Replace uses of deprecated JAX sharding APIs with their new names in jax.sharding.

This change updates:
* {jax.experimental.maps.Mesh, jax.interpreters.pxla.Mesh} to jax.sharding.Mesh
* {jax.experimental.PartitionSpec, jax.experimental.pjit.PartitionSpec, jax.interpreters.pxla.PartitionSpec, jax.pxla.PartitionSpec} to jax.sharding.PartitionSpec
* jax.experimental.maps.NamedSharding to jax.sharding.NamedSharding.

PiperOrigin-RevId: 510027595

docs/Custom_Operation_for_GPUs.md

@@ -479,8 +479,8 @@ We are using `jax.experimental.pjit.pjit` for parallel execution on multiple dev
 Let's first test the forward operation on multiple devices.  We are creating a simple 1D mesh and sharding `x` on all devices.
 
 ```python
-from jax.experimental.maps import Mesh
-from jax.experimental.pjit import PartitionSpec, pjit
+from jax.sharding import Mesh, PartitionSpec
+from jax.experimental.pjit import pjit
 
 
 mesh = Mesh(jax.local_devices(), ("x",))
@@ -777,11 +777,12 @@ import jax.numpy as jnp
 from build import gpu_ops
 from jax import core, dtypes
 from jax.abstract_arrays import ShapedArray
-from jax.experimental.maps import Mesh, xmap
-from jax.experimental.pjit import PartitionSpec, pjit
+from jax.experimental.maps import xmap
+from jax.experimental.pjit import pjit
 from jax.interpreters import mlir, xla
 from jax.interpreters.mlir import ir
 from jax.lib import xla_client
+from jax.sharding import Mesh, PartitionSpec
 from jaxlib.mhlo_helpers import custom_call
 
 

docs/notebooks/xmap_tutorial.ipynb

@@ -169,7 +169,7 @@
    "source": [
     "import jax\n",
     "import numpy as np\n",
-    "from jax.experimental.maps import Mesh\n",
+    "from jax.sharding import Mesh\n",
     "\n",
     "loss = xmap(named_loss, in_axes=in_axes, out_axes=[...],\n",
     "            axis_resources={'batch': 'x'})\n",
@@ -790,7 +790,7 @@
    },
    "outputs": [],
    "source": [
-    "from jax.experimental.maps import Mesh\n",
+    "from jax.sharding import Mesh\n",
     "\n",
     "local = local_matmul(x, x)  # The local function doesn't require the mesh definition\n",
     "with Mesh(*mesh_def):  # Makes the mesh axis names available as resources\n",

docs/notebooks/xmap_tutorial.md

@@ -120,7 +120,7 @@ But on a whim we can decide to parallelize over the batch axis:
 
 import jax
 import numpy as np
-from jax.experimental.maps import Mesh
+from jax.sharding import Mesh
 
 loss = xmap(named_loss, in_axes=in_axes, out_axes=[...],
             axis_resources={'batch': 'x'})
@@ -536,7 +536,7 @@ To introduce the resources in a scope, use the `with Mesh` context manager:
 ```{code-cell} ipython3
 :id: kYdoeaSS9m9f
 
-from jax.experimental.maps import Mesh
+from jax.sharding import Mesh
 
 local = local_matmul(x, x)  # The local function doesn't require the mesh definition
 with Mesh(*mesh_def):  # Makes the mesh axis names available as resources

jax/experimental/custom_partitioning.py

@@ -213,7 +213,7 @@ class custom_partitioning:
       from jax.experimental.custom_partitioning import custom_partitioning
       from jax.experimental.pjit import pjit
       from jax.sharding import PartitionSpec as P
-      from jax.experimental.maps import Mesh
+      from jax.sharding import Mesh
       from jax.numpy.fft import fft
       import regex as re
       import numpy as np

jax/experimental/gda_serialization/serialization.py

@@ -29,7 +29,7 @@ from jax.experimental import global_device_array as gda
 from jax._src import array
 from jax._src import sharding
 from jax._src import typing
-from jax.experimental.maps import Mesh
+from jax.sharding import Mesh
 import jax.numpy as jnp
 import numpy as np
 import tensorstore as ts