmid', 'dout'),
+        eager_sharding=False,
+      )
+      return v2
+
+    v2 = f(v)
+    self.assertEqual(v.out_sharding, (None, 'dout'))
+    self.assertEqual(v[...], 2)
+    self.assertEqual(v2.out_sharding, ('dmid', None, 'dout'))
+    self.assertEqual(v2[...], 10)
+
   @parameterized.product(use_eager_sharding=[True, False])
   def test_eager_sharding_context(self, use_eager_sharding):
     rngs = nnx.Rngs(0)

tests/nnx/transforms_test.py

@@ -5537,12 +5537,9 @@ def dict_scan(carry, x):
 
   def test_no_carry_all_scanned(self):
     def double(x):
-      return (x * 2,)
+      return x * 2
 
-    (ys,) = pure_jax_fancy_scan(
-        double, jnp.arange(5.0),
-        in_axes=(0,), out_axes=(0,),
-    )
+    ys = pure_jax_fancy_scan(double, jnp.arange(5.0), in_axes=0, out_axes=0)
     np.testing.assert_allclose(ys, jnp.arange(5.0) * 2)
 
   def test_reverse(self):
@@ -5651,6 +5648,60 @@ def f(x):
     )
     np.testing.assert_allclose(ys, jnp.arange(5.0) * 2)
 
+  def test_scan_axis_1(self):
+    def cumsum(carry, x):
+      carry = carry + x
+      return carry, carry
+
+    x = jnp.arange(10.0).reshape((2, 5))
+    final_carry, ys = pure_jax_fancy_scan(
+        cumsum, jnp.zeros(2), x,
+        in_axes=(nnx.Carry, 1), out_axes=(nnx.Carry, 1),
+    )
+    np.testing.assert_allclose(final_carry, jnp.array([10.0, 35.0]))
+    expected_ys = jnp.array([
+        [0., 1., 3., 6., 10.],
+        [5., 11., 18., 26., 35.]
+    ])
+    np.testing.assert_allclose(ys, expected_ys)
+
+  def test_scan_axis_negative_1(self):
+    def cumsum(carry, x):
+      carry = carry + x
+      return carry, carry
+
+    x = jnp.arange(10.0).reshape((2, 5))
+    final_carry, ys = pure_jax_fancy_scan(
+        cumsum, jnp.zeros(2), x,
+        in_axes=(nnx.Carry, -1), out_axes=(nnx.Carry, -1),
+    )
+    np.testing.assert_allclose(final_carry, jnp.array([10.0, 35.0]))
+    expected_ys = jnp.array([
+        [0., 1., 3., 6., 10.],
+        [5., 11., 18., 26., 35.]
+    ])
+    np.testing.assert_allclose(ys, expected_ys)
+
+  def test_scan_different_in_out_axes(self):
+    def cumsum(carry, x):
+      carry = carry + x
+      return carry, carry
+
+    x = jnp.arange(10.0).reshape((2, 5))
+    final_carry, ys = pure_jax_fancy_scan(
+        cumsum, jnp.zeros(2), x,
+        in_axes=(nnx.Carry, 1), out_axes=(nnx.Carry, 0),
+    )
+    np.testing.assert_allclose(final_carry, jnp.array([10.0, 35.0]))
+    expected_ys = jnp.array([
+        [0., 5.],
+        [1., 11.],
+        [3., 18.],
+        [6., 26.],
+        [10., 35.]
+    ])
+    np.testing.assert_allclose(ys, expected_ys)
+
 
 if __name__ == '__main__':
   absltest.main()