Delete sharding spec to HloSharding conversion since it's not used an…

…ymore.

PiperOrigin-RevId: 595192496

jax/_src/interpreters/pxla.py

@@ -2475,6 +2475,10 @@ def _get_layouts_from_executable(
   return new_in_layouts, new_out_layouts  # type: ignore
 
 
+def get_logical_mesh_ids(mesh_shape):
+  return np.arange(math.prod(mesh_shape)).reshape(mesh_shape)
+
+
 @weakref_lru_cache
 def _cached_compilation(computation, name, mesh, spmd_lowering,
                         tuple_args, auto_spmd_lowering,
@@ -2528,7 +2532,7 @@ def _cached_compilation(computation, name, mesh, spmd_lowering,
     assert mesh is not None
     opts.auto_spmd_partitioning_mesh_shape = list(mesh.shape.values())
     opts.auto_spmd_partitioning_mesh_ids = (
-        sharding_specs.get_logical_mesh_ids(list(mesh.shape.values()))
+        get_logical_mesh_ids(list(mesh.shape.values()))
         .reshape(-1))
   compile_options.parameter_is_tupled_arguments = tuple_args
   opts.allow_spmd_sharding_propagation_to_output = list(_allow_propagation_to_outputs)

jax/_src/sharding_specs.py

@@ -29,18 +29,15 @@
 
 from __future__ import annotations
 
-import collections
-from collections.abc import Mapping, Sequence
+from collections.abc import Sequence
 import itertools
 import math
-from typing import Any, Union, cast
+from typing import Union
 
 import numpy as np
 
-from jax._src import op_shardings
 from jax._src import util
 from jax._src.lib import pmap_lib
-from jax._src.lib import xla_client as xc
 
 unsafe_map, map = map, util.safe_map
 
@@ -56,9 +53,6 @@
 
 ShardingSpec = pmap_lib.ShardingSpec
 
-OpShardingType = Any
-
-
 
 def _sharding_spec_mesh_shape(self):
   sharded_axis_sizes = []
@@ -76,79 +70,6 @@ def _sharding_spec_mesh_shape(self):
                for a in self.mesh_mapping)
 
 
-def get_logical_mesh_ids(mesh_shape):
-  return np.arange(math.prod(mesh_shape)).reshape(mesh_shape)
-
-
-_MeshAxisName = Any
-
-def sharding_spec_sharding_proto(
-    self, special_axes: Mapping[int, OpShardingType] | None = None
-) -> xc.HloSharding:
-  """Converts a ShardingSpec to an OpSharding proto.
-
-  See
-  https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/xla_data.proto#L601
-  for details on the OpSharding proto.
-  Unfortunately the semantics are not very well described in the proto spec, but
-  the code here might help:
-  https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/compiler/xla/experimental/xla_sharding/xla_sharding.py
-  """
-  special_axes_dict = {} if special_axes is None else special_axes
-  mesh_shape = cast(tuple[int, ...], self.mesh_shape)
-
-  sharded_axes = {}  # maps sharded axis identifiers to mesh axis indices to which they're mapped
-  replicated_maxes = []  # lists mesh axis identifiers to replicate over
-  for maxis, assignment in enumerate(self.mesh_mapping):
-    if isinstance(assignment, Replicated):
-      replicated_maxes.append((maxis, assignment.replicas))
-    elif isinstance(assignment, ShardedAxis):
-      sharded_axes[assignment.axis] = maxis
-    else:
-      util.assert_unreachable(assignment)
-
-  if len(replicated_maxes) == len(self.mesh_mapping) and not special_axes_dict:
-    return xc.HloSharding.replicate()
-
-  mesh_permutation = []
-  new_mesh_shape = []
-  next_sharded_axis = 0
-  for axis, sharding in enumerate(self.sharding):
-    if isinstance(sharding, NoSharding):
-      new_mesh_shape.append(1)  # Add a dummy mesh axis we won't be sharding over
-    elif isinstance(sharding, Chunked):
-      for nchunks in sharding.chunks:
-        maxis = sharded_axes[next_sharded_axis]
-        assert mesh_shape[maxis] == nchunks
-        mesh_permutation.append(maxis)
-        next_sharded_axis += 1
-      new_mesh_shape.append(math.prod(sharding.chunks))
-    elif isinstance(sharding, Unstacked):
-      raise RuntimeError("Cannot convert unstacked sharding specs to XLA OpSharding")
-    else:
-      util.assert_unreachable(sharding)
-
-  # Create a partial sharding proto if tensor is replicated or partitioned
-  # specially over some mesh axes.
-  last_tile_dims = []
-  if replicated_maxes:
-    axes_by_type: dict[OpShardingType, list[_MeshAxisName]] = {}
-    size_by_type: dict[OpShardingType, int] = collections.defaultdict(lambda: 1)
-    assert {x[0] for x in replicated_maxes}.issuperset(set(special_axes_dict.keys()))
-    for axis, size in replicated_maxes:
-      ty = special_axes_dict.get(axis, xc.OpSharding.Type.REPLICATED)
-      axes_by_type.setdefault(ty, []).append(axis)
-      size_by_type[ty] *= size
-    for ty, axes in sorted(axes_by_type.items(), key=lambda x: x[0].value):
-      last_tile_dims.append(ty)
-      new_mesh_shape.append(size_by_type[ty])
-      mesh_permutation.extend(axes)
-
-  return xc.HloSharding.iota_tile(
-      dims=new_mesh_shape, reshape_dims=mesh_shape,
-      transpose_perm=mesh_permutation, subgroup_types=last_tile_dims)
-
-
 def _sharding_spec_indices(self, shape: tuple[int, ...]) -> np.ndarray:
   """Returns NumPy-style indices corresponding to a sharding spec.
 
@@ -163,14 +84,6 @@ def _sharding_spec_indices(self, shape: tuple[int, ...]) -> np.ndarray:
   """
   assert len(shape) == len(self.sharding), (shape, self.sharding)
 
-  has_unstacked = any(isinstance(s, Unstacked) for s in self.sharding)
-  # Take the op sharding indices generation route for pjit/xmap cases.
-  if not has_unstacked:
-    hlo_sharding = sharding_spec_sharding_proto(self)
-    return op_shardings.op_sharding_to_numpy_indices(
-        hlo_sharding, shape, math.prod(self.mesh_shape)
-    ).reshape(self.mesh_shape)
-
   axis_indices: list[Sequence[Index]] = []
   shard_indices_shape = []
   for dim, sharding in enumerate(self.sharding):
@@ -221,7 +134,6 @@ def _sharding_spec_repr(self):
 
 
 ShardingSpec.mesh_shape = property(_sharding_spec_mesh_shape)
-ShardingSpec.sharding_proto = sharding_spec_sharding_proto
 ShardingSpec.indices = _sharding_spec_indices
 # mypy raises: error: Cannot assign to a method  [assignment]
 ShardingSpec.__repr__ = _sharding_spec_repr  # type: ignore

tests/pickle_test.py

@@ -164,12 +164,13 @@ def testPickleSharding(self):
     self.assertEqual(pickle.loads(pickle.dumps(sharding)), sharding)
 
   def testPickleOpSharding(self):
-    sharding = pxla.ShardingSpec((pxla.NoSharding(), pxla.Chunked((2, 2))),
-                                 (pxla.ShardedAxis(0), pxla.ShardedAxis(1)))
-    op_sharding = sharding.sharding_proto().to_proto()
+    op = xc.OpSharding()
+    op.type = xc.OpSharding.Type.OTHER
+    op.tile_assignment_dimensions = [4, 2]
+    op.tile_assignment_devices = [0, 1, 2, 3, 4, 5, 6, 7]
     self.assertTrue(
-        xc.HloSharding.from_proto(pickle.loads(pickle.dumps(op_sharding))),
-        xc.HloSharding.from_proto(op_sharding))
+        xc.HloSharding.from_proto(pickle.loads(pickle.dumps(op))),
+        xc.HloSharding.from_proto(op))
 
   def test_pickle_single_device_sharding(self):
     s = jax.sharding.SingleDeviceSharding(jax.devices()[0])

tests/pmap_test.py

@@ -2988,25 +2988,12 @@ def device_array(x):
           # unsharded
           [(4, 8), pxla.ShardingSpec(sharding=(pxla.NoSharding(), pxla.NoSharding()),
                                      mesh_mapping=())],
-          # partitioned, 1 axis
-          [(4, 8), pxla.ShardingSpec(sharding=(pxla.Chunked([2]), pxla.NoSharding()),
-                                     mesh_mapping=(pxla.ShardedAxis(0),))],
-          # partitioned, 2 axes
-          [(4, 8), pxla.ShardingSpec(sharding=(pxla.Chunked([2]), pxla.Chunked([2])),
-                                     mesh_mapping=map(pxla.ShardedAxis, (0, 1)))],
-          # partitioned, 2 axes, permuted
-          [(4, 8), pxla.ShardingSpec(sharding=(pxla.Chunked([2]), pxla.Chunked([2])),
-                                     mesh_mapping=map(pxla.ShardedAxis, (1, 0)))],
           # replication + sharding
           [(2, 8), pxla.ShardingSpec(sharding=(pxla.Unstacked(2), pxla.NoSharding()),
                                      mesh_mapping=(pxla.ShardedAxis(0), pxla.Replicated(3)))],
           # replication, no sharding
           [(2, 8), pxla.ShardingSpec(sharding=(pxla.NoSharding(), pxla.NoSharding()),
                                      mesh_mapping=(pxla.Replicated(3),))],
-          # multiple replicated axes
-          [(1, 8), pxla.ShardingSpec(sharding=(pxla.Chunked([1]), pxla.Chunked([2])),
-                                     mesh_mapping=(pxla.Replicated(2), pxla.ShardedAxis(0),
-                                                   pxla.Replicated(2), pxla.ShardedAxis(1)))],
           # replicated scalar
           [(), pxla.ShardingSpec(sharding=(),
                                  mesh_mapping=(pxla.Replicated(2), pxla.Replicated(3)))],
@@ -3018,14 +3005,7 @@ def testShardArgs(self, shape, spec, make_arg):
       raise SkipTest
     x = np.arange(math.prod(shape)).reshape(shape)
     arg = make_arg(x)
-    sharding = None
-    if any(isinstance(s, pxla.Unstacked) for s in spec.sharding):
-      sharding = jax.sharding.PmapSharding(jax.devices()[:nshards], spec)
-    else:
-      sharding = jax.sharding.GSPMDSharding(
-          jax.devices()[:nshards],
-          sharding_specs.sharding_spec_sharding_proto(spec))
-
+    sharding = jax.sharding.PmapSharding(jax.devices()[:nshards], spec)
     results = pxla.shard_args(
         jax.devices()[:nshards], [indices], [sharding], [arg]
     )