[sharding_in_types] Make jnp.where broadcast shardings properly when a scalar exists

PiperOrigin-RevId: 705283318

Author: yashk2810

jax/_src/lax/lax.py

@@ -176,6 +176,27 @@ def _broadcast_shapes_uncached(*shapes):
     # Raise ValueError here for backward compatibility.
     raise ValueError(f"Incompatible shapes for broadcasting: shapes={list(shapes)}") from err
 
+def broadcast_shardings(*avals) -> NamedSharding:
+  fst, *rst = avals
+  if not rst:
+    return fst.sharding
+
+  # First check if we need only rank promotion (and not singleton-broadcasting).
+  res_aval = _max(avals, key=lambda a: a.ndim)
+  ndim = res_aval.ndim
+  if ndim == 0 or all(
+      res_aval.sharding.spec[ndim - a.ndim:] == a.sharding.spec for a in avals):
+    return res_aval.sharding
+
+  # Next try singleton-broadcasting, padding out ranks using singletons.
+  aval_list = []
+  for a in avals:
+    new_spec = P(*(None,) * (ndim - a.ndim) + a.sharding.spec)
+    new_shape = (1,) * (ndim - a.ndim) + a.shape
+    aval_list.append(a.update(shape=new_shape,
+                              sharding=a.sharding.with_spec(new_spec)))
+  return broadcasting_sharding_rule('broadcast_shardings', *aval_list)
+
 def _identity(x): return x
 
 def _extract_tracers_dyn_shape(

jax/_src/numpy/util.py

@@ -23,6 +23,7 @@
 from jax._src import config
 from jax._src import core
 from jax._src import dtypes
+from jax._src import api_util
 from jax._src.lax import lax
 from jax._src.util import safe_zip, safe_map
 from jax._src.typing import Array, ArrayLike, DimSize, DType, DTypeLike, Shape
@@ -213,14 +214,18 @@ def promote_args_inexact(fun_name: str, *args: ArrayLike) -> list[Array]:
 @partial(api.jit, inline=True)
 def _broadcast_arrays(*args: ArrayLike) -> list[Array]:
   """Like Numpy's broadcast_arrays but doesn't return views."""
-  shapes = [np.shape(arg) for arg in args]
+  avals = [api_util.shaped_abstractify(arg) for arg in args]
+  shapes = [a.shape for a in avals]
   if not shapes or all(core.definitely_equal_shape(shapes[0], s) for s in shapes):
     return [lax.asarray(arg) for arg in args]
   result_shape = lax.broadcast_shapes(*shapes)
-  return [_broadcast_to(arg, result_shape) for arg in args]
+  result_sharding = (lax.broadcast_shardings(*avals)  # type: ignore
+                     if config.sharding_in_types.value else None)
+  return [_broadcast_to(arg, result_shape, result_sharding) for arg in args]
 
 
-def _broadcast_to(arr: ArrayLike, shape: DimSize | Shape) -> Array:
+def _broadcast_to(arr: ArrayLike, shape: DimSize | Shape, sharding=None
+                  ) -> Array:
   check_arraylike("broadcast_to", arr)
   arr = arr if isinstance(arr, Array) else lax.asarray(arr)
   if not isinstance(shape, tuple) and np.ndim(shape) == 0:
@@ -240,7 +245,8 @@ def _broadcast_to(arr: ArrayLike, shape: DimSize | Shape) -> Array:
     if nlead < 0 or not compatible:
       msg = "Incompatible shapes for broadcasting: {} and requested shape {}"
       raise ValueError(msg.format(arr_shape, shape))
-    return lax.broadcast_in_dim(arr, shape, tuple(range(nlead, len(shape))))
+    return lax.broadcast_in_dim(arr, shape, tuple(range(nlead, len(shape))),
+                                sharding=sharding)
 
 
 # The `jit` on `where` exists to avoid materializing constants in cases like

tests/pjit_test.py

@@ -5548,6 +5548,16 @@ def f(x, x2):
         "AxisTypes should be the same in a tuple subset of PartitionSpec"):
       NamedSharding(mesh2, P(('x', 'y')))
 
+  @jtu.with_user_mesh((2, 2), ('x', 'y'))
+  def test_where_with_scalar(self, mesh):
+    np_inp = np.arange(16.).reshape(8, 2)
+    s = NamedSharding(mesh, P('x', 'y'))
+    x = jax.device_put(np_inp, s)
+
+    out = jnp.where(x > 0, x, 0)
+    self.assertArraysEqual(out, x)
+    self.assertEqual(out.sharding, s)
+
 
 @jtu.pytest_mark_if_available('multiaccelerator')
 class PJitErrorTest(jtu.JaxTestCase):