[XLA:GPU] Add AllReduce{Start,Done} to MLIR LHLO dialect.

PiperOrigin-RevId: 379455720

BUILD

@@ -546,6 +546,7 @@ cc_library(
     hdrs = [
         "include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h",
         "include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops_structs.h",
+        "include/mlir-hlo/utils/lhlo_utils.h",
     ],
     includes = ["include"],
     deps = [
@@ -590,6 +591,7 @@ cc_library(
         ":hlo_ops_base_structs",
         ":hlo_ops_common",
         ":infer_fusibility_op_interface",
+        ":lhlo",
         ":lhlo_gpu_ops_enums",
         ":lhlo_gpu_ops_inc_gen",
         ":lhlo_gpu_ops_structs",

include/mlir-hlo/Dialect/mhlo/IR/lhlo_gpu_ops.td

@@ -230,4 +230,31 @@ def LHLOGPU_CholeskyOp : LHLOGPU_Op<"cholesky"> {
     BoolAttr:$is_lower);
 }
 
+def LHLOGPU_AllReduceStartOp :
+  LHLOGPU_Op<"all_reduce_start", [SameOperandsElementType, SameVariadicOperandSize]> {
+  let summary = "AllReduceStart operator";
+  let description = [{
+    Performs an asynchronous custom reduction across replicas.
+  }];
+  let arguments = (ins
+    Arg<Variadic<LHLO_Buffer>, "", [MemRead]>:$operands,
+    Arg<Variadic<LHLO_Buffer>, "", [MemWrite]>:$results,
+    I64ElementsAttr:$replica_groups,
+    DefaultValuedAttr<BoolAttr, "false">:$constrain_layout,
+    OptionalAttr<ChannelHandle>:$channel_id,
+    DefaultValuedAttr<BoolAttr, "false">:$use_global_device_ids
+  );
+  let regions = (region SizedRegion<1>:$computation);
+  let verifier = [{ return Verify(*this); }];
+}
+
+def LHLOGPU_AllReduceDoneOp:
+  LHLOGPU_Op<"all_reduce_done", [SameVariadicOperandSize]> {
+  let summary = "AllReduceDone operator";
+  let arguments = (ins
+    Arg<Variadic<LHLO_Buffer>, "", [MemRead]>:$operands,
+    Arg<Variadic<LHLO_Buffer>, "", [MemWrite]>:$results
+  );
+}
+
 #endif // LHLO_GPU_OPS

include/mlir-hlo/utils/lhlo_utils.h

@@ -0,0 +1,100 @@
+/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_LHLO_UTILS_H_
+#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_LHLO_UTILS_H_
+
+#include "llvm/ADT/SmallSet.h"
+#include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/Types.h"
+
+namespace mlir {
+namespace lmhlo {
+
+// Verifies replica groups attached to collective communication operations.
+// If the attribute is not empty, it must be a rank 2 tensor, and each replica
+// should appear exactly once. If `is_uniform_sized` is true, then we also check
+// that each group is of the same size. If the operation has
+// `use_global_device_ids` set, then replica group cannot be empty.
+template <typename OpT>
+LogicalResult VerifyReplicaGroups(OpT op, bool is_uniform_sized) {
+  DenseIntElementsAttr attr = op.replica_groups();
+  auto replica_group_type = attr.getType().dyn_cast<RankedTensorType>();
+  if (!replica_group_type || replica_group_type.getRank() != 2 ||
+      !replica_group_type.getElementType().isInteger(/*width=*/64))
+    return op.emitOpError(
+        "replica groups should be a rank 2 tensor of 64 bit integers");
+
+  if (replica_group_type.getShape().equals(ArrayRef<int64_t>{0, 0})) {
+    if (op.use_global_device_ids()) {
+      return op.emitOpError(
+          "if `use_global_device_ids` is set, the replica groups cannot be "
+          "empty");
+    }
+    return success();
+  }
+
+  int64_t max_replica_id_seen = 0;
+  llvm::SmallSet<int64_t, 8> replica_seen;
+  for (int64_t id : attr.getValues<int64_t>()) {
+    // Replica groups are stored in a 2D tensor. If the op supports non-uniform
+    // groups, null replica IDs are stored as -1.
+    if (id == -1) {
+      if (is_uniform_sized) {
+        return op.emitOpError("Invalid replica id -1");
+      }
+      continue;
+    }
+
+    if (!replica_seen.insert(id).second) {
+      return op.emitOpError("replica id #") << id << " seen more than once";
+    }
+    max_replica_id_seen = std::max(max_replica_id_seen, id);
+  }
+
+  for (int64_t id = 0; id <= max_replica_id_seen; id++) {
+    if (!replica_seen.contains(id)) {
+      return op.emitOpError("replica id #")
+             << id << " not seen in replica groups";
+    }
+  }
+  return success();
+}
+
+template <typename OpT>
+static LogicalResult VerifyAllReduce(OpT op) {
+  if (failed(VerifyReplicaGroups(op, /*is_uniform_sized=*/false)))
+    return failure();
+
+  // AllReduce has variadic operands and results that have the same size.
+  // Each member of the operand should have the same type as the corresponding
+  // member of the result.
+  for (auto it : llvm::enumerate(
+           llvm::zip(op.operands().getTypes(), op.results().getTypes()))) {
+    Type operandType = std::get<0>(it.value());
+    Type resultType = std::get<1>(it.value());
+    if (operandType != resultType)
+      return op.emitOpError("requires operand #")
+             << it.index() << " (type: " << operandType << ") and result #"
+             << it.index() << " (type: " << resultType << ") to have same type";
+  }
+  return success();
+}
+
+}  // namespace lmhlo
+}  // namespace mlir
+
+#endif  // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_LHLO_UTILS_H_

lib/Dialect/mhlo/IR/lhlo_gpu_ops.cc

@@ -29,6 +29,7 @@ limitations under the License.
 #include "llvm/ADT/StringRef.h"
 #include "llvm/Support/FormatVariadic.h"
 #include "mlir-hlo/Dialect/mhlo/IR/hlo_ops_common.h"
+#include "mlir-hlo/utils/lhlo_utils.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 #include "mlir/IR/Attributes.h"
 #include "mlir/IR/Builders.h"
@@ -61,6 +62,14 @@ LmhloGpuDialect::LmhloGpuDialect(MLIRContext *context)
 using mlir::hlo::parseWindowAttributes;
 using mlir::hlo::printWindowAttributes;
 
+//===----------------------------------------------------------------------===//
+// AllReduceStartOp
+//===----------------------------------------------------------------------===//
+
+static LogicalResult Verify(AllReduceStartOp op) {
+  return lmhlo::VerifyAllReduce(op);
+}
+
 }  // namespace lmhlo_gpu
 }  // namespace mlir
 

lib/Dialect/mhlo/IR/lhlo_ops.cc

@@ -33,6 +33,7 @@ limitations under the License.
 #include "llvm/Support/FormatVariadic.h"
 #include "mlir-hlo/Dialect/mhlo/IR/hlo_ops_common.h"
 #include "mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h.inc"
+#include "mlir-hlo/utils/lhlo_utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 #include "mlir/IR/Attributes.h"
@@ -86,46 +87,6 @@ static LogicalResult Verify(AbsOp op) {
 // AllToAllOp
 //===----------------------------------------------------------------------===//
 
-// Verifies replica groups attached to collective communication operations.
-// If the attribute is not empty, it must be a rank 2 tensor, and each replica
-// should appear exactly once. If `is_uniform_sized` is true, then we also check
-// that each group is of the same size. If the operation has
-// `use_global_device_id` set, then replica group cannot be empty.
-template <typename OpT>
-LogicalResult VerifyReplicaGroups(OpT op, bool is_uniform_sized) {
-  DenseIntElementsAttr attr = op.replica_groups();
-  auto replica_group_type = attr.getType().dyn_cast<RankedTensorType>();
-  if (!replica_group_type || replica_group_type.getRank() != 2 ||
-      !replica_group_type.getElementType().isInteger(/*width=*/64))
-    return op.emitOpError(
-        "replica groups should be a rank 2 tensor of 64 bit integers");
-
-  if (replica_group_type.getShape().equals(ArrayRef<int64_t>{0, 0}))
-    return success();
-
-  int64_t max_replica_id_seen = 0;
-  llvm::SmallSet<int64_t, 8> replica_seen;
-  for (int64_t id : attr.getValues<int64_t>()) {
-    if (is_uniform_sized && id == -1) {
-      return op.emitOpError("Invalid replica id -1");
-    }
-    if (id != -1) {
-      if (!replica_seen.insert(id).second) {
-        return op.emitOpError("replica id #") << id << " seen more than once";
-      }
-      max_replica_id_seen = std::max(max_replica_id_seen, id);
-    }
-  }
-
-  for (int64_t id = 0; id <= max_replica_id_seen; id++) {
-    if (!replica_seen.contains(id)) {
-      return op.emitOpError("replica id #")
-             << id << " not seen in replica groups";
-    }
-  }
-  return success();
-}
-
 // TODO(jurahul): Add verification for output shape.
 static LogicalResult Verify(AllGatherOp op) {
   return VerifyReplicaGroups(op, /*is_uniform_sized=*/true);
@@ -140,24 +101,7 @@ static LogicalResult Verify(AllToAllOp op) {
 // AllReduceOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult Verify(AllReduceOp op) {
+static LogicalResult Verify(AllReduceOp op) { return VerifyAllReduce(op); }
-  if (failed(VerifyReplicaGroups(op, /*is_uniform_sized=*/false)))
-    return failure();
-
-  // AllReduce has variadic operands and results that have the same size.
-  // Each member of the operand should have the same type as the corresponding
-  // member of the result.
-  for (auto it : llvm::enumerate(
-           llvm::zip(op.operands().getTypes(), op.results().getTypes()))) {
-    Type operandType = std::get<0>(it.value());
-    Type resultType = std::get<1>(it.value());
-    if (operandType != resultType)
-      return op.emitOpError("requires operand #")
-             << it.index() << " (type: " << operandType << ") and result #"
-             << it.index() << " (type: " << resultType << ") to have same type";
-  }
-  return success();
-}
 
 //===----------------------------------------------------------------------===//
 // AllReduceScatterOp