[MLIR:HLO] Extend AllReduce to support multiple inputs and results (to model tuples).

- Instead of SameTypeOperands, add custom verification to check if operands and
  results pairwise have the same type.

PiperOrigin-RevId: 353986341

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

@@ -544,17 +544,18 @@ def LHLO_ReducePrecisionOp: LHLO_Op<"reduce_precision", [SameTypeOperands]>,
   );
 }
 
-def LHLO_AllReduceOp : LHLO_Op<"all_reduce", [SameTypeOperands]>,
+def LHLO_AllReduceOp : LHLO_Op<"all_reduce", [SameVariadicOperandSize]>,
                        BASE_HLO_AllReduceOp {
   let arguments = (ins
-    Arg<LHLO_Buffer, "", [MemRead]>:$operand,
-    Arg<LHLO_Buffer, "", [MemWrite]>:$output,
+    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 LHLO_CollectivePermuteOp: LHLO_Op<"collective_permute", [SameTypeOperands]>,

lib/Dialect/mhlo/IR/lhlo_ops.cc

@@ -56,6 +56,41 @@ LmhloDialect::LmhloDialect(MLIRContext *context)
       >();
 }
 
+//===----------------------------------------------------------------------===//
+// AllReduceOp
+//===----------------------------------------------------------------------===//
+
+static LogicalResult Verify(AllReduceOp op) {
+  // AllReduce had variadic operands and results that have the same size.
+  // Each memeber 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";
+  }
+
+  // Since AllReduce has a single reduction computation attached to it (which is
+  // applied over all the operands and results), they all need to have the same
+  // element type. Since we already check that each operand and corresponding
+  // result has the same type, its sufficient to check just the memref element
+  // type for each operands.
+  Type elementType =
+      op.operands().front().getType().cast<MemRefType>().getElementType();
+  bool allMatch = llvm::all_of(
+      op.operands().drop_front().getType(), [elementType](Type type) {
+        return type.cast<MemRefType>().getElementType() == elementType;
+      });
+  if (!allMatch)
+    return op.emitOpError("requires all operands to have same element type");
+
+  return success();
+}
+
 //===----------------------------------------------------------------------===//
 // ConstOp.
 //===----------------------------------------------------------------------===//

tests/lhlo_ops.mlir

@@ -2,6 +2,36 @@
 
 // -----
 
+func @invalid_allreduce(%input0: memref<2xf32>, %input1: memref<3xf32>) {
+  // expected-error@+1 {{requires operand #1 (type: 'memref<3xf32>') and result #1 (type: 'memref<2xf32>') to have same type}}
+  "lmhlo.all_reduce"(%input0, %input1, %input0, %input0) ({
+    ^bb0(%arg0: tensor<f32>, %arg1: tensor<f32>):
+      %add = mhlo.add %arg0, %arg1 : tensor<f32>
+      "mhlo.return"(%add) : (tensor<f32>) -> ()
+    })
+  {channel_id = {handle = 1 : i64, type = 0 : i64}, constrain_layout = false,
+   replica_groups = dense<[[0, 1, 2, 3], [5, 6, 7, 8]]> : tensor<2x4xi64>,
+   use_global_device_ids = false} : (memref<2xf32>, memref<3xf32>, memref<2xf32>, memref<2xf32>) -> ()
+  return
+}
+
+// -----
+
+func @invalid_allreduce(%input0: memref<2xf32>, %input1: memref<3xf16>) {
+  // expected-error@+1 {{requires all operands to have same element type}}
+  "lmhlo.all_reduce"(%input0, %input1, %input0, %input1) ({
+    ^bb0(%arg0: tensor<f32>, %arg1: tensor<f32>):
+      %add = mhlo.add %arg0, %arg1 : tensor<f32>
+      "mhlo.return"(%add) : (tensor<f32>) -> ()
+    })
+  {channel_id = {handle = 1 : i64, type = 0 : i64}, constrain_layout = false,
+   replica_groups = dense<[[0, 1, 2, 3], [5, 6, 7, 8]]> : tensor<2x4xi64>,
+   use_global_device_ids = false} : (memref<2xf32>, memref<3xf16>, memref<2xf32>, memref<3xf16>) -> ()
+  return
+}
+
+// -----
+
 // CHECK-LABEL: func @ceil
 func @ceil(%input: memref<2x2xf32>, %result: memref<2x2xf32>) {
   "lmhlo.ceil"(%input, %result) : (memref<2x2xf32>, memref<2x2xf32>) -> ()