Add 8bit Tanh support to tflite

Allow output datatypes for custom ops to be more than the output types used in the graph. When an op has multiple outputs, some of them not used will be optimized away. This results in a failure. The change in propagate_array_data_types.cc fix this.

PiperOrigin-RevId: 187198815

1 files changed, 5 insertions, 2 deletions

diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
index f0d107232b..bde947f78d 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
@@ -97,10 +97,13 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) {
     SetDataTypeForAllOutputs(model, op, data_type);
   } else if (op->type == OperatorType::kTensorFlowUnsupported) {
     auto* unsupported_op = static_cast<TensorFlowUnsupportedOperator*>(op);
-    if (unsupported_op->output_data_types.size() != op->outputs.size()) {
+    // Some output tensors from the op could be eliminated by optimization.
+    // This can make unsupported_op->output_data_types have more elements than
+    // op->outputs.
+    if (unsupported_op->output_data_types.size() < op->outputs.size()) {
       return false;
     }
-    for (int i = 0; i < unsupported_op->output_data_types.size(); ++i) {
+    for (int i = 0; i < op->outputs.size(); ++i) {
       auto output = op->outputs[i];
       auto data_type = unsupported_op->output_data_types[i];
       model->GetArray(output).data_type = data_type;