Fix gru_op related code style

Author: guoshengCS

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(