follow comments, refine comment and function name

paddle/gserver/layers/MKLPackedRecurrentLayer.cpp

@@ -59,7 +59,7 @@ void MKLPackedRecurrentLayer::forwardBatch(int batchSize,
         MatrixPtr preBatchValue =
             batchValue_->getBatchValue(n - 1, batchValue->getHeight());
 
-        packed_weight_->compute(batchValue, preBatchValue);
+        packed_weight_->gemm_compute(preBatchValue, batchValue);
       }
       Argument arg;
       arg.value = batchValue;
@@ -96,7 +96,7 @@ void MKLPackedRecurrentLayer::backwardBatch(int batchSize,
 
       if (n != 0) {
         batchValue = batchGrad_->getBatchValue(n - 1, batchGrad->getHeight());
-        packed_weightT_->compute(batchValue, batchGrad);
+        packed_weightT_->gemm_compute(batchGrad, batchValue);
       }
 
       if (backwardByBatch && weight_->getWGrad()) {

paddle/gserver/layers/MKLPackedRecurrentLayer.h

@@ -22,8 +22,8 @@ DECLARE_bool(rnn_use_batch);
 namespace paddle {
 
 /**
- * @brief MKLPackedRecurrentLayer is same with RecurrentLayer but is optimized
- * with MKL cblas packed gemm.
+ * @brief MKLPackedRecurrentLayer is almost the same with RecurrentLayer
+ * but is optimized with MKL cblas packed gemm.
  * More details:
  * https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/mkl/mkl_packed.md
  */
@@ -48,7 +48,7 @@ protected:
                      const int* starts) override;
 
 protected:
-  /// packed_weight_ is contains same data with
+  /// packed_weight_ contains same data with
   /// RecurrentLayer::weight_ but is packed
   std::unique_ptr<MKLPackedWeight> packed_weight_;
   /// packed_weightT_ is the transposition matrix of packed_weight_

paddle/gserver/layers/MKLPackedWeight.h

@@ -22,9 +22,9 @@ namespace paddle {
 
 class MKLPackedWeight {
 protected:
-  /// The pointor of weight
+  /// The pointer of weight
   real *weight_;
-  /// The pointor of cblas packed gemm to weight
+  /// The pointer of cblas packed gemm to weight
   real *packedWeight_;
   size_t height_;
   size_t width_;
@@ -43,7 +43,7 @@ public:
 
   void pack() { pack_(weight_); }
 
-  void compute(MatrixPtr dst, const MatrixPtr src) {
+  void gemm_compute(const MatrixPtr src, MatrixPtr dst) {
     cblas_sgemm_compute(CblasRowMajor,
                         CblasNoTrans,
                         CblasPacked,