quantize: add imatrix and dataset metadata in GGUF (#6658)
* imatrix: save the dataset file used in the output file * llama: support kv overrides type string string * common: factorize KV Overrides parsing between common and server * quantize: add imatrix n entries and dataset KV metadata quantize: factorize KV Overrides parsing between common #6656 * llama: remove kv override str_value initialization as it does not compile on some toolchain * quantize: add imatrix m_last_call as `quantize.imatrix.chunks_count` * quantize: add imatrix filename in KV * llama: add llama_model_kv_override_free * common: add llama_model_kv_override_free common: free kv override if used after model loading * llama: finally move the string KV override value to the stack * llama : minor * no need to add a NUL to the std::vector, std::string can be initialized from a pair of iterators. Co-authored-by: slaren <slarengh@gmail.com> * kv override: ensure string termination --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Pierrick Hymbert
GitHub
parent
017e6999b5
commit
0c4d489e29
9 changed files with 186 additions and 171 deletions
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@ -23,6 +23,7 @@ struct Stats {
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};
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struct StatParams {
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std::string dataset;
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std::string ofile = "imatrix.dat";
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int n_output_frequency = 10;
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int verbosity = 1;
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@ -46,7 +47,7 @@ private:
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std::vector<float> m_src1_data;
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std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
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//
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void save_imatrix(const char * file_name) const;
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void save_imatrix(const char * file_name, const char * dataset) const;
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void keep_imatrix(int ncall) const;
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};
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@ -199,7 +200,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
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}
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void IMatrixCollector::save_imatrix() const {
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save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
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save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
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}
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void IMatrixCollector::keep_imatrix(int ncall) const {
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@ -207,24 +208,33 @@ void IMatrixCollector::keep_imatrix(int ncall) const {
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if (file_name.empty()) file_name = "imatrix.dat";
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file_name += ".at_";
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file_name += std::to_string(ncall);
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save_imatrix(file_name.c_str());
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save_imatrix(file_name.c_str(), m_params.dataset.c_str());
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}
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void IMatrixCollector::save_imatrix(const char * fname) const {
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void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
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std::ofstream out(fname, std::ios::binary);
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int n_entries = m_stats.size();
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out.write((const char*)&n_entries, sizeof(n_entries));
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for (auto& p : m_stats) {
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out.write((const char *) &n_entries, sizeof(n_entries));
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for (const auto & p : m_stats) {
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int len = p.first.size();
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out.write((const char*)&len, sizeof(len));
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out.write((const char *) &len, sizeof(len));
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out.write(p.first.c_str(), len);
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out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
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out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
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int nval = p.second.values.size();
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out.write((const char*)&nval, sizeof(nval));
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if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
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out.write((const char *) &nval, sizeof(nval));
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if (nval > 0) out.write((const char *) p.second.values.data(), nval * sizeof(float));
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}
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// Write the number of call the matrix was computed with
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out.write((const char *) &m_last_call, sizeof(m_last_call));
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// Write the dataset name at the end of the file to later on specify it in quantize
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int n_dataset = strlen(dataset);
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out.write((const char *) &n_dataset, sizeof(n_dataset));
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out.write(dataset, n_dataset);
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if (m_params.verbosity > 0) {
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fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
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fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
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}
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}
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@ -547,6 +557,29 @@ int main(int argc, char ** argv) {
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}
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}
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gpt_params params;
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params.n_batch = 512;
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if (!gpt_params_parse(args.size(), args.data(), params)) {
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return 1;
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}
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params.logits_all = true;
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params.n_batch = std::min(params.n_batch, params.n_ctx);
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print_build_info();
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if (params.seed == LLAMA_DEFAULT_SEED) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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sparams.dataset = params.prompt_file;
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g_collector.set_parameters(std::move(sparams));
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if (!combine_files.empty()) {
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@ -585,28 +618,6 @@ int main(int argc, char ** argv) {
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}
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}
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gpt_params params;
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params.n_batch = 512;
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if (!gpt_params_parse(args.size(), args.data(), params)) {
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return 1;
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}
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params.logits_all = true;
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params.n_batch = std::min(params.n_batch, params.n_ctx);
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print_build_info();
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if (params.seed == LLAMA_DEFAULT_SEED) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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llama_backend_init();
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llama_numa_init(params.numa);
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@ -1,6 +1,6 @@
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set(TARGET quantize)
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add_executable(${TARGET} quantize.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
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target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
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target_include_directories(${TARGET} PRIVATE ../../common)
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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@ -8,7 +8,6 @@
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#include <unordered_map>
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#include <fstream>
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#include <cmath>
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#include <algorithm>
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struct quant_option {
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std::string name;
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@ -53,6 +52,10 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", },
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};
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static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file";
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static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset";
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static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count";
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static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count";
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static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
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std::string ftype_str;
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@ -113,7 +116,7 @@ static void usage(const char * executable) {
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exit(1);
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}
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static void load_imatrix(const std::string & imatrix_file, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
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static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
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std::ifstream in(imatrix_file.c_str(), std::ios::binary);
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if (!in) {
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printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
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@ -160,18 +163,33 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
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printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
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}
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}
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printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
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// latest imatrix version contains the dataset filename at the end of the file
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int m_last_call = 0;
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if (in.peek() != EOF) {
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in.read((char *)&m_last_call, sizeof(m_last_call));
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int dataset_len;
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in.read((char *)&dataset_len, sizeof(dataset_len));
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std::vector<char> dataset_as_vec(dataset_len);
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in.read(dataset_as_vec.data(), dataset_len);
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imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
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printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
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}
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printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
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return m_last_call;
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}
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static void prepare_imatrix(const std::string & imatrix_file,
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static int prepare_imatrix(const std::string & imatrix_file,
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std::string & imatrix_dataset,
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const std::vector<std::string> & included_weights,
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const std::vector<std::string> & excluded_weights,
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std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
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int m_last_call = -1;
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if (!imatrix_file.empty()) {
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load_imatrix(imatrix_file, imatrix_data);
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m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data);
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}
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if (imatrix_data.empty()) {
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return;
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return m_last_call;
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}
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if (!excluded_weights.empty()) {
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for (auto& name : excluded_weights) {
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@ -197,6 +215,7 @@ static void prepare_imatrix(const std::string & imatrix_file,
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if (!imatrix_data.empty()) {
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printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
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}
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return m_last_call;
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}
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static ggml_type parse_ggml_type(const char * arg) {
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@ -211,43 +230,6 @@ static ggml_type parse_ggml_type(const char * arg) {
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return result;
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}
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static bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
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const char* sep = strchr(data, '=');
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if (sep == nullptr || sep - data >= 128) {
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fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
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return false;
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}
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llama_model_kv_override kvo;
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std::strncpy(kvo.key, data, sep - data);
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kvo.key[sep - data] = 0;
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sep++;
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if (strncmp(sep, "int:", 4) == 0) {
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sep += 4;
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
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kvo.int_value = std::atol(sep);
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} else if (strncmp(sep, "float:", 6) == 0) {
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sep += 6;
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
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kvo.float_value = std::atof(sep);
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} else if (strncmp(sep, "bool:", 5) == 0) {
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sep += 5;
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
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if (std::strcmp(sep, "true") == 0) {
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kvo.bool_value = true;
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} else if (std::strcmp(sep, "false") == 0) {
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kvo.bool_value = false;
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} else {
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fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
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return false;
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}
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} else {
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fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
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return false;
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}
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overrides.emplace_back(std::move(kvo));
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return true;
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}
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int main(int argc, char ** argv) {
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if (argc < 3) {
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usage(argv[0]);
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@ -316,10 +298,43 @@ int main(int argc, char ** argv) {
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usage(argv[0]);
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}
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std::string imatrix_dataset;
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std::unordered_map<std::string, std::vector<float>> imatrix_data;
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prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data);
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int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
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if (!imatrix_data.empty()) {
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params.imatrix = &imatrix_data;
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{
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llama_model_kv_override kvo;
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std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
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strncpy(kvo.val_str, imatrix_file.c_str(), 127);
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kvo.val_str[127] = '\0';
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kv_overrides.emplace_back(std::move(kvo));
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}
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if (!imatrix_dataset.empty()) {
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llama_model_kv_override kvo;
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std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
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strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
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kvo.val_str[127] = '\0';
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kv_overrides.emplace_back(std::move(kvo));
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}
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{
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llama_model_kv_override kvo;
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std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
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kvo.val_i64 = imatrix_data.size();
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kv_overrides.emplace_back(std::move(kvo));
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}
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if (m_last_call > 0) {
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llama_model_kv_override kvo;
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std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
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kvo.val_i64 = m_last_call;
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kv_overrides.emplace_back(std::move(kvo));
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}
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}
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if (!kv_overrides.empty()) {
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kv_overrides.emplace_back();
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@ -2392,7 +2392,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
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printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict);
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printf(" --override-kv KEY=TYPE:VALUE\n");
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printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
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printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
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printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
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printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
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printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
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printf(" --chat-template JINJA_TEMPLATE\n");
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@ -2823,43 +2823,11 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
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invalid_param = true;
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break;
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}
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char * sep = strchr(argv[i], '=');
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if (sep == nullptr || sep - argv[i] >= 128) {
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fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
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invalid_param = true;
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break;
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}
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struct llama_model_kv_override kvo;
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std::strncpy(kvo.key, argv[i], sep - argv[i]);
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kvo.key[sep - argv[i]] = 0;
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sep++;
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if (strncmp(sep, "int:", 4) == 0) {
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sep += 4;
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
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kvo.int_value = std::atol(sep);
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} else if (strncmp(sep, "float:", 6) == 0) {
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sep += 6;
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
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kvo.float_value = std::atof(sep);
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} else if (strncmp(sep, "bool:", 5) == 0) {
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sep += 5;
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
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if (std::strcmp(sep, "true") == 0) {
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kvo.bool_value = true;
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} else if (std::strcmp(sep, "false") == 0) {
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kvo.bool_value = false;
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} else {
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fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
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invalid_param = true;
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break;
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}
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} else {
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if (!parse_kv_override(argv[i], params.kv_overrides)) {
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fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
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invalid_param = true;
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break;
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}
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params.kv_overrides.push_back(kvo);
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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server_print_usage(argv[0], default_params, default_sparams);
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