Do not differentiage integers in the eager API.

This is similar to the change made in:
https://github.com/tensorflow/tensorflow/commit/f63750645826df65b05cad505546a86f0e347674
for backpropagation during graph construction via tf.gradients()

PiperOrigin-RevId: 195878952

1 files changed, 29 insertions, 7 deletions

diff --git a/tensorflow/c/eager/tape.h b/tensorflow/c/eager/tape.h
index 8026076b9e..e9ed3395c4 100644
--- a/tensorflow/c/eager/tape.h
+++ b/tensorflow/c/eager/tape.h
@@ -130,13 +130,15 @@ class GradientTape {
     }
   }
 
-  bool ShouldRecord(gtl::ArraySlice<int64> tensor_ids);
+  bool ShouldRecord(gtl::ArraySlice<int64> tensor_ids,
+                    gtl::ArraySlice<tensorflow::DataType> dtypes);
 
   void Watch(int64 tensor_id);
 
   void RecordOperation(const string& op_type,
                        gtl::ArraySlice<TapeTensor> output_tensors,
                        gtl::ArraySlice<int64> input_tensor_id,
+                       gtl::ArraySlice<tensorflow::DataType> input_dtypes,
                        BackwardFunction* backward_function,
                        const std::function<void()>& backward_function_deleter);
 
@@ -170,12 +172,30 @@ class GradientTape {
 
 // Template instantiations here
 
+inline bool IsDtypeTrainable(DataType dtype) {
+  switch (dtype) {
+    case DT_HALF:
+    case DT_BFLOAT16:
+    case DT_FLOAT:
+    case DT_DOUBLE:
+    case DT_COMPLEX64:
+    case DT_COMPLEX128:
+    case DT_RESOURCE:
+    case DT_VARIANT:
+      return true;
+    default:
+      return false;
+  }
+}
+
 template <typename Gradient, typename BackwardFunction>
 bool GradientTape<Gradient, BackwardFunction>::ShouldRecord(
-    gtl::ArraySlice<int64> tensor_ids) {
-  for (int64 i : tensor_ids) {
-    if (tensor_tape_.find(i) != tensor_tape_.end()) {
-      return true;
+    gtl::ArraySlice<int64> tensor_ids,
+    gtl::ArraySlice<tensorflow::DataType> dtypes) {
+  CHECK_EQ(tensor_ids.size(), dtypes.size());
+  for (int i = 0; i < tensor_ids.size(); ++i) {
+    if (tensor_tape_.find(tensor_ids[i]) != tensor_tape_.end()) {
+      return IsDtypeTrainable(dtypes[i]);
     }
   }
   return false;
@@ -189,9 +209,11 @@ void GradientTape<Gradient, BackwardFunction>::Watch(int64 tensor_id) {
 template <typename Gradient, typename BackwardFunction>
 void GradientTape<Gradient, BackwardFunction>::RecordOperation(
     const string& op_type, gtl::ArraySlice<TapeTensor> output_tensors,
-    gtl::ArraySlice<int64> input_tensor_id, BackwardFunction* backward_function,
+    gtl::ArraySlice<int64> input_tensor_id,
+    gtl::ArraySlice<tensorflow::DataType> input_dtypes,
+    BackwardFunction* backward_function,
     const std::function<void()>& backward_function_deleter) {
-  if (!ShouldRecord(input_tensor_id)) {
+  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
     backward_function_deleter();
     return;
   }