Division by zero in TensorFlow Lite division operations

@@ -216,9 +216,23 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_OK(context,
                     GetOutputSafe(context, node, kOutputTensor, &output));
 
-  if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
+  // TODO(b/193904910): This can written with C++ templates
+#define TF_LITE_CHECK_DIV_NON_ZERO(data_type)                       \
+  const auto* input2_data = GetTensorData<data_type>(input2);       \
+  const size_t input2_elements = input2->bytes / sizeof(data_type); \
+  for (size_t i = 0; i < input2_elements; i++) {                    \
+    TF_LITE_ENSURE(context, input2_data[i] != 0);                   \
+  }
+
+  if (output->type == kTfLiteFloat32) {
+    // Div by zero seems ok in this case, just like in TF case infinities are
+    // returned. So we don't do a check at this point.
+    EvalDiv<kernel_type>(context, node, params, data, input1, input2, output);
+  } else if (output->type == kTfLiteInt32) {
+    TF_LITE_CHECK_DIV_NON_ZERO(int32_t);
     EvalDiv<kernel_type>(context, node, params, data, input1, input2, output);
   } else if (output->type == kTfLiteUInt8) {
+    TF_LITE_CHECK_DIV_NON_ZERO(uint8_t);
     TF_LITE_ENSURE_OK(
         context, EvalQuantized<kernel_type>(context, node, params, data, input1,
                                             input2, output));
@@ -229,6 +243,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
         output->type);
     return kTfLiteError;
   }
+#undef TF_LITE_CHECK_DIV_NON_ZERO
 
   return kTfLiteOk;
 }