diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index f2c16c9dce2..2f8f092303e 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -1571,6 +1571,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation,
   TF_RETURN_IF_ERROR(ExpectArray(lhs, "lhs of convolution"));
   TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of convolution"));
 
+  if (feature_group_count <= 0) {
+    return InvalidArgument(
+        "feature_group_count must be a positive number, got %d",
+        feature_group_count);
+  }
   if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) {
     return InvalidArgument(
         "Convolution with different element types: %s and %s.",
@@ -1684,14 +1689,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation,
   const int64 kernel_output_features =
       rhs.dimensions(dnums.kernel_output_feature_dimension());
 
-  if (input_features != kernel_input_features * feature_group_count) {
+  if (input_features % feature_group_count != 0 ||
+      input_features / feature_group_count != kernel_input_features) {
     return InvalidArgument(
-        "Expected LHS feature dimension (value %d) to match RHS "
-        "input feature dimension * feature_group_count (value %d * %d = %d); "
+        "Expected LHS feature dimension (value %d) to be a multiple of "
+        "feature_group_count (value %d), and LHS feature dimension / "
+        "feature_group_count = RHS feature dimension (value %d); "
         "got <conv>(%s, %s)\n"
         "Dimension numbers: {%s}.",
-        input_features, kernel_input_features, feature_group_count,
-        kernel_input_features * feature_group_count,
+        input_features, feature_group_count, kernel_input_features,
         ShapeUtil::HumanString(lhs), ShapeUtil::HumanString(rhs),
         dnums.DebugString());
   }