Remove physical_hlo_sharding from TyRules.

The only caller of `physical_op_sharding` outside of TyRules was mlir.py. This CL also changes lower_jaxpr_to_fun to only accept logical arg_shardings and result_shardings which are XLACompatiableShardings.

PiperOrigin-RevId: 616267810

Author: yashk2810

jax/_src/interpreters/mlir.py

@@ -819,16 +819,19 @@ class LoweringResult(NamedTuple):
 _platforms_with_donation = ["cpu", "cuda", "rocm", "tpu"]
 
 
-def _to_logical_op_sharding(
+def _to_physical_op_sharding(
     aval: core.AbstractValue, sharding: XLACompatibleSharding | None,
-) -> xc.HloSharding | None:
+) -> xc.OpSharding | None:
   if sharding is None:
     return None
   assert isinstance(sharding, sharding_impls.XLACompatibleSharding)
   if isinstance(aval, AbstractRef):
-    return _to_logical_op_sharding(aval.inner_aval, sharding)
+    return _to_physical_op_sharding(aval.inner_aval, sharding)
   assert isinstance(aval, (core.ShapedArray, core.DShapedArray))
-  return sharding._to_xla_hlo_sharding(aval.ndim)
+  if dtypes.issubdtype(aval.dtype, dtypes.extended):
+    sharding = aval.dtype._rules.physical_sharding(aval, sharding)
+    aval = core.physical_aval(aval)
+  return sharding._to_xla_hlo_sharding(aval.ndim).to_proto()  # type: ignore
 
 
 def _to_xla_layout(layout: XLACompatibleLayout | None | LayoutRequest) -> str | None:
@@ -941,13 +944,6 @@ def lower_jaxpr_to_module(
   else:
     dim_vars = ()
 
-  arg_op_shardings = (
-      map(_to_logical_op_sharding, jaxpr.in_avals, arg_shardings)
-      if arg_shardings is not None else arg_shardings)
-  result_op_shardings = (
-      map(_to_logical_op_sharding, jaxpr.out_avals, result_shardings)
-      if result_shardings is not None else result_shardings)
-
   arg_layouts = (map(_to_xla_layout, in_layouts) if in_layouts is not None
                   else in_layouts)
   result_layouts = (map(_to_xla_layout, out_layouts) if out_layouts is not None
@@ -978,8 +974,8 @@ def lower_jaxpr_to_module(
         replace_tokens_with_dummy=replace_tokens_with_dummy,
         num_output_tokens=0,
         replicated_args=replicated_args,
-        arg_shardings=arg_op_shardings,
-        result_shardings=result_op_shardings,
+        arg_shardings=arg_shardings,
+        result_shardings=result_shardings,
         input_output_aliases=input_output_aliases,
         xla_donated_args=xla_donated_args,
         arg_names=arg_names,
@@ -1123,8 +1119,8 @@ def lower_jaxpr_to_fun(
     public: bool = False,
     replace_tokens_with_dummy: bool = False,
     replicated_args: Sequence[bool] | None = None,
-    arg_shardings: Sequence[xc.HloSharding | None] | None = None,
-    result_shardings: Sequence[xc.HloSharding | None] | None = None,
+    arg_shardings: Sequence[XLACompatibleSharding | None] | None = None,
+    result_shardings: Sequence[XLACompatibleSharding | None] | None = None,
     use_sharding_annotations: bool = True,
     input_output_aliases: Sequence[int | None] | None = None,
     xla_donated_args: Sequence[bool] | None = None,
@@ -1483,15 +1479,6 @@ def wrap_with_memory_kind(
   return op.result
 
 
-def _to_physical_op_sharding(
-    aval: core.AbstractValue | None, sharding: xc.HloSharding | None
-) -> xc.OpSharding | None:
-  if (isinstance(aval, core.ShapedArray) and dtypes.issubdtype(aval.dtype, dtypes.extended)
-      and sharding is not None):
-    return aval.dtype._rules.physical_hlo_sharding(aval, sharding).to_proto()
-  return None if sharding is None else sharding.to_proto()  # type: ignore
-
-
 def _emit_lowering_rule_as_fun(lowering_rule,
                                ctx: LoweringRuleContext) -> func_dialect.FuncOp:
   """Emits the contents of a lowering rule as a private function."""

jax/_src/lax/lax.py

@@ -5110,10 +5110,6 @@ def handler(bufs):
       return core.DArray(aval, phys_handler(bufs))
     return handler
 
-  @staticmethod
-  def physical_hlo_sharding(aval, hlo_sharding: xc.HloSharding) -> xc.HloSharding:
-    return hlo_sharding
-
   @staticmethod
   def logical_sharding(aval, phys_sharding):
     return phys_sharding

jax/_src/pjit.py

@@ -1617,10 +1617,8 @@ def _pjit_cached_lower_jaxpr_to_fun(ctx, name, jaxpr, effects, in_shardings,
 
   func = mod_ctx.cached_primitive_lowerings.get(key, None)
   if func is None:
-    arg_shardings = [None if is_unspecified(i) else i._to_xla_hlo_sharding(aval.ndim)
-                     for aval, i in zip(ctx.avals_in, in_shardings)]
-    result_shardings = [None if is_unspecified(o) else o._to_xla_hlo_sharding(aval.ndim)
-                        for aval, o in zip(ctx.avals_out, out_shardings)]
+    arg_shardings = [None if is_unspecified(i) else i for i in in_shardings]
+    result_shardings = [None if is_unspecified(o) else o for o in out_shardings]
     # TODO(b/228598865): inlined calls cannot have shardings set directly on the
     # inputs or outputs because they are lost during MLIR->HLO conversion.
     # using_sharding_annotation=False means we add an identity operation instead.

jax/_src/prng.py

@@ -342,8 +342,7 @@ def make_key_array_phys_sharding(aval, sharding):
   else:
     hlos = sharding._to_xla_hlo_sharding(aval.ndim)
     return GSPMDSharding(
-        sharding._device_assignment,
-        KeyTyRules.physical_hlo_sharding(aval, hlos))
+        sharding._device_assignment, physical_hlo_sharding(aval, hlos))
 
 
 def get_logical_gspmd_sharding(aval, phys_sharding):
@@ -361,6 +360,17 @@ def get_logical_gspmd_sharding(aval, phys_sharding):
                        xc.HloSharding.from_proto(logical_op_sharding))
 
 
+def physical_hlo_sharding(aval, hlo_sharding: xc.HloSharding) -> xc.HloSharding:
+    key_shape = aval.dtype._impl.key_shape
+    new_op_sharding = hlo_sharding.to_proto().clone()  # type: ignore
+    partitions, num_replicas = op_shardings.get_num_ways_dim_sharded(
+        hlo_sharding)
+    suffix = [] if num_replicas == 1 else [num_replicas]
+    tad = partitions + [1] * len(key_shape) + suffix
+    new_op_sharding.tile_assignment_dimensions = tad
+    return xc.HloSharding.from_proto(new_op_sharding)
+
+
 class KeyTyRules:
 
   @staticmethod
@@ -382,17 +392,6 @@ def physical_element_aval(dtype) -> core.ShapedArray:
   def physical_const(val) -> Array:
     return val._base_array
 
-  @staticmethod
-  def physical_hlo_sharding(aval, hlo_sharding: xc.HloSharding) -> xc.HloSharding:
-    key_shape = aval.dtype._impl.key_shape
-    new_op_sharding = hlo_sharding.to_proto().clone()  # type: ignore
-    partitions, num_replicas = op_shardings.get_num_ways_dim_sharded(
-        hlo_sharding)
-    suffix = [] if num_replicas == 1 else [num_replicas]
-    tad = partitions + [1] * len(key_shape) + suffix
-    new_op_sharding.tile_assignment_dimensions = tad
-    return xc.HloSharding.from_proto(new_op_sharding)
-
   @staticmethod
   def physical_sharding(
       aval, sharding: XLACompatibleSharding) -> XLACompatibleSharding:

jax/experimental/shard_map.py

@@ -567,13 +567,13 @@ def _xla_shard(ctx: mlir.LoweringRuleContext, mesh, auto, names,
                aval_in, aval_out, x):
   manual_proto = pxla.manual_proto(aval_in, frozenset(mesh.axis_names) - auto, mesh)
   axes = {name: i for i, ns in names.items() for name in ns}
-  shard_proto = NamedSharding(
-      mesh, sharding_impls.array_mapping_to_axis_resources(axes)  # type: ignore
-  )._to_xla_hlo_sharding(aval_in.ndim)
+  ns = NamedSharding(mesh, sharding_impls.array_mapping_to_axis_resources(axes))  # type: ignore
   if dtypes.issubdtype(aval_in.dtype, dtypes.extended):
-    shard_proto = aval_in.dtype._rules.physical_hlo_sharding(aval_in, shard_proto)
+    ns = aval_in.dtype._rules.physical_sharding(aval_in, ns)
+    aval_in = core.physical_aval(aval_in)
+  shard_proto = ns._to_xla_hlo_sharding(aval_in.ndim).to_proto()
   unspecified = set(range(aval_in.ndim)) if auto else set()
-  sx = mlir.wrap_with_sharding_op(ctx, x, aval_in, shard_proto.to_proto(),  # type: ignore
+  sx = mlir.wrap_with_sharding_op(ctx, x, aval_in, shard_proto,  # type: ignore
                                   unspecified_dims=unspecified)
   return [mlir.wrap_with_full_to_shard_op(ctx, sx, aval_out, manual_proto, set())]
 
@@ -583,13 +583,13 @@ def _xla_unshard(ctx: mlir.LoweringRuleContext, mesh, auto, names,
   manual_proto = pxla.manual_proto(aval_in, frozenset(mesh.axis_names) - auto, mesh)
   sx = mlir.wrap_with_sharding_op(ctx, x, aval_in, manual_proto, unspecified_dims=set())
   axes = {name: i for i, ns in names.items() for name in ns}
-  shard_proto = NamedSharding(
-      mesh, sharding_impls.array_mapping_to_axis_resources(axes)  # type: ignore
-  )._to_xla_hlo_sharding(aval_out.ndim)
+  ns = NamedSharding(mesh, sharding_impls.array_mapping_to_axis_resources(axes))  # type: ignore
   if dtypes.issubdtype(aval_out.dtype, dtypes.extended):
-    shard_proto = aval_out.dtype._rules.physical_hlo_sharding(aval_out, shard_proto)
+    ns = aval_out.dtype._rules.physical_sharding(aval_out, ns)
+    aval_out = core.physical_aval(aval_out)
+  shard_proto = ns._to_xla_hlo_sharding(aval_out.ndim).to_proto()
   unspecified = set(range(aval_out.ndim)) if auto else set()
-  return mlir.wrap_with_shard_to_full_op(ctx, sx, aval_out, shard_proto.to_proto(),
+  return mlir.wrap_with_shard_to_full_op(ctx, sx, aval_out, shard_proto,
                                          unspecified)  # type: ignore
 
 def _pspec_mhlo_attrs(names: AxisNames, aval: core.AbstractValue) -> str:

tests/lax_test.py

@@ -45,7 +45,6 @@
 from jax._src.interpreters import pxla
 from jax._src.internal_test_util import lax_test_util
 from jax._src.lax import lax as lax_internal
-from jax._src.lib import xla_client as xc
 from jax._src.lib import xla_extension_version
 from jax._src.util import NumpyComplexWarning
 
@@ -2989,14 +2988,6 @@ class FooTyRules:
   def physical_element_aval(dtype) -> core.ShapedArray:
     return core.ShapedArray((2,), jnp.dtype('uint32'))
 
-  @staticmethod
-  def physical_hlo_sharding(aval, hlo_sharding: xc.HloSharding):
-    op_sharding_proto = hlo_sharding.to_proto()
-    new_op_sharding = op_sharding_proto.clone()
-    tad = list(new_op_sharding.tile_assignment_dimensions)
-    new_op_sharding.tile_assignment_dimensions = [*tad, 1]
-    return xc.HloSharding.from_proto(new_op_sharding)
-
   @staticmethod
   def logical_sharding(aval, phys_sharding):
     return phys_sharding