Port type inference for 6 ops from StableHLO to MHLO

Ops:
  1) AfterAllOp: openxla/stablehlo#708.
  2) CreateTokenOp: openxla/stablehlo#711.
  3) DynamicUpdateSliceOp: openxla/stablehlo#686 and openxla/stablehlo#757.
  4) OptimizationBarrierOp: openxla/stablehlo#575.
  5) OutfeedOp: openxla/stablehlo#713.
  6) SendOp: openxla/stablehlo#580.

This PR prepares for migration from producing MHLO to producing StableHLO by
aligning type inference between dialects, so that switching from one to another
doesn't need changes to calls to Python builders.

PiperOrigin-RevId: 495336100

jax/_src/ad_checkpoint.py

@@ -33,6 +33,7 @@
 from jax._src.api_util import flatten_fun, shaped_abstractify
 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.traceback_util import api_boundary
 from jax._src.util import (unzip2, wraps, split_list, partition_list, safe_map,
@@ -621,7 +622,10 @@ def _optimization_barrier_lowering_rule(ctx, *args):
   barrier_types = map(mlir.aval_to_ir_types, ctx.avals_in)
   flat_barrier_types = util.flatten(barrier_types)
   flat_args = mlir.flatten_lowering_ir_args(args)
-  barrier_op = mhlo.OptimizationBarrierOp(flat_barrier_types, flat_args)
+  if xc.mlir_api_version < 40:
+    barrier_op = mhlo.OptimizationBarrierOp(flat_barrier_types, flat_args)
+  else:
+    barrier_op = mhlo.OptimizationBarrierOp(flat_args)
   return util.unflatten(barrier_op.results, map(len, barrier_types))
 
 def _optimization_barrier(arg):

jax/_src/lax/control_flow/loops.py

@@ -41,6 +41,7 @@
 from jax._src.lax import lax
 from jax._src.lax import slicing
 from jax._src.lax import windowed_reductions
+from jax._src.lib import xla_client as xc
 from jax._src.lib.mlir import ir
 from jax._src.lib.mlir.dialects import mhlo
 from jax._src.numpy.ufuncs import logaddexp
@@ -1571,7 +1572,10 @@ def _pred_bcast_select_mhlo(ctx,
   if x_y_aval is core.abstract_token:
     x, = xs
     y, = ys
-    return [mhlo.AfterAllOp(mlir.aval_to_ir_type(x_y_aval), [x, y]).result]
+    if xc.mlir_api_version < 40:
+      return [mhlo.AfterAllOp(mlir.aval_to_ir_type(x_y_aval), [x, y]).result]
+    else:
+      return [mhlo.AfterAllOp([x, y]).result]
   else:
     assert isinstance(x_y_aval, core.ShapedArray), x_y_aval
     x, = xs

jax/_src/lax/lax.py

@@ -4133,7 +4133,10 @@ def create_token(_=None):
 
 def _create_token_lowering(ctx, *operands):
   aval_out, = ctx.avals_out
-  return mhlo.CreateTokenOp(mlir.aval_to_ir_type(aval_out)).results
+  if xc.mlir_api_version < 40:
+    return mhlo.CreateTokenOp(mlir.aval_to_ir_type(aval_out)).results
+  else:
+    return mhlo.CreateTokenOp().results
 
 mlir.register_lowering(create_token_p, _create_token_lowering)
 
@@ -4156,7 +4159,10 @@ def _after_all_abstract_eval(*operands):
 
 def _after_all_lowering(ctx, *operands):
   aval_out, = ctx.avals_out
-  return mhlo.AfterAllOp(mlir.aval_to_ir_type(aval_out), operands).results
+  if xc.mlir_api_version < 40:
+    return mhlo.AfterAllOp(mlir.aval_to_ir_type(aval_out), operands).results
+  else:
+    return mhlo.AfterAllOp(operands).results
 
 mlir.register_lowering(after_all_p, _after_all_lowering)
 
@@ -4252,11 +4258,17 @@ def _outfeed_abstract_eval(token, *xs, partitions):
 
 def _outfeed_lowering(ctx, token, *xs, partitions):
   token_aval = ctx.avals_in[0]
-  outfeed = mhlo.OutfeedOp(
-      mlir.aval_to_ir_type(token_aval),
-      mlir.flatten_lowering_ir_args(xs),
-      token,
-      outfeed_config=ir.StringAttr.get(''))
+  if xc.mlir_api_version < 40:
+    outfeed = mhlo.OutfeedOp(
+        mlir.aval_to_ir_type(token_aval),
+        mlir.flatten_lowering_ir_args(xs),
+        token,
+        outfeed_config=ir.StringAttr.get(''))
+  else:
+    outfeed = mhlo.OutfeedOp(
+        mlir.flatten_lowering_ir_args(xs),
+        token,
+        outfeed_config=ir.StringAttr.get(''))
   if partitions is not None:
     mlir.set_sharding(outfeed, xla.sharding_to_proto(partitions))
   return outfeed.results

jax/interpreters/mlir.py

@@ -302,8 +302,12 @@ def _device_array_constant_handler(val, canonicalize_types):
 for t in device_array.device_array_types:
   register_constant_handler(t, _device_array_constant_handler)
 
-register_constant_handler(
-  core.Token, lambda _, __: [mhlo.CreateTokenOp(mhlo.TokenType.get()).result])
+def _token_constant_handler(val, canonicalize_types):
+  if mlir_api_version < 40:
+    return [mhlo.CreateTokenOp(mhlo.TokenType.get()).result]
+  else:
+    return [mhlo.CreateTokenOp().result]
+register_constant_handler(core.Token, _token_constant_handler)
 
 # Source locations
 
@@ -760,8 +764,11 @@ def token_type() -> Sequence[ir.Type]:
   return [mhlo.TokenType.get()]
 
 def create_token() -> Token:
-  return wrap_singleton_ir_values(
-      mhlo.CreateTokenOp(mhlo.TokenType.get()).result)
+  if mlir_api_version < 40:
+    return wrap_singleton_ir_values(
+        mhlo.CreateTokenOp(mhlo.TokenType.get()).result)
+  else:
+    return wrap_singleton_ir_values(mhlo.CreateTokenOp().result)
 
 class TokenSet:
   """An immutable container of tokens to be used to lower effectful jaxprs. When lowering
@@ -980,7 +987,10 @@ def aval_to_types(aval):
     args: List[List[ir.Value]] = []
     for aval, arg in zip(jaxpr.in_avals, unflattened_args):
       if replace_tokens_with_dummy and aval is core.abstract_token:
-        args.append(mhlo.CreateTokenOp(mhlo.TokenType.get()).results)
+        if mlir_api_version < 40:
+          args.append(mhlo.CreateTokenOp(mhlo.TokenType.get()).results)
+        else:
+          args.append(mhlo.CreateTokenOp().results)
       else:
         args.append(arg)
     callee_name_stack = xla.extend_name_stack(ctx.name_stack,
@@ -1345,8 +1355,11 @@ def dynamic_update_slice(ctx: LoweringRuleContext, aval_out, x, update, *,
         ctx, aval_out, x, update, *start_indices)
 
   # TODO(necula): handle dynamic shapes
-  return mhlo.DynamicUpdateSliceOp(aval_to_ir_type(aval_out), x, update,
-                                  start_indices).result
+  if mlir_api_version < 40:
+    return mhlo.DynamicUpdateSliceOp(aval_to_ir_type(aval_out), x, update,
+                                    start_indices).result
+  else:
+    return mhlo.DynamicUpdateSliceOp(x, update, start_indices).result
 
 def full_like_aval(ctx: LoweringRuleContext, value, aval: core.ShapedArray) -> ir.Value:
   """Returns an IR constant shaped full of `value` shaped like `aval`."""
@@ -1602,8 +1615,12 @@ def send_to_host(channel: int, token: mhlo.TokenType, operand: Any,
                  aval: core.ShapedArray, name: str, *,
                  sharding: Optional[xc.OpSharding] = None) -> ir.Value:
   channel_handle = mhlo.ChannelHandle.get(channel, SEND_TO_HOST_TYPE)
-  send_op = mhlo.SendOp(mhlo.TokenType.get(), [operand], token, channel_handle,
-                        is_host_transfer=ir.BoolAttr.get(True))
+  if mlir_api_version < 40:
+    send_op = mhlo.SendOp(mhlo.TokenType.get(), [operand], token, channel_handle,
+                          is_host_transfer=ir.BoolAttr.get(True))
+  else:
+    send_op = mhlo.SendOp([operand], token, channel_handle,
+                          is_host_transfer=ir.BoolAttr.get(True))
   dtype_str = _dtype_to_xla_type_string(aval.dtype)
   if dtype_str in {"f64", "s64", "u64", "c64", "c128"}:
     raise NotImplementedError("64-bit types not supported.")
@@ -1652,7 +1669,10 @@ def _emit_tpu_python_callback(
     *,
     sharding: Optional[xc.OpSharding] = None
 ) -> Tuple[List[ir.Value], Any, Any]:
-  token = token or mhlo.CreateTokenOp(mhlo.TokenType.get()).result
+  if mlir_api_version < 40:
+    token = token or mhlo.CreateTokenOp(mhlo.TokenType.get()).result
+  else:
+    token = token or mhlo.CreateTokenOp().result
   _wrapped_callback = callback
 
   send_channels = []

jax/interpreters/pxla.py

@@ -2258,8 +2258,12 @@ def _mhlo_unshard(ctx: mlir.LoweringRuleContext, aval, axis_env, out_axis, xs, p
     idxs = [_unravel_index_mhlo(axis_env)] + [zero] * len(dims)
     broadcast_result = mhlo.BroadcastOp(
         x, mlir.dense_int_elements([1])).result
-    padded = mhlo.DynamicUpdateSliceOp(
-        padded.type, padded, broadcast_result, idxs).result
+    if xc.mlir_api_version < 40:
+      padded = mhlo.DynamicUpdateSliceOp(
+          padded.type, padded, broadcast_result, idxs).result
+    else:
+      padded = mhlo.DynamicUpdateSliceOp(
+          padded, broadcast_result, idxs).result
     replica_groups = mlir.dense_int_elements(
       xla.axis_groups(axis_env, axis_env.names[-1]))
     out = mhlo.CrossReplicaSumOp(padded, replica_groups).result

jaxlib/gpu_solver.py

@@ -498,7 +498,7 @@ def _sytrd_mhlo(platform, gpu_solver, dtype, a, *, lower):
         ir.RankedTensorType.get(s_type.shape, a_type.element_type), s)
     offsets = tuple(mhlo.ConstantOp(intattr(i))
                     for i in ((0,) * len(batch_dims) + (0, 1)))
-    a = mhlo.DynamicUpdateSliceOp(a.type, a, s, offsets).result
+    a = mhlo.DynamicUpdateSliceOp(a, s, offsets).result
 
   return a, d, e, taus, info
 

tests/lax_test.py

@@ -47,6 +47,7 @@
 from jax._src import lax_reference
 from jax._src.util import prod
 from jax._src.lax import lax as lax_internal
+from jax._src.lib import xla_client as xc
 
 from jax.config import config
 config.parse_flags_with_absl()
@@ -2883,8 +2884,11 @@ def dynamic_update_slice_mlir(ctx, aval_out, x, update, *start_indices):
     aval_out, = ctx.avals_out
     dtype = dtypes.canonicalize_dtype(np.dtype('int64'))
     start_indices = (*start_indices, mlir.ir_constant(np.array(0, dtype=dtype)))
-    return mhlo.DynamicUpdateSliceOp(mlir.aval_to_ir_type(aval_out), x, update,
-                                     start_indices).result
+    if xc.mlir_api_version < 40:
+      return mhlo.DynamicUpdateSliceOp(mlir.aval_to_ir_type(aval_out), x, update,
+                                       start_indices).result
+    else:
+      return mhlo.DynamicUpdateSliceOp(x, update, start_indices).result
 
   @staticmethod
   def broadcast_in_dim_mlir(ctx, aval_out, x, broadcast_dimensions):