Refactor legacy mode

tools/imatrix/imatrix.cpp

@@ -160,7 +160,7 @@ static std::vector<float> compute_tensor_averages(const Stats & tstats) {
     return vec;
 }
 
-static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e) {
+static bool compute_vector_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e) {
     if (e.values.size() % e.counts.size() != 0) {
         LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.values.size());
         return -1;;
@@ -172,7 +172,6 @@ static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, co
 
     const int n_mat = e.counts.size();
     const int row_size = e.values.size() / n_mat;
-    const int calc_mode = e.activations.empty() ? 2 : 1;
 
     std::vector<float> activations;
 
@@ -203,7 +202,15 @@ static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, co
     const float std_deviation   = std::sqrt(std::max(0.0f, variance));
     float entropy               = 0;
 
-    if (calc_mode == 1) {
+    if (e.activations.empty()) {
+        if (sum > 0) {
+            for (const auto act : activations) {
+                if (const float p = act / sum; p > 0) {
+                    entropy -= p * std::log2(p);
+                }
+            }
+        }
+    } else {
         float div = 0.0;
         std::vector<float> weights(activations.size());
         for (size_t i = 0; i < activations.size(); ++i) {
@@ -218,14 +225,6 @@ static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, co
                 if (p > 0.0) entropy -= p * std::log2(p);
             }
         }
-    } else {
-        if (sum > 0) {
-            for (const auto act : activations) {
-                if (const float p = act / sum; p > 0) {
-                    entropy -= p * std::log2(p);
-                }
-            }
-        }
     }
 
     int z_score = 0;
@@ -247,7 +246,7 @@ static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, co
     ts.entropy      = entropy;
     ts.zd_score     = static_cast<float>(z_score) / ts.elements;
 
-    return calc_mode;
+    return e.activations.empty();
 }
 
 static void compute_tensor_statistics(std::vector<tensor_statistics> & tstats) {
@@ -1257,7 +1256,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c
 
 static bool show_statistics(const common_params & params) {
     std::vector<tensor_statistics> ts;
-    int tensor_calc_mode = 0;
+    bool legacy_mode = false;
 
     if (params.in_files.empty() || params.in_files.size() > 1) {
         LOG_ERR("\nError: a single imatrix file is required to compute tensor statistics\n\n");
@@ -1265,7 +1264,7 @@ static bool show_statistics(const common_params & params) {
     }
     if (g_collector.load_imatrix(params.in_files[0].c_str())) {
         for (const auto & [name, stats] :g_collector.get_mstats()) {
-            tensor_calc_mode =compute_vector_statistics(ts, name, stats);
+            legacy_mode = compute_vector_statistics(ts, name, stats);
         }
     } else {
         LOG_ERR("\nError: %s is not a valid imatrix file\n\n", params.in_files[0].c_str());
@@ -1300,7 +1299,7 @@ static bool show_statistics(const common_params & params) {
     LOG_INF("\n%6s\t%18s\t%13s\t%8s\t%8s\t%7s\t%15s\t%13s\t%12s\t%s\t%5s\t%10s\n",
         "Layer",
         "Tensor",
-        tensor_calc_mode == 1 ? "L₂ Norm" : "Σ(Act²)",
+        legacy_mode ? "Σ(Act²)" : "L₂ Norm",
         "Min",
         "Max",
         "μ",
@@ -1327,7 +1326,7 @@ static bool show_statistics(const common_params & params) {
         LOG_INF("%5s\t%-20s\t%11.2f\t%10.4f\t%10.4f\t%8.2f\t%8.2f\t%7d\t%12.4f\t%7.2f%%\t%6.2f%%\t%10.4f\n",
                 layer.c_str(),
                 name.c_str(),
-                tensor_calc_mode == 1 ? tstat.l2_norm : tstat.sum_values,
+                legacy_mode == 1 ? tstat.sum_values : tstat.l2_norm,
                 tstat.min_values,
                 tstat.max_values,
                 tstat.mean_values,
@@ -1361,7 +1360,7 @@ static bool show_statistics(const common_params & params) {
     LOG_INF("\nComputing aggregated statistics per layer (%ld layers)\n", layers);
     LOG_INF("\n%6s\t%13s\t%5s\t%10s\n",
     "Layer",
-    tensor_calc_mode == 1 ? "L₂ Norm" : "Σ(Act²)",
+    legacy_mode ? "Σ(Act²)" : "L₂ Norm",
     "ZD",
     "CosSim");
     LOG_INF("============================================\n");
@@ -1375,7 +1374,7 @@ static bool show_statistics(const common_params & params) {
         const float l2_norm = (ll2n != lyr_l2_norm.end()) ? ll2n->second : 0.0f;
         LOG_INF("%5d\t%11.2f\t%6.2f%%\t%10.4f\n",
             layer,
-            tensor_calc_mode == 1 ? l2_norm: lyr_sum,
+            legacy_mode ? lyr_sum : l2_norm,
             100.0f * lyr_zd,
             lyr_cs);
     }