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llama.cpp
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Refactor variable name

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Ed Addario 2025-08-04 22:15:50 +01:00
parent f1c2a4ca3f
commit c39c4e2a33
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1 changed files with 35 additions and 35 deletions
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70
tools/imatrix/imatrix.cpp
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@ -40,7 +40,7 @@ static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
struct Stats { struct Stats {
std::vector<float> in_sum; std::vector<float> in_sum;
std::vector<float> in_sum2; std::vector<float> values;
std::vector<int64_t> counts; std::vector<int64_t> counts;
}; };
@ -130,7 +130,7 @@ 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.in_sum.empty() ? tstats.in_sum.size() : tstats.in_sum2.size(); const size_t len = !tstats.in_sum.empty() ? tstats.in_sum.size() : tstats.values.size();
if (len == 0 || len % n_mat != 0) return {}; if (len == 0 || len % n_mat != 0) return {};
const size_t row = len / n_mat; const size_t row = len / n_mat;
@ -152,7 +152,7 @@ static std::vector<float> compute_tensor_averages(const Stats & tstats) {
if (c <= 0) return {}; if (c <= 0) return {};
const size_t off = m * row; const size_t off = m * row;
for (size_t j = 0; j < row; ++j) { for (size_t j = 0; j < row; ++j) {
vec.push_back(tstats.in_sum2[off + j] / c); vec.push_back(tstats.values[off + j] / c);
} }
} }
} }
@ -161,8 +161,8 @@ static std::vector<float> compute_tensor_averages(const Stats & tstats) {
} }
static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e) { static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e) {
if (e.in_sum2.size() % e.counts.size() != 0) { if (e.values.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()); LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.values.size());
return -1;; return -1;;
} }
if (e.counts.empty()) { if (e.counts.empty()) {
@ -171,17 +171,17 @@ static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, co
} }
const int n_mat = e.counts.size(); const int n_mat = e.counts.size();
const int row_size = e.in_sum2.size() / n_mat; const int row_size = e.values.size() / n_mat;
const int calc_mode = e.in_sum.empty() ? 2 : 1; const int calc_mode = e.in_sum.empty() ? 2 : 1;
std::vector<float> activations; std::vector<float> activations;
if (e.in_sum.empty()) { if (e.in_sum.empty()) {
activations.reserve(e.in_sum2.size()); activations.reserve(e.values.size());
for (int i = 0; i < n_mat; ++i) { for (int i = 0; i < n_mat; ++i) {
for (int j = 0; j < row_size; ++j) { for (int j = 0; j < row_size; ++j) {
activations.push_back(e.in_sum2[i*row_size + j] / e.counts[i]); activations.push_back(e.values[i*row_size + j] / e.counts[i]);
} }
} }
} else { } else {
@ -420,13 +420,13 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
// broadcast, when loading an old imatrix // broadcast, when loading an old imatrix
e.counts.resize(n_as, e.counts[0]); e.counts.resize(n_as, e.counts[0]);
} }
if (e.in_sum2.empty()) { if (e.values.empty()) {
e.in_sum.resize(src1->ne[0]*n_as, 0); e.in_sum.resize(src1->ne[0]*n_as, 0);
e.in_sum2.resize(src1->ne[0]*n_as, 0); e.values.resize(src1->ne[0]*n_as, 0);
e.counts.resize(n_as, 0); e.counts.resize(n_as, 0);
} }
else if (e.in_sum2.size() != (size_t)src1->ne[0]*n_as) { else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.in_sum2.size(), (int)(src1->ne[0]*n_as)); LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)(src1->ne[0]*n_as));
exit(1); //GGML_ABORT("fatal error"); exit(1); //GGML_ABORT("fatal error");
} }
else if (e.counts.size() != (size_t)n_as) { else if (e.counts.size() != (size_t)n_as) {
@ -454,9 +454,9 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
for (int64_t j = 0; j < src1->ne[0]; ++j) { for (int64_t j = 0; j < src1->ne[0]; ++j) {
e.in_sum[e_start + j] += x[j]; e.in_sum[e_start + j] += x[j];
e.in_sum2[e_start + j] += x[j] * x[j]; e.values[e_start + j] += x[j] * x[j];
if (!std::isfinite((float)e.in_sum2[e_start + j])) { if (!std::isfinite((float)e.values[e_start + j])) {
LOG_ERR("%f detected in %s\n", (float)e.in_sum2[e_start + j], wname.c_str()); LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
exit(1); exit(1);
} }
} }
@ -478,13 +478,13 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
auto & e = m_stats[wname]; auto & e = m_stats[wname];
const int64_t n_mat = src1->ne[2] * src1->ne[3]; const int64_t n_mat = src1->ne[2] * src1->ne[3];
if (e.in_sum2.empty()) { if (e.values.empty()) {
e.in_sum.resize(src1->ne[0] * n_mat, 0); e.in_sum.resize(src1->ne[0] * n_mat, 0);
e.in_sum2.resize(src1->ne[0] * n_mat, 0); e.values.resize(src1->ne[0] * n_mat, 0);
e.counts.resize(n_mat, 0); e.counts.resize(n_mat, 0);
} }
else if (e.in_sum2.size() != (size_t)(src1->ne[0] * n_mat)) { else if (e.values.size() != (size_t)(src1->ne[0] * n_mat)) {
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.in_sum2.size(), (int)(src1->ne[0] * n_mat)); LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)(src1->ne[0] * n_mat));
exit(1); //GGML_ABORT("fatal error"); exit(1); //GGML_ABORT("fatal error");
} }
else if (e.counts.size() != (size_t)n_mat) { else if (e.counts.size() != (size_t)n_mat) {
@ -502,9 +502,9 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
e.counts[mat_id]++; e.counts[mat_id]++;
for (int64_t j = 0; j < src1->ne[0]; ++j) { for (int64_t j = 0; j < src1->ne[0]; ++j) {
e.in_sum[mat_start + j] += x[j]; e.in_sum[mat_start + j] += x[j];
e.in_sum2[mat_start + j] += x[j] * x[j]; e.values[mat_start + j] += x[j] * x[j];
if (!std::isfinite((float)e.in_sum2[j])) { if (!std::isfinite((float)e.values[j])) {
LOG_ERR("%f detected in %s\n", (float)e.in_sum2[j], wname.c_str()); LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str());
exit(1); exit(1);
} }
} }
@ -593,14 +593,14 @@ void IMatrixCollector::save_imatrix_legacy(int32_t ncall) const {
// ceiling division to avoid accidental zeros // ceiling division to avoid accidental zeros
const int32_t ncall = (*std::max_element(stat.counts.begin(), stat.counts.end()) + (chunk_size - 1)) / chunk_size; const int32_t ncall = (*std::max_element(stat.counts.begin(), stat.counts.end()) + (chunk_size - 1)) / chunk_size;
out.write((const char *) &ncall, sizeof(ncall)); out.write((const char *) &ncall, sizeof(ncall));
const int32_t nval = stat.in_sum2.size(); const int32_t nval = stat.values.size();
const int32_t nmat = stat.counts.size(); const int32_t nmat = stat.counts.size();
out.write((const char *) &nval, sizeof(nval)); out.write((const char *) &nval, sizeof(nval));
if (nval > 0 && nmat > 0) { if (nval > 0 && nmat > 0) {
std::vector<float> tmp(nval); std::vector<float> tmp(nval);
for (int32_t i = 0; i < nval; i++) { for (int32_t i = 0; i < nval; i++) {
float count = static_cast<float>(stat.counts[i / (nval / nmat)]); float count = static_cast<float>(stat.counts[i / (nval / nmat)]);
float value = stat.in_sum2[i]; float value = stat.values[i];
if (count == 0.0f) { if (count == 0.0f) {
// store 1 for partial data // store 1 for partial data
value = 1.0f; value = 1.0f;
@ -676,7 +676,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
to_store.push_back(kv.first); to_store.push_back(kv.first);
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.in_sum.size(), GGML_MEM_ALIGN); data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.in_sum.size(), GGML_MEM_ALIGN);
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.in_sum2.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) * kv.second.counts.size(), GGML_MEM_ALIGN);
} }
@ -711,7 +711,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
for (const auto & name : to_store) { for (const auto & name : to_store) {
const auto & stat = m_stats.at(name); const auto & stat = m_stats.at(name);
const int32_t nval = (int32_t) stat.in_sum2.size(); const int32_t nval = (int32_t) stat.values.size();
const int32_t nmat = (int32_t) stat.counts.size(); const int32_t nmat = (int32_t) stat.counts.size();
if (nval > 0 && nmat > 0) { if (nval > 0 && nmat > 0) {
struct ggml_tensor * in_sum2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat); struct ggml_tensor * in_sum2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat);
@ -720,7 +720,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
ggml_format_name(counts, "%s.counts", name.c_str()); ggml_format_name(counts, "%s.counts", name.c_str());
for (int32_t j = 0; j < nval; ++j) { for (int32_t j = 0; j < nval; ++j) {
((float *) in_sum2->data)[j] = (float) stat.in_sum2[j]; ((float *) in_sum2->data)[j] = (float) stat.values[j];
} }
for (int32_t j = 0; j < nmat; ++j) { for (int32_t j = 0; j < nmat; ++j) {
((float *) counts->data)[j] = (float) stat.counts[j]; ((float *) counts->data)[j] = (float) stat.counts[j];
@ -787,8 +787,8 @@ bool IMatrixCollector::load_imatrix_legacy(const char * fname) {
return false; return false;
} }
if (e.in_sum2.empty()) { if (e.values.empty()) {
e.in_sum2.resize(nval, 0.0f); e.values.resize(nval, 0.0f);
e.counts.resize(1, 0); e.counts.resize(1, 0);
} }
@ -802,7 +802,7 @@ bool IMatrixCollector::load_imatrix_legacy(const char * fname) {
// Recreate the state as expected by save_imatrix(), and correct for weighted sum. // Recreate the state as expected by save_imatrix(), and correct for weighted sum.
for (int i = 0; i < nval; i++) { for (int i = 0; i < nval; i++) {
e.in_sum2[i] += tmp[i] * chunk_size; e.values[i] += tmp[i] * chunk_size;
} }
// The legacy format doesn't distinguish the counts for different experts // The legacy format doesn't distinguish the counts for different experts
for (size_t j = 0; j < e.counts.size(); ++j) { for (size_t j = 0; j < e.counts.size(); ++j) {
@ -922,11 +922,11 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
auto & e = m_stats[name]; auto & e = m_stats[name];
int64_t nval = ggml_nelements(in_sum2); int64_t nval = ggml_nelements(in_sum2);
if (e.in_sum2.empty()) { if (e.values.empty()) {
e.in_sum2.resize(nval, 0.0f); e.values.resize(nval, 0.0f);
e.in_sum.resize(nval, 0.0f); e.in_sum.resize(nval, 0.0f);
} else if ((size_t) nval != e.in_sum2.size()) { } else if ((size_t) nval != e.values.size()) {
LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.in_sum2.size()); LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.values.size());
gguf_free(ctx_gguf); gguf_free(ctx_gguf);
ggml_free(ctx); ggml_free(ctx);
return false; return false;
@ -947,7 +947,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
// Recreate the state as expected by save_imatrix() // Recreate the state as expected by save_imatrix()
for (int64_t j = 0; j < nval; j++) { for (int64_t j = 0; j < nval; j++) {
e.in_sum2[j] += ((const float *) in_sum2->data)[j]; e.values[j] += ((const float *) in_sum2->data)[j];
} }
for (int64_t j = 0; j < ncounts; j++) { for (int64_t j = 0; j < ncounts; j++) {
e.counts[j] += std::lround(((const float *) counts->data)[j]); e.counts[j] += std::lround(((const float *) counts->data)[j]);