refine cpu gru batch mode

paddle/fluid/operators/math/sequence2batch.h

@@ -92,7 +92,7 @@ class LoDTensor2BatchFunctor {
     // Calculate the start position of each batch.
     // example:  sequences = {s0, s1, s2}
     //           s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
-    //           num_batch = 5,
+    //           max_seqlen = 5,
     //           batchIndex = {b0, b1, b2, b3, b4}
     //           b0: 1 0 2, b1: 1 0 2, b2: 1 0 2, b3: 1 0, b4: 1
     //           batch_start_positions[6] = {0, 3, 6, 9, 11, 12}
@@ -109,7 +109,7 @@ class LoDTensor2BatchFunctor {
     //               where 1 is the second sequence,
     //                     0 is the first sequence,
     //                     2 is the third sequence.
-    // The num_batch represents batch size after rearranging the
+    // The max_seqlen represents batch size after rearranging the
     // input LodTensor. It is also the maximum length of input sequence.
 
     paddle::framework::LoD batch_lods;
@@ -118,8 +118,8 @@ class LoDTensor2BatchFunctor {
     batch_lods.emplace_back(std::vector<size_t>{0});
 
     // batch_lods[0] is the start positions for batch LoDTensor
-    int num_batch = seq_info[0].length;
-    batch_lods[0].resize(static_cast<size_t>(num_batch + 1));
+    int max_seqlen = seq_info[0].length;
+    batch_lods[0].resize(static_cast<size_t>(max_seqlen + 1));
     // batch_lods[1] is the raw index in the input LoDTensor
     batch_lods[1].resize(static_cast<size_t>(lod_tensor.dims()[0]));
     // batch_lods[2] is the sort order for the input LoDTensor.
@@ -128,7 +128,7 @@ class LoDTensor2BatchFunctor {
     size_t* batch_starts = batch_lods[0].data();
     size_t* seq2batch_idx = batch_lods[1].data();
     batch_starts[0] = 0;
-    for (int n = 0; n < num_batch; n++) {
+    for (int n = 0; n < max_seqlen; n++) {
       auto batch_id = static_cast<int>(batch_starts[n]);
       for (size_t i = 0; i < seq_info.size(); ++i) {
         int seq_len = seq_info[i].length;