Fix gru_op related code style

paddle/operators/gru_op.h

@@ -71,8 +71,8 @@ class GRUKernel : public framework::OpKernel<T> {
 
     int frame_size = hidden_dims[1];
     math::hl_gru_value<T> gru_value;
-    gru_value.gateWeight = const_cast<T*>(weight_data);
-    gru_value.stateWeight =
+    gru_value.gate_weight = const_cast<T*>(weight_data);
+    gru_value.state_weight =
         const_cast<T*>(weight_data + 2 * frame_size * frame_size);
     Tensor ordered_h0;
     const size_t* order = batch_gate->lod()[2].data();
@@ -82,9 +82,9 @@ class GRUKernel : public framework::OpKernel<T> {
       // to reorder.
       ReorderInitState<Place, T>(context.device_context(), *h0, order,
                                  &ordered_h0, true);
-      gru_value.prevOutValue = ordered_h0.data<T>();
+      gru_value.prev_out_value = ordered_h0.data<T>();
     } else {
-      gru_value.prevOutValue = nullptr;
+      gru_value.prev_out_value = nullptr;
     }
     auto batch_starts = batch_gate->lod()[0];
     size_t num_batch = batch_starts.size() - 1;
@@ -96,14 +96,14 @@ class GRUKernel : public framework::OpKernel<T> {
       Tensor gate_t = batch_gate->Slice(bstart, bend);
       Tensor reset_hidden_prev_t = batch_reset_hidden_prev->Slice(bstart, bend);
       Tensor hidden_t = batch_hidden->Slice(bstart, bend);
-      gru_value.outputValue = hidden_t.data<T>();
-      gru_value.gateValue = gate_t.data<T>();
-      gru_value.resetOutputValue = reset_hidden_prev_t.data<T>();
+      gru_value.output_value = hidden_t.data<T>();
+      gru_value.gate_value = gate_t.data<T>();
+      gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
       math::GRUUnitFunctor<Place, T>::compute(
           dev_ctx, gru_value, frame_size, cur_batch_size,
           math::ActiveType(context.Attr<std::string>("activation")),
           math::ActiveType(context.Attr<std::string>("gate_activation")));
-      gru_value.prevOutValue = gru_value.outputValue;
+      gru_value.prev_out_value = gru_value.output_value;
     }
 
     math::Batch2LoDTensorFunctor<Place, T> to_seq;
@@ -169,20 +169,20 @@ class GRUGradKernel : public framework::OpKernel<T> {
     to_batch(dev_ctx, *hidden_grad, batch_hidden_grad, false, is_reverse);
 
     math::hl_gru_value<T> gru_value;
-    gru_value.gateWeight = const_cast<T*>(weight_data);
-    gru_value.stateWeight =
+    gru_value.gate_weight = const_cast<T*>(weight_data);
+    gru_value.state_weight =
         const_cast<T*>(weight_data + 2 * frame_size * frame_size);
 
     math::hl_gru_grad<T> gru_grad;
     if (weight_grad) {
-      gru_grad.gateWeightGrad =
+      gru_grad.gate_weight_grad =
           weight_grad->mutable_data<T>(context.GetPlace());
       zero(dev_ctx, weight_grad, static_cast<T>(0.0));
-      gru_grad.stateWeightGrad =
+      gru_grad.state_weight_grad =
           weight_grad->data<T>() + 2 * frame_size * frame_size;
     } else {
-      gru_grad.gateWeightGrad = nullptr;
-      gru_grad.stateWeightGrad = nullptr;
+      gru_grad.gate_weight_grad = nullptr;
+      gru_grad.state_weight_grad = nullptr;
     }
 
     auto batch_starts = batch_hidden_grad.lod()[0];
@@ -193,27 +193,27 @@ class GRUGradKernel : public framework::OpKernel<T> {
       int cur_batch_size = bend - bstart;
 
       Tensor gate_t = batch_gate->Slice(bstart, bend);
-      gru_value.gateValue = gate_t.data<T>();
+      gru_value.gate_value = gate_t.data<T>();
       Tensor reset_hidden_prev_t = batch_reset_hidden_prev->Slice(bstart, bend);
-      gru_value.resetOutputValue = reset_hidden_prev_t.data<T>();
+      gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
 
       Tensor hidden_grad_t = batch_hidden_grad.Slice(bstart, bend);
-      gru_grad.outputGrad = hidden_grad_t.data<T>();
+      gru_grad.output_grad = hidden_grad_t.data<T>();
       Tensor gate_grad_t = batch_gate_grad.Slice(bstart, bend);
-      gru_grad.gateGrad = gate_grad_t.data<T>();
+      gru_grad.gate_grad = gate_grad_t.data<T>();
       Tensor reset_hidden_prev_grad_t =
           batch_reset_hidden_prev_grad.Slice(bstart, bend);
-      gru_grad.resetOutputGrad = reset_hidden_prev_grad_t.data<T>();
+      gru_grad.reset_output_grad = reset_hidden_prev_grad_t.data<T>();
       if (n == 0) {
-        gru_value.prevOutValue = h0 ? ordered_h0.data<T>() : nullptr;
-        gru_grad.prevOutGrad =
+        gru_value.prev_out_value = h0 ? ordered_h0.data<T>() : nullptr;
+        gru_grad.prev_out_grad =
             h0 && h0_grad ? ordered_h0_grad.data<T>() : nullptr;
       } else {
         int bstart_pre = static_cast<int>(batch_starts[n - 1]);
         Tensor hidden_prev_t = batch_hidden->Slice(bstart_pre, bstart);
-        gru_value.prevOutValue = hidden_prev_t.data<T>();
+        gru_value.prev_out_value = hidden_prev_t.data<T>();
         Tensor hidden_prev_grad_t = batch_hidden_grad.Slice(bstart_pre, bstart);
-        gru_grad.prevOutGrad = hidden_prev_grad_t.data<T>();
+        gru_grad.prev_out_grad = hidden_prev_grad_t.data<T>();
       }
 
       math::GRUUnitGradFunctor<Place, T>::compute(

paddle/operators/math/detail/gru_gpu_kernel.h

@@ -27,174 +27,174 @@ namespace math {
 namespace detail {
 
 /*
- * threads(framePerBlock, batchPerBlock)
- * grid(frameBlocks, batchBlocks)
+ * threads(frame_per_block, batch_per_block)
+ * grid(frame_blocks, batch_blocks)
  */
-template <class OpResetOutput, bool isBatch, typename T>
-__global__ void KeGruForwardResetOutput(OpResetOutput opResetOutput,
-                                        T *gateValue, T *resetOutputValue,
-                                        T *prevOutputValue, int frameSize,
-                                        int batchSize,
+template <class OpResetOutput, bool is_batch, typename T>
+__global__ void KeGruForwardResetOutput(OpResetOutput op_reset_output,
+                                        T *gate_value, T *reset_output_value,
+                                        T *prev_output_value, int frame_size,
+                                        int batch_size,
                                         activation_mode_t active_gate) {
-  const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
-  if (frameIdx >= frameSize) return;
-
-  int batchIdx = 0;
-  if (isBatch) {
-    batchIdx = blockIdx.y * blockDim.y + threadIdx.y;
-    if (batchIdx >= batchSize) return;
-    gateValue += batchIdx * 3 * frameSize;
-    resetOutputValue += batchIdx * frameSize;
+  const int frame_idx = block_idx.x * block_dim.x + thread_idx.x;
+  if (frame_idx >= frame_size) return;
+
+  int batch_idx = 0;
+  if (is_batch) {
+    batch_idx = block_idx.y * block_dim.y + thread_idx.y;
+    if (batch_idx >= batch_size) return;
+    gate_value += batch_idx * 3 * frame_size;
+    reset_output_value += batch_idx * frame_size;
   }
 
-  T rPrevOut = 0;
-  T rValueResetOutput;
-  T rValueUpdateGate = gateValue[frameIdx + frameSize * 0];
-  T rValueResetGate = gateValue[frameIdx + frameSize * 1];
+  T r_prev_out = 0;
+  T r_value_reset_output;
+  T r_value_update_gate = gate_value[frame_idx + frame_size * 0];
+  T r_value_reset_gate = gate_value[frame_idx + frame_size * 1];
 
-  if (prevOutputValue) {
-    if (isBatch) prevOutputValue += batchIdx * frameSize;
-    rPrevOut = prevOutputValue[frameIdx];
+  if (prev_output_value) {
+    if (is_batch) prev_output_value += batch_idx * frame_size;
+    r_prev_out = prev_output_value[frame_idx];
   }
 
-  opResetOutput(rValueUpdateGate, rValueResetGate, rPrevOut, rValueResetOutput,
-                active_gate);
+  op_reset_output(r_value_update_gate, r_value_reset_gate, r_prev_out,
+                  r_value_reset_output, active_gate);
 
-  gateValue[frameIdx + frameSize * 0] = rValueUpdateGate;
-  gateValue[frameIdx + frameSize * 1] = rValueResetGate;
-  resetOutputValue[frameIdx] = rValueResetOutput;
+  gate_value[frame_idx + frame_size * 0] = r_value_update_gate;
+  gate_value[frame_idx + frame_size * 1] = r_value_reset_gate;
+  reset_output_value[frame_idx] = r_value_reset_output;
 }
 
 /*
- * threads(framePerBlock, batchPerBlock)
- * grid(frameBlocks, batchBlocks)
+ * threads(frame_per_block, batch_per_block)
+ * grid(frame_blocks, batch_blocks)
  */
-template <class OpFinalOutput, bool isBatch, typename T>
-__global__ void KeGruForwardFinalOutput(OpFinalOutput opFinalOutput,
-                                        T *gateValue, T *prevOutputValue,
-                                        T *outputValue, int frameSize,
-                                        int batchSize,
+template <class OpFinalOutput, bool is_batch, typename T>
+__global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
+                                        T *gate_value, T *prev_output_value,
+                                        T *output_value, int frame_size,
+                                        int batch_size,
                                         activation_mode_t active_node) {
-  const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
-  if (frameIdx >= frameSize) return;
-  int batchIdx = 0;
-  if (isBatch) {
-    batchIdx = blockIdx.y * blockDim.y + threadIdx.y;
-    if (batchIdx >= batchSize) return;
-    gateValue += batchIdx * 3 * frameSize;
-    outputValue += batchIdx * frameSize;
+  const int frame_idx = block_idx.x * block_dim.x + thread_idx.x;
+  if (frame_idx >= frame_size) return;
+  int batch_idx = 0;
+  if (is_batch) {
+    batch_idx = block_idx.y * block_dim.y + thread_idx.y;
+    if (batch_idx >= batch_size) return;
+    gate_value += batch_idx * 3 * frame_size;
+    output_value += batch_idx * frame_size;
   }
 
-  T rOutput;
-  T rPrevOut = 0;
-  T rValueUpdateGate = gateValue[frameIdx + frameSize * 0];
-  T rValueFrameState = gateValue[frameIdx + frameSize * 2];
+  T r_output;
+  T r_prev_out = 0;
+  T r_value_update_gate = gate_value[frame_idx + frame_size * 0];
+  T r_value_frame_state = gate_value[frame_idx + frame_size * 2];
 
-  if (prevOutputValue) {
-    if (isBatch) prevOutputValue += batchIdx * frameSize;
-    rPrevOut = prevOutputValue[frameIdx];
+  if (prev_output_value) {
+    if (is_batch) prev_output_value += batch_idx * frame_size;
+    r_prev_out = prev_output_value[frame_idx];
   }
 
-  opFinalOutput(rValueUpdateGate, rValueFrameState, rPrevOut, rOutput,
-                active_node);
+  op_final_output(r_value_update_gate, r_value_frame_state, r_prev_out,
+                  r_output, active_node);
 
-  gateValue[frameIdx + frameSize * 2] = rValueFrameState;
-  outputValue[frameIdx] = rOutput;
+  gate_value[frame_idx + frame_size * 2] = r_value_frame_state;
+  output_value[frame_idx] = r_output;
 }
 
 /*
- * threads(framePerBlock, batchPerBlock)
- * grid(frameBlocks, batchBlocks)
+ * threads(frame_per_block, batch_per_block)
+ * grid(frame_blocks, batch_blocks)
  */
-template <class OpStateGrad, bool isBatch, typename T>
-__global__ void KeGruBackwardStateGrad(OpStateGrad opStateGrad, T *gateValue,
-                                       T *gateGrad, T *prevOutValue,
-                                       T *prevOutGrad, T *outputGrad,
-                                       int frameSize, int batchSize,
+template <class OpStateGrad, bool is_batch, typename T>
+__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,
                                        activation_mode_t active_node) {
-  const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
-  if (frameIdx >= frameSize) return;
-  int batchIdx = 0;
-  if (isBatch) {
-    batchIdx = blockIdx.y * blockDim.y + threadIdx.y;
-    if (batchIdx >= batchSize) return;
-    gateValue += batchIdx * 3 * frameSize;
-    gateGrad += batchIdx * 3 * frameSize;
-    outputGrad += batchIdx * frameSize;
+  const int frame_idx = block_idx.x * block_dim.x + thread_idx.x;
+  if (frame_idx >= frame_size) return;
+  int batch_idx = 0;
+  if (is_batch) {
+    batch_idx = block_idx.y * block_dim.y + thread_idx.y;
+    if (batch_idx >= batch_size) return;
+    gate_value += batch_idx * 3 * frame_size;
+    gate_grad += batch_idx * 3 * frame_size;
+    output_grad += batch_idx * frame_size;
   }
 
-  T rUpdateGateGrad;
-  T rFrameStateGrad;
-  T rPrevOutValue = 0;
-  T rPrevOutGrad = 0;
-  T rUpdateGateValue = gateValue[frameIdx + frameSize * 0];
-  T rFrameStateValue = gateValue[frameIdx + frameSize * 2];
-  T rOutGrad = outputGrad[frameIdx];
+  T r_update_gate_grad;
+  T r_frame_state_grad;
+  T r_prev_out_value = 0;
+  T r_prev_out_grad = 0;
+  T r_update_gate_value = gate_value[frame_idx + frame_size * 0];
+  T r_frame_state_value = gate_value[frame_idx + frame_size * 2];
+  T r_out_grad = output_grad[frame_idx];
 
-  if (prevOutValue && prevOutGrad) {
-    if (isBatch) prevOutValue += batchIdx * frameSize;
-    rPrevOutValue = prevOutValue[frameIdx];
+  if (prev_out_value && prev_out_grad) {
+    if (is_batch) prev_out_value += batch_idx * frame_size;
+    r_prev_out_value = prev_out_value[frame_idx];
 
-    if (isBatch) prevOutGrad += batchIdx * frameSize;
-    rPrevOutGrad = prevOutGrad[frameIdx];
+    if (is_batch) prev_out_grad += batch_idx * frame_size;
+    r_prev_out_grad = prev_out_grad[frame_idx];
   }
 
-  opStateGrad(rUpdateGateValue, rUpdateGateGrad, rFrameStateValue,
-              rFrameStateGrad, rPrevOutValue, rPrevOutGrad, rOutGrad,
-              active_node);
+  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);
 
-  gateGrad[frameIdx + frameSize * 0] = rUpdateGateGrad;
-  gateGrad[frameIdx + frameSize * 2] = rFrameStateGrad;
-  if (prevOutGrad) {
-    prevOutGrad[frameIdx] = rPrevOutGrad;
+  gate_grad[frame_idx + frame_size * 0] = r_update_gate_grad;
+  gate_grad[frame_idx + frame_size * 2] = r_frame_state_grad;
+  if (prev_out_grad) {
+    prev_out_grad[frame_idx] = r_prev_out_grad;
   }
 }
 
 /*
- * threads(framePerBlock, batchPerBlock)
- * grid(frameBlocks, batchBlocks)
+ * threads(frame_per_block, batch_per_block)
+ * grid(frame_blocks, batch_blocks)
  */
-template <class OpResetGrad, bool isBatch, typename T>
-__global__ void KeGruBackwardResetGrad(OpResetGrad opResetGrad, T *gateValue,
-                                       T *gateGrad, T *prevOutValue,
-                                       T *prevOutGrad, T *resetOutputGrad,
-                                       int frameSize, int batchSize,
+template <class OpResetGrad, bool is_batch, typename T>
+__global__ void KeGruBackwardResetGrad(OpResetGrad op_reset_grad, T *gate_value,
+                                       T *gate_grad, T *prev_out_value,
+                                       T *prev_out_grad, T *reset_output_grad,
+                                       int frame_size, int batch_size,
                                        activation_mode_t active_gate) {
-  const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
-  if (frameIdx >= frameSize) return;
-  int batchIdx = 0;
-  if (isBatch) {
-    batchIdx = blockIdx.y * blockDim.y + threadIdx.y;
-    if (batchIdx >= batchSize) return;
-    gateValue += batchIdx * 3 * frameSize;
-    gateGrad += batchIdx * 3 * frameSize;
-    resetOutputGrad += batchIdx * frameSize;
+  const int frame_idx = block_idx.x * block_dim.x + thread_idx.x;
+  if (frame_idx >= frame_size) return;
+  int batch_idx = 0;
+  if (is_batch) {
+    batch_idx = block_idx.y * block_dim.y + thread_idx.y;
+    if (batch_idx >= batch_size) return;
+    gate_value += batch_idx * 3 * frame_size;
+    gate_grad += batch_idx * 3 * frame_size;
+    reset_output_grad += batch_idx * frame_size;
   }
 
-  T rResetGateGrad;
-  T rPrevOutValue = 0;
-  T rPrevOutGrad = 0;
-  T rResetOutputGrad = 0;
-  T rUpdateGateValue = gateValue[frameIdx + frameSize * 0];
-  T rUpdateGateGrad = gateGrad[frameIdx + frameSize * 0];
-  T rResetGateValue = gateValue[frameIdx + frameSize * 1];
-
-  if (prevOutValue && prevOutGrad) {
-    if (isBatch) prevOutValue += batchIdx * frameSize;
-    if (isBatch) prevOutGrad += batchIdx * frameSize;
-    rPrevOutValue = prevOutValue[frameIdx];
-    rPrevOutGrad = prevOutGrad[frameIdx];
-    rResetOutputGrad = resetOutputGrad[frameIdx];
+  T r_reset_gate_grad;
+  T r_prev_out_value = 0;
+  T r_prev_out_grad = 0;
+  T r_reset_output_grad = 0;
+  T r_update_gate_value = gate_value[frame_idx + frame_size * 0];
+  T r_update_gate_grad = gate_grad[frame_idx + frame_size * 0];
+  T r_reset_gate_value = gate_value[frame_idx + frame_size * 1];
+
+  if (prev_out_value && prev_out_grad) {
+    if (is_batch) prev_out_value += batch_idx * frame_size;
+    if (is_batch) prev_out_grad += batch_idx * frame_size;
+    r_prev_out_value = prev_out_value[frame_idx];
+    r_prev_out_grad = prev_out_grad[frame_idx];
+    r_reset_output_grad = reset_output_grad[frame_idx];
   }
 
-  opResetGrad(rUpdateGateValue, rUpdateGateGrad, rResetGateValue,
-              rResetGateGrad, rPrevOutValue, rPrevOutGrad, rResetOutputGrad,
-              active_gate);
+  op_reset_grad(r_update_gate_value, r_update_gate_grad, r_reset_gate_value,
+                r_reset_gate_grad, r_prev_out_value, r_prev_out_grad,
+                r_reset_output_grad, active_gate);
 
-  gateGrad[frameIdx + frameSize * 0] = rUpdateGateGrad;
-  gateGrad[frameIdx + frameSize * 1] = rResetGateGrad;
-  if (prevOutGrad) {
-    prevOutGrad[frameIdx] = rPrevOutGrad;
+  gate_grad[frame_idx + frame_size * 0] = r_update_gate_grad;
+  gate_grad[frame_idx + frame_size * 1] = r_reset_gate_grad;
+  if (prev_out_grad) {
+    prev_out_grad[frame_idx] = r_prev_out_grad;
   }
 }
 }  // namespace detail

paddle/operators/math/detail/gru_kernel.h

@@ -28,23 +28,25 @@ namespace forward {
 template <typename T>
 class gru_resetOutput {
  public:
-  HOSTDEVICE void operator()(T &valueUpdateGate, T &valueResetGate, T &prevOut,
-                             T &valueResetOutput, activation_mode_t actGate) {
-    valueUpdateGate = activation(valueUpdateGate, actGate);
-    valueResetGate = activation(valueResetGate, actGate);
-    valueResetOutput = prevOut * valueResetGate;
+  HOSTDEVICE void operator()(T &value_update_gate, T &value_reset_gate,
+                             T &prev_out, T &value_reset_output,
+                             activation_mode_t act_gate) {
+    value_update_gate = activation(value_update_gate, act_gate);
+    value_reset_gate = activation(value_reset_gate, act_gate);
+    value_reset_output = prev_out * value_reset_gate;
   }
 #ifndef __NVCC__
 #ifndef __AVX__
   static const bool avx = false;
 #else
   static const bool avx = true;
-  HOSTDEVICE void operator()(__m256 &valueUpdateGate, __m256 &valueResetGate,
-                             __m256 &prevOut, __m256 &valueResetOutput,
-                             activation_mode_t actGate) {
-    valueUpdateGate = activation(valueUpdateGate, actGate);
-    valueResetGate = activation(valueResetGate, actGate);
-    valueResetOutput = _mm256_mul_ps(prevOut, valueResetGate);
+  HOSTDEVICE void operator()(__m256 &value_update_gate,
+                             __m256 &value_reset_gate, __m256 &prev_out,
+                             __m256 &value_reset_output,
+                             activation_mode_t act_gate) {
+    value_update_gate = activation(value_update_gate, act_gate);
+    value_reset_gate = activation(value_reset_gate, act_gate);
+    value_reset_output = _mm256_mul_ps(prev_out, value_reset_gate);
   }
 #endif
 #endif
@@ -53,24 +55,26 @@ class gru_resetOutput {
 template <typename T>
 class gru_finalOutput {
  public:
-  HOSTDEVICE void operator()(T &valueUpdateGate, T &valueFrameState, T &prevOut,
-                             T &valueOutput, activation_mode_t actInput) {
-    valueFrameState = activation(valueFrameState, actInput);
-    valueOutput = prevOut - (valueUpdateGate * prevOut) +
-                  (valueUpdateGate * valueFrameState);
+  HOSTDEVICE void operator()(T &value_update_gate, T &value_frame_state,
+                             T &prev_out, T &value_output,
+                             activation_mode_t act_input) {
+    value_frame_state = activation(value_frame_state, act_input);
+    value_output = prev_out - (value_update_gate * prev_out) +
+                   (value_update_gate * value_frame_state);
   }
 #ifndef __NVCC__
 #ifndef __AVX__
   static const bool avx = false;
 #else
   static const bool avx = true;
-  HOSTDEVICE void operator()(__m256 &valueUpdateGate, __m256 &valueFrameState,
-                             __m256 &prevOut, __m256 &valueOutput,
-                             activation_mode_t actInput) {
-    valueFrameState = activation(valueFrameState, actInput);
-    valueOutput = _mm256_add_ps(
-        _mm256_sub_ps(prevOut, _mm256_mul_ps(valueUpdateGate, prevOut)),
-        _mm256_mul_ps(valueUpdateGate, valueFrameState));
+  HOSTDEVICE void operator()(__m256 &value_update_gate,
+                             __m256 &value_frame_state, __m256 &prev_out,
+                             __m256 &value_output,
+                             activation_mode_t act_input) {
+    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));
   }
 #endif
 #endif
@@ -82,34 +86,37 @@ namespace backward {
 template <typename T>
 class gru_stateGrad {
  public:
-  HOSTDEVICE void operator()(T &valueUpdateGate, T &gradUpdateGate,
-                             T &valueFrameState, T &gradFrameState,
-                             T &valuePrevOut, T &gradPrevOut, T &gradOutput,
-                             activation_mode_t actInput) {
-    gradUpdateGate = (gradOutput * valueFrameState);
-    gradUpdateGate -= (gradOutput * valuePrevOut);
-    gradPrevOut -= (gradOutput * valueUpdateGate);
-    gradPrevOut += gradOutput;
-    gradFrameState =
-        activation(gradOutput * valueUpdateGate, valueFrameState, actInput);
+  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, activation_mode_t 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);
   }
 #ifndef __NVCC__
 #ifndef __AVX__
   static const bool avx = false;
 #else
   static const bool avx = true;
-  HOSTDEVICE void operator()(__m256 &valueUpdateGate, __m256 &gradUpdateGate,
-                             __m256 &valueFrameState, __m256 &gradFrameState,
-                             __m256 &valuePrevOut, __m256 &gradPrevOut,
-                             __m256 &gradOutput, activation_mode_t actInput) {
-    gradUpdateGate = _mm256_mul_ps(gradOutput, valueFrameState);
-    gradUpdateGate =
-        _mm256_sub_ps(gradUpdateGate, _mm256_mul_ps(gradOutput, valuePrevOut));
-    gradPrevOut = _mm256_add_ps(
-        _mm256_sub_ps(gradPrevOut, _mm256_mul_ps(gradOutput, valueUpdateGate)),
-        gradOutput);
-    gradFrameState = activation(_mm256_mul_ps(gradOutput, valueUpdateGate),
-                                valueFrameState, actInput);
+  HOSTDEVICE void operator()(__m256 &value_update_gate,
+                             __m256 &grad_update_gate,
+                             __m256 &value_frame_state,
+                             __m256 &grad_frame_state, __m256 &value_prev_out,
+                             __m256 &grad_prev_out, __m256 &grad_output,
+                             activation_mode_t 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);
   }
 #endif
 #endif
@@ -118,30 +125,32 @@ class gru_stateGrad {
 template <typename T>
 class gru_resetGrad {
  public:
-  HOSTDEVICE void operator()(T &valueUpdateGate, T &gradUpdateGate,
-                             T &valueResetGate, T &gradResetGate,
-                             T &valuePrevOut, T &gradPrevOut,
-                             T &gradResetOutput, activation_mode_t actGate) {
-    gradResetGate = (gradResetOutput * valuePrevOut);
-    gradPrevOut += (gradResetOutput * valueResetGate);
-    gradUpdateGate = activation(gradUpdateGate, valueUpdateGate, actGate);
-    gradResetGate = activation(gradResetGate, valueResetGate, actGate);
+  HOSTDEVICE void operator()(T &value_update_gate, T &grad_update_gate,
+                             T &value_reset_gate, T &grad_reset_gate,
+                             T &value_prev_out, T &grad_prev_out,
+                             T &grad_reset_output, activation_mode_t act_gate) {
+    grad_reset_gate = (grad_reset_output * value_prev_out);
+    grad_prev_out += (grad_reset_output * value_reset_gate);
+    grad_update_gate =
+        activation(grad_update_gate, value_update_gate, act_gate);
+    grad_reset_gate = activation(grad_reset_gate, value_reset_gate, act_gate);
   }
 #ifndef __NVCC__
 #ifndef __AVX__
   static const bool avx = false;
 #else
   static const bool avx = true;
-  HOSTDEVICE void operator()(__m256 &valueUpdateGate, __m256 &gradUpdateGate,
-                             __m256 &valueResetGate, __m256 &gradResetGate,
-                             __m256 &valuePrevOut, __m256 &gradPrevOut,
-                             __m256 &gradResetOutput,
-                             activation_mode_t actGate) {
-    gradResetGate = _mm256_mul_ps(gradResetOutput, valuePrevOut);
-    gradPrevOut = _mm256_add_ps(gradPrevOut,
-                                _mm256_mul_ps(gradResetOutput, valueResetGate));
-    gradUpdateGate = activation(gradUpdateGate, valueUpdateGate, actGate);
-    gradResetGate = activation(gradResetGate, valueResetGate, actGate);
+  HOSTDEVICE void operator()(__m256 &value_update_gate,
+                             __m256 &grad_update_gate, __m256 &value_reset_gate,
+                             __m256 &grad_reset_gate, __m256 &value_prev_out,
+                             __m256 &grad_prev_out, __m256 &grad_reset_output,
+                             activation_mode_t act_gate) {
+    grad_reset_gate = _mm256_mul_ps(grad_reset_output, value_prev_out);
+    grad_prev_out = _mm256_add_ps(
+        grad_prev_out, _mm256_mul_ps(grad_reset_output, value_reset_gate));
+    grad_update_gate =
+        activation(grad_update_gate, value_update_gate, act_gate);
+    grad_reset_gate = activation(grad_reset_gate, value_reset_gate, act_gate);
   }
 #endif
 #endif

paddle/operators/math/gru_compute.cc

@@ -21,29 +21,29 @@ namespace math {
 template <typename T>
 struct GRUUnitFunctor<platform::CPUPlace, T> {
   static void compute(const platform::DeviceContext &context,
-                      hl_gru_value<T> value, int frameSize, int batchSize,
+                      hl_gru_value<T> value, int frame_size, int batch_size,
                       activation_mode_t active_node,
                       activation_mode_t active_gate) {
 #ifndef __NVCC__
-    if (value.prevOutValue) {
+    if (value.prev_out_value) {
       math::gemm<platform::CPUPlace, T>(
-          context, false, false, batchSize, frameSize * 2, frameSize, 1,
-          value.prevOutValue, frameSize, value.gateWeight, frameSize * 2, 1,
-          value.gateValue, frameSize * 3);
+          context, false, false, batch_size, frame_size * 2, frame_size, 1,
+          value.prev_out_value, frame_size, value.gate_weight, frame_size * 2,
+          1, value.gate_value, frame_size * 3);
     }
 
     detail::forward_reset_output(detail::forward::gru_resetOutput<T>(), value,
-                                 frameSize, batchSize, active_gate);
+                                 frame_size, batch_size, active_gate);
 
-    if (value.prevOutValue) {
+    if (value.prev_out_value) {
       math::gemm<platform::CPUPlace, T>(
-          context, false, false, batchSize, frameSize, frameSize, 1,
-          value.resetOutputValue, frameSize, value.stateWeight, frameSize, 1,
-          value.gateValue + frameSize * 2, frameSize * 3);
+          context, false, false, batch_size, frame_size, frame_size, 1,
+          value.reset_output_value, frame_size, value.state_weight, frame_size,
+          1, value.gate_value + frame_size * 2, frame_size * 3);
     }
 
     detail::forward_final_output(detail::forward::gru_finalOutput<T>(), value,
-                                 frameSize, batchSize, active_node);
+                                 frame_size, batch_size, active_node);
 #endif
   }
 };
@@ -51,41 +51,43 @@ struct GRUUnitFunctor<platform::CPUPlace, T> {
 template <typename T>
 struct GRUUnitGradFunctor<platform::CPUPlace, T> {
   static void compute(const platform::DeviceContext &context,
-                      hl_gru_value<T> value, hl_gru_grad<T> grad, int frameSize,
-                      int batchSize, activation_mode_t active_node,
+                      hl_gru_value<T> value, hl_gru_grad<T> grad,
+                      int frame_size, int batch_size,
+                      activation_mode_t active_node,
                       activation_mode_t active_gate) {
 #ifndef __NVCC__
     detail::backward_state_grad(detail::backward::gru_stateGrad<T>(), value,
-                                grad, frameSize, batchSize, active_node);
+                                grad, frame_size, batch_size, active_node);
 
-    if (value.prevOutValue && grad.prevOutGrad) {
+    if (value.prev_out_value && grad.prev_out_grad) {
       math::gemm<platform::CPUPlace, T>(
-          context, false, true, batchSize, frameSize, frameSize, 1,
-          grad.gateGrad + frameSize * 2, frameSize * 3, value.stateWeight,
-          frameSize, 0, grad.resetOutputGrad, frameSize);
+          context, false, true, batch_size, frame_size, frame_size, 1,
+          grad.gate_grad + frame_size * 2, frame_size * 3, value.state_weight,
+          frame_size, 0, grad.reset_output_grad, frame_size);
 
-      if (grad.stateWeightGrad) {
+      if (grad.state_weight_grad) {
         math::gemm<platform::CPUPlace, T>(
-            context, true, false, frameSize, frameSize, batchSize, 1,
-            value.resetOutputValue, frameSize, grad.gateGrad + frameSize * 2,
-            frameSize * 3, 1, grad.stateWeightGrad, frameSize);
+            context, true, false, frame_size, frame_size, batch_size, 1,
+            value.reset_output_value, frame_size,
+            grad.gate_grad + frame_size * 2, frame_size * 3, 1,
+            grad.state_weight_grad, frame_size);
       }
     }
 
     detail::backward_reset_grad(detail::backward::gru_resetGrad<T>(), value,
-                                grad, frameSize, batchSize, active_gate);
+                                grad, frame_size, batch_size, active_gate);
 
-    if (grad.prevOutGrad && value.prevOutValue) {
+    if (grad.prev_out_grad && value.prev_out_value) {
       math::gemm<platform::CPUPlace, T>(
-          context, false, true, batchSize, frameSize, frameSize * 2, 1,
-          grad.gateGrad, frameSize * 3, value.gateWeight, frameSize * 2, 1,
-          grad.prevOutGrad, frameSize);
+          context, false, true, batch_size, frame_size, frame_size * 2, 1,
+          grad.gate_grad, frame_size * 3, value.gate_weight, frame_size * 2, 1,
+          grad.prev_out_grad, frame_size);
 
-      if (grad.gateWeightGrad) {
+      if (grad.gate_weight_grad) {
         math::gemm<platform::CPUPlace, T>(
-            context, true, false, frameSize, frameSize * 2, batchSize, 1,
-            value.prevOutValue, frameSize, grad.gateGrad, frameSize * 3, 1,
-            grad.gateWeightGrad, frameSize * 2);
+            context, true, false, frame_size, frame_size * 2, batch_size, 1,
+            value.prev_out_value, frame_size, grad.gate_grad, frame_size * 3, 1,
+            grad.gate_weight_grad, frame_size * 2);
       }
     }
 #endif

paddle/operators/math/gru_compute.cu

@@ -21,66 +21,66 @@ namespace math {
 template <typename T>
 struct GRUUnitFunctor<platform::GPUPlace, T> {
   static void compute(const platform::DeviceContext &context,
-                      hl_gru_value<T> value, int frameSize, int batchSize,
+                      hl_gru_value<T> value, int frame_size, int batch_size,
                       activation_mode_t active_node,
                       activation_mode_t active_gate) {
     auto stream =
         reinterpret_cast<const platform::CUDADeviceContext &>(context).stream();
     dim3 threads;
     dim3 grid;
-    if (batchSize == 1) {
-      int framePerBlock = frameSize <= 1024 ? frameSize : 1024;
-      int frameBlocks = (frameSize + 1024 - 1) / 1024;
-      threads = dim3(framePerBlock, 1);
-      grid = dim3(frameBlocks, 1);
+    if (batch_size == 1) {
+      int frame_per_block = frame_size <= 1024 ? frame_size : 1024;
+      int frame_blocks = (frame_size + 1024 - 1) / 1024;
+      threads = dim3(frame_per_block, 1);
+      grid = dim3(frame_blocks, 1);
     } else {
       threads = dim3(32, 32);
-      grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 32 - 1) / 32);
+      grid = dim3((frame_size + 32 - 1) / 32, (batch_size + 32 - 1) / 32);
     }
 
-    if (value.prevOutValue) {
+    if (value.prev_out_value) {
       math::gemm<platform::GPUPlace, T>(
-          context, false, false, batchSize, frameSize * 2, frameSize, 1,
-          value.prevOutValue, frameSize, value.gateWeight, frameSize * 2, 1,
-          value.gateValue, frameSize * 3);
+          context, false, false, batch_size, frame_size * 2, frame_size, 1,
+          value.prev_out_value, frame_size, value.gate_weight, frame_size * 2,
+          1, value.gate_value, frame_size * 3);
     }
 
-    if (batchSize == 1) {
+    if (batch_size == 1) {
       detail::KeGruForwardResetOutput<detail::forward::gru_resetOutput<T>,
-                                      /* isBatch= */ false,
+                                      /* is_batch= */ false,
                                       T><<<grid, threads, 0, stream>>>(
-          detail::forward::gru_resetOutput<T>(), value.gateValue,
-          value.resetOutputValue, value.prevOutValue, frameSize, batchSize,
-          active_gate);
+          detail::forward::gru_resetOutput<T>(), value.gate_value,
+          value.reset_output_value, value.prev_out_value, frame_size,
+          batch_size, active_gate);
     } else {
       detail::KeGruForwardResetOutput<detail::forward::gru_resetOutput<T>,
-                                      /* isBatch= */ true,
+                                      /* is_batch= */ true,
                                       T><<<grid, threads, 0, stream>>>(
-          detail::forward::gru_resetOutput<T>(), value.gateValue,
-          value.resetOutputValue, value.prevOutValue, frameSize, batchSize,
-          active_gate);
+          detail::forward::gru_resetOutput<T>(), value.gate_value,
+          value.reset_output_value, value.prev_out_value, frame_size,
+          batch_size, active_gate);
     }
 
-    if (value.prevOutValue) {
+    if (value.prev_out_value) {
       math::gemm<platform::GPUPlace, T>(
-          context, false, false, batchSize, frameSize, frameSize, 1,
-          value.resetOutputValue, frameSize, value.stateWeight, frameSize, 1,
-          value.gateValue + frameSize * 2, frameSize * 3);
+          context, false, false, batch_size, frame_size, frame_size, 1,
+          value.reset_output_value, frame_size, value.state_weight, frame_size,
+          1, value.gate_value + frame_size * 2, frame_size * 3);
     }
 
-    if (batchSize == 1) {
+    if (batch_size == 1) {
       detail::KeGruForwardFinalOutput<detail::forward::gru_finalOutput<T>,
-                                      /* isBatch= */ false,
+                                      /* is_batch= */ false,
                                       T><<<grid, threads, 0, stream>>>(
-          detail::forward::gru_finalOutput<T>(), value.gateValue,
-          value.prevOutValue, value.outputValue, frameSize, batchSize,
+          detail::forward::gru_finalOutput<T>(), value.gate_value,
+          value.prev_out_value, value.output_value, frame_size, batch_size,
           active_node);
     } else {
       detail::KeGruForwardFinalOutput<detail::forward::gru_finalOutput<T>,
-                                      /* isBatch= */ true,
+                                      /* is_batch= */ true,
                                       T><<<grid, threads, 0, stream>>>(
-          detail::forward::gru_finalOutput<T>(), value.gateValue,
-          value.prevOutValue, value.outputValue, frameSize, batchSize,
+          detail::forward::gru_finalOutput<T>(), value.gate_value,
+          value.prev_out_value, value.output_value, frame_size, batch_size,
           active_node);
     }
   }
@@ -89,80 +89,82 @@ struct GRUUnitFunctor<platform::GPUPlace, T> {
 template <typename T>
 struct GRUUnitGradFunctor<platform::GPUPlace, T> {
   static void compute(const platform::DeviceContext &context,
-                      hl_gru_value<T> value, hl_gru_grad<T> grad, int frameSize,
-                      int batchSize, activation_mode_t active_node,
+                      hl_gru_value<T> value, hl_gru_grad<T> grad,
+                      int frame_size, int batch_size,
+                      activation_mode_t active_node,
                       activation_mode_t active_gate) {
     auto stream =
         reinterpret_cast<const platform::CUDADeviceContext &>(context).stream();
     dim3 threads;
     dim3 grid;
-    if (batchSize == 1) {
-      int framePerBlock = frameSize <= 1024 ? frameSize : 1024;
-      int frameBlocks = (frameSize + 1024 - 1) / 1024;
-      threads = dim3(framePerBlock, 1);
-      grid = dim3(frameBlocks, 1);
+    if (batch_size == 1) {
+      int frame_per_block = frame_size <= 1024 ? frame_size : 1024;
+      int frame_blocks = (frame_size + 1024 - 1) / 1024;
+      threads = dim3(frame_per_block, 1);
+      grid = dim3(frame_blocks, 1);
     } else {
       threads = dim3(32, 32);
-      grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 32 - 1) / 32);
+      grid = dim3((frame_size + 32 - 1) / 32, (batch_size + 32 - 1) / 32);
     }
 
-    if (batchSize == 1) {
+    if (batch_size == 1) {
       detail::KeGruBackwardStateGrad<
           detail::backward::gru_stateGrad<T>,
-          /* isBatch= */ false><<<grid, threads, 0, stream>>>(
-          detail::backward::gru_stateGrad<T>(), value.gateValue, grad.gateGrad,
-          value.prevOutValue, grad.prevOutGrad, grad.outputGrad, frameSize,
-          batchSize, active_node);
+          /* 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);
     } else {
       detail::KeGruBackwardStateGrad<
           detail::backward::gru_stateGrad<T>,
-          /* isBatch= */ true><<<grid, threads, 0, stream>>>(
-          detail::backward::gru_stateGrad<T>(), value.gateValue, grad.gateGrad,
-          value.prevOutValue, grad.prevOutGrad, grad.outputGrad, frameSize,
-          batchSize, active_node);
+          /* 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);
     }
 
-    if (value.prevOutValue && grad.prevOutGrad) {
+    if (value.prev_out_value && grad.prev_out_grad) {
       math::gemm<platform::GPUPlace, T>(
-          context, false, true, batchSize, frameSize, frameSize, 1,
-          grad.gateGrad + frameSize * 2, frameSize * 3, value.stateWeight,
-          frameSize, 0, grad.resetOutputGrad, frameSize);
+          context, false, true, batch_size, frame_size, frame_size, 1,
+          grad.gate_grad + frame_size * 2, frame_size * 3, value.state_weight,
+          frame_size, 0, grad.reset_output_grad, frame_size);
 
-      if (grad.stateWeightGrad) {
+      if (grad.state_weight_grad) {
         math::gemm<platform::GPUPlace, T>(
-            context, true, false, frameSize, frameSize, batchSize, 1,
-            value.resetOutputValue, frameSize, grad.gateGrad + frameSize * 2,
-            frameSize * 3, 1, grad.stateWeightGrad, frameSize);
+            context, true, false, frame_size, frame_size, batch_size, 1,
+            value.reset_output_value, frame_size,
+            grad.gate_grad + frame_size * 2, frame_size * 3, 1,
+            grad.state_weight_grad, frame_size);
       }
     }
 
-    if (batchSize == 1) {
+    if (batch_size == 1) {
       detail::KeGruBackwardResetGrad<
           detail::backward::gru_resetGrad<T>,
-          /* isBatch= */ false><<<grid, threads, 0, stream>>>(
-          detail::backward::gru_resetGrad<T>(), value.gateValue, grad.gateGrad,
-          value.prevOutValue, grad.prevOutGrad, grad.resetOutputGrad, frameSize,
-          batchSize, active_gate);
+          /* is_batch= */ false><<<grid, threads, 0, stream>>>(
+          detail::backward::gru_resetGrad<T>(), value.gate_value,
+          grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
+          grad.reset_output_grad, frame_size, batch_size, active_gate);
     } else {
       detail::KeGruBackwardResetGrad<
           detail::backward::gru_resetGrad<T>,
-          /* isBatch= */ true><<<grid, threads, 0, stream>>>(
-          detail::backward::gru_resetGrad<T>(), value.gateValue, grad.gateGrad,
-          value.prevOutValue, grad.prevOutGrad, grad.resetOutputGrad, frameSize,
-          batchSize, active_gate);
+          /* is_batch= */ true><<<grid, threads, 0, stream>>>(
+          detail::backward::gru_resetGrad<T>(), value.gate_value,
+          grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
+          grad.reset_output_grad, frame_size, batch_size, active_gate);
     }
 
-    if (grad.prevOutGrad && value.prevOutValue) {
+    if (grad.prev_out_grad && value.prev_out_value) {
       math::gemm<platform::GPUPlace, T>(
-          context, false, true, batchSize, frameSize, frameSize * 2, 1,
-          grad.gateGrad, frameSize * 3, value.gateWeight, frameSize * 2, 1,
-          grad.prevOutGrad, frameSize);
+          context, false, true, batch_size, frame_size, frame_size * 2, 1,
+          grad.gate_grad, frame_size * 3, value.gate_weight, frame_size * 2, 1,
+          grad.prev_out_grad, frame_size);
 
-      if (grad.gateWeightGrad) {
+      if (grad.gate_weight_grad) {
         math::gemm<platform::GPUPlace, T>(
-            context, true, false, frameSize, frameSize * 2, batchSize, 1,
-            value.prevOutValue, frameSize, grad.gateGrad, frameSize * 3, 1,
-            grad.gateWeightGrad, frameSize * 2);
+            context, true, false, frame_size, frame_size * 2, batch_size, 1,
+            value.prev_out_value, frame_size, grad.gate_grad, frame_size * 3, 1,
+            grad.gate_weight_grad, frame_size * 2);
       }
     }
   }

paddle/operators/math/gru_compute.h

@@ -22,28 +22,28 @@ namespace math {
 // TODO(guosheng): refine code style in gru_compute
 template <typename T>
 struct hl_gru_value {
-  T *gateWeight;
-  T *stateWeight;
-  T *gateValue;
-  T *resetOutputValue;
-  T *outputValue;
-  T *prevOutValue;
+  T *gate_weight;
+  T *state_weight;
+  T *gate_value;
+  T *reset_output_value;
+  T *output_value;
+  T *prev_out_value;
 };
 
 template <typename T>
 struct hl_gru_grad {
-  T *gateWeightGrad;
-  T *stateWeightGrad;
-  T *gateGrad;
-  T *resetOutputGrad;
-  T *outputGrad;
-  T *prevOutGrad;
+  T *gate_weight_grad;
+  T *state_weight_grad;
+  T *gate_grad;
+  T *reset_output_grad;
+  T *output_grad;
+  T *prev_out_grad;
 };
 
 template <typename Place, typename T>
 struct GRUUnitFunctor {
   static void compute(const platform::DeviceContext &context,
-                      hl_gru_value<T> value, int frameSize, int batchSize,
+                      hl_gru_value<T> value, int frame_size, int batch_size,
                       activation_mode_t active_node,
                       activation_mode_t active_gate);
 };
@@ -51,8 +51,9 @@ struct GRUUnitFunctor {
 template <typename Place, typename T>
 struct GRUUnitGradFunctor {
   static void compute(const platform::DeviceContext &context,
-                      hl_gru_value<T> value, hl_gru_grad<T> grad, int frameSize,
-                      int batchSize, activation_mode_t active_node,
+                      hl_gru_value<T> value, hl_gru_grad<T> grad,
+                      int frame_size, int batch_size,
+                      activation_mode_t active_node,
                       activation_mode_t active_gate);
 };