improve naming due to codemod

faiss/IndexAdditiveQuantizerFastScan.cpp

@@ -32,30 +32,31 @@ IndexAdditiveQuantizerFastScan::IndexAdditiveQuantizerFastScan(
 }
 
 void IndexAdditiveQuantizerFastScan::init(
-        AdditiveQuantizer* aq_2,
+        AdditiveQuantizer* aq_init,
         MetricType metric,
         int bbs) {
-    FAISS_THROW_IF_NOT(aq_2 != nullptr);
-    FAISS_THROW_IF_NOT(!aq_2->nbits.empty());
-    FAISS_THROW_IF_NOT(aq_2->nbits[0] == 4);
+    FAISS_THROW_IF_NOT(aq_init != nullptr);
+    FAISS_THROW_IF_NOT(!aq_init->nbits.empty());
+    FAISS_THROW_IF_NOT(aq_init->nbits[0] == 4);
     if (metric == METRIC_INNER_PRODUCT) {
         FAISS_THROW_IF_NOT_MSG(
-                aq_2->search_type == AdditiveQuantizer::ST_LUT_nonorm,
+                aq_init->search_type == AdditiveQuantizer::ST_LUT_nonorm,
                 "Search type must be ST_LUT_nonorm for IP metric");
     } else {
         FAISS_THROW_IF_NOT_MSG(
-                aq_2->search_type == AdditiveQuantizer::ST_norm_lsq2x4 ||
-                        aq_2->search_type == AdditiveQuantizer::ST_norm_rq2x4,
+                aq_init->search_type == AdditiveQuantizer::ST_norm_lsq2x4 ||
+                        aq_init->search_type ==
+                                AdditiveQuantizer::ST_norm_rq2x4,
                 "Search type must be lsq2x4 or rq2x4 for L2 metric");
     }
 
-    this->aq = aq_2;
+    this->aq = aq_init;
     if (metric == METRIC_L2) {
-        M = aq_2->M + 2; // 2x4 bits AQ
+        M = aq_init->M + 2; // 2x4 bits AQ
     } else {
-        M = aq_2->M;
+        M = aq_init->M;
     }
-    init_fastscan(aq_2->d, M, 4, metric, bbs);
+    init_fastscan(aq_init->d, M, 4, metric, bbs);
 
     max_train_points = 1024 * ksub * M;
 }

faiss/utils/NeuralNet.cpp

@@ -212,19 +212,23 @@ nn::Int32Tensor2D QINCoStep::encode(
     // repeated codebook
     Tensor2D zqs_r(n * K, d);  // size n, K, d
     Tensor2D cc(n * K, d * 2); // size n, K, d * 2
-    size_t d_2 = this->d;
 
-    auto copy_row = [d_2](Tensor2D& t, size_t i, size_t j, const float* data) {
-        assert(i <= t.shape[0] && j <= t.shape[1]);
-        memcpy(t.data() + i * t.shape[1] + j, data, sizeof(float) * d_2);
-    };
+    size_t local_d = this->d;
+
+    auto copy_row =
+            [local_d](Tensor2D& t, size_t i, size_t j, const float* data) {
+                assert(i <= t.shape[0] && j <= t.shape[1]);
+                memcpy(t.data() + i * t.shape[1] + j,
+                       data,
+                       sizeof(float) * local_d);
+            };
 
     // manual broadcasting
     for (size_t i = 0; i < n; i++) {
         for (size_t j = 0; j < K; j++) {
-            copy_row(zqs_r, i * K + j, 0, codebook.data() + j * d_2);
-            copy_row(cc, i * K + j, 0, codebook.data() + j * d_2);
-            copy_row(cc, i * K + j, d_2, xhat.data() + i * d_2);
+            copy_row(zqs_r, i * K + j, 0, codebook.data() + j * d);
+            copy_row(cc, i * K + j, 0, codebook.data() + j * d);
+            copy_row(cc, i * K + j, d, xhat.data() + i * d);
         }
     }
 
@@ -237,13 +241,13 @@ nn::Int32Tensor2D QINCoStep::encode(
 
     // add the xhat
     for (size_t i = 0; i < n; i++) {
-        float* zqs_r_row = zqs_r.data() + i * K * d_2;
-        const float* xhat_row = xhat.data() + i * d_2;
+        float* zqs_r_row = zqs_r.data() + i * K * d;
+        const float* xhat_row = xhat.data() + i * d;
         for (size_t l = 0; l < K; l++) {
-            for (size_t j = 0; j < d_2; j++) {
+            for (size_t j = 0; j < d; j++) {
                 zqs_r_row[j] += xhat_row[j];
             }
-            zqs_r_row += d_2;
+            zqs_r_row += d;
         }
     }
 
@@ -252,31 +256,31 @@ nn::Int32Tensor2D QINCoStep::encode(
     float* res = nullptr;
     if (residuals) {
         FAISS_THROW_IF_NOT(
-                residuals->shape[0] == n && residuals->shape[1] == d_2);
+                residuals->shape[0] == n && residuals->shape[1] == d);
         res = residuals->data();
     }
 
     for (size_t i = 0; i < n; i++) {
-        const float* q = x.data() + i * d_2;
-        const float* db = zqs_r.data() + i * K * d_2;
+        const float* q = x.data() + i * d;
+        const float* db = zqs_r.data() + i * K * d;
         float dis_min = HUGE_VALF;
         int64_t idx = -1;
         for (size_t j = 0; j < K; j++) {
-            float dis = fvec_L2sqr(q, db, d_2);
+            float dis = fvec_L2sqr(q, db, d);
             if (dis < dis_min) {
                 dis_min = dis;
                 idx = j;
             }
-            db += d_2;
+            db += d;
         }
         codes.v[i] = idx;
         if (res) {
-            const float* xhat_row = xhat.data() + i * d_2;
-            const float* xhat_next_row = zqs_r.data() + (i * K + idx) * d_2;
-            for (size_t j = 0; j < d_2; j++) {
+            const float* xhat_row = xhat.data() + i * d;
+            const float* xhat_next_row = zqs_r.data() + (i * K + idx) * d;
+            for (size_t j = 0; j < d; j++) {
                 res[j] = xhat_next_row[j] - xhat_row[j];
             }
-            res += d_2;
+            res += d;
         }
     }
     return codes;

faiss/utils/utils.cpp

@@ -589,9 +589,9 @@ int64_t count_gt(int64_t n, const T* row, T threshold) {
 } // namespace
 
 template <typename T>
-void CombinerRangeKNN<T>::compute_sizes(int64_t* L_res_2) {
-    this->L_res = L_res_2;
-    L_res_2[0] = 0;
+void CombinerRangeKNN<T>::compute_sizes(int64_t* L_res_init) {
+    this->L_res = L_res_init;
+    L_res_init[0] = 0;
     int64_t j = 0;
     for (int64_t i = 0; i < nq; i++) {
         int64_t n_in;
@@ -602,11 +602,11 @@ void CombinerRangeKNN<T>::compute_sizes(int64_t* L_res_2) {
             n_in = lim_remain[j + 1] - lim_remain[j];
             j++;
         }
-        L_res_2[i + 1] = n_in; // L_res_2[i] + n_in;
+        L_res_init[i + 1] = n_in; // L_res_init[i] + n_in;
     }
     // cumsum
     for (int64_t i = 0; i < nq; i++) {
-        L_res_2[i + 1] += L_res_2[i];
+        L_res_init[i + 1] += L_res_init[i];
     }
 }