Remove code present to support jaxlib < 0.5.1.

The new minimum xla_extension_version is 317 and the new mlir_api_version is 58.

jax/_src/array.py

@@ -40,7 +40,6 @@ from jax._src.interpreters import xla
 from jax._src.layout import AutoLayout, DeviceLocalLayout, Layout
 from jax._src.lib import xla_client as xc
 from jax._src.lib import xla_extension as xe
-from jax._src.lib import xla_extension_version
 from jax._src.sharding import Sharding
 from jax._src.sharding_impls import (
     PmapSharding, SingleDeviceSharding,
@@ -212,90 +211,51 @@ class ArrayImpl(basearray.Array):
       arrays = self._check_and_rearrange(arrays, self._sharding, self.aval)
     self._arrays = arrays
 
-  if xla_extension_version >= 310:
-    def _check_and_rearrange(self, arrays, sharding, aval):
-      device_id_to_buffer = {_get_device(db).id: db for db in arrays}
+  def _check_and_rearrange(self, arrays, sharding, aval):
+    device_id_to_buffer = {_get_device(db).id: db for db in arrays}
 
-      addressable_dev = sharding.addressable_devices
-      if len(arrays) != len(addressable_dev):
-        raise ValueError(
-            f"Expected {len(addressable_dev)} per-device arrays "
-            "(this is how many devices are addressable by the sharding), but "
-            f"got {len(arrays)}")
+    addressable_dev = sharding.addressable_devices
+    if len(arrays) != len(addressable_dev):
+      raise ValueError(
+          f"Expected {len(addressable_dev)} per-device arrays "
+          "(this is how many devices are addressable by the sharding), but "
+          f"got {len(arrays)}")
 
-      array_device_ids = set(device_id_to_buffer.keys())
-      addressable_device_ids = {d.id for d in addressable_dev}
-      if len(array_device_ids) != len(arrays):
-        buffer_device_ids = [_get_device(db).id for db in arrays]
-        raise ValueError(
-            "When making an array from single-device arrays, the input arrays"
-            " must be from distinct devices, but got device IDs"
-            f" {buffer_device_ids}")
+    array_device_ids = set(device_id_to_buffer.keys())
+    addressable_device_ids = {d.id for d in addressable_dev}
+    if len(array_device_ids) != len(arrays):
+      buffer_device_ids = [_get_device(db).id for db in arrays]
+      raise ValueError(
+          "When making an array from single-device arrays, the input arrays"
+          " must be from distinct devices, but got device IDs"
+          f" {buffer_device_ids}")
 
-      # Calculate a symmetric difference because the device ids between sharding
-      # and _arrays should match.
-      diff = array_device_ids ^ addressable_device_ids
-      if diff:
-        dev_in_sharding_not_in_arrays = addressable_device_ids - array_device_ids
-        dev_in_arrays_not_in_sharding = array_device_ids - addressable_device_ids
-        err_msg = (
-            "Addressable devices and per-device arrays devices do not match.")
-        if dev_in_sharding_not_in_arrays:
-          err_msg += (f" Sharding contains devices {dev_in_sharding_not_in_arrays} "
-                      "that are not present in per-device arrays.")
-        if dev_in_arrays_not_in_sharding:
-          err_msg += (f" Per-device arrays contain devices {dev_in_arrays_not_in_sharding} "
-                      "that are not present in the sharding.")
-        raise ValueError(err_msg)
+    # Calculate a symmetric difference because the device ids between sharding
+    # and _arrays should match.
+    diff = array_device_ids ^ addressable_device_ids
+    if diff:
+      dev_in_sharding_not_in_arrays = addressable_device_ids - array_device_ids
+      dev_in_arrays_not_in_sharding = array_device_ids - addressable_device_ids
+      err_msg = (
+          "Addressable devices and per-device arrays devices do not match.")
+      if dev_in_sharding_not_in_arrays:
+        err_msg += (f" Sharding contains devices {dev_in_sharding_not_in_arrays} "
+                    "that are not present in per-device arrays.")
+      if dev_in_arrays_not_in_sharding:
+        err_msg += (f" Per-device arrays contain devices {dev_in_arrays_not_in_sharding} "
+                    "that are not present in the sharding.")
+      raise ValueError(err_msg)
 
-      _validate_shape_and_dtype_for_per_device_arrays(
-          arrays,
-          sharding=sharding,
-          aval=aval,
-          expected_shape=sharding.shard_shape(aval.shape),
-      )
+    _validate_shape_and_dtype_for_per_device_arrays(
+        arrays,
+        sharding=sharding,
+        aval=aval,
+        expected_shape=sharding.shard_shape(aval.shape),
+    )
 
-      # Rearrange arrays based on the device assignment.
-      addressable_da = sharding._addressable_device_assignment
-      return [device_id_to_buffer[device.id] for device in addressable_da]
-  else:
-    def _check_and_rearrange(self):  # type: ignore
-      device_id_to_buffer = {_get_device(db).id: db for db in self._arrays}
-
-      addressable_dev = self.sharding.addressable_devices
-      if len(self._arrays) != len(addressable_dev):
-        raise ValueError(
-            f"Expected {len(addressable_dev)} per-device arrays "
-            "(this is how many devices are addressable by the sharding), but "
-            f"got {len(self._arrays)}")
-
-      array_device_ids = set(device_id_to_buffer.keys())
-      addressable_device_ids = {d.id for d in addressable_dev}
-      # Calculate a symmetric difference because the device ids between sharding
-      # and _arrays should match.
-      diff = array_device_ids ^ addressable_device_ids
-      if diff:
-        dev_in_sharding_not_in_arrays = addressable_device_ids - array_device_ids
-        dev_in_arrays_not_in_sharding = array_device_ids - addressable_device_ids
-        err_msg = (
-            "Addressable devices and per-device arrays devices do not match.")
-        if dev_in_sharding_not_in_arrays:
-          err_msg += (f" Sharding contains devices {dev_in_sharding_not_in_arrays} "
-                      "that are not present in per-device arrays.")
-        if dev_in_arrays_not_in_sharding:
-          err_msg += (f" Per-device arrays contain devices {dev_in_arrays_not_in_sharding} "
-                      "that are not present in the sharding.")
-        raise ValueError(err_msg)
-
-      _validate_shape_and_dtype_for_per_device_arrays(
-          self._arrays,
-          sharding=self.sharding,
-          aval=self.aval,
-          expected_shape=self.sharding.shard_shape(self.shape),
-      )
-      # Rearrange arrays based on the device assignment.
-      addressable_da = self.sharding._addressable_device_assignment
-      self._arrays = [device_id_to_buffer[device.id] for device in addressable_da]
+    # Rearrange arrays based on the device assignment.
+    addressable_da = sharding._addressable_device_assignment
+    return [device_id_to_buffer[device.id] for device in addressable_da]
 
   @property
   def shape(self) -> Shape:
@@ -652,18 +612,9 @@ class ArrayImpl(basearray.Array):
       db.block_until_ready()
     return self
 
-  if xla_extension_version >= 314:
-    @use_cpp_method()
-    def _single_device_array_to_np_array_did_copy(self) -> tuple[np.ndarray, bool]:  # type: ignore
-      ...  # pytype: disable=bad-return-type
-
-  else:
-    @use_cpp_method()
-    def _single_device_array_to_np_array(self):
-      return np.asarray(self._arrays[0])
-
-    def _single_device_array_to_np_array_did_copy(self) -> tuple[np.ndarray, bool]:
-      return cast(np.ndarray, self._single_device_array_to_np_array()), True
+  @use_cpp_method()
+  def _single_device_array_to_np_array_did_copy(self) -> tuple[np.ndarray, bool]:  # type: ignore
+    ...  # pytype: disable=bad-return-type
 
   @use_cpp_method()
   def _copy_single_device_array_to_host_async(self):

jax/_src/compiler.py

@@ -33,7 +33,6 @@ from jax._src import profiler
 from jax._src import traceback_util
 from jax._src.interpreters import mlir
 from jax._src.lib import xla_client as xc
-from jax._src.lib import xla_extension_version
 from jax._src.lib.mlir import ir
 import numpy as np
 
@@ -191,13 +190,12 @@ def get_compile_options(
 
   build_options.exec_time_optimization_effort = config.exec_time_optimization_effort.value
   build_options.memory_fitting_effort = config.memory_fitting_effort.value
-  if xla_extension_version >= 316:
-    build_options.optimization_level = config.EffortLevel(
-        config.optimization_level.value
-    ).value
-    build_options.memory_fitting_level = config.EffortLevel(
-        config.memory_fitting_level.value
-    ).value
+  build_options.optimization_level = config.EffortLevel(
+      config.optimization_level.value
+  ).value
+  build_options.memory_fitting_level = config.EffortLevel(
+      config.memory_fitting_level.value
+  ).value
 
   # This is a temporary workaround to simplify the AutoPGLE usage.
   # TODO(b/376647494): Remove once the bug is fixed.
@@ -212,10 +210,9 @@ def get_compile_options(
     # Some overrides are passed directly on build_options.
     overrides_on_build_options = [
         "exec_time_optimization_effort", "memory_fitting_effort"]
-    if xla_extension_version >= 316:
-      overrides_on_build_options.extend(
-          ["optimization_level", "memory_fitting_level"]
-      )
+    overrides_on_build_options.extend(
+        ["optimization_level", "memory_fitting_level"]
+    )
 
     env_options_overrides = dict(env_options_overrides)
     for name in overrides_on_build_options:

jax/_src/dispatch.py

@@ -45,7 +45,6 @@ from jax._src.interpreters import pxla
 from jax._src.interpreters import xla
 from jax._src.layout import DeviceLocalLayout, Layout
 from jax._src.lib import xla_client as xc
-from jax._src.lib import xla_extension_version
 from jax._src.mesh import AbstractMesh, Mesh
 from jax._src.monitoring import record_event_duration_secs, record_event_time_span
 from jax._src.partition_spec import PartitionSpec
@@ -419,12 +418,8 @@ def _different_device_order_reshard(x, target_sharding, copy: CopySemantics):
 
 def _reorder_shards(x, new_s, copy_semantics: CopySemantics):
   """Reorders array shards to match the order indicated by the new sharding."""
-  if xla_extension_version >= 304:
-    xc_copy_semantics = pxla.to_xc_copy_semantics([copy_semantics])[0]
-    return xc.reorder_shards(x, new_s, xc_copy_semantics)  # type: ignore
-  else:
-    assert copy_semantics == CopySemantics.ALIAS
-    return array.make_array_from_single_device_arrays(x.shape, new_s, x._arrays)
+  xc_copy_semantics = pxla.to_xc_copy_semantics([copy_semantics])[0]
+  return xc.reorder_shards(x, new_s, xc_copy_semantics)  # type: ignore
 
 
 @dataclasses.dataclass(frozen=True)

jax/_src/dtypes.py

@@ -33,7 +33,6 @@ import ml_dtypes
 import numpy as np
 
 from jax._src import config
-from jax._src.lib import xla_extension_version
 from jax._src.typing import Array, DType, DTypeLike
 from jax._src.util import set_module, StrictABC
 
@@ -518,7 +517,7 @@ _complex_types: list[JAXType] = [
 # only meant for the `jnp.isdtype` and we want to be conservative and not allow
 # StringDType to be used in there.
 _string_types: list[JAXType] = []
-if hasattr(np.dtypes, 'StringDType') and xla_extension_version >= 311:
+if hasattr(np.dtypes, 'StringDType'):
   _string_types: list[JAXType] = [np.dtypes.StringDType()]  # type: ignore
 
 _jax_dtype_set = {

jax/_src/interpreters/mlir.py

@@ -55,7 +55,6 @@ from jax._src.sharding_impls import (AUTO, NamedSharding,
                                      SdyArraySharding, SdyArrayShardingList)
 from jax._src.lib import xla_client as xc
 from jax._src.lib import xla_extension
-from jax._src.lib import xla_extension_version
 from jax._src.lib.mlir import dialects, ir, passmanager
 from jax._src.lib.mlir.dialects import func as func_dialect, hlo
 from jax._src.lib.mlir import register_jax_dialects
@@ -479,20 +478,13 @@ def _traceback_to_location(ctx: ModuleContext, tb: xc.Traceback) -> ir.Location:
     loc = ctx.traceback_caches.location_cache.get(code_lasti, None)
     if loc is None:
       frame = source_info_util.raw_frame_to_frame(code, lasti)
-      if xla_extension_version >= 309:
-        file_loc = ir.Location.file(
-            get_canonical_source_file(frame.file_name, ctx.traceback_caches),
-            frame.start_line,
-            frame.start_column,
-            frame.end_line,
-            frame.end_column,
-        )
-      else:
-        file_loc = ir.Location.file(
-            get_canonical_source_file(frame.file_name, ctx.traceback_caches),
-            frame.start_line,
-            frame.start_column,
-        )
+      file_loc = ir.Location.file(
+          get_canonical_source_file(frame.file_name, ctx.traceback_caches),
+          frame.start_line,
+          frame.start_column,
+          frame.end_line,
+          frame.end_column,
+      )
       loc = ir.Location.name(frame.function_name, childLoc=file_loc)
       ctx.traceback_caches.location_cache[code_lasti] = loc
     frame_locs.append(loc)

jax/_src/numpy/lax_numpy.py

@@ -49,7 +49,6 @@ from jax._src.lax import lax as lax_internal
 from jax._src.lax.lax import (PrecisionLike,_array_copy,
                               _sort_le_comparator, _sort_lt_comparator)
 from jax._src.lib import xla_client as xc
-from jax._src.lib import xla_extension_version
 from jax._src.numpy.array_creation import (empty, empty_like, full,
                                            ones, ones_like, zeros, zeros_like)
 from jax._src.numpy import indexing
@@ -5357,11 +5356,6 @@ def _make_string_array(
     ndmin: int = 0,
     device: xc.Device | Sharding | None = None,
 ) -> Array:
-  if xla_extension_version < 311:
-    raise TypeError(
-        "String arrays are not supported in JAX before XLA extension version"
-        " 311."
-    )
   if not isinstance(object, np.ndarray):
     raise TypeError(
         "Currently, string arrays can only be made from NumPy"

jax/_src/pallas/triton/pallas_call_registration.py

@@ -25,7 +25,6 @@ import jax._src.core as jax_core
 from jax._src.interpreters import mlir
 from jax._src.lib import triton
 from jax._src.lib import gpu_triton as triton_kernel_call_lib
-from jax._src.lib import version as jaxlib_version
 from jax._src.lib.mlir import ir
 from jax._src.pallas import core as pallas_core
 from jax._src.pallas.triton import lowering
@@ -98,7 +97,7 @@ def pallas_call_lowering(
   module_op.write_bytecode(buf)
 
   # TODO(b/394629193): Remove True once the bug is fixed.
-  if True or jaxlib_version < (0, 5, 1):
+  if True:
     # AOT Triton compilation is only available on jaxlib 0.5.1+.
     out_types = [
       ir.RankedTensorType.get(bm.array_shape_dtype.shape,

jax/experimental/jax2tf/call_tf.py

@@ -591,12 +591,7 @@ def _call_tf_lowering(
   call = func_dialect.CallOp(callee_result_types,
                              ir.FlatSymbolRefAttr.get(fn),
                              tuple(args_op) + captured_ops)
-  if result_shape.is_tuple() and xla_client.mlir_api_version < 58:
-    # In API version 58, the results are always flattened.
-    flat_results = [hlo.get_tuple_element(call, mlir.i32_attr(i))
-                    for i in range(len(result_shapes))]
-  else:
-    flat_results = call.results
+  flat_results = call.results
 
   if ordered:
     raise NotImplementedError(

jax/experimental/transfer.py

@@ -16,7 +16,6 @@
 import jax
 from typing import Any, TYPE_CHECKING
 from jax._src.lib import xla_client as _xc
-from jax._src.lib import xla_extension_version
 from jax._src.util import use_cpp_class, use_cpp_method
 
 class TransferConnection:
@@ -42,7 +41,7 @@ class TransferConnection:
     return tree.unflatten(self._pull_flat(uuid, backend, xs_flat))
 
 
-if not TYPE_CHECKING and xla_extension_version >= 305:
+if not TYPE_CHECKING:
   TransferConnection = use_cpp_class(_xc._xla.TransferConnection)(TransferConnection)
 
 
@@ -67,8 +66,7 @@ class TransferServer:
     self._await_pull_flat(uuid, jax.tree.flatten(arrays)[0])
 
 
-if not TYPE_CHECKING and xla_extension_version >= 305:
+if not TYPE_CHECKING:
   TransferServer = use_cpp_class(_xc._xla.TransferServer)(TransferServer)
 
-if xla_extension_version >= 305:
-  start_transfer_server = _xc._xla.start_transfer_server
+start_transfer_server = _xc._xla.start_transfer_server

tests/api_test.py

@@ -61,7 +61,6 @@ from jax._src.interpreters import mlir
 from jax._src.interpreters import partial_eval as pe
 from jax._src.compilation_cache import is_persistent_cache_enabled
 from jax._src.lib import xla_extension
-from jax._src.lib import xla_extension_version
 import jax._src.util as jax_util
 from jax.ad_checkpoint import checkpoint_name, checkpoint as new_checkpoint
 import jax.custom_batching
@@ -1371,8 +1370,6 @@ class JitTest(jtu.BufferDonationTestCase):
     def f(x):
       return jnp.sqrt(x**2) + 1.0
 
-    if xla_extension_version < 316:
-      self.skipTest("Requires XLA extension version >= 316")
     f_jit = jit(
         f,
         compiler_options={
@@ -1386,8 +1383,6 @@ class JitTest(jtu.BufferDonationTestCase):
     def f(x):
       return jnp.sqrt(x**2) + 1.0
 
-    if xla_extension_version < 316:
-      self.skipTest("Requires XLA extension version >= 316")
     f_jit = jit(
         f,
         compiler_options={

tests/array_test.py

@@ -29,7 +29,6 @@ from jax._src import op_shardings
 from jax._src import test_util as jtu
 from jax._src import xla_bridge as xb
 from jax._src.lib import xla_client as xc
-from jax._src.lib import xla_extension_version
 from jax._src.lib.mlir import dialects, ir
 from jax._src.util import safe_zip
 from jax._src.mesh import AxisTypes
@@ -825,8 +824,6 @@ class JaxArrayTest(jtu.JaxTestCase):
   @parameterized.product(dtype=jtu.dtypes.all + jtu.dtypes.custom_floats)
   @jtu.run_on_devices("gpu")
   def test_pinned_host_npy_value_doesnt_cache(self, dtype):
-    if xla_extension_version < 314:
-      self.skipTest("Requires XLA extension version >= 314")
     # see https://github.com/jax-ml/jax/issues/26216
     d_tensor = jnp.array(0, dtype=dtype)
     d_sharding = d_tensor.sharding

tests/colocated_python_test.py

@@ -25,7 +25,6 @@ from absl.testing import parameterized
 import jax
 from jax._src import config
 from jax._src import test_util as jtu
-from jax._src.lib import xla_extension_version
 from jax.experimental import colocated_python
 from jax.experimental.colocated_python import serialization
 from jax.extend.ifrt_programs import ifrt_programs
@@ -383,11 +382,6 @@ class ColocatedPythonTest(jtu.JaxTestCase):
         del colocated_python._testing_global_state
 
   def testStringProcessing(self):
-    if xla_extension_version < 315:
-      self.skipTest(
-          "String support for colocated Python requires xla_extension_version"
-          " >= 315"
-      )
     if np.lib.NumpyVersion(np.__version__) < "2.0.0":
       self.skipTest("StringDType requires NumPy 2.0.0 or later")
     cpu_devices = _colocated_cpu_devices(jax.local_devices())
@@ -431,11 +425,6 @@ class ColocatedPythonTest(jtu.JaxTestCase):
     )
 
   def testBinaryDataProcessing(self):
-    if xla_extension_version < 315:
-      self.skipTest(
-          "String support for colocated Python requires xla_extension_version"
-          " >= 315"
-      )
     if np.lib.NumpyVersion(np.__version__) < "2.0.0":
       self.skipTest("StringDType requires NumPy 2.0.0 or later")
     cpu_devices = _colocated_cpu_devices(jax.local_devices())

tests/debugging_primitives_test.py

@@ -25,7 +25,6 @@ from jax._src import ad_checkpoint
 from jax._src import debugging
 from jax._src import dispatch
 from jax._src import test_util as jtu
-from jax._src.lib import xla_extension_version
 import jax.numpy as jnp
 import numpy as np
 
@@ -62,9 +61,6 @@ class DebugCallbackTest(jtu.JaxTestCase):
   @jtu.skip_on_flag("jax_skip_slow_tests", True)
   @jtu.run_on_devices("cpu")
   def test_async_deadlock(self):
-    if xla_extension_version < 306:
-      self.skipTest("deadlock expected")
-
     # See https://github.com/jax-ml/jax/issues/25861
     def print_it(i, maxiter):
       self.assertIsInstance(i, jax.Array)

tests/ffi_test.py

@@ -32,7 +32,7 @@ from jax._src import dispatch
 from jax._src import test_util as jtu
 from jax._src.interpreters import mlir
 from jax._src.layout import DeviceLocalLayout
-from jax._src.lib import lapack, xla_extension_version
+from jax._src.lib import lapack
 from jax._src.lib.mlir.dialects import hlo
 from jax._src.lax import linalg as lax_linalg_internal
 from jax.experimental.shard_map import shard_map
@@ -333,8 +333,6 @@ def ffi_call_geqrf(x, _use_extend=False, **kwargs):
 class BatchPartitioningTest(jtu.JaxTestCase):
   def setUp(self):
     super().setUp()
-    if xla_extension_version < 313:
-      self.skipTest("Requires XLA extension version >= 313")
     # Register callbacks before checking the number of devices to make sure
     # that we're testing the registration path, even if we can't run the tests.
     for target_name in ["lapack_sgeqrf_ffi", "cusolver_geqrf_ffi",

tests/layout_test.py

@@ -23,7 +23,6 @@ from jax.sharding import NamedSharding, PartitionSpec as P, SingleDeviceSharding
 from jax._src import config
 from jax._src.layout import Layout, DeviceLocalLayout as DLL
 from jax._src import test_util as jtu
-from jax._src.lib import xla_extension_version
 from jax._src.util import safe_zip
 from jax.experimental.compute_on import compute_on
 
@@ -33,12 +32,6 @@ jtu.request_cpu_devices(8)
 
 class LayoutTest(jtu.JaxTestCase):
 
-  # Remove this setUp once the released xla_extension_version is >= 308.
-  def setUp(self):
-    if xla_extension_version < 308 and not jtu.test_device_matches(['tpu', 'gpu']):
-      self.skipTest("Layouts do not work on CPU backend yet.")
-    super().setUp()
-
   def test_auto_layout(self):
     mesh = jtu.create_mesh((2, 2), ('x', 'y'))
     shape1 = (128, 128)

tests/linalg_test.py

@@ -1707,8 +1707,6 @@ class ScipyLinalgTest(jtu.JaxTestCase):
     if pivoting:
       if not jtu.test_device_matches(["cpu", "gpu"]):
         self.skipTest("Pivoting is only supported on CPU and GPU.")
-      if jtu.test_device_matches(["gpu"]) and jtu.jaxlib_version() <= (0, 5, 0):
-        self.skipTest("Pivoting is only supported on GPU for jaxlib > 0.5.0")
     rng = jtu.rand_default(self.rng())
     jsp_func = partial(jax.scipy.linalg.qr, mode=mode, pivoting=pivoting)
     sp_func = partial(scipy.linalg.qr, mode=mode, pivoting=pivoting)

tests/magma_linalg_test.py

@@ -120,8 +120,6 @@ class MagmaLinalgTest(jtu.JaxTestCase):
   )
   @jtu.run_on_devices("gpu")
   def testPivotedQrFactorization(self, shape, dtype):
-    if jtu.jaxlib_version() <= (0, 5, 0):
-      self.skipTest("qr with `pivoting=True` on GPU requires jaxlib version > 0.5.0")
     if not gpu_solver.has_magma():
       self.skipTest("MAGMA is not installed or can't be loaded.")
     rng = jtu.rand_default(self.rng())
@@ -133,8 +131,6 @@ class MagmaLinalgTest(jtu.JaxTestCase):
     self._CompileAndCheck(lax_func, args_maker)
 
   def testPivotedQrFactorizationMagmaConfig(self):
-    if jtu.jaxlib_version() <= (0, 5, 0):
-      self.skipTest("qr with `pivoting=True` on GPU requires jaxlib version > 0.5.0")
     if not gpu_solver.has_magma():
       self.skipTest("MAGMA is not installed or can't be loaded.")
     rng = jtu.rand_default(self.rng())

tests/memories_test.py

@@ -26,7 +26,6 @@ from jax import lax
 from jax._src import test_util as jtu
 from jax._src import xla_bridge as xb
 from jax._src.layout import DeviceLocalLayout as DLL, Layout
-from jax._src.lib import xla_extension_version
 from jax._src import config
 from jax.ad_checkpoint import checkpoint_name, checkpoint as new_checkpoint
 import jax.numpy as jnp
@@ -1664,8 +1663,6 @@ class ComputeOffload(jtu.BufferDonationTestCase):
 class StreamAnnotationTest(jtu.JaxTestCase):
 
   def test_stream_annotation_inside_shmap(self):
-    if xla_extension_version < 313:
-      self.skipTest("Requires xla_extension_version >= 313")
     if not jtu.test_device_matches(["gpu"]):
       self.skipTest("Stream annotation is only supported on GPU.")
     mesh = jtu.create_mesh((2, 2), ('x', 'y'))

tests/mosaic/gpu_test.py

@@ -29,7 +29,6 @@ import jax
 from jax._src import config
 from jax._src import test_util as jtu
 from jax._src.interpreters import mlir
-from jax._src.lib import version as jaxlib_version
 from jax._src.lib.mlir import ir
 from jax._src.lib.mlir import passmanager
 from jax._src.lib.mlir.dialects import arith
@@ -2701,9 +2700,6 @@ class UtilsTest(TestCase):
 class SerializationTest(absltest.TestCase):
 
   def test_pass_is_registered(self):
-    if jaxlib_version < (0, 5, 1):
-      self.skipTest("Test requires jaxlib 0.5.1 or later")
-
     ctx = mlir.make_ir_context()
     ctx.allow_unregistered_dialects = True
     with ir.Location.unknown(ctx):

tests/pjit_test.py

@@ -62,7 +62,6 @@ from jax._src.lib.mlir import dialects
 from jax._src import xla_bridge
 from jax._src.lib import xla_client as xc
 from jax._src.lib import xla_extension
-from jax._src.lib import xla_extension_version
 from jax._src.util import curry, unzip2
 
 config.parse_flags_with_absl()
@@ -7147,9 +7146,6 @@ class PJitErrorTest(jtu.JaxTestCase):
     f(arr, 2., 3.)  # doesn't crash
 
   def test_named_sharding_of_none(self):
-    if xla_extension_version < 309:
-      raise unittest.SkipTest("NamedSharding does't reject None.")
-
     mesh = jtu.create_mesh((2,), ('x',))
     with self.assertRaisesRegex(TypeError, 'Unexpected None'):
       jax.NamedSharding(mesh, None)

tests/ragged_collective_test.py

@@ -37,8 +37,6 @@ class RaggedCollectiveTest(jtu.JaxTestCase):
     super().setUp()
     if jtu.test_device_matches(['cpu']):
       self.skipTest('ragged-all-to-all is not supported on CPU')
-    if jtu.jaxlib_version() < (0, 5, 1):
-      self.skipTest('ragged-all-to-all is not supported on jaxlib version < 0.5.1')
 
   @parameterized.named_parameters(
       dict(

tests/string_array_test.py

@@ -18,7 +18,6 @@ import jax
 from jax import numpy as jnp
 from jax._src import config
 from jax._src import test_util as jtu
-from jax._src.lib import xla_extension_version
 import numpy as np
 
 config.parse_flags_with_absl()
@@ -29,12 +28,6 @@ class StringArrayTest(jtu.JaxTestCase):
 
   def setUp(self):
     super().setUp()
-    if xla_extension_version < 311:
-      self.skipTest(
-          "Skipping this test because the current XLA extension version:"
-          f" {xla_extension_version} is older than 309, the oldest version with"
-          " string array support."
-      )
     if not hasattr(np.dtypes, "StringDType"):
       self.skipTest(
           "Skipping this test because the numpy.dtype.StringDType is not"