Have EnsureBiasVector create bias vectors that already have the

constant value 0 and already have their shape set from the output
activations shape; instead of having it create dummy placeholders
and relying on PropagateFixedSizes to create the constant array.
Rationale: It wasn't PropagateFixedSizes's job to create constant
arrays, and that broke down in a case where the bias vectors not
being constant prevented FuseBinaryIntoPrecedingAffine from running.

PiperOrigin-RevId: 202365030

1 files changed, 0 insertions, 50 deletions

diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
index c9c9f13d2e..cee14b257f 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
@@ -120,49 +120,7 @@ void ComputeBinaryOperatorOutputSize(const Shape& input_shape_x,
   CHECK(output_array->has_shape());
 }
 
-int GetOutputDepthFromWeights(const Model& model, const Operator& op) {
-  const string& weights_name = op.inputs[1];
-  const auto& weights_shape = model.GetArray(weights_name).shape();
-  if (op.type == OperatorType::kConv ||
-      op.type == OperatorType::kFullyConnected) {
-    return weights_shape.dims(0);
-  } else if (op.type == OperatorType::kDepthwiseConv) {
-    return weights_shape.dims(3);
-  } else {
-    LOG(FATAL) << "Unhandled operator type";
-  }
-}
-
-bool EnsureBiasVectorShape(Model* model, Operator* op) {
-  const string& weights_name = op->inputs[1];
-  const auto& weights_array = model->GetArray(weights_name);
-  // Yield until weights shape has been resolved.
-  if (!weights_array.has_shape()) {
-    return false;
-  }
-
-  if (op->inputs.size() < 3) {
-    return false;
-  }
-  auto& bias_array = model->GetArray(op->inputs[2]);
-  if (bias_array.has_shape()) {
-    return true;
-  }
-
-  const int output_depth = GetOutputDepthFromWeights(*model, *op);
-  bias_array.copy_shape(Shape({output_depth}));
-
-  auto& float_buffer = bias_array.GetMutableBuffer<ArrayDataType::kFloat>();
-  float_buffer.data.resize(output_depth, 0);
-
-  return true;
-}
-
 void ProcessConvOperator(Model* model, ConvOperator* op) {
-  if (!EnsureBiasVectorShape(model, op)) {
-    return;
-  }
-
   const auto& input_array = model->GetArray(op->inputs[0]);
   // Yield until input dims have been resolved.
   if (!input_array.has_shape()) {
@@ -292,10 +250,6 @@ void ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) {
 }
 
 void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) {
-  if (!EnsureBiasVectorShape(model, op)) {
-    return;
-  }
-
   const auto& input_array = model->GetArray(op->inputs[0]);
   // Yield until input dims have been resolved.
   if (!input_array.has_shape()) {
@@ -410,10 +364,6 @@ void ProcessOpWithShapeInput(Model* model, Operator* op) {
 }
 
 void ProcessFullyConnectedOperator(Model* model, FullyConnectedOperator* op) {
-  if (!EnsureBiasVectorShape(model, op)) {
-    return;
-  }
-
   const auto& input_array = model->GetArray(op->inputs[0]);
   // Yield until input dims have been resolved.
   if (!input_array.has_shape()) {