Support shard_map with sharding in types. Right now only full manual …

…mode is supported.

This change also adds AxisTypes to Mesh which are `User`, `Auto` and `Collective`.

In the following changes, I'll remove the `config.sharding_in_types` flag and we'll enter into various modes via AxisTypes mentioned on the mesh.

PiperOrigin-RevId: 693048291

jax/_src/core.py

@@ -1657,7 +1657,10 @@ def str_short(self, short_dtypes=False):
     dt_str = dt_str.replace('void', 'float0')
     if hasattr(self, 'sharding') and self.sharding is not None:
       shapestr = ','.join(_get_shape_sharding_str(self.shape, self.sharding.spec))
-      return f'{dt_str}[{shapestr}]'
+      axis_types = self.sharding.mesh.axis_types
+      axt = (f"{{{', '.join(_get_axis_type_str(axis_types))}}}"
+             if axis_types is not None else '')
+      return f'{dt_str}[{shapestr}]{axt}'
     else:
       shapestr = ','.join(map(str, self.shape))
       return f'{dt_str}[{shapestr}]'
@@ -1669,6 +1672,23 @@ def _len(self, ignored_tracer):
       raise TypeError("len() of unsized object") from err  # same as numpy error
 
 
+def _get_axis_type_str(axis_types):
+  from jax._src.mesh import AxisTypes  # type: ignore
+
+  d = defaultdict(list)
+  for axis, t in axis_types.items():
+    d[t].append(axis)
+  for t, axes in d.items():
+    a = ','.join(a for a in axes)
+    a = a if len(a) == 1 else f"({a})"
+    if t == AxisTypes.Collective:
+      yield f"C:{a}"
+    elif t == AxisTypes.User:
+      yield f"U:{a}"
+    else:
+      assert t == AxisTypes.Auto
+      yield f"A:{a}"
+
 def _get_shape_sharding_str(shape, spec):
   for s1, s2 in zip(shape, spec):
     if s2 is None:

jax/_src/lax/lax.py

@@ -2203,14 +2203,13 @@ def multi_sharding_in_dim(ctx, ops, in_avals, out_aval):
   for op, in_aval in zip(ops, in_avals):
     if in_aval.sharding == out_aval.sharding or in_aval.sharding is None:
       out.append(op)
+    elif in_aval.sharding.mesh.are_all_axis_types_collective:
+      out.append(op)
     else:
       # TODO(yashkatariya, dougalm): If `in_aval.sharding` contains
       # CompilerShardingAxis, then specify `unspecified_dims` via
       # `wrap_with_sharding_op`.
-      if config.use_shardy_partitioner.value:
-        sp = in_aval.sharding._to_sdy_sharding(in_aval.ndim)
-      else:
-        sp = in_aval.sharding._to_xla_hlo_sharding(in_aval.ndim).to_proto()
+      sp = in_aval.sharding._to_xla_hlo_sharding(in_aval.ndim).to_proto()
       out.append(mlir.wrap_with_sharding_op(ctx, op, out_aval, sp))
   return out
 
@@ -2227,10 +2226,9 @@ def _nary_lower_hlo(op: Callable, ctx,
 
   out = op(*args)
   if config.sharding_in_types.value:
-    if config.use_shardy_partitioner.value:
-      out_sp = aval_out.sharding._to_sdy_sharding(aval_out.ndim)
-    else:
-      out_sp = aval_out.sharding._to_xla_hlo_sharding(aval_out.ndim).to_proto()
+    if aval_out.sharding.mesh.are_all_axis_types_collective:
+      return [out]
+    out_sp = aval_out.sharding._to_xla_hlo_sharding(aval_out.ndim).to_proto()
     return [mlir.wrap_with_sharding_op(ctx, out, aval_out, out_sp)]
   else:
     return [out]

jax/_src/mesh.py

@@ -18,6 +18,7 @@
 import collections
 from collections.abc import Hashable, Sequence
 import contextlib
+import enum
 import functools
 import math
 import threading
@@ -101,6 +102,12 @@ def _get_local_mesh(global_mesh: Mesh, process_index: int) -> Mesh:
   return Mesh(global_mesh.devices[subcube_indices_tuple], global_mesh.axis_names)
 
 
+class AxisTypes(enum.Enum):
+  Auto = enum.auto()
+  User = enum.auto()
+  Collective = enum.auto()
+
+
 _mesh_object_dict = {}  # type: ignore
 
 
@@ -157,9 +164,11 @@ class Mesh(contextlib.ContextDecorator):
 
   devices: np.ndarray
   axis_names: tuple[MeshAxisName, ...]
+  axis_types: dict[str, AxisTypes] | None
 
   def __new__(cls, devices: np.ndarray | Sequence[xc.Device],
-              axis_names: str | Sequence[MeshAxisName]):
+              axis_names: str | Sequence[MeshAxisName],
+              axis_types: dict[str, AxisTypes] | None = None):
     if not isinstance(devices, np.ndarray):
       devices = np.array(devices)
     if isinstance(axis_names, str):
@@ -175,7 +184,10 @@ def __new__(cls, devices: np.ndarray | Sequence[xc.Device],
           f"devices.ndim == {devices.ndim} and "
           f"len(axis_names) == {len(axis_names)}.")
 
-    key = (axis_names, devices.shape, tuple(devices.flat))
+    # TODO(yashkatariya): If axis_types is None, set all axes to AUTO.
+    axis_types_tuple = (None if axis_types is None else
+                        tuple(axis_types.items()))
+    key = (axis_names, devices.shape, tuple(devices.flat), axis_types_tuple)
     val = _mesh_object_dict.get(key, None)
     if val is not None:
       return val
@@ -184,11 +196,13 @@ def __new__(cls, devices: np.ndarray | Sequence[xc.Device],
     self.devices = devices.copy()
     self.devices.flags.writeable = False
     self.axis_names = axis_names
+    self.axis_types = axis_types
+    self._axis_types_tuple = axis_types_tuple
     _mesh_object_dict[key] = self
     return self
 
   def __reduce__(self):
-    return (type(self), (self.devices, self.axis_names))
+    return (type(self), (self.devices, self.axis_names, self.axis_types))
 
   def __eq__(self, other):
     if not isinstance(other, Mesh):
@@ -199,12 +213,14 @@ def __eq__(self, other):
       return True
     return (self.axis_names == other.axis_names and
             self.devices.shape == other.devices.shape and
+            self._axis_types_tuple == other._axis_types_tuple and
             self._internal_device_list == other._internal_device_list)
 
   def __hash__(self):
     if not hasattr(self, '_hash'):
       self._hash = hash(
-          (self.axis_names, self._internal_device_list, self.devices.shape))
+          (self.axis_names, self._internal_device_list, self.devices.shape,
+           self._axis_types_tuple))
     return self._hash
 
   def __setattr__(self, name, value):
@@ -301,7 +317,8 @@ def __str__(self):
   def _repr(self):
     if self.empty:
       return "Mesh(device_ids=[], axis_names=())"
-    return f"Mesh(device_ids={self.device_ids!r}, axis_names={self.axis_names!r})"
+    atr = '' if self.axis_types is None else f", axis_types={self.axis_types}"
+    return f"Mesh(device_ids={self.device_ids!r}, axis_names={self.axis_names!r}{atr})"
 
   def __repr__(self):
     return self._repr
@@ -313,7 +330,7 @@ def local_devices(self):
 
   @functools.cached_property
   def abstract_mesh(self):
-    return AbstractMesh(self.shape_tuple)
+    return AbstractMesh(self.shape_tuple, self.axis_types)
 
 
 EMPTY_ENV = ResourceEnv(Mesh(np.empty((), dtype=object), ()))
@@ -338,25 +355,32 @@ class AbstractMesh:
   details.
   """
 
-  def __init__(self, shape_tuple: tuple[tuple[str, int], ...]):
+  def __init__(self, shape_tuple: tuple[tuple[str, int], ...],
+               axis_types: dict[str, AxisTypes] | None = None):
     self.shape_tuple = shape_tuple
+    self.axis_types = axis_types
     if self.shape_tuple:
       self._axis_names, self._axis_sizes = list(zip(*self.shape_tuple))
     else:
       self._axis_names, self._axis_sizes = (), ()
+    # TODO(yashkatariya): If axis_types is None, set all axes to AUTO.
+    self._axis_types_tuple = (None if axis_types is None else
+                              tuple(axis_types.items()))
 
   def __hash__(self):
-    return hash(self.shape_tuple)
+    return hash((self.shape_tuple, self._axis_types_tuple))
 
   def __eq__(self, other):
     if not isinstance(other, AbstractMesh):
       return False
     if id(self) == id(other):
       return True
-    return self.shape_tuple == other.shape_tuple
+    return (self.shape_tuple == other.shape_tuple and
+            self._axis_types_tuple == other._axis_types_tuple)
 
   def __repr__(self):
-    return f"AbstractMesh({self.shape_tuple})"
+    atr = '' if self.axis_types is None else f", axis_types={self.axis_types}"
+    return f"AbstractMesh({self.shape_tuple}{atr})"
 
   @property
   def axis_names(self):
@@ -382,6 +406,12 @@ def _internal_device_list(self):
   def empty(self):
     return self.size == 0
 
+  @functools.cached_property
+  def are_all_axis_types_collective(self) -> bool:
+    if self.axis_types is None:
+      return False
+    return all(t == AxisTypes.Collective for t in self.axis_types.values())
+
   @property
   def devices(self):
     _raise_value_error("devices")

jax/_src/partition_spec.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 class _UnconstrainedPartitionSingleton:
 
   def __repr__(self):
@@ -48,3 +50,21 @@ def __repr__(self):
 
   def __reduce__(self):
     return (PartitionSpec, tuple(self))
+
+  def _normalized_spec(self, ndim: int) -> PartitionSpec:
+    out = []  # type: ignore
+    for p in self:
+      if p is None:
+        out.append(None)
+      elif p == self.UNCONSTRAINED:
+        out.append(p)
+      elif isinstance(p, (list, tuple)):
+        if len(p) == 1:
+          out.append(p[0])
+        else:
+          out.append(tuple(p))
+      else:
+        out.append(p)
+    if len(out) < ndim:
+      out.extend([None] * (ndim - len(out)))
+    return PartitionSpec(*out)

jax/_src/sharding_impls.py

@@ -361,19 +361,7 @@ def with_memory_kind(self, kind: str) -> NamedSharding:
     return NamedSharding(self.mesh, self.spec, memory_kind=kind)
 
   def _normalized_spec(self, ndim: int) -> PartitionSpec:
-    out = []  # type: ignore
-    for p in self._parsed_pspec:
-      if p is None:
-        raise ValueError("UNCONSTRAINED is not supported yet.")
-      if not p:
-        out.append(None)
-      elif isinstance(p, tuple) and len(p) == 1:
-        out.append(p[0])
-      else:
-        out.append(p)
-    if len(out) < ndim:
-      out.extend([None] * (ndim - len(out)))
-    return PartitionSpec(*out)
+    return self.spec._normalized_spec(ndim)
 
   def _to_xla_hlo_sharding(self, num_dimensions: int) -> xc.HloSharding:
     return named_sharding_to_xla_hlo_sharding(self, num_dimensions)

jax/experimental/shard_map.py

@@ -46,7 +46,7 @@
 from jax._src import traceback_util
 from jax._src import util
 from jax._src.core import Tracer
-from jax._src.mesh import AbstractMesh, Mesh
+from jax._src.mesh import AbstractMesh, Mesh, AxisTypes
 from jax._src.api import _shared_code_pmap, _prepare_pmap
 from jax._src.lax import (lax, parallel as lax_parallel, slicing,
                           windowed_reductions, convolution, fft, linalg,
@@ -528,17 +528,32 @@ def _unshard_aval(mesh: Mesh, names: AxisNames, aval: core.AbstractValue
     raise NotImplementedError(f"Unsupported aval type: {type(aval)}")
 
 def _shard_shaped_array(mesh: Mesh, names: AxisNames, aval: core.AbstractValue
-                       ) -> core.AbstractValue:
+                        ) -> core.AbstractValue:
   assert isinstance(aval, core.ShapedArray)
-  return aval.update(tuple(sz // prod(mesh.shape[n] for n in names.get(i, ()))
-                            for i, sz in enumerate(aval.shape)))
+  new_shape = tuple(sz // prod(mesh.shape[n] for n in names.get(i, ()))
+                    for i, sz in enumerate(aval.shape))
+  if config.sharding_in_types.value:
+    axis_names = mesh.axis_names
+    new_mesh = AbstractMesh(
+        mesh.shape_tuple,
+        dict(zip(axis_names, [AxisTypes.Collective] * len(axis_names))))
+    new_sharding = NamedSharding(new_mesh, P(*[None] * aval.ndim))
+  else:
+    new_sharding = None
+  return aval.update(shape=new_shape, sharding=new_sharding)
 core.shard_aval_handlers[core.ShapedArray] = _shard_shaped_array
 
 def _unshard_shaped_array(mesh: Mesh, names: AxisNames,
                           aval: core.AbstractValue,) -> core.AbstractValue:
   assert isinstance(aval, core.ShapedArray)
-  return aval.update(tuple(sz * prod(mesh.shape[n] for n in names.get(i, ()))
-                            for i, sz in enumerate(aval.shape)))
+  new_shape = tuple(sz * prod(mesh.shape[n] for n in names.get(i, ()))
+                    for i, sz in enumerate(aval.shape))
+  if config.sharding_in_types.value:
+    spec = _names_to_pspec(names)._normalized_spec(aval.ndim)
+    new_sharding = NamedSharding(AbstractMesh(mesh.shape_tuple), spec)
+  else:
+    new_sharding = None
+  return aval.update(shape=new_shape, sharding=new_sharding)
 core.unshard_aval_handlers[core.ShapedArray] = _unshard_shaped_array
 
 # Type-checking

tests/pjit_test.py

@@ -5201,6 +5201,30 @@ def f(x):
     self.assertArraysEqual(out, np_inp)
     self.assertEqual(out.sharding, NamedSharding(mesh, P('x', None)))
 
+  def test_shard_map_full_manual(self):
+    mesh = jtu.create_mesh((2, 2), ('x', 'y'))
+    np_inp = np.arange(16).reshape(8, 2)
+    arr = jax.device_put(np_inp, NamedSharding(mesh, P('x', 'y')))
+    arr2 = jax.device_put(np_inp, NamedSharding(mesh, P('x', 'y')))
+
+    def g(x, y):
+      self.assertTrue(x.sharding.mesh.are_all_axis_types_collective)
+      self.assertTrue(y.sharding.mesh.are_all_axis_types_collective)
+      return x * y
+
+    @jax.jit
+    def f(x, y):
+      z = shard_map(g, mesh=mesh, in_specs=(x.sharding.spec, y.sharding.spec),
+                    out_specs=P('x', 'y'))(x, y)
+      self.assertEqual(z.sharding.spec, P('x', 'y'))
+      out = z * 2
+      self.assertEqual(out.sharding.spec, P('x', 'y'))
+      return out
+
+    out = f(arr, arr2)
+    self.assertArraysEqual(out, (np_inp * np_inp) * 2)
+    self.assertEqual(out.sharding, NamedSharding(mesh, P('x', 'y')))
+
 
 @jtu.pytest_mark_if_available('multiaccelerator')
 class PJitErrorTest(jtu.JaxTestCase):