(NFC) Prepare for migration from producing MHLO to producing StableHLO

This CL renames occurrences of "mhlo" in: 1) names, 2) tests, 3) prose in order
to prepare for the upcoming migration.

Unchanged occurrences:
  1) Public API that contains "mhlo", e.g. XlaLowering.mhlo and the "mhlo"
     argument value in Lowering.as_text and Lowering.compiler_ir.
  2) Documentation (changelog, JEPs, IR examples, etc).
  3) One rare situation where prose says "StableHLO" and "MHLO" in one sentence,
     so both are necessary to disambiguate.

PiperOrigin-RevId: 495771153

Author: Eugene Burmako

build/build_wheel.py

@@ -169,7 +169,7 @@ def prepare_wheel(sources_path):
   copy_to_jaxlib("__main__/jaxlib/init.py", dst_filename="__init__.py")
   copy_to_jaxlib(f"__main__/jaxlib/cpu_feature_guard.{pyext}")
   copy_to_jaxlib("__main__/jaxlib/lapack.py")
-  copy_to_jaxlib("__main__/jaxlib/mhlo_helpers.py")
+  copy_to_jaxlib("__main__/jaxlib/hlo_helpers.py")
   copy_to_jaxlib("__main__/jaxlib/ducc_fft.py")
   copy_to_jaxlib("__main__/jaxlib/gpu_prng.py")
   copy_to_jaxlib("__main__/jaxlib/gpu_linalg.py")

jax/_src/ad_checkpoint.py

@@ -34,7 +34,7 @@
 from jax._src.lax import lax as lax_internal
 from jax._src.lax import convolution as lax_convolution
 from jax._src.lib import xla_client as xc
-from jax._src.lib.mlir.dialects import mhlo
+from jax._src.lib.mlir.dialects import hlo
 from jax._src.traceback_util import api_boundary
 from jax._src.util import (unzip2, wraps, split_list, partition_list, safe_map,
                            safe_zip, merge_lists, weakref_lru_cache)
@@ -623,9 +623,9 @@ def _optimization_barrier_lowering_rule(ctx, *args):
   flat_barrier_types = util.flatten(barrier_types)
   flat_args = mlir.flatten_lowering_ir_args(args)
   if xc.mlir_api_version < 40:
-    barrier_op = mhlo.OptimizationBarrierOp(flat_barrier_types, flat_args)
+    barrier_op = hlo.OptimizationBarrierOp(flat_barrier_types, flat_args)
   else:
-    barrier_op = mhlo.OptimizationBarrierOp(flat_args)
+    barrier_op = hlo.OptimizationBarrierOp(flat_args)
   return util.unflatten(barrier_op.results, map(len, barrier_types))
 
 def _optimization_barrier(arg):