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Move duplicated imatrix code into single common imatrix-loader.cpp (#22445)

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* Deduplicate imatrix loading code

* Add back LLAMA_TRACE, early exit on quantize missing metadata
This commit is contained in:
Bartowski
2026-06-04 17:45:40 +02:00
committed by GitHub
parent 21444c822e
commit e7bcf1c3a8
5 changed files with 303 additions and 359 deletions
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tools/imatrix/imatrix.cpp
+50 -194
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@@ -1,5 +1,6 @@
#include "arg.h"
#include "common.h"
#include "imatrix-loader.h"
#include "log.h"
#include "llama.h"
#include "gguf.h"
@@ -34,10 +35,6 @@ static void print_usage(int, char ** argv) {
LOG("\n");
}
static const char * const LLM_KV_IMATRIX_DATASETS = "imatrix.datasets";
static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
struct Stats {
std::vector<float> values;
std::vector<int64_t> counts;
@@ -65,7 +62,6 @@ public:
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix_legacy(int32_t ncall = -1) const;
void save_imatrix(int32_t n_chunk = -1) const;
bool load_imatrix_legacy(const char * fname);
bool load_imatrix(const char * file_name);
const std::unordered_map<std::string, Stats> & get_mstats() const { return m_stats; }
private:
@@ -624,204 +620,63 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
ggml_free(ctx);
}
bool IMatrixCollector::load_imatrix_legacy(const char * fname) {
std::ifstream in(fname, std::ios::binary);
if (!in) {
LOG_ERR("%s: failed to open %s\n", __func__, fname);
return false;
}
int n_entries;
in.read((char *) &n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
LOG_ERR("%s: no data in file %s\n", __func__, fname);
return false;
}
// Guess the chunk size because it's not stored in the file
const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
for (int i = 0; i < n_entries; ++i) {
int32_t len = 0;
in.read((char *) &len, sizeof(len));
std::vector<char> name_as_vec(len + 1);
in.read((char *) name_as_vec.data(), len);
if (in.fail()) {
LOG_ERR("%s: failed reading name for entry %d from %s\n", __func__, i + 1, fname);
return false;
}
name_as_vec[len] = 0;
std::string name{ name_as_vec.data() };
auto & e = m_stats[std::move(name)];
int32_t ncall = 0;
in.read((char *) &ncall, sizeof(ncall));
int32_t nval = 0;
in.read((char *) &nval, sizeof(nval));
if (in.fail() || nval < 1) {
LOG_ERR("%s: failed reading number of values for entry %d\n", __func__, i);
m_stats = {};
return false;
}
if (e.values.empty()) {
e.values.resize(nval, 0.0f);
e.counts.resize(1, 0);
}
std::vector<float> tmp(nval);
in.read((char *) tmp.data(), nval * sizeof(float));
if (in.fail()) {
LOG_ERR("%s: failed reading data for entry %d\n", __func__, i);
m_stats = {};
return false;
}
// Recreate the state as expected by save_imatrix(), and correct for weighted sum.
for (int i = 0; i < nval; i++) {
e.values[i] += tmp[i] * chunk_size;
}
// The legacy format doesn't distinguish the counts for different experts
for (size_t j = 0; j < e.counts.size(); ++j) {
e.counts[j] += ncall * chunk_size;
}
}
{
// TODO: extract into its own method; this is also used by the GGUF-based format
// Calculate the last chunk count
int64_t max_count = 0;
for (const auto & stats : m_stats) {
for (int64_t count : stats.second.counts) {
if (count > max_count) {
max_count = count;
}
}
}
m_last_chunk = max_count / (chunk_size);
}
{
// Read the number of calls the matrix was computed with
int32_t n_calls;
in.read((char *) &n_calls, sizeof(n_calls));
// ignore it because it's not important
}
// Read the dataset path to include it when writing to GGUF
if (!in.fail()){
int32_t len = 0;
in.read((char *) &len, sizeof(len));
if (!in.fail()) {
std::vector<char> dataset;
dataset.resize(len + 1, 0);
in.read(dataset.data(), len);
if (!in.fail()) {
m_datasets.push_back(dataset.data());
}
}
}
return true;
}
// Using GGUF as the file format, for greater extensibility
bool IMatrixCollector::load_imatrix(const char * file_name) {
struct ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ false, // the data is needed
/* .ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params);
if (!ctx_gguf) {
return this->load_imatrix_legacy(file_name);
}
const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
if (n_entries < 1) {
LOG_ERR("%s: no data in file %s\n", __func__, file_name);
gguf_free(ctx_gguf);
ggml_free(ctx);
common_imatrix loaded;
if (!common_imatrix_load(file_name, loaded)) {
return false;
}
const int64_t datasets_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS);
if (datasets_key != -1 && gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key);
m_datasets.reserve(m_datasets.size() + n);
for (int64_t i = 0; i < n; ++i) {
m_datasets.push_back(gguf_get_arr_str(ctx_gguf, datasets_key, i));
}
}
const std::string in_sum2_suffix{ ".in_sum2" };
const std::string counts_suffix{ ".counts" };
// Could re-use m_stats instead, but this allows
// checking for completeness of *each* loaded imatrix file
// and also makes it easier to re-use a similar implementation in quantize.cpp
// Using an ordered map to get a deterministic iteration order.
std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
std::string name = cur->name;
if (name.empty()) { continue; }
if (string_remove_suffix(name, in_sum2_suffix)) {
// in_sum2
sums_counts_for[std::move(name)].first = cur;
} else if (string_remove_suffix(name, counts_suffix)) {
// counts
sums_counts_for[std::move(name)].second = cur;
} else {
// ignore other tensors
}
}
for (const auto & sc : sums_counts_for) {
const std::string & name = sc.first;
const struct ggml_tensor * in_sum2 = sc.second.first;
const struct ggml_tensor * counts = sc.second.second;
if (!in_sum2 || !counts) {
LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
const bool is_legacy = loaded.is_legacy;
for (auto & [name, entry] : loaded.entries) {
auto & e = m_stats[name];
int64_t nval = ggml_nelements(in_sum2);
if (e.values.empty()) {
e.values.resize(nval, 0.0f);
} 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.values.size());
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
if (is_legacy) {
// Legacy format: sums contain (raw_sum/raw_count)*ncall, counts contain {ncall}
// Reconstruct raw form by multiplying by chunk_size
if (e.values.empty()) {
e.values.resize(entry.sums.size(), 0.0f);
e.counts.resize(1, 0);
}
for (size_t j = 0; j < entry.sums.size(); ++j) {
e.values[j] += entry.sums[j] * chunk_size;
}
for (size_t j = 0; j < e.counts.size(); ++j) {
e.counts[j] += entry.counts[0] * chunk_size;
}
} else {
// GGUF format: raw sums and counts, accumulate directly
const int64_t nval = entry.sums.size();
const int64_t ncounts = entry.counts.size();
int64_t ncounts = ggml_nelements(counts);
if (e.counts.empty()) {
e.counts.resize(ncounts, 0);
} else if (e.counts.size() == 1 && ncounts > 1) {
// broadcast, when loading an old imatrix
e.counts.resize(ncounts, e.counts[0]);
} else if ((size_t) ncounts != e.counts.size()) {
LOG_ERR("%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) ncounts, e.counts.size());
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
if (e.values.empty()) {
e.values.resize(nval, 0.0f);
} 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.values.size());
return false;
}
// Recreate the state as expected by save_imatrix()
for (int64_t j = 0; j < nval; j++) {
e.values[j] += ((const float *) in_sum2->data)[j];
}
for (int64_t j = 0; j < ncounts; j++) {
e.counts[j] += std::lround(((const float *) counts->data)[j]);
if (e.counts.empty()) {
e.counts.resize(ncounts, 0);
} else if (e.counts.size() == 1 && ncounts > 1) {
e.counts.resize(ncounts, e.counts[0]);
} else if ((size_t) ncounts != e.counts.size()) {
LOG_ERR("%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) ncounts, e.counts.size());
return false;
}
for (int64_t j = 0; j < nval; ++j) {
e.values[j] += entry.sums[j];
}
for (int64_t j = 0; j < ncounts; ++j) {
e.counts[j] += entry.counts[j];
}
}
}
// TODO: extract into its own method; this is also used by the legacy format
m_datasets.insert(m_datasets.end(), loaded.datasets.begin(), loaded.datasets.end());
// Calculate the last chunk count
int64_t max_count = 0;
for (const auto & stats : m_stats) {
@@ -831,10 +686,8 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
}
}
}
m_last_chunk = max_count / (m_params.n_ctx / m_params.n_parallel);
m_last_chunk = max_count / chunk_size;
gguf_free(ctx_gguf);
ggml_free(ctx);
return true;
}
@@ -1218,6 +1071,9 @@ int main(int argc, char ** argv) {
return 1;
}
// set_params before show_statistics so load_imatrix has valid n_ctx/n_parallel
g_collector.set_params(params);
if (params.show_statistics) {
if (!show_statistics(params)) {
return 1;