Any devices passed to jax.sharding.Mesh are required to be hashable.

This is true for mock devices or user specific devices and jax.devices() too.

Fix the tests so that the mock devices are hashable.

PiperOrigin-RevId: 561103167

CHANGELOG.md

@@ -24,6 +24,9 @@ Remember to align the itemized text with the first line of an item within a list
     `JAX_TRACEBACK_FILTERING=off` (for one unfiltered traceback).
   * jax2tf default serialization version is now 7, which introduces new shape
     [safety assertions](https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#errors-in-presence-of-shape-polymorphism).
+  * Devices passed to `jax.sharding.Mesh` should be hashable. This specifically
+    applies to mock devices or user created devices. `jax.devices()` are
+    already hashable.
 
 * Breaking changes:
   * jax2tf now uses native serialization by default. See

jax/_src/mesh.py

@@ -90,6 +90,35 @@ class ResourceEnv(NamedTuple):
     return f"ResourceEnv({self.physical_mesh!r}, {self.loops!r})"
 
 
+@functools.lru_cache(maxsize=128)
+def _get_local_mesh(global_mesh: Mesh, process_index: int) -> Mesh:
+  if global_mesh.empty:
+      return global_mesh
+  is_local_device = np.vectorize(
+      lambda d: d.process_index == process_index, otypes=[bool])(global_mesh.devices)
+  subcube_indices = []
+  # We take the smallest slice of each dimension that doesn't skip any local device.
+  for axis in range(global_mesh.devices.ndim):
+    other_axes = util.tuple_delete(tuple(range(global_mesh.devices.ndim)), axis)
+    # NOTE: This re-reduces over many axes multiple times, so we could definitely
+    #       optimize it, but I hope it won't be a bottleneck anytime soon.
+    local_slices = is_local_device.any(other_axes, keepdims=False)
+    nonzero_indices = np.flatnonzero(local_slices)
+    start, end = int(np.min(nonzero_indices)), int(np.max(nonzero_indices))
+    subcube_indices.append(slice(start, end + 1))
+  subcube_indices = tuple(subcube_indices)
+  # We only end up with all conditions being true if the local devices formed a
+  # subcube of the full array. This is because we were biased towards taking a
+  # "hull" spanned by the devices, and in case the local devices don't form a
+  # subcube that hull will contain non-local devices.
+  if not is_local_device[subcube_indices].all():
+    raise ValueError(
+        "When passing host local inputs to pjit or xmap, devices "
+        "connected to a single host must form a contiguous subcube of the "
+        "global device mesh")
+  return Mesh(global_mesh.devices[subcube_indices], global_mesh.axis_names)
+
+
 _mesh_object_dict = {}  # type: ignore
 
 
@@ -156,18 +185,7 @@ class Mesh(contextlib.ContextDecorator):
     axis_names = tuple(axis_names)
     assert devices.ndim == len(axis_names)
 
-    flat_devices = tuple(devices.flat)
-
-    # TODO(yashkatariya): Make Mock Devices hashable and them remove this
-    # workaround
-    _use_cache = True
-    try:
-      hash(flat_devices[0])
-    except:
-      _use_cache = False
-
-    if _use_cache:
-      key = (axis_names, devices.shape, flat_devices)
+    key = (axis_names, devices.shape, tuple(devices.flat))
     val = _mesh_object_dict.get(key, None)
     if val is not None:
       return val
@@ -176,7 +194,6 @@ class Mesh(contextlib.ContextDecorator):
     self.devices = devices.copy()
     self.devices.flags.writeable = False
     self.axis_names = axis_names
-    if _use_cache:
     _mesh_object_dict[key] = self
     return self
 
@@ -248,36 +265,12 @@ class Mesh(contextlib.ContextDecorator):
   def is_multi_process(self):
     return self.devices.size != len(self.local_devices)
 
-  @functools.cached_property
+  @property
   def local_mesh(self):
     return self._local_mesh(xb.process_index())
 
   def _local_mesh(self, process_index):
-    if self.empty:
-      return self
-    is_local_device = np.vectorize(
-        lambda d: d.process_index == process_index, otypes=[bool])(self.devices)
-    subcube_indices = []
-    # We take the smallest slice of each dimension that doesn't skip any local device.
-    for axis in range(self.devices.ndim):
-      other_axes = util.tuple_delete(tuple(range(self.devices.ndim)), axis)
-      # NOTE: This re-reduces over many axes multiple times, so we could definitely
-      #       optimize it, but I hope it won't be a bottleneck anytime soon.
-      local_slices = is_local_device.any(other_axes, keepdims=False)
-      nonzero_indices = np.flatnonzero(local_slices)
-      start, end = int(np.min(nonzero_indices)), int(np.max(nonzero_indices))
-      subcube_indices.append(slice(start, end + 1))
-    subcube_indices = tuple(subcube_indices)
-    # We only end up with all conditions being true if the local devices formed a
-    # subcube of the full array. This is because we were biased towards taking a
-    # "hull" spanned by the devices, and in case the local devices don't form a
-    # subcube that hull will contain non-local devices.
-    if not is_local_device[subcube_indices].all():
-      raise ValueError(
-          "When passing host local inputs to pjit or xmap, devices "
-          "connected to a single host must form a contiguous subcube of the "
-          "global device mesh")
-    return Mesh(self.devices[subcube_indices], self.axis_names)
+    return _get_local_mesh(self, process_index)
 
   @functools.cached_property
   def device_ids(self):

tests/mesh_utils_test.py

@@ -28,7 +28,7 @@ from jax.sharding import Mesh
 from jax._src import test_util
 
 
-@dataclasses.dataclass
+@dataclasses.dataclass(frozen=True)
 class MockTpuDevice:
   """Mock TPU device for testing."""
   id: int