Fill zeros for experts with zero counts to preserve shape

tools/imatrix/imatrix.cpp

@@ -132,25 +132,34 @@ static std::vector<float> compute_tensor_averages(const Stats & tstats) {
     const size_t n_mat = tstats.counts.size();
     const size_t len = !tstats.activations.empty() ? tstats.activations.size() : tstats.values.size();
 
-    if (len == 0 || len % n_mat != 0) return {};
+    if (len == 0 || n_mat == 0 || len % n_mat != 0) { return {}; }
+
     const size_t row = len / n_mat;
     std::vector<float> vec;
     vec.reserve(len);
 
     if (tstats.activations.empty()) {
+        // Use mean of squares; fill zeros for experts with zero counts to preserve shape
         for (size_t m = 0; m < n_mat; ++m) {
             const float c = (float)tstats.counts[m];
-            if (c <= 0) return {};
             const size_t off = m * row;
+            if (c <= 0.0f) {
+                vec.insert(vec.end(), row, 0.0f);
+                continue;
+            }
             for (size_t j = 0; j < row; ++j) {
                 vec.push_back(tstats.values[off + j] / c);
             }
         }
     } else {
+        // Use mean; fill zeros for experts with zero counts to preserve shape
         for (size_t m = 0; m < n_mat; ++m) {
             const float c = (float)tstats.counts[m];
-            if (c <= 0) return {};
             const size_t off = m * row;
+            if (c <= 0.0f) {
+                vec.insert(vec.end(), row, 0.0f);
+                continue;
+            }
             for (size_t j = 0; j < row; ++j) {
                 vec.push_back(tstats.activations[off + j] / c);
             }