1 files changed, 20 insertions, 27 deletions

diff --git a/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h b/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h
index f7e39248c..96e1d9127 100644
--- a/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h
+++ b/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h
@@ -56,28 +56,22 @@ inline void BinaryArithmeticOp(const BinaryArithmeticOpParam &params, const Shap
   const int size = MatchingElementsSize(input1_shape, input2_shape, output_shape);
   for (int i = 0; i < size; i++)
   {
-    output_data[i] =
-        ActivationFunctionWithMinMax(fn(input1_data[i], input2_data[i]),
-                                     params.float_activation_min, params.float_activation_max);
+    output_data[i] = ActivationFunctionWithMinMax(
+      fn(input1_data[i], input2_data[i]), params.float_activation_min, params.float_activation_max);
   }
 }
 
 template <typename T>
-inline void BroadcastBinaryArithmeticOpSlowQuant8(
-    const BinaryArithmeticOpParam &params, const Shape &input1_shape, const T *input1_data,
-    const Shape &input2_shape, const T *input2_data, const Shape &output_shape, T *output_data,
-    const std::function<T(const BinaryArithmeticOpParam &params, const T &, const T &)> &fn)
+inline typename std::enable_if_t<is_quant8<T>::value> BroadcastBinaryArithmeticOpSlow(
+  const BinaryArithmeticOpParam &params, const Shape &input1_shape, const T *input1_data,
+  const Shape &input2_shape, const T *input2_data, const Shape &output_shape, T *output_data,
+  const std::function<T(const BinaryArithmeticOpParam &params, const T &, const T &)> &fn)
 {
   NdArrayDesc<4> desc1;
   NdArrayDesc<4> desc2;
   NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2);
   const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape);
 
-  if ((params.quantized_activation_min < 0) && (params.quantized_activation_max > 255))
-  {
-    throw std::runtime_error{"Support only for Quant8."};
-  }
-
   // Comment from tensorflow lite:
   //
   // In Tensorflow, the dimensions are canonically named (batch_number, row,
@@ -99,11 +93,10 @@ inline void BroadcastBinaryArithmeticOpSlowQuant8(
       {
         for (int c = 0; c < extended_output_shape.Dims(3); ++c)
         {
-          output_data[Offset(extended_output_shape, b, y, x, c)] =
-              ActivationFunctionWithMinMax<uint8_t>(
-                  fn(params, input1_data[SubscriptToIndex(desc1, b, y, x, c)],
-                     input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
-                  params.quantized_activation_min, params.quantized_activation_max);
+          output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax<T>(
+            fn(params, input1_data[SubscriptToIndex(desc1, b, y, x, c)],
+               input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
+            params.quantized_activation_min, params.quantized_activation_max);
         }
       }
     }
@@ -143,9 +136,9 @@ inline void BroadcastBinaryArithmeticOpSlow(const BinaryArithmeticOpParam &param
         for (int c = 0; c < extended_output_shape.Dims(3); ++c)
         {
           output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax<T>(
-              fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)],
-                 input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
-              params.quantized_activation_min, params.quantized_activation_max);
+            fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)],
+               input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
+            params.quantized_activation_min, params.quantized_activation_max);
         }
       }
     }
@@ -154,9 +147,9 @@ inline void BroadcastBinaryArithmeticOpSlow(const BinaryArithmeticOpParam &param
 
 template <>
 inline void BroadcastBinaryArithmeticOpSlow(
-    const BinaryArithmeticOpParam &params, const Shape &input1_shape, const float *input1_data,
-    const Shape &input2_shape, const float *input2_data, const Shape &output_shape,
-    float *output_data, const std::function<float(const float &, const float &)> &fn)
+  const BinaryArithmeticOpParam &params, const Shape &input1_shape, const float *input1_data,
+  const Shape &input2_shape, const float *input2_data, const Shape &output_shape,
+  float *output_data, const std::function<float(const float &, const float &)> &fn)
 {
   NdArrayDesc<4> desc1;
   NdArrayDesc<4> desc2;
@@ -171,10 +164,10 @@ inline void BroadcastBinaryArithmeticOpSlow(
       {
         for (int c = 0; c < extended_output_shape.Dims(3); ++c)
         {
-          output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax(
-              fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)],
-                 input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
-              params.float_activation_min, params.float_activation_max);
+          output_data[Offset(extended_output_shape, b, y, x, c)] =
+            ActivationFunctionWithMinMax(fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)],
+                                            input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
+                                         params.float_activation_min, params.float_activation_max);
         }
       }
     }