1 files changed, 103 insertions, 0 deletions
diff --git a/compiler/logo/src/Passes/ResolveRedundantReshapePass.cpp b/compiler/logo/src/Passes/ResolveRedundantReshapePass.cpp
new file mode 100644
index 000000000..da4af15c1
--- /dev/null
+++ b/compiler/logo/src/Passes/ResolveRedundantReshapePass.cpp
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <logo/ResolveRedundantReshapePass.h>
+
+#include <loco/Service/ShapeInference.h>
+
+#include <loco.h>
+
+#include <cassert>
+
+namespace
+{
+
+bool shape_inference_done(loco::FixedReshape *reshape)
+{
+  return loco::shape_known(reshape) && loco::shape_known(reshape->input());
+}
+
+bool are_same_tensor_shapes(const loco::NodeShape &lhs, const loco::NodeShape &rhs)
+{
+  assert(lhs.domain() == loco::Domain::Tensor);
+  assert(rhs.domain() == loco::Domain::Tensor);
+
+  auto lts = lhs.as<loco::TensorShape>();
+  auto rts = rhs.as<loco::TensorShape>();
+
+  if (lts.rank() != rts.rank())
+    return false;
+
+  for (uint32_t axis = 0; axis < lts.rank(); ++axis)
+  {
+    assert(lts.dim(axis).known());
+    assert(rts.dim(axis).known());
+    if (lts.dim(axis).value() != rts.dim(axis).value())
+      return false;
+  }
+  return true;
+}
+
+/// @return  true when 'reshape' has same input and output shape
+bool is_redundant_reshape(loco::FixedReshape *reshape)
+{
+  auto input_shape = loco::shape_get(reshape->input());
+  auto output_shape = loco::shape_get(reshape);
+
+  // Note that FixedReshape's input and output are always tensor
+  return are_same_tensor_shapes(input_shape, output_shape);
+}
+
+} // namespace
+
+namespace logo
+{
+
+/**
+ * @brief  Bypass redundant FixedReshape
+ *
+ * Before:
+ *
+ *   In ----- FixedReshape ----- [Out]*
+ *
+ * After:
+ *
+ *   In ------------------------ [Out]*
+ *    \
+ *     ------ FixedReshape
+ */
+bool ResolveRedundantReshapePass::run(loco::Graph *graph)
+{
+  bool changed = false;
+  for (auto node : loco::postorder_traversal(loco::output_nodes(graph)))
+  {
+    if (auto reshape = dynamic_cast<loco::FixedReshape *>(node))
+    {
+      if (shape_inference_done(reshape))
+      {
+        if (is_redundant_reshape(reshape))
+        {
+          replace(reshape).with(reshape->input());
+          changed = true;
+        }
+      }
+    }
+  }
+
+  return changed;
+}
+
+} // namespace logo