Support tuples in custom_partitioning.

PiperOrigin-RevId: 738154413

jax/_src/custom_partitioning.py

@@ -179,7 +179,7 @@ def _custom_partitioning_partition(arg_shapes, arg_shardings, result_shape,
       for sharding, s in zip(result_shardings, result_shapes)
   ]
   closed_jaxpr = jax.make_jaxpr(lower_fn, axis_env=list(mesh.shape.items()))(
-      *tiled_args
+      *info.in_tree.unflatten(tiled_args)
   )
   if ([(o.shape, o.dtype) for o in closed_jaxpr.out_avals] !=
       [(t.shape, t.dtype) for t in tiled_results]):

tests/pjit_test.py

@@ -1680,6 +1680,47 @@ class CustomPartitionerTest(jtu.JaxTestCase):
     jit_f = jax.jit(f, in_shardings=s, out_shardings=s)
     self.assertArraysEqual(x, jit_f(x))
 
+  @jtu.with_mesh([('x', 4), ('y', 2)])
+  def test_custom_partitioner_pytree_inputs(self):
+    self.skip_if_custom_partitioning_not_supported()
+
+    def partition(mesh, arg_shapes, result_shape):
+      def lower_fn(xs):
+        x, y, z = xs
+        return x + y + z
+
+      return (
+          mesh,
+          lower_fn,
+          arg_shapes[0][0].sharding,
+          jax.tree.map(lambda x: x.sharding, arg_shapes),
+      )
+
+    def infer_sharding_from_operands(mesh, arg_shapes, result_shape):
+      return arg_shapes[0][0].sharding
+
+    def propagate_user_sharding(mesh, user_shape):
+      return user_shape.sharding
+
+    @custom_partitioning
+    def f(xs):
+      x, y, z = xs
+      return x + y + z
+
+    f.def_partition(
+        infer_sharding_from_operands=infer_sharding_from_operands,
+        partition=partition,
+        propagate_user_sharding=propagate_user_sharding,
+        sharding_rule='i j, i j, i j -> i j',
+    )
+
+    def f2(a):
+      return a + f((a, a, a))
+
+    pjit_f = pjit(f2, in_shardings=(P(None, 'x')), out_shardings=P('x'))
+    x = np.asarray(np.random.randint(0, 20, (32, 16)), dtype=np.float32)
+    self.assertArraysEqual(x * 4, pjit_f(x))
+
 
 @jtu.pytest_mark_if_available('multiaccelerator')
 class AutoShardingPjitTest(jtu.JaxTestCase):