diff --git a/include/mlir-hlo/Dialect/mhlo/IR/chlo_ops.td b/include/mlir-hlo/Dialect/mhlo/IR/chlo_ops.td
index c9db345..9a42d95 100644
--- a/include/mlir-hlo/Dialect/mhlo/IR/chlo_ops.td
+++ b/include/mlir-hlo/Dialect/mhlo/IR/chlo_ops.td
@@ -708,4 +708,49 @@ def HLOClient_BroadcastSelectOp : HLOClient_Op<
   }];
 }
 
+//===----------------------------------------------------------------------===//
+// Helper ops
+//===----------------------------------------------------------------------===//
+
+def HLOClient_MinimumBroadcastShapesOp :
+    HLOClient_Op<"minimum_broadcast_shapes", [NoSideEffect]> {
+  string summary = "Minimizes the rank of two or more shapes to be broadcasted";
+
+  string description = [{
+    Given two or more 1D tensors representing shapes, returns one 1D tensor for
+    each operand, where operand `i` corresponds to output `i`.
+
+    The returned tensors have the property that they specify a shape which is a
+    reshape of the corresponding input shape, and the broadcasted output shape
+    (using shape::BroadcastOp) of the returned shapes is a reshape of the
+    broadcasted output shape of the input shapes. Among all possibilities with
+    this property, the one is chosen which minimizes the rank of each returned
+    shape.
+
+    The general idea of this op is that it can be used for ops which have a
+    broadcasting semantic to operate on shapes with a possibly smaller rank
+    while preserving equivalence of the computed values. After computing the
+    result of the op using reshaped operands, the result can be reshaped to the
+    result that would have been originally computed.
+
+    Here is an example with two input shapes:
+
+    ```mlir
+    chlo.minimum_broadcast_shapes [1, 2, 3, 1, 2, 1],
+                                     [1, 1, 1, 2, 3] -> [6, 2, 1], [2, 3]
+    ```
+
+    The broadcasted output shape of the operands is [1, 2, 3, 1, 2, 3], the
+    broadcasted output shape of the outputs is [6, 2, 3]. These two shapes are
+    reshapes of each other, and also each output is a reshape of the
+    corresponding input.
+  }];
+
+  let arguments = (ins Variadic<1DTensorOf<[Index]>>:$shapes);
+  let results = (outs Variadic<1DTensorOf<[Index]>>:$results);
+
+  let assemblyFormat = "$shapes attr-dict `:` type($shapes) `->` type($results)";
+
+}
+
 #endif  // CHLO_OPS
diff --git a/lib/Dialect/mhlo/IR/chlo_ops.cc b/lib/Dialect/mhlo/IR/chlo_ops.cc
index 57ae271..3a1caa9 100644
--- a/lib/Dialect/mhlo/IR/chlo_ops.cc
+++ b/lib/Dialect/mhlo/IR/chlo_ops.cc
@@ -337,6 +337,26 @@ static LogicalResult Verify(ConstantLikeOp op) {
   return success();
 }
 
+//===----------------------------------------------------------------------===//
+// MinimumBroadcastShapesOp
+//===----------------------------------------------------------------------===//
+static LogicalResult Verify(MinimumBroadcastShapesOp op) {
+  // Check that the number of operands matches the number of outputs.
+  unsigned result_shapes_count = op.results().size();
+  unsigned operand_shapes_count = op.shapes().size();
+  if (operand_shapes_count != result_shapes_count) {
+    return op.emitOpError()
+           << "number of operand shapes (" << operand_shapes_count
+           << ") does not match number of result shapes ("
+           << result_shapes_count << ")";
+  }
+  if (operand_shapes_count < 2) {
+    return op.emitOpError() << "number of operand shapes ("
+                            << operand_shapes_count << ") should be >= 2";
+  }
+  return success();
+}
+
 LogicalResult ConstantLikeOp::inferReturnTypeComponents(
     MLIRContext* context, Optional<Location> location, ValueRange operands,
     DictionaryAttr attributes, RegionRange regions,
diff --git a/tests/chlo_ops.mlir b/tests/chlo_ops.mlir
new file mode 100644
index 0000000..a4d5f79
--- /dev/null
+++ b/tests/chlo_ops.mlir
@@ -0,0 +1,26 @@
+// RUN: mlir-hlo-opt %s -verify-diagnostics -split-input-file | mlir-hlo-opt | FileCheck %s
+
+// CHECK-LABEL: func @minimum_broadcast_shapes
+func @minimum_broadcast_shapes(%lhs: tensor<?xindex>, %rhs: tensor<?xindex>)
+    -> (tensor<?xindex>, tensor<?xindex>) {
+  %0, %1 = chlo.minimum_broadcast_shapes %lhs, %rhs :
+      tensor<?xindex>, tensor<?xindex> -> tensor<?xindex>, tensor<?xindex>
+  return %0, %1 : tensor<?xindex>, tensor<?xindex>
+}
+
+// -----
+
+func @minimum_broadcast_shapes_mismatch_operand_and_result_count(%lhs: tensor<?xindex>, %rhs: tensor<?xindex>) {
+  // expected-error @+1{{number of operand shapes (2) does not match number of result shapes (1)}}
+  %0 = chlo.minimum_broadcast_shapes %lhs, %rhs :
+      tensor<?xindex>, tensor<?xindex> -> tensor<?xindex>
+  return
+}
+
+// -----
+
+func @minimum_broadcast_shapes_one_operand(%arg: tensor<?xindex>) {
+  // expected-error @+1{{number of operand shapes (1) should be >= 2}}
+  %0 = chlo.minimum_broadcast_shapes %arg : tensor<?xindex> -> tensor<?xindex>
+  return
+}