1 files changed, 27 insertions, 12 deletions
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index ae295cc8b5..4c8cbe4275 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1403,6 +1403,33 @@ inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims,
                output_data, output_dims);
 }
 
+inline void Div(const float* input1_data, const Dims<4>& input1_dims,
+                const float* input2_data, const Dims<4>& input2_dims,
+                float output_activation_min, float output_activation_max,
+                float* output_data, const Dims<4>& output_dims) {
+  const int batches =
+      MatchingArraySize(input1_dims, 3, input2_dims, 3, output_dims, 3);
+  const int height =
+      MatchingArraySize(input1_dims, 2, input2_dims, 2, output_dims, 2);
+  const int width =
+      MatchingArraySize(input1_dims, 1, input2_dims, 1, output_dims, 1);
+  const int depth =
+      MatchingArraySize(input1_dims, 0, input2_dims, 0, output_dims, 0);
+  for (int b = 0; b < batches; ++b) {
+    for (int y = 0; y < height; ++y) {
+      for (int x = 0; x < width; ++x) {
+        for (int c = 0; c < depth; ++c) {
+          output_data[Offset(output_dims, c, x, y, b)] =
+              ActivationFunctionWithMinMax(
+                  input1_data[Offset(input1_dims, c, x, y, b)] /
+                      input2_data[Offset(input2_dims, c, x, y, b)],
+                  output_activation_min, output_activation_max);
+        }
+      }
+    }
+  }
+}
+
 // TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary
 // dimensionality if the runtime code does a single loop over one dimension
 // that handles broadcasting as the base case. The code generator would then
@@ -1444,18 +1471,6 @@ void BroadcastDiv(const T* input1_data, const Dims<4>& input1_dims,
   }
 }
 
-inline void Div(const float* input1_data, const Dims<4>& input1_dims,
-                const float* input2_data, const Dims<4>& input2_dims,
-                float output_activation_min, float output_activation_max,
-                float* output_data, const Dims<4>& output_dims) {
-  const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims);
-  for (int i = 0; i < flat_size; ++i) {
-    output_data[i] = ActivationFunctionWithMinMax(
-        input1_data[i] / input2_data[i], output_activation_min,
-        output_activation_max);
-  }
-}
-
 inline void Sub(const float* input1_data, const Dims<4>& input1_dims,
                 const float* input2_data, const Dims<4>& input2_dims,
                 float output_activation_min, float output_activation_max,