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llama.cpp
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Fix legacy_mode getting overwritten on each tensor bug

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Ed Addario 2025-10-28 18:27:19 +00:00
parent 8fd2aca8ec
commit af3b6aca22
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1 changed files with 48 additions and 43 deletions
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91
tools/imatrix/imatrix.cpp
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@ -1260,15 +1260,16 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c
static bool show_statistics(const common_params & params) {
std::vector<tensor_statistics> ts;
bool legacy_mode = false;
bool legacy_mode = true;
if (params.in_files.empty() || params.in_files.size() > 1) {
LOG_ERR("\nError: a single imatrix file is required to compute tensor statistics\n\n");
return false;
}
if (g_collector.load_imatrix(params.in_files[0].c_str())) {
for (const auto & [name, stats] :g_collector.get_mstats()) {
legacy_mode = compute_vector_statistics(ts, name, stats);
for (const auto & [name, stats] : g_collector.get_mstats()) {
const bool is_legacy = compute_vector_statistics(ts, name, stats);
legacy_mode = legacy_mode && is_legacy;
}
} else {
LOG_ERR("\nError: %s is not a valid imatrix file\n\n", params.in_files[0].c_str());
@ -1286,19 +1287,21 @@ static bool show_statistics(const common_params & params) {
explicit tensor_comparer(const bool legacy) : legacy_mode(legacy) {}
bool operator()(const tensor_statistics & a, const tensor_statistics & b) const {
std::string layer, name_a, name_b;
std::string layer;
std::string name_a;
std::string name_b;
process_tensor_name(a.tensor, layer, name_a);
process_tensor_name(b.tensor, layer, name_b);
return legacy_mode ? name_a < name_b || (name_a == name_b && a.sum_values > b.sum_values) :
name_a < name_b || (name_a == name_b && a.cossim > b.cossim);
return legacy_mode ? name_a < name_b || (name_a == name_b && a.sum_values > b.sum_values)
: name_a < name_b || (name_a == name_b && a.cossim > b.cossim);
}
};
std::sort(ts.begin(), ts.end(), tensor_comparer(legacy_mode));
struct layer_stats {
float layer_sum = 0.0f;
float layer_zd = 0.0f;
int n = 0;
float layer_zd = 0.0f;
int n = 0;
};
std::map<int, layer_stats> ls;
@ -1319,6 +1322,7 @@ static bool show_statistics(const common_params & params) {
LOG_INF(
"=============================================================================================================="
"=============================================================\n");
for (const auto & tstat : ts) {
std::string layer, name;
process_tensor_name(tstat.tensor, layer, name);
@ -1326,36 +1330,37 @@ static bool show_statistics(const common_params & params) {
int blk;
try {
blk = std::stoi(layer);
} catch (const std::exception & e) {
blk = -1; // not a block layer
} catch (const std::exception &) {
blk = -1; // not a block layer
}
const float h_norm = tstat.elements > 1 ? 100.0f * (tstat.entropy / std::log2((float) tstat.elements)) : 0.0f;
const float ecs = 100.0f * std::exp(-0.01f * tstat.l2_norm) * std::pow(std::fabs(tstat.cossim), 10.0f);
LOG_INF("%5s\t%-20s\t%11.2f\t%10.4f\t%10.4f\t%8.2f\t%8.2f\t%7d\t%10.2f%%\t%10.4f\t%6.2f%%\t%10.4f\n",
layer.c_str(),
name.c_str(),
legacy_mode ? tstat.sum_values : tstat.l2_norm,
tstat.min_values,
tstat.max_values,
tstat.mean_values,
tstat.std_deviation,
tstat.elements,
100.0f * (tstat.entropy / std::log2(tstat.elements)),
legacy_mode ? tstat.entropy : 100.0f * std::exp(-0.01f * tstat.l2_norm) * std::pow(fabs(tstat.cossim), 10.0f),
100.0f * tstat.zd_score,
tstat.cossim);
const float zd = tstat.elements * tstat.zd_score;
layer.c_str(), name.c_str(),
legacy_mode ? tstat.sum_values : tstat.l2_norm,
tstat.min_values,
tstat.max_values,
tstat.mean_values,
tstat.std_deviation,
tstat.elements,
h_norm,
legacy_mode ? tstat.entropy : ecs,
100.0f * tstat.zd_score,
tstat.cossim);
const float zd = tstat.elements * tstat.zd_score;
if (ls.find(blk) != ls.end()) {
ls[blk].layer_sum += tstat.sum_values;
ls[blk].layer_zd += zd;
ls[blk].n += tstat.elements;
ls[blk].layer_zd += zd;
ls[blk].n += tstat.elements;
} else {
layer_stats temp_ls;
temp_ls.layer_sum = tstat.sum_values;
temp_ls.layer_zd = zd;
temp_ls.n = tstat.elements;
ls[blk] = temp_ls;
temp_ls.layer_zd = zd;
temp_ls.n = tstat.elements;
ls[blk] = temp_ls;
}
}
@ -1366,11 +1371,11 @@ static bool show_statistics(const common_params & params) {
const auto layers = std::count_if(ls.begin(), ls.end(), [](const auto & kv) { return kv.first >= 0; });
LOG_INF("\nComputing layer statistics (%ld layers)\n", layers);
LOG_INF("\n%6s\t%13s\t%6s\t%11s\t%6s\n",
"Layer",
legacy_mode ? "Σ(Act²)" : "L₂ Norm",
"ZD",
"CosSim",
legacy_mode ? "" : "ECS");
"Layer",
legacy_mode ? "Σ(Act²)" : "L₂ Norm",
"ZD",
"CosSim",
legacy_mode ? "" : "ECS");
if (legacy_mode) {
LOG_INF("============================================\n");
} else {
@ -1384,17 +1389,17 @@ static bool show_statistics(const common_params & params) {
const float layer_l2n = ll2n != layer_l2_norm.end() ? ll2n->second : 0.0f;
if (legacy_mode) {
LOG_INF("%5d\t%11.2f\t%6.2f%%\t%11.4f\n",
layer,
stats.layer_sum,
100.0f * stats.layer_zd / stats.n,
layer_cs);
layer,
stats.layer_sum,
100.0f * stats.layer_zd / stats.n,
layer_cs);
} else {
LOG_INF("%5d\t%11.2f\t%6.2f%%\t%11.4f\t%8.4f\n",
layer,
layer_l2n,
100.0f * stats.layer_zd / stats.n,
layer_cs,
100.0f * std::exp(-0.01f * layer_l2n) * std::pow(fabs(layer_cs), 10.0f));
layer,
layer_l2n,
100.0f * stats.layer_zd / stats.n,
layer_cs,
100.0f * std::exp(-0.01f * layer_l2n) * std::pow(std::fabs(layer_cs), 10.0f));
}
}
LOG_INF("\n");