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llama.cpp
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Fix bug when vectors are zero

This commit is contained in:
Ed Addario 2025-11-06 15:12:09 +00:00
parent 8bd9d87d3e
commit b2b7175e19
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GPG Key ID: E7875815A3230993
1 changed files with 23 additions and 25 deletions
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48
tools/imatrix/imatrix.cpp
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@ -328,11 +328,7 @@ static void compute_layer_statistics(const std::vector<tensor_statistics> & tsta
};
static const std::regex pattern(R"(blk\.(\d+)\.)");
std::unordered_map<std::string, const tensor_statistics*> tidx;
tidx.reserve(tstats.size());
for (const auto & ts : tstats) { tidx[ts.tensor] = &ts; }
std::map<int, layer_aggregation> agr;
std::map<int, layer_aggregation> l_agr;
for (const auto & ts : tstats) {
std::smatch match;
if (!std::regex_search(ts.tensor, match, pattern)) { continue; }
@ -341,36 +337,35 @@ static void compute_layer_statistics(const std::vector<tensor_statistics> & tsta
std::string prev_lyr(ts.tensor);
prev_lyr.replace(match.position(1), match.length(1), std::to_string(blk - 1));
if (tidx.find(prev_lyr) == tidx.end()) { continue; }
auto it_curr = stats_map.find(ts.tensor);
auto it_prev = stats_map.find(prev_lyr);
if (it_curr == stats_map.end() || it_prev == stats_map.end()) { continue; }
const auto curr_avg = compute_tensor_averages(it_curr->second);
const auto prev_avg = compute_tensor_averages(it_prev->second);
if (curr_avg.empty() || prev_avg.empty() || curr_avg.size() != prev_avg.size()) { continue; }
if (curr_avg.empty() || prev_avg.empty()) { continue; }
// Allow minor length mismatches by using the overlap
const size_t n = std::min(curr_avg.size(), prev_avg.size());
if (n == 0) { continue; }
// Compute statistics for each tensor pair individually
double dot_prod = 0.0;
double norm1_sq = 0.0;
double norm2_sq = 0.0;
double l2_dist_sq = 0.0;
for (size_t i = 0; i < curr_avg.size(); ++i) {
const double c_val = curr_avg[i];
const double p_val = prev_avg[i];
dot_prod += c_val * p_val;
norm1_sq += c_val * c_val;
norm2_sq += p_val * p_val;
const double diff = c_val - p_val;
l2_dist_sq += diff * diff;
for (size_t i = 0; i < n; ++i) {
const double a = curr_avg[i];
const double b = prev_avg[i];
dot_prod += a * b;
norm1_sq += a * a;
norm2_sq += b * b;
const double d = a - b;
l2_dist_sq += d * d;
}
if (norm1_sq == 0.0 && norm2_sq == 0.0) { continue; }
// Accumulate statistics for the layer
auto & entry = agr[blk];
auto & entry = l_agr[blk];
entry.sum_dot_prod += dot_prod;
entry.sum_norm1_sq += norm1_sq;
entry.sum_norm2_sq += norm2_sq;
@ -379,23 +374,26 @@ static void compute_layer_statistics(const std::vector<tensor_statistics> & tsta
}
// Compute aggregated layer statistics
for (auto & kv : agr) {
for (const auto & kv : l_agr) {
const int layer = kv.first;
const auto & agg = kv.second;
if (agg.n_tensors == 0) { continue; }
// Compute aggregated Cosine Similarity
float cossim = 0.0f;
if (agg.sum_norm1_sq > 0.0 && agg.sum_norm2_sq > 0.0) {
cossim = agg.sum_dot_prod / (std::sqrt(agg.sum_norm1_sq) * std::sqrt(agg.sum_norm2_sq));
cossim = (float)(agg.sum_dot_prod / (std::sqrt(agg.sum_norm1_sq) * std::sqrt(agg.sum_norm2_sq)));
cossim = std::min(cossim, 1.0f);
cossim = std::max(cossim, -1.0f);
} else if (agg.sum_norm1_sq == 0.0 && agg.sum_norm2_sq == 0.0) {
cossim = 1.0f;
cossim = 1.0f; // both vectors are zero then CosSim is 1
} else {
cossim = 0.0f; // One zero and the other non-zero then CosSim is 0
}
layer_cossim[kv.first] = cossim;
// Compute aggregated L2 Distance (Euclidean Distance)
layer_l2_dist[kv.first] = (float)std::sqrt(agg.sum_l2_dist_sq);
layer_cossim[layer] = cossim;
layer_l2_dist[layer] = (float)std::sqrt(agg.sum_l2_dist_sq);
}
}