Add broadcasting functionality for Div and Sub ops.

tensorflow/contrib/lite/kernels/div.cc

@@ -106,6 +106,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
 #undef TF_LITE_DIV
 }
 
+
+
 template <KernelType kernel_type>
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
@@ -118,7 +120,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   if (output->type == kTfLiteFloat32) {
     EvalFloat<kernel_type>(context, node, params, data, input1, input2, output);
   } else {
-    context->ReportError(context, "Inputs and outputs not all float types.");
+    context->ReportError(context,
+                         "Div only supports FLOAT32 and quantized UINT8 now.");
     return kTfLiteError;
   }
 

tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h

@@ -3938,7 +3938,7 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims,
   using FixedPointAccum = gemmlowp::FixedPoint<int32, kAccumulationIntegerBits>;
   using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>;
 
-  gemmlowp::ScopedProfilingLabel label("Softmax/8bit");
+gemmlowp::ScopedProfilingLabel label("Softmax/8bit");
   const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
   const int height = MatchingArraySize(input_dims, 2, output_dims, 2);
   const int width = MatchingArraySize(input_dims, 1, output_dims, 1);

tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h

@@ -1255,6 +1255,33 @@ 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
@@ -1296,18 +1323,6 @@ 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,

tensorflow/contrib/lite/kernels/sub.cc

@@ -174,7 +174,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
     EvalQuantized<kernel_type>(context, node, params, data, input1, input2,
                                output);
   } else {
-    context->ReportError(context, "Inputs and outputs not all float types.");
+    context->ReportError(context,
+                         "Inputs and outputs not all float|unit8 types.");
     return kTfLiteError;
   }