Add weighted statistics per layer

Author: EAddario

tools/imatrix/imatrix.cpp

@@ -13,6 +13,7 @@
 #include <vector>
 #include <fstream>
 #include <unordered_map>
+#include <map>
 #include <algorithm>
 #include <regex>
 #include <numeric>
@@ -729,18 +730,73 @@ int main(int argc, char ** argv) {
         };
         std::sort(ts.begin(), ts.end(), tensor_comparer());
 
+        struct weighted_stats {
+            float weighted_bias = 0.0f;
+            float weighted_zd = 0.0f;
+            float weighted_cossim = 0.0f;
+            int total_elements = 0;
+        };
+        std::map<int, weighted_stats> ws;
+
         LOG_INF("\nComputing statistics for %s (%d tensors)\n", params.in_files[0].c_str(), static_cast<int>(ts.size()));
         LOG_INF("\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n",
             " Layer", "       Tensor", "          Σ(Bias)", "  Min", "            Max", "           μ", "   σ", " % Active", "N", "   Entropy", "E (norm)", "ZD", "  CosSim");
         LOG_INF("=========================================================================================================================================================================\n");
         for (const auto & tstat : ts) {
             std::string layer, name;
             process_tensor_name(tstat.tensor, layer, name);
+
+            int blk;
+            try {
+                blk = std::stoi(layer);
+            } catch (const std::exception & e) {
+                blk = -1; // not a block layer
+            }
+
             LOG_INF("%5s\t%-20s\t%10.2f\t%8.4f\t%11.4f\t%6.2f\t%6.2f\t%8.2f%%\t%6d\t%10.4f\t%6.2f%%\t%10.2f%%\t%8.4f\n",
                 layer.c_str(), name.c_str(), tstat.total_bias, tstat.min_bias, tstat.max_bias, tstat.mean_bias, tstat.stddev,
                 tstat.active * 100.0f, tstat.elements, tstat.entropy, 100.0f * (tstat.entropy / std::log2(tstat.elements)),
                 100.0f * tstat.zd, tstat.cossim);
+
+            const float weighted_bias   = tstat.elements * tstat.total_bias;
+            const float weighted_zd     = tstat.elements * tstat.zd;
+            const float weighted_cossim = tstat.elements * tstat.cossim;
+
+            if (ws.find(blk) != ws.end()) {
+                ws[blk].weighted_bias += weighted_bias;
+                ws[blk].weighted_zd += weighted_zd;
+                ws[blk].weighted_cossim += weighted_cossim;
+                ws[blk].total_elements += tstat.elements;
+            } else {
+                weighted_stats temp_ws;
+                temp_ws.weighted_bias = weighted_bias;
+                temp_ws.weighted_zd = weighted_zd;
+                temp_ws.weighted_cossim = weighted_cossim;
+                temp_ws.total_elements = tstat.elements;
+                ws[blk] = temp_ws;
+            }
         }
+
+        const int layers = std::count_if(ws.begin(), ws.end(), [](const auto & kv) { return kv.first >= 0; });
+        LOG_INF("\nComputing weighted statistics per layer (%d layers)\n", layers);
+        LOG_INF("\n%s\t%s\t%s\t%s\n", "  Layer", "      Σ(Bias)", "       ZD", "CosSim");
+        LOG_INF("===============================================\n");
+
+        for (const auto & [first, second] : ws) {
+            const auto & layer = first;
+            const auto & stats = second;
+
+            if (stats.total_elements == 0) continue;
+
+            if (layer >= 0) {
+                const float bias = stats.weighted_bias / stats.total_elements;
+                const float zd = stats.weighted_zd / stats.total_elements;
+                const float cossim = stats.weighted_cossim / stats.total_elements;
+
+                LOG_INF("%5d\t%14.2f\t%10.4f%%\t%6.4f\n", layer, bias, 100.0f * zd, cossim);
+            }
+        }
+
         LOG_INF("\n");
 
         return 0;