1 files changed, 24 insertions, 14 deletions

diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc
index d496f5ae5e..fcf30bd347 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc
@@ -32,21 +32,34 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) {
   const auto* matmul_op =
       static_cast<const TensorFlowMatMulOperator*>(matmul_it->get());
 
+  // Handling transposition of the first input here isn't very simple because
+  // we need to know the actual shape in order to produce a proper
+  // TransposeOperator.  However, the second input is supposed to be 2D, so we
+  // can actually handle transposition of that matrix, which happens to be more
+  // common anyway.
+  CHECK(!matmul_op->transpose_a);
+
   // Reorder the axes on the second input. TensorFlow uses row-major ordering
   // on both inputs, however this is inefficient for the FullyConnected
   // operator. We'll transpose the second input to be in column-major order now
   // and let constant propagation optimize things (if possible).
-  auto* transpose_op = new TransposeOperator;
-  transpose_op->inputs = {
-      matmul_op->inputs[1],
-      CreateInt32Array(
-          model,
-          AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose/perm"),
-          {1, 0})};
-  transpose_op->outputs = {
-      AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose")};
-  model->GetOrCreateArray(transpose_op->outputs[0]);
-  model->operators.emplace(matmul_it, transpose_op);
+  string input_lhs = matmul_op->inputs[0];
+  string input_rhs = matmul_op->inputs[1];
+  if (!matmul_op->transpose_b) {
+    auto* transpose_op = new TransposeOperator;
+    transpose_op->inputs = {
+        matmul_op->inputs[1],
+        CreateInt32Array(model,
+                         AvailableArrayName(
+                             *model, matmul_op->inputs[1] + "/transpose/perm"),
+                         {1, 0})};
+    transpose_op->outputs = {
+        AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose")};
+    model->GetOrCreateArray(transpose_op->outputs[0]);
+    model->operators.emplace(matmul_it, transpose_op);
+
+    input_rhs = transpose_op->outputs[0];
+  }
 
   // Refresh iterator.
   matmul_it = model->operators.begin();
@@ -57,9 +70,6 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) {
   }
   DCHECK_EQ(matmul_it->get(), matmul_op);
 
-  string input_lhs = matmul_op->inputs[0];
-  string input_rhs = transpose_op->outputs[0];
-
   // Construct the new FullyConnectedOperator.
   auto* fc_op = new FullyConnectedOperator;
   fc_op->outputs = matmul_op->outputs;