Adds a kernel that checks whether vector is zero or not.

PiperOrigin-RevId: 197633182

1 files changed, 2 insertions, 6 deletions

diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc
index 3374923e6e..1b942a1910 100644
--- a/tensorflow/contrib/lite/kernels/fully_connected.cc
+++ b/tensorflow/contrib/lite/kernels/fully_connected.cc
@@ -101,6 +101,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
     input_size *= input->dims->data[i];
   }
 
+  TF_LITE_ENSURE_EQ(context, NumDimensions(filter), 2);
   const int batch_size = input_size / filter->dims->data[1];
   const int num_units = filter->dims->data[0];
 
@@ -109,8 +110,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
     TF_LITE_ENSURE_EQ(context, NumElements(bias), SizeOfDimension(filter, 0));
   }
 
-  TF_LITE_ENSURE_EQ(context, NumDimensions(filter), 2);
-
   // Note that quantized inference requires that all tensors have their
   // parameters set. This is usually done during quantized training.
   TfLiteType data_type = input->type;
@@ -218,11 +217,8 @@ TfLiteStatus EvalPieQuantized(TfLiteContext* context, TfLiteNode* node,
     tensor_utils::ZeroVector(output->data.f, batch_size * num_units);
   }
 
-  // TODO(mirkov): change std::minmax_element with a vectorized call.
-  auto minmax_element =
-      std::minmax_element(input->data.f, input->data.f + total_input_size);
   // Save matrix multiplication computation for all zero input.
-  if (*minmax_element.first == 0.0 && *minmax_element.second == 0.0) {
+  if (tensor_utils::IsZeroVector(input->data.f, total_input_size)) {
     tensor_utils::ApplyActivationToVector(output->data.f,
                                           batch_size * num_units,
                                           params->activation, output->data.f);