Put abstract_mesh on every eqn so that we can preserve it during `eva…

…l_jaxpr` and `check_jaxpr` roundtrip.

Also allow users to enter into `Auto`/`User` mode inside jit along all or some axes.

Add checks to make sure that avals inside a context match the surrounding context. This check happens inside `abstract_eval` rules but maybe we need a more central place for it which we can create later on.

PiperOrigin-RevId: 707128096

jax/_src/api.py

@@ -2187,8 +2187,6 @@ def _infer_src_sharding(src, x) -> Sharding | None:
     return src  # pytype: disable=bad-return-type
   if isinstance(x, array.ArrayImpl):
     return x.sharding
-  if config.sharding_in_types.value and hasattr(x, 'sharding'):
-    return x.sharding
   if isinstance(x, core.Tracer):
     val = x.to_concrete_value()
     if val is not None and isinstance(val, array.ArrayImpl):

jax/_src/core.py

@@ -273,10 +273,12 @@ def __init__(self, compute_type: str | None, threefry_partitionable: bool,
                xla_metadata=None):
     self.compute_type = compute_type
     self.threefry_partitionable = threefry_partitionable
+    self.cur_abstract_mesh = mesh_lib.get_abstract_mesh()
     self.xla_metadata = xla_metadata
     self._managers = [
         (compute_on.extend_compute_type, self.compute_type),
         (config.threefry_partitionable.__call__, self.threefry_partitionable),
+        (mesh_lib.set_abstract_mesh, self.cur_abstract_mesh),
         (xla_metadata_lib.set_xla_metadata, self.xla_metadata),
     ]
 
@@ -292,6 +294,7 @@ def __repr__(self):
     return (
         f"JaxprEqnContext(compute_type={self.compute_type}, "
         f"threefry_partitionable={self.threefry_partitionable}, "
+        f"cur_abstract_mesh={self.cur_abstract_mesh}, "
         f"xla_metadata={self.xla_metadata})"
     )
 
@@ -535,6 +538,17 @@ def write(v: Var, val: Any) -> None:
     clean_up_dead_vars(eqn, env, lu)
   return map(read, jaxpr.outvars)
 
+def check_avals_context_mesh(avals, prim_name):
+  if config.sharding_in_types.value:
+    for a in avals:
+      cur_mesh = mesh_lib.get_abstract_mesh()
+      if a.sharding.mesh != cur_mesh:
+        raise ValueError(
+            f"For primitive {prim_name}, context mesh {cur_mesh} should match"
+            f" the aval mesh {a.sharding.mesh} for shape {a.str_short()}. This"
+            " error occurs at source: "
+            f" {source_info_util.summarize(source_info_util.current())}")
+
 
 # -------------------- tracing --------------------
 
@@ -1622,7 +1636,10 @@ def get_sharding(sharding, ndim):
   from jax._src.sharding_impls import NamedSharding  # type: ignore
 
   if sharding is not None:
-    assert len(sharding.spec) == ndim
+    if len(sharding.spec) != ndim:
+      raise ValueError(
+          "Length of sharding.spec must be equal to aval's ndim. Got"
+          f" sharding.spec {sharding.spec} and aval.ndim {ndim}")
     return _maybe_modify_sharding(sharding)
 
   context_mesh = mesh_lib.get_abstract_mesh()
@@ -2518,17 +2535,18 @@ def write(v: Var, a: AbstractValue) -> None:
       in_avals = [x.aval for x in in_atoms]  # use in_atoms for dyn shapes
 
       # Compute the type of the primitive application.
-      if prim in custom_typechecks:
-        out_type, eqn_effects = custom_typechecks[prim](
-          ctx_factory, *in_atoms, **eqn.params)
-      elif prim.call_primitive:
-        out_type, eqn_effects = _check_call(ctx_factory, prim, in_atoms,
+      with eqn.ctx.manager:
+        if prim in custom_typechecks:
+          out_type, eqn_effects = custom_typechecks[prim](
+            ctx_factory, *in_atoms, **eqn.params)
+        elif prim.call_primitive:
+          out_type, eqn_effects = _check_call(ctx_factory, prim, in_atoms,
+                                              eqn.params)
+        elif prim.map_primitive:
+          out_type, eqn_effects = _check_map(ctx_factory, prim, in_avals,
                                             eqn.params)
-      elif prim.map_primitive:
-        out_type, eqn_effects = _check_map(ctx_factory, prim, in_avals,
-                                           eqn.params)
-      else:
-        out_type, eqn_effects = check_eqn(prim, in_avals, eqn.params)
+        else:
+          out_type, eqn_effects = check_eqn(prim, in_avals, eqn.params)
 
       # Check the computed effect type matches the eqn's annotation, and is
       # included in the jaxpr's annotation.

jax/_src/dispatch.py

@@ -522,8 +522,6 @@ def _batched_device_put_impl(
 device_put_p.def_impl(_batched_device_put_impl)
 
 def _device_put_abstract_eval(*xs, devices, srcs, copy_semantics):
-  if config.sharding_in_types.value:
-    return [x.update(sharding=s) for x, s in zip(xs, devices)]
   return xs
 device_put_p.def_abstract_eval(_device_put_abstract_eval)
 
@@ -566,12 +564,6 @@ def _tpu_gpu_device_put_lowering(ctx, *xs, devices, srcs, copy_semantics):
   # TODO(yashkatariya): Maybe we should add the custom calls anyways if it's
   # being used inside jit? Atleast for now, this preserves the old behavior.
   if ctx.module_context.all_default_mem_kind:
-    if config.sharding_in_types.value:
-      return [
-          mlir.wrap_with_sharding_op(
-              ctx, x, a, a.sharding._to_xla_hlo_sharding(a.ndim).to_proto())
-          for x, a in zip(xs, ctx.avals_out)
-      ]
     return xs
   def lower(x, device, aval, out_aval):
     if (isinstance(device, (Sharding, TransferToMemoryKind)) and
@@ -597,12 +589,6 @@ def lower(x, device, aval, out_aval):
 
 
 def _common_device_put_lowering(ctx, *xs, devices, srcs, copy_semantics):
-  if config.sharding_in_types.value:
-    return [
-        mlir.wrap_with_sharding_op(
-            ctx, x, a, a.sharding._to_xla_hlo_sharding(a.ndim).to_proto())
-        for x, a in zip(xs, ctx.avals_out)
-    ]
   return xs
 mlir.register_lowering(device_put_p, _common_device_put_lowering)
 

jax/_src/interpreters/pxla.py

@@ -2162,6 +2162,8 @@ def _abstract_to_concrete_mesh(abstract_mesh):
 
   out = []
   for s, a in zip(shardings, avals):
+    # Remove the `UnconstrainedSingleton` logic after UNCONSTRAINED is supported
+    # in out_shardings at top level jit.
     if (isinstance(s, UnspecifiedValue) and a.sharding is not None and
         all(not isinstance(s, UnconstrainedSingleton) for s in a.sharding.spec)):
       out.append(NamedSharding(_abstract_to_concrete_mesh(a.sharding.mesh),

jax/_src/lax/parallel.py

@@ -687,6 +687,7 @@ def _allreduce_effectful_abstract_eval(*args, axes, axis_index_groups):
       raise ValueError(f"axis_index_groups can only be used with reductions over "
                        f"named axes, but got: {axes}")
   if config.sharding_in_types.value:
+    core.check_avals_context_mesh(args, 'all_reduce')
     out_avals = [
         ShapedArray(lax._reduce_op_shape_rule(arg, axes=pos_axes), arg.dtype,
                     sharding=lax._reduce_op_sharding_rule(arg, axes=pos_axes))

jax/_src/lax/utils.py

@@ -53,6 +53,7 @@ def standard_abstract_eval(prim, shape_rule, dtype_rule, weak_type_rule,
   weak_type = weak_type_rule(*avals, **kwargs)
   least_specialized = type(max(avals, key=_get_array_abstraction_level))
   if least_specialized is core.ShapedArray:
+    core.check_avals_context_mesh(avals, prim.name)
     return core.ShapedArray(
         shape_rule(*avals, **kwargs), dtype_rule(*avals, **kwargs),
         weak_type=weak_type,
@@ -78,6 +79,7 @@ def standard_multi_result_abstract_eval(
   if least_specialized is core.ShapedArray:
     out_shapes = shape_rule(*avals, **kwargs)
     out_dtypes = dtype_rule(*avals, **kwargs)
+    core.check_avals_context_mesh(avals, prim.name)
     out_shardings = (sharding_rule(*avals, **kwargs)
                      if config.sharding_in_types.value else
                      [None] * len(out_shapes))

jax/_src/mesh.py

@@ -350,6 +350,9 @@ def local_devices(self):
   def abstract_mesh(self):
     return AbstractMesh(self.shape_tuple, axis_types=self.axis_types)
 
+  def with_axis_types(self, new_axis_types) -> Mesh:
+    return Mesh(self.devices, self.axis_names, axis_types=new_axis_types)
+
 
 EMPTY_ENV = ResourceEnv(Mesh(np.empty((), dtype=object), ()))
 
@@ -396,8 +399,9 @@ def __eq__(self, other):
             self._axis_types_tuple == other._axis_types_tuple)
 
   def __repr__(self):
+    mesh_repr = ", ".join(f"'{n}': {v}" for n, v in self.shape_tuple)
     atr = f", axis_types={self.axis_types}"
-    return f"AbstractMesh({self.shape_tuple}{atr})"
+    return f"AbstractMesh({mesh_repr}{atr})"
 
   @property
   def axis_names(self):
@@ -427,6 +431,9 @@ def _internal_device_list(self):
   def empty(self):
     return self.size == 0
 
+  def with_axis_types(self, new_axis_types) -> AbstractMesh:
+    return AbstractMesh(self.shape_tuple, axis_types=new_axis_types)
+
   @functools.cached_property
   def _are_all_axes_collective(self) -> bool:
     return all(t == AxisTypes.Collective for t in self.axis_types.keys())

jax/_src/pjit.py

@@ -61,8 +61,8 @@
 from jax._src.lib.mlir.dialects import func as func_dialect
 from jax._src.lib import jax_jit
 from jax._src.lib import xla_client as xc
-from jax._src import sharding
 from jax._src.mesh import AbstractMesh
+from jax._src.sharding import Sharding
 from jax._src.sharding_impls import (
     NamedSharding, GSPMDSharding,
     SingleDeviceSharding, PmapSharding, AUTO, UNSPECIFIED, UnspecifiedValue,
@@ -73,7 +73,7 @@
 from jax._src.tree_util import (
     tree_flatten, tree_unflatten, treedef_is_leaf, tree_structure, tree_leaves,
     treedef_children, broadcast_prefix, all_leaves, prefix_errors, keystr,
-    PyTreeDef, none_leaf_registry as none_lr)
+    PyTreeDef, none_leaf_registry as none_lr, tree_map)
 from jax._src.util import (
     HashableFunction, safe_map, safe_zip, wraps,
     distributed_debug_log, split_list, weakref_lru_cache,
@@ -1027,7 +1027,7 @@ def hashable_pytree(pytree):
 def _create_sharding_for_array(mesh, x, name, api_name):
   if x is None and (mesh is None or mesh.empty):
     return UNSPECIFIED
-  if isinstance(x, (AUTO, UnspecifiedValue, sharding.Sharding)):
+  if isinstance(x, (AUTO, UnspecifiedValue, Sharding)):
     return x
   if mesh is None:
     msg = ('jax.jit only supports `Sharding`s being passed to'
@@ -1339,7 +1339,7 @@ def _check_and_canonicalize_out_shardings(
     out_shardings_treedef, out_shardings_leaves, out_layouts_treedef,
     out_layouts_leaves, out_tree, out_avals, debug_info, device_or_backend_set):
   orig_out_shardings = tree_unflatten(out_shardings_treedef, out_shardings_leaves)
-  if isinstance(orig_out_shardings, (UnspecifiedValue, sharding.Sharding)):
+  if isinstance(orig_out_shardings, (UnspecifiedValue, Sharding)):
     out_shardings_flat = (orig_out_shardings,) * len(out_avals)
   else:
     out_shardings_flat = flatten_axis_resources(
@@ -1571,7 +1571,7 @@ def _resolve_in_shardings(args, pjit_in_shardings: Sequence[PjitSharding]
           else:
             resolved_in_shardings.append(arg_s)
         else:
-          assert isinstance(arg_s, sharding.Sharding)
+          assert isinstance(arg_s, Sharding)
           if dispatch.is_single_device_sharding(arg_s):
             resolved_in_shardings.append(UNSPECIFIED)
           else:
@@ -1903,7 +1903,7 @@ def _pjit_typecheck(ctx_factory, *in_atoms, jaxpr, **params):
 core.custom_typechecks[pjit_p] = _pjit_typecheck
 
 
-def _pjit_abstract_eval(*args, jaxpr, **_):
+def _pjit_abstract_eval(*args, jaxpr, out_shardings, **_):
   return jaxpr.out_avals, jaxpr.effects
 pjit_p.def_effectful_abstract_eval(_pjit_abstract_eval)
 
@@ -2016,7 +2016,7 @@ def _pjit_batcher(axis_data, vals_in, dims_in,
 batching.ragged_prop_rules[pjit_p] = batching.ragged_mask_no_op_rule
 
 def _pjit_batcher_for_sharding(
-    s: sharding.Sharding | UnspecifiedValue,
+    s: Sharding | UnspecifiedValue,
     dim: int, spmd_axis_name: tuple[str, ...] | None, mesh, ndim: int):
   if isinstance(s, UnspecifiedValue):
     return s
@@ -2673,6 +2673,74 @@ def _sharding_constraint_batcher(
 batching.fancy_primitive_batchers[sharding_constraint_p] = _sharding_constraint_batcher
 batching.skippable_batchers[sharding_constraint_p] = lambda _: ()
 
+# -------------------- sharding_cast ---------------------------
+
+def _check_mesh_shape_same(src_sharding, dst_sharding, aval):
+  if src_sharding.mesh.shape_tuple != dst_sharding.mesh.shape_tuple:
+    raise ValueError(
+        f'Mesh shape of the input {src_sharding.mesh.shape_tuple} does not'
+        ' match the mesh shape of the target sharding'
+        f' {dst_sharding.mesh.shape_tuple} for shape {aval.str_short()}')
+
+def sharding_cast(xs, shardings):
+  if isinstance(shardings, NamedSharding):
+    return tree_map(lambda x: sharding_cast_p.bind(
+        x, src_sharding=x.sharding, dst_sharding=shardings), xs)
+
+  x_flat, treedef = tree_flatten(xs)
+  shardings_flat = flatten_axes("sharding_cast shardings", treedef, shardings)
+  out_flat = [sharding_cast_p.bind(x, src_sharding=x.sharding, dst_sharding=s)
+              for x, s in safe_zip(x_flat, shardings_flat)]
+  return tree_unflatten(treedef, out_flat)
+
+sharding_cast_p = core.Primitive('sharding_cast')
+def _sharding_cast_abstract_eval(aval, src_sharding, dst_sharding):
+  _check_mesh_shape_same(src_sharding, dst_sharding, aval)
+  return aval.update(sharding=dst_sharding)
+sharding_cast_p.def_abstract_eval(_sharding_cast_abstract_eval)
+
+def _sharding_cast_impl(x, src_sharding, dst_sharding):
+  aval = shaped_abstractify(x)
+  _check_mesh_shape_same(x.sharding, dst_sharding, aval)
+  new_mesh = x.sharding.mesh.with_axis_types(dst_sharding.mesh.axis_types)
+  concrete_dst_sharding = NamedSharding(new_mesh, dst_sharding.spec)
+  # TODO(yashkatariya): Replace this with `dispatch.apply_primitive(...)`
+  return api.jit(_identity_fn, out_shardings=concrete_dst_sharding)(x)
+sharding_cast_p.def_impl(_sharding_cast_impl)
+
+def _sharding_cast_transpose_rule(ct, _, src_sharding, dst_sharding):
+  return [sharding_cast_p.bind(ct, src_sharding=dst_sharding,
+                               dst_sharding=src_sharding)]
+ad.deflinear2(sharding_cast_p, _sharding_cast_transpose_rule)
+
+def _sharding_cast_hlo_lowering(ctx, x_node, *, src_sharding, dst_sharding):
+  aval, = ctx.avals_in
+  aval_out, = ctx.avals_out
+  proto = dst_sharding._to_xla_hlo_sharding(aval.ndim).to_proto()
+  return [mlir.lower_sharding_under_shit(ctx, x_node, aval_out, proto)]
+mlir.register_lowering(sharding_cast_p, _sharding_cast_hlo_lowering)
+
+# TODO(yashkatariya): Comment this in after vmap ShiT tests are added.
+# def _sharding_cast_batcher(axis_data, vals_in, dims_in, src_sharding,
+#                            dst_sharding):
+#   if axis_data.spmd_name is not None:
+#     used = {n for ns in dst_sharding.spec
+#             for n in (ns if isinstance(ns, tuple) else (ns,))}
+#     if set(axis_data.spmd_name) & used:
+#       raise ValueError(
+#           f'vmap spmd_axis_name {axis_data.spmd_name} cannot '
+#           f'appear in sharding_cast spec, but got spec {dst_sharding.spec}')
+#   x, = vals_in
+#   d, = dims_in
+
+#   val = None if axis_data.spmd_name is None else axis_data.spmd_name
+#   new_spec = PartitionSpec(*util.tuple_insert(dst_sharding.spec, d, val))
+#   vmapped_dst_sharding = NamedSharding(dst_sharding.mesh, new_spec)
+#   y = sharding_cast_p.bind(x, src_sharding=src_sharding,
+#                            dst_sharding=vmapped_dst_sharding)
+#   return y, d
+# batching.fancy_primitive_batchers[sharding_cast_p] = _sharding_cast_batcher
+# batching.skippable_batchers[sharding_cast_p] = lambda _: ()
 
 # -------------------- helpers --------------------
 

jax/_src/sharding_impls.py

@@ -69,15 +69,18 @@ def _check_mesh_resource_axis(mesh, parsed_pspec, _manual_axes):
 
 @util.cache(max_size=128, trace_context_in_key=False)
 def _check_axis_type_consistency(mesh, parsed_pspec):
-  if mesh.axis_types is None:
-    return
   for p in parsed_pspec:
     if p is not None:
       if not all(mesh._name_to_type[p[0]] == mesh._name_to_type[r] for r in p):
         raise ValueError(
             'AxisTypes should be the same in a tuple subset of PartitionSpec:'
             f' {parsed_pspec.get_partition_spec()}. Got subset {p} with axis'
             f' types: ({", ".join(str(mesh._name_to_type[r]) for r in p)})')
+  if mesh_lib.AxisTypes.Auto not in mesh.axis_types and None in parsed_pspec:
+    raise ValueError(
+        f'PartitionSpec {parsed_pspec.get_partition_spec()} cannot contain'
+        ' `P.UNCONSTRAINED` when no mesh axis_types are `Auto`. Got mesh'
+        f' axis_types: {mesh.axis_types}')
 
 
 def hashed_index(x) -> int:
@@ -271,11 +274,15 @@ def __init__(
     self._parsed_pspec = preprocess(self.mesh, self.spec, _parsed_pspec)
 
   def __repr__(self):
-    mesh_repr = ", ".join(f"'{k}': {v}" for k, v in self.mesh.shape.items())
     mem = '' if self.memory_kind is None else f', memory_kind={self.memory_kind}'
     ldi = ('' if self._logical_device_ids is None else
            f', logical_device_ids={self._logical_device_ids}')
-    return f'NamedSharding(mesh=Mesh({mesh_repr}), spec={self.spec}{mem}{ldi})'
+    if isinstance(self.mesh, mesh_lib.AbstractMesh):
+      mesh_repr = f"{self.mesh}"
+    else:
+      nv_str = ", ".join(f"'{n}': {v}" for n, v in self.mesh.shape.items())
+      mesh_repr = f"Mesh({nv_str})"
+    return f'NamedSharding(mesh={mesh_repr}, spec={self.spec}{mem}{ldi})'
 
   def __reduce__(self):
     return (type(self), (self.mesh, self.spec),
@@ -381,6 +388,9 @@ def with_spec(self, spec: PartitionSpec | Sequence[Any]) -> NamedSharding:
       spec = PartitionSpec(*spec)
     return NamedSharding(self.mesh, spec, memory_kind=self.memory_kind)
 
+  def with_mesh(self, new_mesh: mesh_lib.Mesh) -> NamedSharding:
+    return NamedSharding(new_mesh, self.spec, memory_kind=self.memory_kind)
+
   def _to_xla_hlo_sharding(self, num_dimensions: int) -> xc.HloSharding:
     return named_sharding_to_xla_hlo_sharding(self, num_dimensions)
 

jax/experimental/jax2tf/tests/primitives_test.py

@@ -172,6 +172,8 @@ def test_primitive_coverage(self):
         continue
       if p.name == "sharding_constraint":
         continue
+      if p.name == "sharding_cast":
+        continue
       # TODO: Remove once tensorflow is 2.10.0 everywhere.
       if p.name == "optimization_barrier":
         continue