Added layout_inference for UnidirectionalRnn

Added layout_inference so that can support tflite cases
Modified copyright of code
Modified case name and value name in UnidirectionalRnn unittest

Type: Code Improvement
Signed-off-by: Feiyue Chen <Feiyue.Chen@verisilicon.com>

src/tim/transform/layout_inference.cc

@@ -64,6 +64,7 @@
 #include "ops/transpose_layout_inference.h"
 #include "ops/unidirectional_lstm_layout_inference.h"
 #include "ops/broadcast_layout_inference.h"
+#include "ops/unidirectional_rnn_layout_inference.h"
 
 #include <algorithm>
 #include <deque>
@@ -267,6 +268,7 @@ std::vector<std::shared_ptr<vx::Tensor>> HandleLayoutInfer(
     REGIST_LAYOUT_INFERENCE(VSI_NN_OP_CONV3D, Conv3d);
     REGIST_LAYOUT_INFERENCE(VSI_NN_OP_LSTM_OVXLIB, UnidirectionalLstm);
     REGIST_LAYOUT_INFERENCE(VSI_NN_OP_EXPAND_BROADCAST, Broadcast);
+    REGIST_LAYOUT_INFERENCE(VSI_NN_OP_UNIDIRECTIONAL_SEQUENCE_RNN, UnidirectionalRnn);
     REGIST_LOGICAL_LAYOUT_INFERENCE(VSI_NN_OP_LOGICAL_OPS);
     REGIST_REDUCE_LAYOUT_INFERENCE(VSI_NN_OP_REDUCE);
     // use default layout inference

src/tim/transform/ops/unidirectional_rnn_layout_inference.h

@@ -0,0 +1,99 @@
+/****************************************************************************
+ *
+ *    Copyright (c) 2022 Vivante Corporation
+ *
+ *    Permission is hereby granted, free of charge, to any person obtaining a
+ *    copy of this software and associated documentation files (the "Software"),
+ *    to deal in the Software without restriction, including without limitation
+ *    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ *    and/or sell copies of the Software, and to permit persons to whom the
+ *    Software is furnished to do so, subject to the following conditions:
+ *
+ *    The above copyright notice and this permission notice shall be included in
+ *    all copies or substantial portions of the Software.
+ *
+ *    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ *    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ *    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ *    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ *    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ *    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ *    DEALINGS IN THE SOFTWARE.
+ *
+ *****************************************************************************/
+#ifndef TIM_LAYOUT_INFER_UNIDIRECTIONAL_RNN_LAYOUT_INFERENCE_H_
+#define TIM_LAYOUT_INFER_UNIDIRECTIONAL_RNN_LAYOUT_INFERENCE_H_
+
+#include "tim/vx/ops/reshape.h"
+#include "tim/vx/ops/nbg.h"
+#include "tim/vx/ops/transpose.h"
+#include "tim/vx/ops/batchnorm.h"
+#include "tim/vx/ops/clip.h"
+
+#include "ops/op_layout_inference.h"
+#include "permute_vector.h"
+#include "builtin_op_impl.h"
+
+namespace tim {
+namespace transform {
+
+class UnidirectionalRnnLayoutInfer : public OpLayoutInfer {
+ public:
+  UnidirectionalRnnLayoutInfer(
+      const std::shared_ptr<vx::Operation> op,
+      std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
+      : OpLayoutInfer(op, context) {}
+
+  // reverse any applied permute on it's input tensor
+  void OnInputs(
+      std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
+    ReverseInputsPermuteVector();
+
+    auto cloned_op = op_->Clone(context_->infer_graph_);
+
+    for (const auto& i_src : op_->impl()->InputsTensor()) {
+      std::shared_ptr<vx::Tensor> infer_tensor;
+      std::shared_ptr<IPermuteVector> required_pv;
+      if ((i_src->IsConstTensor() &&
+              !(i_src->GetSpec().attr_ & vx::TensorAttribute::INPUT))) {
+        infer_tensor = context_->infer_graph_->CreateTensor(
+            i_src->GetSpec(), i_src->GetDataRef());
+        context_->UpdateTensorMap(i_src, infer_tensor);
+      }
+      if (i_src->GetId() == (uint32_t)-1) {
+        infer_tensor = context_->infer_graph_->CreateTensorPlaceHolder();
+        context_->UpdateTensorMap(i_src, infer_tensor);
+      }
+      required_pv = MakeShared(i_src->GetShape().size());
+      context_->SetPermuteVector(i_src, required_pv);
+    }
+
+
+    for (const auto& i_src : op_->impl()->InputsTensor()) {
+      (*cloned_op).BindInput(context_->GetMapedTensor(i_src));
+    }
+
+    
+    std::vector<std::shared_ptr<IPermuteVector>> required_pv_lst;
+    for (auto out_tensor : op_->impl()->OutputsTensor()) {
+      std::shared_ptr<vx::Tensor> infer_tensor;
+       if (out_tensor->GetId() == (uint32_t)-1) {
+        out_tensor = context_->infer_graph_->CreateTensorPlaceHolder();
+      }
+      required_pv_lst.push_back(MakeShared(out_tensor->GetShape().size()));
+    }
+    auto out_infer = CreateOutputsTensor(required_pv_lst);
+
+    (*cloned_op).BindOutputs(out_infer);
+    uint32_t i = 0;
+    for (auto out_tensor : op_->impl()->OutputsTensor()) {
+      context_->SetPermuteVector(out_tensor, required_pv_lst[i++]);
+      next_tensors.push_back(out_tensor);
+    }
+  }
+};
+
+}  // namespace transform
+}  // namespace tim
+
+#endif

src/tim/vx/ops/unidirectional_sequence_rnn.cc

@@ -1,6 +1,6 @@
 /****************************************************************************
 *
-*    Copyright (c) 2020 Vivante Corporation
+*    Copyright (c) 2022 Vivante Corporation
 *
 *    Permission is hereby granted, free of charge, to any person obtaining a
 *    copy of this software and associated documentation files (the "Software"),

src/tim/vx/ops/unidirectional_sequence_rnn_test.cc

@@ -1,6 +1,6 @@
 /****************************************************************************
 *
-*    Copyright (c) 2021 Vivante Corporation
+*    Copyright (c) 2022 Vivante Corporation
 *
 *    Permission is hereby granted, free of charge, to any person obtaining a
 *    copy of this software and associated documentation files (the "Software"),
@@ -28,19 +28,19 @@
 #include "test_utils.h"
 #include "gtest/gtest.h"
 
-TEST(UnidirectionalSequenceRnn, shape_2_3_4_float_sigmoid) {
+TEST(UnidirectionalSequenceRnn, shape_2_3_2_float_sigmoid) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 
-    uint32_t input_size = 2, batch_size = 3, num_units = 4;
+    uint32_t input_size = 2, batch_size = 3, num_units = 4, time_step = 2;
 
-    tim::vx::ShapeType input_shape({input_size, batch_size, 2});
+    tim::vx::ShapeType input_shape({input_size, batch_size, time_step});
     tim::vx::ShapeType weights_shape({input_size, num_units});
     tim::vx::ShapeType recurrent_weights_shape({num_units, num_units});
     tim::vx::ShapeType bias_shape({num_units});
     tim::vx::ShapeType recurrent_bias_shape({num_units});
     tim::vx::ShapeType state_in_shape({num_units, batch_size});
-    tim::vx::ShapeType output_shape({num_units, batch_size, 2});
+    tim::vx::ShapeType output_shape({num_units, batch_size, time_step});
     tim::vx::ShapeType state_out_shape({num_units, batch_size});
 
     tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
@@ -144,19 +144,19 @@ TEST(UnidirectionalSequenceRnn, shape_2_3_4_float_sigmoid) {
 }
 
 
-TEST(UnidirectionalSequenceRnn, shape_2_3_4_float_relu) {
+TEST(UnidirectionalSequenceRnn, shape_2_3_2_float_relu) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 
-    uint32_t input_size = 2, batch_size = 3, num_units = 4;
+    uint32_t input_size = 2, batch_size = 3, num_units = 4, time_step = 2;
 
-    tim::vx::ShapeType input_shape({input_size, batch_size, 2});
+    tim::vx::ShapeType input_shape({input_size, batch_size, time_step});
     tim::vx::ShapeType weights_shape({input_size, num_units});
     tim::vx::ShapeType recurrent_weights_shape({num_units, num_units});
     tim::vx::ShapeType bias_shape({num_units});
     tim::vx::ShapeType recurrent_bias_shape({num_units});
     tim::vx::ShapeType state_in_shape({num_units, batch_size});
-    tim::vx::ShapeType output_shape({num_units, batch_size, 2});
+    tim::vx::ShapeType output_shape({num_units, batch_size, time_step});
     tim::vx::ShapeType state_out_shape({num_units, batch_size});
 
     tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,