add gru_unit op

src/common/types.cpp

@@ -66,6 +66,7 @@ const char *G_OP_TYPE_CONV_TRANSPOSE = "conv2d_transpose";
 const char *G_OP_TYPE_PRELU = "prelu";
 const char *G_OP_TYPE_LOOKUP_TABLE = "lookup_table";
 const char *G_OP_TYPE_GRU = "gru";
+const char *G_OP_TYPE_GRU_UNIT = "gru_unit";
 const char *G_OP_TYPE_CRF = "crf_decoding";
 const char *G_OP_TYPE_BILINEAR_INTERP = "bilinear_interp";
 const char *G_OP_TYPE_FLATTEN = "flatten";
@@ -149,6 +150,9 @@ std::unordered_map<
         {G_OP_TYPE_GRU,
          {{"Input", "H0", "Weight", "Bias"},
           {"BatchGate", "BatchResetHiddenPrev", "BatchHidden", "Hidden"}}},
+        {G_OP_TYPE_GRU_UNIT,
+         {{"Input", "HiddenPrev", "Weight", "Bias"},
+          {"Gate", "ResetHiddenPrev", "Hidden"}}},
         {G_OP_TYPE_CRF, {{"Emission", "Transition", "Label"}, {"ViterbiPath"}}},
         {G_OP_TYPE_BILINEAR_INTERP, {{"OutSize", "X"}, {"Out"}}},
         {G_OP_TYPE_FLATTEN, {{"X"}, {"Out"}}},

src/operators/gru_unit_op.cpp

+/* Copyright (c) 2018 PaddlePaddle Authors. 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. */
+
+#ifdef GRU_UNIT_OP
+
+#include "operators/gru_unit_op.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <typename DeviceType, typename T>
+void GruUnitOp<DeviceType, T>::InferShape() const {
+  auto input_dims = this->param_.InputInput()->dims();
+  auto hidden_prev_dims = this->param_.InputHiddenPrev()->dims();
+  auto weight_dims = this->param_.InputWeight()->dims();
+  int batch_size = input_dims[0];
+  int input_size = input_dims[1];
+  int frame_size = hidden_prev_dims[1];
+  int weight_height = weight_dims[0];
+  int weight_width = weight_dims[1];
+  PADDLE_MOBILE_ENFORCE(
+      (input_size == frame_size * 3),
+      "The input_size must be 3 times of frame_size in GRUUnitOp.");
+  PADDLE_MOBILE_ENFORCE(
+      (weight_height == frame_size),
+      "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+  PADDLE_MOBILE_ENFORCE(
+      (weight_width == frame_size * 3),
+      "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+  if (this->param_.InputBias()) {
+    auto bias_dims = this->param_.InputBias()->dims();
+    int bias_height = bias_dims[0];
+    int bias_width = bias_dims[1];
+    PADDLE_MOBILE_ENFORCE((bias_height == 1),
+                          "The shape of Bias must be [1, frame_size * 3].");
+    PADDLE_MOBILE_ENFORCE((bias_width == frame_size * 3),
+                          "The shape of Bias must be [1, frame_size * 3].");
+  }
+  this->param_.OutGate()->Resize({batch_size, frame_size * 3});
+  this->param_.OutResetHiddenPrev()->Resize({batch_size, frame_size});
+  this->param_.OutHidden()->Resize({batch_size, frame_size});
+}
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+namespace ops = paddle_mobile::operators;
+#ifdef PADDLE_MOBILE_CPU
+REGISTER_OPERATOR_CPU(gru_unit, ops::GruUnitOp);
+#endif
+#ifdef PADDLE_MOBILE_MALI_GPU
+#endif
+#ifdef PADDLE_MOBILE_FPGA
+#endif
+
+#ifdef PADDLE_MOBILE_CL
+#endif
+
+#endif
\ No newline at end of file

src/operators/gru_unit_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. 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. */
+
+#ifdef GRU_UNIT_OP
+
+#pragma once
+
+#include "framework/operator.h"
+#include "operators/kernel/gru_unit_kernel.h"
+#include "operators/op_param.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <typename DeviceType, typename T>
+class GruUnitOp : public framework::OperatorWithKernel<
+                      DeviceType, GruUnitParam<DeviceType>,
+                      operators::GruUnitKernel<DeviceType, T>> {
+ public:
+  GruUnitOp(const std::string &type, const VariableNameMap &inputs,
+            const VariableNameMap &outputs, const AttributeMap &attrs,
+            std::shared_ptr<Scope> scope)
+      : framework::OperatorWithKernel<DeviceType, GruUnitParam<DeviceType>,
+                                      operators::GruUnitKernel<DeviceType, T>>(
+            type, inputs, outputs, attrs, scope){};
+  void InferShape() const override;
+};
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif

src/operators/kernel/arm/gru_unit_kernel.cpp

+/* Copyright (c) 2018 PaddlePaddle Authors. 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. */
+
+#ifdef GRU_UNIT_OP
+
+#include "operators/kernel/gru_unit_kernel.h"
+#include "operators/kernel/central-arm-func/gru_unit_arm_func.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <>
+bool GruUnitKernel<CPU, float>::Init(GruUnitParam<CPU> *param) {
+  return true;
+}
+
+template <>
+void GruUnitKernel<CPU, float>::Compute(const GruUnitParam<CPU> &param) {
+	GruUnitCompute(param);
+}
+
+template class GruUnitKernel<CPU, float>;
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif

src/operators/kernel/central-arm-func/gru_unit_arm_func.h

+/* Copyright (c) 2018 PaddlePaddle Authors. 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. */
+
+#ifdef GRU_UNIT_OP
+
+#pragma once
+
+#include <operators/math/gru_compute.h>
+#include "operators/math/math_function.h"
+#include "operators/kernel/activation_kernel.h"
+#include "operators/math/gemm.h"
+#include "operators/op_param.h"
+namespace paddle_mobile {
+namespace operators {
+
+template <typename P>
+void GruUnitCompute(const GruUnitParam<CPU>& param) {
+  auto* input = param.InputInput();
+  auto* hidden_prev = param.InputHiddenPrev();
+  auto* weight = param.InputWeight();
+  auto* bias = param.InputBias();
+  auto* gate = param.OutGate();
+  auto* reset_hidden_prev = param.OutResetHiddenPrev();
+  auto* hidden = param.OutHidden();
+
+  if (bias) {
+    math::RowwiseAdd<CPU, float> add_bias;
+    add_bias(*gate, *bias, gate);
+  }
+
+  int batch_size = input->dims()[0];
+  int frame_size = hidden_prev->dims()[1];
+  const P* weight_data = weight->data<P>();
+  math::GRUMetaValue<P> gru_value;
+  gru_value.gate_weight = const_cast<P*>(weight_data);
+  gru_value.state_weight =
+      const_cast<P*>(weight_data + 2 * frame_size * frame_size);
+  gru_value.output_value = hidden->data<P>();
+  gru_value.prev_out_value = gru_value.output_value;
+  gru_value.gate_value = gate->data<P>();
+  gru_value.reset_output_value = reset_hidden_prev->data<P>();
+  auto active_node = math::GetActivationType(param.Activation());
+  auto active_gate = math::GetActivationType(param.GateActivation());
+  math::GRUUnitFunctor<CPU, float>::compute(gru_value, frame_size, batch_size,
+                                            active_node, active_gate);
+}
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif
\ No newline at end of file

src/operators/kernel/gru_unit_kernel.h

+/* Copyright (c) 2018 PaddlePaddle Authors. 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. */
+
+#ifdef GRU_UNIT_OP
+
+#pragma once
+
+#include "framework/operator.h"
+#include "operators/op_param.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <typename DeviceType, typename T>
+class GruUnitKernel
+    : public framework::OpKernelBase<DeviceType, GruUnitParam<DeviceType>> {
+ public:
+  void Compute(const GruUnitParam<DeviceType>& param);
+  bool Init(GruUnitParam<DeviceType>* param);
+};
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif
\ No newline at end of file

src/operators/math/activation.h

@@ -45,6 +45,19 @@ inline ActivationType GetActivationType(const std::string &type) {
   PADDLE_MOBILE_THROW_EXCEPTION("Not support activation type.");
 }
 
+inline ActivationType GetActivationType(const int type) {
+  if (type == 0) {
+    return ActivationType::IDENTITY;
+  } else if (type == 1) {
+    return ActivationType::SIGMOID;
+  } else if (type == 2) {
+    return ActivationType::TANH;
+  } else if (type == 3) {
+    return ActivationType::RELU;
+  }
+  PADDLE_MOBILE_THROW_EXCEPTION("Not support activation type.");
+}
+
 #if defined(__ARM_NEON__) || defined(__ARM_NEON)
 template <ActivationType Act = IDENTITY>
 inline float32x4_t vActiveq_f32(const float32x4_t &x) {

src/operators/op_param.h

@@ -74,6 +74,13 @@ class OpParam {
   static T *InputH0From(const VariableNameMap &inputs, const Scope &scope) {
     return GetVarValue<T>("H0", inputs, scope);
   }
+
+  template <typename T>
+  static T *InputHiddenPrevFrom(const VariableNameMap &inputs,
+                                const Scope &scope) {
+    return GetVarValue<T>("HiddenPrev", inputs, scope);
+  }
+
   template <typename T>
   static T *InputAlphaFrom(const VariableNameMap &inputs, const Scope &scope) {
     return GetVarValue<T>("Alpha", inputs, scope);
@@ -214,6 +221,11 @@ class OpParam {
     return GetVarValue<T>("BatchGate", outputs, scope);
   }
 
+  template <typename T>
+  static T *OutputGateFrom(const VariableNameMap &outputs, const Scope &scope) {
+    return GetVarValue<T>("Gate", outputs, scope);
+  }
+
   template <typename T>
   static T *OutputViterbiPathFrom(const VariableNameMap &outputs,
                                   const Scope &scope) {
@@ -225,6 +237,12 @@ class OpParam {
     return GetVarValue<T>("BatchResetHiddenPrev", outputs, scope);
   }
 
+  template <typename T>
+  static T *OutputResetHiddenPrevFrom(const VariableNameMap &outputs,
+                                      const Scope &scope) {
+    return GetVarValue<T>("ResetHiddenPrev", outputs, scope);
+  }
+
   template <typename T>
   static T *OutputBatchHiddenFrom(const VariableNameMap &outputs,
                                   const Scope &scope) {
@@ -2444,6 +2462,51 @@ class GruParam : public OpParam {
 };
 #endif
 
+#ifdef GRU_UNIT_OP
+template <typename Dtype>
+class GruUnitParam : public OpParam {
+  typedef typename DtypeTensorTrait<Dtype>::gtype GType;
+
+ public:
+  GruUnitParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
+               const AttributeMap &attrs, const Scope &scope) {
+    input_input_ = InputFrom<GType>(inputs, scope);
+    input_hidden_prev_ = InputHiddenPrevFrom<GType>(inputs, scope);
+    input_bias_ = InputBiasFrom<GType>(inputs, scope);
+    input_weight_ = InputWeightFrom<GType>(inputs, scope);
+
+    output_gate_ = OutputGateFrom<GType>(outputs, scope);
+    output_reset_hidden_prev_ =
+        OutputResetHiddenPrevFrom<GType>(outputs, scope);
+    output_hidden_ = OutputHiddenFrom<GType>(outputs, scope);
+    activation_ = GetAttr<int>("activation", attrs);
+    gate_activation_ = GetAttr<int>("gate_activation", attrs);
+  }
+  const GType *InputInput() const { return input_input_; }
+  const GType *InputWeight() const { return input_weight_; }
+  const GType *InputHiddenPrev() const { return input_hidden_prev_; }
+  const GType *InputBias() const { return input_bias_; }
+  const int &Activation() const { return activation_; }
+  const int &GateActivation() const { return gate_activation_; }
+
+  GType *OutGate() const { return output_gate_; }
+  GType *OutResetHiddenPrev() const { return output_reset_hidden_prev_; }
+  GType *OutHidden() const { return output_hidden_; }
+
+ private:
+  GType *input_input_;
+  GType *input_hidden_prev_;
+  GType *input_bias_;
+  GType *input_weight_;
+
+  GType *output_gate_;
+  GType *output_reset_hidden_prev_;
+  GType *output_hidden_;
+  int activation_;
+  int gate_activation_;
+};
+#endif
+
 #ifdef FLATTEN_OP
 template <typename Dtype>
 class FlattenParam : public OpParam {

tools/op.cmake

@@ -256,6 +256,7 @@ if(NOT FOUND_MATCH)
   set(IM2SEQUENCE_OP ON)
   set(LOOKUP_OP ON)
   set(GRU_OP ON)
+  set(GRU_UNIT_OP ON)
   set(CRF_OP ON)
   set(BILINEAR_INTERP_OP ON)
   set(SPLIT_OP ON)
@@ -450,6 +451,10 @@ if (GRU_OP)
   add_definitions(-DGRU_OP)
 endif()
 
+if (GRU_UNIT_OP)
+  add_definitions(-DGRU_UNIT_OP)
+endif()
+
 if (CRF_OP)
   add_definitions(-DCRF_OP)
 endif()