happyz
/
llama.cpp
mirror of https://github.com/ggerganov/llama.cpp.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Refactor lambda into compute_tensor_averages() function

This commit is contained in:
Ed Addario 2025-08-03 13:03:21 +01:00
parent 5324558132
commit fce05aac9e
No known key found for this signature in database
GPG Key ID: E7875815A3230993
1 changed files with 37 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

68
tools/imatrix/imatrix.cpp
View File

@ -127,6 +127,39 @@ static void process_tensor_name(const std::string & input, std::string & layer,
}
}
static std::vector<float> compute_tensor_averages(const Stats & tstats) {
if (tstats.counts.empty()) return {};
const size_t n_mat = tstats.counts.size();
const size_t len = !tstats.in_sum.empty() ? tstats.in_sum.size() : tstats.in_sum2.size();
if (len == 0 || len % n_mat != 0) return {};
const size_t row = len / n_mat;
std::vector<float> vec;
vec.reserve(len);
if (!tstats.in_sum.empty()) {
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;
for (size_t j = 0; j < row; ++j) {
vec.push_back(tstats.in_sum[off + j] / c);
}
}
} else {
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;
for (size_t j = 0; j < row; ++j) {
vec.push_back(tstats.in_sum2[off + j] / c);
}
}
}
return vec;
}
static int compute_tensor_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e) {
if (e.in_sum2.size() % e.counts.size() != 0) {
LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.in_sum2.size());
@ -222,33 +255,6 @@ static int compute_tensor_statistics(std::vector<tensor_statistics> & tstats, co
static void compute_layer_statistics(std::vector<tensor_statistics> & tstats) {
static const std::regex pattern(R"(blk\.(\d+)\.)");
auto build_avg = [](const Stats & s) -> std::vector<float> {
if (s.counts.empty()) return {};
const size_t n_mat = s.counts.size();
const size_t len = !s.in_sum.empty() ? s.in_sum.size()
: s.in_sum2.size();
if (len == 0 || len % n_mat != 0) return {};
const size_t row = len / n_mat;
std::vector<float> v;
v.reserve(len);
if (!s.in_sum.empty()) {
for (size_t m = 0; m < n_mat; ++m) {
const float c = (float)s.counts[m];
if (c <= 0) return {};
const size_t off = m*row;
for (size_t j = 0; j < row; ++j) v.push_back(s.in_sum[off+j]/c);
}
} else {
for (size_t m = 0; m < n_mat; ++m) {
const float c = (float)s.counts[m];
if (c <= 0) return {};
const size_t off = m*row;
for (size_t j = 0; j < row; ++j) v.push_back(s.in_sum2[off+j]/c);
}
}
return v;
};
// compute the cosine similarity between the same tensors in consecutive layers
for (auto & ts : tstats) {
ts.cossim = 0;
@ -261,8 +267,8 @@ static void compute_layer_statistics(std::vector<tensor_statistics> & tstats) {
auto prev = std::find_if(tstats.begin(), tstats.end(),
[tname](const tensor_statistics & t) { return t.tensor == tname; });
if (prev == tstats.end()) continue;
const auto curr_avg = build_avg(ts.stats);
const auto prev_avg = build_avg(prev->stats);
const auto curr_avg = compute_tensor_averages(ts.stats);
const auto prev_avg = compute_tensor_averages(prev->stats);
if (curr_avg.size() == prev_avg.size() && !curr_avg.empty()) {
float dot_prod = 0.0f, vec1 = 0.0f, vec2 = 0.0f;
for (size_t i = 0; i < curr_avg.size(); ++i) {
@ -288,8 +294,8 @@ static void compute_layer_statistics(std::vector<tensor_statistics> & tstats) {
auto prev = std::find_if(tstats.begin(), tstats.end(),
[tname](const tensor_statistics & t) { return t.tensor == tname; });
if (prev == tstats.end()) continue;
const auto cur_avg = build_avg(ts.stats);
const auto prev_avg = build_avg(prev->stats);
const auto cur_avg = compute_tensor_averages(ts.stats);
const auto prev_avg = compute_tensor_averages(prev->stats);
if (cur_avg.empty() || prev_avg.empty() || cur_avg.size() != prev_avg.size()) continue;
float dist = 0.0;