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llama.cpp
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Minor refactoring

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Ed Addario 2025-10-28 23:03:52 +00:00
parent 0b0381c94c
commit b5068df804
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1 changed files with 9 additions and 15 deletions
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22
tools/imatrix/imatrix.cpp
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@ -128,41 +128,35 @@ static void process_tensor_name(const std::string & input, std::string & layer,
} }
static std::vector<float> compute_tensor_averages(const Stats & tstats) { static std::vector<float> compute_tensor_averages(const Stats & tstats) {
if (tstats.counts.empty()) return {}; if (tstats.counts.empty()) { return {}; }
const size_t n_mat = tstats.counts.size(); const size_t n_mat = tstats.counts.size();
const size_t len = !tstats.activations.empty() ? tstats.activations.size() : tstats.values.size(); const size_t len = !tstats.activations.empty() ? tstats.activations.size() : tstats.values.size();
if (len == 0 || n_mat == 0 || len % n_mat != 0) { return {}; } if (len == 0 || n_mat == 0 || len % n_mat != 0) { return {}; }
const size_t row = len / n_mat; const size_t row = len / n_mat;
std::vector<float> vec; std::vector<float> vec;
vec.reserve(len); vec.reserve(len);
if (tstats.activations.empty()) { if (tstats.activations.empty()) {
// Use mean of squares; fill zeros for experts with zero counts to preserve shape // Mean of squares
for (size_t m = 0; m < n_mat; ++m) { for (size_t m = 0; m < n_mat; ++m) {
const float c = (float)tstats.counts[m]; const auto c = (float)tstats.counts[m];
const size_t off = m * row; const size_t off = m * row;
if (c <= 0.0f) { if (c <= 0.0f) {
vec.insert(vec.end(), row, 0.0f); vec.insert(vec.end(), row, 0.0f); // zero-fill rows for experts with zero count to preserve shape
continue; continue;
} }
for (size_t j = 0; j < row; ++j) { for (size_t j = 0; j < row; ++j) { vec.push_back(tstats.values[off + j] / c); }
vec.push_back(tstats.values[off + j] / c);
}
} }
} else { } else {
// Use mean; fill zeros for experts with zero counts to preserve shape // Mean
for (size_t m = 0; m < n_mat; ++m) { for (size_t m = 0; m < n_mat; ++m) {
const float c = (float) tstats.counts[m]; const float c = (float) tstats.counts[m];
const size_t off = m * row; const size_t off = m * row;
if (c <= 0.0f) { if (c <= 0.0f) {
vec.insert(vec.end(), row, 0.0f); vec.insert(vec.end(), row, 0.0f); // zero-fill rows for experts with zero count to preserve shape
continue; continue;
} }
for (size_t j = 0; j < row; ++j) { for (size_t j = 0; j < row; ++j) { vec.push_back(tstats.activations[off + j] / c); }
vec.push_back(tstats.activations[off + j] / c);
}
} }
} }