Merge pull request #15161 from jacquesqiao/gru-add-mode

gru add origin mode

paddle/fluid/API.spec

@@ -70,8 +70,8 @@ paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param
 paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
 paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
 paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None))
-paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None))
-paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid'))
+paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False))
+paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False))
 paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.crf_decoding ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None)

paddle/fluid/operators/gru_op.cc

@@ -137,6 +137,10 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool, defalut: False) "
                   "whether to compute reversed GRU.")
         .SetDefault(false);
+    AddAttr<bool>("origin_mode",
+                  "bool"
+                  "use origin mode in article https://arxiv.org/abs/1412.3555")
+        .SetDefault(false);
     AddComment(R"DOC(
 GRU Operator implements part calculations of the complete GRU as following:
 
@@ -221,6 +225,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
  public:
   void BatchCompute(const framework::ExecutionContext& context) const {
     using DeviceContext = paddle::platform::CPUDeviceContext;
+    bool origin_mode = context.Attr<bool>("origin_mode");
     auto* input = context.Input<LoDTensor>("Input");
     auto* h0 = context.Input<Tensor>("H0");
     auto* weight = context.Input<Tensor>("Weight");
@@ -327,7 +332,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
 
         math::detail::forward_final_output(
             math::detail::forward::gru_finalOutput<T>(), gru_value, frame_size,
-            cur_batch_size, active_node);
+            cur_batch_size, active_node, origin_mode);
 
         gru_value.prev_out_value = gru_value.output_value;
       }
@@ -351,7 +356,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
 
         math::GRUUnitFunctor<DeviceContext, T>::compute(
             dev_ctx, gru_value, frame_size, cur_batch_size, active_node,
-            active_gate);
+            active_gate, origin_mode);
 
         gru_value.prev_out_value = gru_value.output_value;
       }

paddle/fluid/operators/gru_op.cu.cc

@@ -21,6 +21,7 @@ template <typename DeviceContext, typename T>
 class GRUKernel : public framework::OpKernel<T> {
  public:
   void BatchCompute(const framework::ExecutionContext& context) const {
+    bool origin_mode = context.Attr<bool>("origin_mode");
     auto* input = context.Input<LoDTensor>("Input");
     auto* h0 = context.Input<Tensor>("H0");
     auto* weight = context.Input<Tensor>("Weight");
@@ -87,7 +88,7 @@ class GRUKernel : public framework::OpKernel<T> {
       gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
       math::GRUUnitFunctor<DeviceContext, T>::compute(
           dev_ctx, gru_value, frame_size, cur_batch_size, active_node,
-          active_gate);
+          active_gate, origin_mode);
       gru_value.prev_out_value = gru_value.output_value;
     }
 

paddle/fluid/operators/gru_op.h

@@ -41,6 +41,7 @@ template <typename DeviceContext, typename T>
 class GRUGradKernel : public framework::OpKernel<T> {
  public:
   void BatchCompute(const framework::ExecutionContext& context) const {
+    bool origin_mode = context.Attr<bool>("origin_mode");
     auto* h0 = context.Input<Tensor>("H0");
     auto* weight = context.Input<Tensor>("Weight");
     const T* weight_data = weight->data<T>();
@@ -146,7 +147,7 @@ class GRUGradKernel : public framework::OpKernel<T> {
 
       math::GRUUnitGradFunctor<DeviceContext, T>::compute(
           dev_ctx, gru_value, gru_grad, frame_size, cur_batch_size, active_node,
-          active_gate);
+          active_gate, origin_mode);
     }
     if (input_grad) {
       input_grad->mutable_data<T>(context.GetPlace());

paddle/fluid/operators/gru_unit_op.cc

@@ -111,6 +111,13 @@ class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
                  "The activation type used in update gate and reset gate.")
         .SetDefault(sigmoid)
         .InEnum({identity, sigmoid, tanh, relu});
+    AddAttr<bool>("origin_mode",
+                  "bool"
+                  "use origin mode in article <Learning Phrase Representations "
+                  "using RNN Encoder–Decoder\n"
+                  "for Statistical Machine "
+                  "Translation>(https://arxiv.org/pdf/1406.1078.pdf)")
+        .SetDefault(false);
     AddComment(R"DOC(
 GRUUnit Operator implements partial calculations of the GRU unit as following:
 

paddle/fluid/operators/gru_unit_op.h

@@ -113,7 +113,11 @@ class GRUUnitKernel : public framework::OpKernel<T> {
     auto c = g.slice(c_offsets, extents);  // output candidate
 
     // calculate final output
-    h.device(place) = u * (c - h_p) + h_p;
+    if (context.Attr<bool>("origin_mode")) {
+      h.device(place) = c + u * (h_p - c);  // (1 - u) * c + u * h_p
+    } else {
+      h.device(place) = u * (c - h_p) + h_p;  // u * c + (1 - u) * h_p
+    }
   }
 };
 
@@ -180,11 +184,19 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
     auto c = g.slice(c_offsets, extents);  // output candidate
 
     // backward for unactivated update gate
-    ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
-                   d_g.slice(u_offsets, extents), d_h * (c - h_p));
-    // backward for unactivated output candidate
-    ActGradCompute(context.Attr<int>("activation"), place, c, c,
-                   d_g.slice(c_offsets, extents), d_h * u);
+    if (context.Attr<bool>("origin_mode")) {
+      ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
+                     d_g.slice(u_offsets, extents), d_h * (h_p - c));
+      // backward for unactivated output candidate
+      ActGradCompute(context.Attr<int>("activation"), place, c, c,
+                     d_g.slice(c_offsets, extents), d_h * (1 - u));
+    } else {
+      ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
+                     d_g.slice(u_offsets, extents), d_h * (c - h_p));
+      // backward for unactivated output candidate
+      ActGradCompute(context.Attr<int>("activation"), place, c, c,
+                     d_g.slice(c_offsets, extents), d_h * u);
+    }
     // backward for reset_hidden_prev
     auto blas = math::GetBlas<DeviceContext, T>(context);
     blas.GEMM(false, true, batch_size, frame_size, frame_size, 1,
@@ -213,7 +225,11 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
       T* hidden_prev_grad_data =
           hidden_prev_grad->mutable_data<T>(context.GetPlace());
       auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
-      d_h_p.device(place) = d_r_h_p * r + d_h * (u.constant(T(1)) - u);
+      if (context.Attr<bool>("origin_mode")) {
+        d_h_p.device(place) = d_r_h_p * r + d_h * u;
+      } else {
+        d_h_p.device(place) = d_r_h_p * r + d_h * (1 - u);
+      }
       blas.GEMM(false, true, batch_size, frame_size, frame_size * 2, 1,
                 gate_grad_data, frame_size * 3, weight_data, frame_size * 2, 1,
                 hidden_prev_grad_data, frame_size);

paddle/fluid/operators/math/detail/gru_cpu_kernel.h

@@ -56,7 +56,8 @@ template <class OpFinalOutput, typename T>
 void hl_naive_gru_forward_final_output(OpFinalOutput op_final_output,
                                        T *gate_value, T *prev_output_value,
                                        T *output_value, int frame_size,
-                                       ActivationType active_node) {
+                                       ActivationType active_node,
+                                       bool origin_mode) {
   T r_value_update_gate;
   T r_value_frame_state;
   T r_prev_out = 0;
@@ -72,7 +73,7 @@ void hl_naive_gru_forward_final_output(OpFinalOutput op_final_output,
     }
 
     op_final_output(&r_value_update_gate, &r_value_frame_state, &r_prev_out,
-                    &r_output, active_node);
+                    &r_output, active_node, origin_mode);
 
     frame_state[i] = r_value_frame_state;
     output_value[i] = r_output;
@@ -146,7 +147,8 @@ template <class OpFinalOutput, typename T>
 void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
                                      T *gate_value, T *prev_output_value,
                                      T *output_value, int frame_size,
-                                     ActivationType active_node) {
+                                     ActivationType active_node,
+                                     bool origin_mode) {
 #ifdef __AVX__
   __m256 r_value_update_gate, r_value_update_gate_last = _mm256_set1_ps(0.0f);
   __m256 r_value_frame_state, r_value_frame_state_last = _mm256_set1_ps(0.0f);
@@ -180,7 +182,7 @@ void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
     }
 
     op_final_output(&r_value_update_gate, &r_value_frame_state, &r_prev_out,
-                    &r_output, active_node);
+                    &r_output, active_node, origin_mode);
 
     _mm256_storeu_ps(reinterpret_cast<float *>(frame_state + i),
                      r_value_frame_state);
@@ -190,7 +192,7 @@ void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
   if (rest > 0) {
     i = n - block;
     op_final_output(&r_value_update_gate_last, &r_value_frame_state_last,
-                    &r_prev_out_last, &r_output, active_node);
+                    &r_prev_out_last, &r_output, active_node, origin_mode);
 
     _mm256_storeu_ps(reinterpret_cast<float *>(frame_state + i),
                      r_value_frame_state_last);
@@ -227,17 +229,18 @@ inline void forward_reset_output(OpResetOutput op_reset_output,
 template <class OpFinalOutput, typename T>
 inline void forward_final_output(OpFinalOutput op_final_output,
                                  GRUMetaValue<T> value, int frame_size,
-                                 int batch_size, ActivationType active_node) {
+                                 int batch_size, ActivationType active_node,
+                                 bool origin_mode) {
   for (int b = 0; b < batch_size; b++) {
     if (OpFinalOutput::avx && (frame_size > static_cast<int>(8 - 1)) &&
         (sizeof(T) == 4)) {
       hl_avx_gru_forward_final_output(op_final_output, value.gate_value,
                                       value.prev_out_value, value.output_value,
-                                      frame_size, active_node);
+                                      frame_size, active_node, origin_mode);
     } else {
       hl_naive_gru_forward_final_output(
           op_final_output, value.gate_value, value.prev_out_value,
-          value.output_value, frame_size, active_node);
+          value.output_value, frame_size, active_node, origin_mode);
     }
 
     value.gate_value += frame_size * 3;
@@ -253,7 +256,8 @@ void hl_naive_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
                                       T *gate_grad, T *prev_out_value,
                                       T *prev_out_grad, T *output_grad,
                                       int frame_size,
-                                      ActivationType active_node) {
+                                      ActivationType active_node,
+                                      bool origin_mode) {
   T r_update_gate_value;
   T r_update_gate_grad;
   T r_frame_state_value;
@@ -279,7 +283,7 @@ void hl_naive_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
 
     op_state_grad(&r_update_gate_value, &r_update_gate_grad,
                   &r_frame_state_value, &r_frame_state_grad, &r_prev_out_value,
-                  &r_prev_out_grad, &r_out_grad, active_node);
+                  &r_prev_out_grad, &r_out_grad, active_node, origin_mode);
 
     update_gate_grad[i] = r_update_gate_grad;
     frame_state_grad[i] = r_frame_state_grad;
@@ -338,8 +342,8 @@ template <class OpStateGrad, typename T>
 void hl_avx_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
                                     T *gate_grad, T *prev_out_value,
                                     T *prev_out_grad, T *output_grad,
-                                    int frame_size,
-                                    ActivationType active_node) {
+                                    int frame_size, ActivationType active_node,
+                                    bool origin_mode) {
 #ifdef __AVX__
   __m256 r_update_gate_value;
   __m256 r_update_gate_grad;
@@ -368,7 +372,7 @@ void hl_avx_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
 
     op_state_grad(&r_update_gate_value, &r_update_gate_grad,
                   &r_frame_state_value, &r_frame_state_grad, &r_prev_out_value,
-                  &r_prev_out_grad, &r_out_grad, active_node);
+                  &r_prev_out_grad, &r_out_grad, active_node, origin_mode);
 
     update_gate_grad[i] = r_update_gate_grad;
     frame_state_grad[i] = r_frame_state_grad;
@@ -431,16 +435,18 @@ template <class OpStateGrad, typename T>
 inline void backward_state_grad(OpStateGrad op_state_grad,
                                 GRUMetaValue<T> value, GRUMetaGrad<T> grad,
                                 int frame_size, int batch_size,
-                                ActivationType active_node) {
+                                ActivationType active_node, bool origin_mode) {
   for (int b = 0; b < batch_size; b++) {
     if (OpStateGrad::avx && !(frame_size & (8 - 1)) && (sizeof(T) == 4)) {
-      hl_avx_gru_backward_state_grad(
-          op_state_grad, value.gate_value, grad.gate_grad, value.prev_out_value,
-          grad.prev_out_grad, grad.output_grad, frame_size, active_node);
+      hl_avx_gru_backward_state_grad(op_state_grad, value.gate_value,
+                                     grad.gate_grad, value.prev_out_value,
+                                     grad.prev_out_grad, grad.output_grad,
+                                     frame_size, active_node, origin_mode);
     } else {
-      hl_naive_gru_backward_state_grad(
-          op_state_grad, value.gate_value, grad.gate_grad, value.prev_out_value,
-          grad.prev_out_grad, grad.output_grad, frame_size, active_node);
+      hl_naive_gru_backward_state_grad(op_state_grad, value.gate_value,
+                                       grad.gate_grad, value.prev_out_value,
+                                       grad.prev_out_grad, grad.output_grad,
+                                       frame_size, active_node, origin_mode);
     }
 
     value.gate_value += frame_size * 3;

paddle/fluid/operators/math/detail/gru_gpu_kernel.h

@@ -72,7 +72,8 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
                                         T *gate_value, T *prev_output_value,
                                         T *output_value, int frame_size,
                                         int batch_size,
-                                        ActivationType active_node) {
+                                        ActivationType active_node,
+                                        bool origin_mode) {
   const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
   if (frame_idx >= frame_size) return;
   int batch_idx = 0;
@@ -94,7 +95,7 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
   }
 
   op_final_output(&r_value_update_gate, &r_value_frame_state, &r_prev_out,
-                  &r_output, active_node);
+                  &r_output, active_node, origin_mode);
 
   gate_value[frame_idx + frame_size * 2] = r_value_frame_state;
   output_value[frame_idx] = r_output;
@@ -109,7 +110,8 @@ __global__ void KeGruBackwardStateGrad(OpStateGrad op_state_grad, T *gate_value,
                                        T *gate_grad, T *prev_out_value,
                                        T *prev_out_grad, T *output_grad,
                                        int frame_size, int batch_size,
-                                       ActivationType active_node) {
+                                       ActivationType active_node,
+                                       bool origin_mode) {
   const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
   if (frame_idx >= frame_size) return;
   int batch_idx = 0;
@@ -139,7 +141,7 @@ __global__ void KeGruBackwardStateGrad(OpStateGrad op_state_grad, T *gate_value,
 
   op_state_grad(&r_update_gate_value, &r_update_gate_grad, &r_frame_state_value,
                 &r_frame_state_grad, &r_prev_out_value, &r_prev_out_grad,
-                &r_out_grad, active_node);
+                &r_out_grad, active_node, origin_mode);
 
   gate_grad[frame_idx + frame_size * 0] = r_update_gate_grad;
   gate_grad[frame_idx + frame_size * 2] = r_frame_state_grad;

paddle/fluid/operators/math/detail/gru_kernel.h

@@ -57,10 +57,16 @@ class gru_finalOutput {
  public:
   HOSTDEVICE void operator()(T *value_update_gate, T *value_frame_state,
                              T *prev_out, T *value_output,
-                             ActivationType act_input) {
+                             ActivationType act_input, bool origin_mode) {
     *value_frame_state = activation(*value_frame_state, act_input);
-    *value_output = *prev_out - ((*value_update_gate) * (*prev_out)) +
-                    ((*value_update_gate) * (*value_frame_state));
+    if (origin_mode) {
+      *value_output = ((*value_update_gate) * (*prev_out)) +
+                      *value_frame_state -
+                      ((*value_update_gate) * (*value_frame_state));
+    } else {
+      *value_output = *prev_out - ((*value_update_gate) * (*prev_out)) +
+                      ((*value_update_gate) * (*value_frame_state));
+    }
   }
 #ifndef __NVCC__
 #ifndef __AVX__
@@ -69,11 +75,20 @@ class gru_finalOutput {
   static const bool avx = true;
   HOSTDEVICE void operator()(__m256 *value_update_gate,
                              __m256 *value_frame_state, __m256 *prev_out,
-                             __m256 *value_output, ActivationType act_input) {
+                             __m256 *value_output, ActivationType act_input,
+                             bool origin_mode) {
     *value_frame_state = activation(*value_frame_state, act_input);
-    *value_output = _mm256_add_ps(
-        _mm256_sub_ps(*prev_out, _mm256_mul_ps(*value_update_gate, *prev_out)),
-        _mm256_mul_ps(*value_update_gate, *value_frame_state));
+    if (origin_mode) {
+      *value_output = _mm256_sub_ps(
+          _mm256_add_ps(_mm256_mul_ps(*value_update_gate, *prev_out),
+                        *value_frame_state),
+          _mm256_mul_ps(*value_update_gate, *value_frame_state));
+    } else {
+      *value_output = _mm256_add_ps(
+          _mm256_sub_ps(*prev_out,
+                        _mm256_mul_ps(*value_update_gate, *prev_out)),
+          _mm256_mul_ps(*value_update_gate, *value_frame_state));
+    }
   }
 #endif
 #endif
@@ -88,13 +103,23 @@ class gru_stateGrad {
   HOSTDEVICE void operator()(T *value_update_gate, T *grad_update_gate,
                              T *value_frame_state, T *grad_frame_state,
                              T *value_prev_out, T *grad_prev_out,
-                             T *grad_output, ActivationType act_input) {
-    *grad_update_gate = (*grad_output * (*value_frame_state));
-    *grad_update_gate -= (*grad_output * (*value_prev_out));
-    *grad_prev_out -= (*grad_output * (*value_update_gate));
-    *grad_prev_out += *grad_output;
-    *grad_frame_state = activation(*grad_output * (*value_update_gate),
-                                   *value_frame_state, act_input);
+                             T *grad_output, ActivationType act_input,
+                             bool origin_mode) {
+    if (origin_mode) {
+      *grad_update_gate =
+          (*grad_output) * ((*value_prev_out) - (*value_frame_state));
+      *grad_prev_out += (*grad_output * (*value_update_gate));
+      *grad_frame_state = activation(
+          *grad_output * (static_cast<T>(1.0) - (*value_update_gate)),
+          *value_frame_state, act_input);
+    } else {
+      *grad_update_gate =
+          (*grad_output) * ((*value_frame_state) - (*value_prev_out));
+      *grad_prev_out +=
+          (*grad_output * (static_cast<T>(1.0) - *value_update_gate));
+      *grad_frame_state = activation(*grad_output * (*value_update_gate),
+                                     *value_frame_state, act_input);
+    }
   }
 #ifndef __NVCC__
 #ifndef __AVX__
@@ -106,17 +131,27 @@ class gru_stateGrad {
                              __m256 *value_frame_state,
                              __m256 *grad_frame_state, __m256 *value_prev_out,
                              __m256 *grad_prev_out, __m256 *grad_output,
-                             ActivationType act_input) {
-    *grad_update_gate = _mm256_mul_ps(*grad_output, *value_frame_state);
-    *grad_update_gate = _mm256_sub_ps(
-        *grad_update_gate, _mm256_mul_ps(*grad_output, *value_prev_out));
-    *grad_prev_out = _mm256_add_ps(
-        _mm256_sub_ps(*grad_prev_out,
-                      _mm256_mul_ps(*grad_output, *value_update_gate)),
-        *grad_output);
-    *grad_frame_state =
-        activation(_mm256_mul_ps(*grad_output, *value_update_gate),
-                   *value_frame_state, act_input);
+                             ActivationType act_input, bool origin_mode) {
+    if (origin_mode) {
+      *grad_update_gate = _mm256_mul_ps(
+          *grad_output, _mm256_sub_ps(*value_prev_out, *value_frame_state));
+      *grad_prev_out = _mm256_add_ps(
+          *grad_prev_out, _mm256_mul_ps(*grad_output, *value_update_gate));
+      *grad_frame_state = activation(
+          _mm256_mul_ps(*grad_output, _mm256_sub_ps(_mm256_set1_ps(1.0f),
+                                                    *value_update_gate)),
+          *value_frame_state, act_input);
+    } else {
+      *grad_update_gate = _mm256_mul_ps(
+          *grad_output, _mm256_sub_ps(*value_frame_state, *value_prev_out));
+      *grad_prev_out = _mm256_add_ps(
+          *grad_prev_out,
+          _mm256_mul_ps(*grad_output, _mm256_sub_ps(_mm256_set1_ps(1.0f),
+                                                    *value_update_gate)));
+      *grad_frame_state =
+          activation(_mm256_mul_ps(*grad_output, *value_update_gate),
+                     *value_frame_state, act_input);
+    }
   }
 #endif
 #endif

paddle/fluid/operators/math/gru_compute.cc

@@ -23,7 +23,8 @@ struct GRUUnitFunctor<platform::CPUDeviceContext, T> {
   static void compute(const platform::CPUDeviceContext &context,
                       GRUMetaValue<T> value, int frame_size, int batch_size,
                       const detail::ActivationType active_node,
-                      const detail::ActivationType active_gate) {
+                      const detail::ActivationType active_gate,
+                      bool origin_mode) {
 #ifndef __NVCC__
     auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
     if (value.prev_out_value) {
@@ -43,7 +44,8 @@ struct GRUUnitFunctor<platform::CPUDeviceContext, T> {
     }
 
     detail::forward_final_output(detail::forward::gru_finalOutput<T>(), value,
-                                 frame_size, batch_size, active_node);
+                                 frame_size, batch_size, active_node,
+                                 origin_mode);
 #endif
   }
 };
@@ -54,10 +56,12 @@ struct GRUUnitGradFunctor<platform::CPUDeviceContext, T> {
                       GRUMetaValue<T> value, GRUMetaGrad<T> grad,
                       int frame_size, int batch_size,
                       const detail::ActivationType active_node,
-                      const detail::ActivationType active_gate) {
+                      const detail::ActivationType active_gate,
+                      bool origin_mode) {
 #ifndef __NVCC__
     detail::backward_state_grad(detail::backward::gru_stateGrad<T>(), value,
-                                grad, frame_size, batch_size, active_node);
+                                grad, frame_size, batch_size, active_node,
+                                origin_mode);
     auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
     if (value.prev_out_value && grad.prev_out_grad) {
       blas.GEMM(false, true, batch_size, frame_size, frame_size, 1,

paddle/fluid/operators/math/gru_compute.cu

@@ -24,7 +24,8 @@ struct GRUUnitFunctor<platform::CUDADeviceContext, T> {
   static void compute(const platform::CUDADeviceContext &context,
                       GRUMetaValue<T> value, int frame_size, int batch_size,
                       const detail::ActivationType active_node,
-                      const detail::ActivationType active_gate) {
+                      const detail::ActivationType active_gate,
+                      bool origin_mode) {
     auto stream = context.stream();
     dim3 threads;
     dim3 grid;
@@ -73,14 +74,14 @@ struct GRUUnitFunctor<platform::CUDADeviceContext, T> {
                                       T><<<grid, threads, 0, stream>>>(
           detail::forward::gru_finalOutput<T>(), value.gate_value,
           value.prev_out_value, value.output_value, frame_size, batch_size,
-          active_node);
+          active_node, origin_mode);
     } else {
       detail::KeGruForwardFinalOutput<detail::forward::gru_finalOutput<T>,
                                       /* is_batch= */ true,
                                       T><<<grid, threads, 0, stream>>>(
           detail::forward::gru_finalOutput<T>(), value.gate_value,
           value.prev_out_value, value.output_value, frame_size, batch_size,
-          active_node);
+          active_node, origin_mode);
     }
   }
 };
@@ -91,7 +92,8 @@ struct GRUUnitGradFunctor<platform::CUDADeviceContext, T> {
                       GRUMetaValue<T> value, GRUMetaGrad<T> grad,
                       int frame_size, int batch_size,
                       const detail::ActivationType active_node,
-                      const detail::ActivationType active_gate) {
+                      const detail::ActivationType active_gate,
+                      bool origin_mode) {
     auto stream = context.stream();
     dim3 threads;
     dim3 grid;
@@ -111,14 +113,14 @@ struct GRUUnitGradFunctor<platform::CUDADeviceContext, T> {
           /* is_batch= */ false><<<grid, threads, 0, stream>>>(
           detail::backward::gru_stateGrad<T>(), value.gate_value,
           grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
-          grad.output_grad, frame_size, batch_size, active_node);
+          grad.output_grad, frame_size, batch_size, active_node, origin_mode);
     } else {
       detail::KeGruBackwardStateGrad<
           detail::backward::gru_stateGrad<T>,
           /* is_batch= */ true><<<grid, threads, 0, stream>>>(
           detail::backward::gru_stateGrad<T>(), value.gate_value,
           grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
-          grad.output_grad, frame_size, batch_size, active_node);
+          grad.output_grad, frame_size, batch_size, active_node, origin_mode);
     }
 
     auto blas = math::GetBlas<platform::CUDADeviceContext, T>(context);

paddle/fluid/operators/math/gru_compute.h

@@ -44,7 +44,8 @@ struct GRUUnitFunctor {
   static void compute(const DeviceContext &context, GRUMetaValue<T> value,
                       int frame_size, int batch_size,
                       const detail::ActivationType active_node,
-                      const detail::ActivationType active_gate);
+                      const detail::ActivationType active_gate,
+                      bool origin_mode);
 };
 
 template <typename DeviceContext, typename T>
@@ -52,7 +53,8 @@ struct GRUUnitGradFunctor {
   static void compute(const DeviceContext &context, GRUMetaValue<T> value,
                       GRUMetaGrad<T> grad, int frame_size, int batch_size,
                       const detail::ActivationType active_node,
-                      const detail::ActivationType active_gate);
+                      const detail::ActivationType active_gate,
+                      bool origin_mode);
 };
 
 }  // namespace math

python/paddle/fluid/layers/nn.py

@@ -864,12 +864,14 @@ def dynamic_gru(input,
                 is_reverse=False,
                 gate_activation='sigmoid',
                 candidate_activation='tanh',
-                h_0=None):
+                h_0=None,
+                origin_mode=False):
     """
     **Gated Recurrent Unit (GRU) Layer**
 
-    Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
-    Sequence Modeling <https://arxiv.org/abs/1412.3555>`_ .
+    if origin_mode is False, then the equation of a gru step is from paper
+    `Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
+    Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_ .
 
     The formula is as follows:
 
@@ -883,6 +885,21 @@ def dynamic_gru(input,
 
         h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
 
+
+    if origin_mode is True then the equation is from paper
+    Learning Phrase Representations using RNN Encoder-Decoder for Statistical
+    Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
+
+    .. math::
+
+        u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
+
+        r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
+
+        \\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
+
+        h_t & = u_t \odot h_{t-1} + (1-u_t) \odot \\tilde{h_t}
+
     The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
     is the update gate and reset gate activation function and :math:`sigmoid`
     is usually used for it. :math:`act_c` is the activation function for
@@ -980,7 +997,8 @@ def dynamic_gru(input,
         attrs={
             'is_reverse': is_reverse,
             'gate_activation': gate_activation,
-            'activation': candidate_activation
+            'activation': candidate_activation,
+            'origin_mode': origin_mode
         })
     return hidden
 
@@ -991,9 +1009,14 @@ def gru_unit(input,
              param_attr=None,
              bias_attr=None,
              activation='tanh',
-             gate_activation='sigmoid'):
+             gate_activation='sigmoid',
+             origin_mode=False):
     """
-    GRU unit layer. The equation of a gru step is:
+    **GRU unit layer**
+
+    if origin_mode is True, then the equation of a gru step is from paper
+    `Learning Phrase Representations using RNN Encoder-Decoder for Statistical
+    Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
 
         .. math::
             u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
@@ -1002,7 +1025,21 @@ def gru_unit(input,
 
             m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
 
-            h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
+            h_t & = dot(u_t, h_{t-1}) + dot((1-u_t), m_t)
+
+    if origin_mode is False, then the equation of a gru step is from paper
+    `Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
+    Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_
+
+        .. math::
+            u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
+
+            r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
+
+            m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
+
+            h_t & = dot((1-u_t), h_{t-1}) + dot(u_t, m_t)
+
 
     The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
     of the equation above, the :math:`z_t` is split into 3 parts -

python/paddle/fluid/tests/unittests/test_gru_op.py

@@ -31,7 +31,8 @@ def gru(
         is_reverse,
         act_state,
         act_gate,
-        dtype='float32'):
+        dtype='float32',
+        origin_mode=False):
     def _seq_to_batch(lod, is_reverse):
         idx_in_seq_list = []
         seq_lens = lod[0]
@@ -66,7 +67,10 @@ def gru(
         w_c = w.flatten()[D * D * 2:].reshape((D, D))
         c = act_state(np.dot(r_h_p, w_c) + g[:, D * 2:])
         g = np.hstack((u_r, c))
-        h = u * c + (1 - u) * h_p
+        if origin_mode:
+            h = (1 - u) * c + u * h_p
+        else:
+            h = u * c + (1 - u) * h_p
         return g, r_h_p, h
 
     T = sum(lod[0])
@@ -110,6 +114,7 @@ class TestGRUOp(OpTest):
         self.act_state = 'tanh'
         self.act_gate = 'sigmoid'
         self.dtype = 'float64'
+        self.origin_mode = False
         self.set_confs()
 
         T = sum(self.lod[0])
@@ -126,7 +131,8 @@ class TestGRUOp(OpTest):
 
         batch_gate, batch_reset_hidden_prev, batch_hidden, hidden = gru(
             input, self.lod, h0, weight, bias, self.is_reverse,
-            ACTIVATION[self.act_state], ACTIVATION[self.act_gate], self.dtype)
+            ACTIVATION[self.act_state], ACTIVATION[self.act_gate], self.dtype,
+            self.origin_mode)
         self.inputs = {'Input': (input, self.lod), 'Weight': weight}
 
         if self.with_bias:
@@ -145,7 +151,8 @@ class TestGRUOp(OpTest):
         self.attrs = {
             'activation': self.act_state,
             'gate_activation': self.act_gate,
-            'is_reverse': self.is_reverse
+            'is_reverse': self.is_reverse,
+            'origin_mode': self.origin_mode
         }
 
     def test_check_output(self):
@@ -155,12 +162,24 @@ class TestGRUOp(OpTest):
         self.check_grad(['Input', 'H0', 'Weight', 'Bias'], ['Hidden'])
 
 
+class TestGRUOriginMode(TestGRUOp):
+    def set_confs(self):
+        self.origin_mode = True
+
+
 class TestGRUOp2(TestGRUOp):
     def set_confs(self):
         self.D = 19
         self.dtype = 'float32'
 
 
+class TestGRUOp2OriginMode(TestGRUOp):
+    def set_confs(self):
+        self.D = 19
+        self.dtype = 'float32'
+        self.origin_mode = True
+
+
 class TestGRUOpNoInitial(TestGRUOp):
     def set_confs(self):
         self.with_h0 = False
@@ -182,5 +201,11 @@ class TestGRUOpReverse(TestGRUOp):
         self.is_reverse = True
 
 
+class TestGRUOpReverseOriginMode(TestGRUOp):
+    def set_confs(self):
+        self.is_reverse = True
+        self.origin_mode = True
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_gru_unit_op.py

@@ -53,7 +53,7 @@ class TestGRUUnitOp(OpTest):
         GRUActivationType.relu: relu,
     }
 
-    def set_inputs(self):
+    def set_inputs(self, origin_mode=False):
         batch_size = self.batch_size
         frame_size = self.frame_size
         self.op_type = 'gru_unit'
@@ -68,10 +68,11 @@ class TestGRUUnitOp(OpTest):
         }
         self.attrs = {
             'activation': GRUActivationType.tanh,
-            'gate_activation': GRUActivationType.sigmoid
+            'gate_activation': GRUActivationType.sigmoid,
+            'origin_mode': origin_mode
         }
 
-    def set_outputs(self):
+    def set_outputs(self, origin_mode=False):
         # GRU calculations
         batch_size = self.batch_size
         frame_size = self.frame_size
@@ -93,7 +94,10 @@ class TestGRUUnitOp(OpTest):
         c = self.activate[self.attrs['activation']](np.dot(r_h_p, w_c) +
                                                     g[:, frame_size * 2:])
         g = np.hstack((u_r, c))
-        h = u * c + (1 - u) * h_p
+        if origin_mode:
+            h = (1 - u) * c + u * h_p
+        else:
+            h = u * c + (1 - u) * h_p
         self.outputs = {
             'Gate': g.astype('float64'),
             'ResetHiddenPrev': r_h_p.astype('float64'),
@@ -111,8 +115,14 @@ class TestGRUUnitOp(OpTest):
         self.check_grad(['Input', 'HiddenPrev', 'Weight'], ['Hidden'])
 
 
+class TestGRUUnitOpOriginMode(TestGRUUnitOp):
+    def setUp(self):
+        self.set_inputs(origin_mode=True)
+        self.set_outputs(origin_mode=True)
+
+
 class TestGRUUnitOpWithBias(TestGRUUnitOp):
-    def set_inputs(self):
+    def set_inputs(self, origin_mode=False):
         batch_size = self.batch_size
         frame_size = self.frame_size
         super(TestGRUUnitOpWithBias, self).set_inputs()
@@ -120,7 +130,8 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
             -0.1, 0.1, (1, frame_size * 3)).astype('float64')
         self.attrs = {
             'activation': GRUActivationType.identity,
-            'gate_activation': GRUActivationType.sigmoid
+            'gate_activation': GRUActivationType.sigmoid,
+            'origin_mode': origin_mode
         }
 
     def test_check_grad(self):
@@ -132,5 +143,11 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
             no_grad_set=set('Input'))
 
 
+class TestGRUUnitOpWithBiasOriginMode(TestGRUUnitOpWithBias):
+    def setUp(self):
+        self.set_inputs(origin_mode=True)
+        self.set_outputs(origin_mode=True)
+
+
 if __name__ == '__main__':
     unittest.main()