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llama.cpp
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Save tensor statistics to imatrix file

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Ed Addario 2026-01-22 15:05:15 +00:00
parent 509b1caf40
commit f3323b6503
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1 changed files with 58 additions and 1 deletions
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59
tools/imatrix/imatrix.cpp
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@ -753,11 +753,26 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.activations.size(), GGML_MEM_ALIGN);
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN);
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN);
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * 10, GGML_MEM_ALIGN);
}
// deterministic tensor name order
std::sort(to_store.begin(), to_store.end());
// Compute per-tensor statistics
std::vector<tensor_statistics> tstats;
tstats.reserve(m_stats.size());
bool legacy;
for (const auto & kv : m_stats) {
compute_vector_statistics(tstats, kv.first, kv.second, legacy);
}
if (!tstats.empty()) { compute_tensor_statistics(tstats); }
// index by tensor name
std::unordered_map<std::string, const tensor_statistics *> tstat_index;
tstat_index.reserve(tstats.size());
for (const auto & ts : tstats) { tstat_index[ts.tensor] = &ts; }
struct ggml_init_params params = {
/* .mem_size = */ data_size,
/* .mem_buffer = */ NULL,
@ -814,6 +829,48 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
gguf_add_tensor(ctx_gguf, in_sum);
}
}
// Store per-tensor statistics as a small 1D tensor
{
float nan = std::numeric_limits<float>::quiet_NaN();
float min = 0.0f;
float max = 0.0f;
float mean = 0.0f;
float stddev = 0.0f;
float h_norm = 0.0f;
float zd_score = 0.0f;
float sum_sq = 0.0f;
float l2_dist = 0.0f;
float cossim = 0.0f;
float pcc = 0.0f;
auto it_ts = tstat_index.find(name);
if (it_ts != tstat_index.end() && it_ts->second != nullptr) {
sum_sq = it_ts->second->sum_values;
h_norm = it_ts->second->elements > 0 ? 100.0f * (it_ts->second->entropy / std::log2f((float)it_ts->second->elements)) : nan;
zd_score = it_ts->second->zd_score;
l2_dist = it_ts->second->l2_dist;
cossim = it_ts->second->cossim;
pcc = it_ts->second->pearson;
min = it_ts->second->min_values;
max = it_ts->second->max_values;
mean = it_ts->second->mean_values;
stddev = it_ts->second->std_deviation;
}
struct ggml_tensor * stats_t = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 10);
ggml_format_name(stats_t, "%s.stats", name.c_str());
((float *)stats_t->data)[0] = sum_sq;
((float *)stats_t->data)[1] = h_norm;
((float *)stats_t->data)[2] = zd_score;
((float *)stats_t->data)[3] = l2_dist;
((float *)stats_t->data)[4] = cossim;
((float *)stats_t->data)[5] = pcc;
((float *)stats_t->data)[6] = min;
((float *)stats_t->data)[7] = max;
((float *)stats_t->data)[8] = mean;
((float *)stats_t->data)[9] = stddev;
gguf_add_tensor(ctx_gguf, stats_t);
}
}
gguf_write_to_file(ctx_gguf, fname.c_str(), false);
@ -1404,7 +1461,7 @@ static bool show_statistics(const common_params & params) {
std::string name;
process_tensor_name(tstat.tensor, layer, name);
const float h_norm = tstat.elements > 1 ? 100.0f * (tstat.entropy / std::log2((float) tstat.elements)) : 0.0f;
const float h_norm = tstat.elements > 1 ? 100.0f * (tstat.entropy / std::log2f((float)tstat.elements)) : std::numeric_limits<float>::quiet_NaN();
int blk;
try { blk = std::stoi(layer); } catch (...) { blk = -1; }