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https://github.com/ggerganov/llama.cpp.git
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Adding some imatrix tools (#5302)
* imatrix: adding --combine and --continue-from * imatrix: be able to start from a specific chunk --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@ -36,6 +36,8 @@ public:
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void set_parameters(StatParams&& params) { m_params = std::move(params); }
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bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
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void save_imatrix() const;
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bool load_imatrix(const char * file_name, bool add);
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static bool load_imatrix(const char * file_name, std::unordered_map<std::string, Stats>& imatrix);
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private:
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std::unordered_map<std::string, Stats> m_stats;
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StatParams m_params;
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@ -189,6 +191,57 @@ void IMatrixCollector::save_imatrix(const char * fname) const {
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}
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}
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bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map<std::string, Stats>& imatrix_data) {
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std::ifstream in(imatrix_file, std::ios::binary);
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if (!in) {
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printf("%s: failed to open %s\n",__func__,imatrix_file);
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return false;
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}
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int n_entries;
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in.read((char*)&n_entries, sizeof(n_entries));
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if (in.fail() || n_entries < 1) {
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printf("%s: no data in file %s\n", __func__, imatrix_file);
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return false;
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}
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for (int i = 0; i < n_entries; ++i) {
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int len; in.read((char *)&len, sizeof(len));
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std::vector<char> name_as_vec(len+1);
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in.read((char *)name_as_vec.data(), len);
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if (in.fail()) {
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printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file);
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return false;
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}
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name_as_vec[len] = 0;
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std::string name{name_as_vec.data()};
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auto& e = imatrix_data[std::move(name)];
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int ncall;
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in.read((char*)&ncall, sizeof(ncall));
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int nval;
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in.read((char *)&nval, sizeof(nval));
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if (in.fail() || nval < 1) {
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printf("%s: failed reading number of values for entry %d\n",__func__,i);
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imatrix_data = {};
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return false;
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}
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e.values.resize(nval);
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in.read((char*)e.values.data(), nval*sizeof(float));
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if (in.fail()) {
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printf("%s: failed reading data for entry %d\n",__func__,i);
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imatrix_data = {};
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return false;
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}
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e.ncall = ncall;
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}
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return true;
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}
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bool IMatrixCollector::load_imatrix(const char * file_name, bool add) {
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if (!add) {
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m_stats.clear();
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}
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return load_imatrix(file_name, m_stats);
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}
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static IMatrixCollector g_collector;
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static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
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@ -269,7 +322,7 @@ static void process_logits(
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}
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}
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static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl) {
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static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
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const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
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const int n_ctx = llama_n_ctx(ctx);
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@ -282,6 +335,15 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
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auto tim2 = std::chrono::high_resolution_clock::now();
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fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
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if (from_chunk > 0) {
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if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) {
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fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk);
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return false;
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}
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fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx);
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tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx);
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}
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if (int(tokens.size()) < 2*n_ctx) {
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fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx,
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n_ctx);
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@ -402,7 +464,10 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
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int main(int argc, char ** argv) {
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StatParams sparams;
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std::string prev_result_file;
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std::string combine_files;
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bool compute_ppl = true;
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int from_chunk = 0;
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std::vector<char*> args;
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args.push_back(argv[0]);
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int iarg = 1;
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@ -423,6 +488,13 @@ int main(int argc, char ** argv) {
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compute_ppl = false;
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} else if (arg == "--keep-imatrix") {
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sparams.keep_every = std::stoi(argv[++iarg]);
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} else if (arg == "--continue-from") {
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prev_result_file = argv[++iarg];
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} else if (arg == "--combine") {
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combine_files = argv[++iarg];
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}
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else if (arg == "--from-chunk") {
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from_chunk = std::stoi(argv[++iarg]);
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} else {
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args.push_back(argv[iarg]);
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}
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@ -436,14 +508,50 @@ int main(int argc, char ** argv) {
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}
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}
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g_collector.set_parameters(std::move(sparams));
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if (!combine_files.empty()) {
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std::vector<std::string> files;
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size_t pos = 0;
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while (true) {
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auto new_pos = combine_files.find(',', pos);
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if (new_pos != std::string::npos) {
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files.emplace_back(combine_files.substr(pos, new_pos - pos));
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pos = new_pos + 1;
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} else {
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files.emplace_back(combine_files.substr(pos));
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break;
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}
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}
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if (files.size() < 2) {
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fprintf(stderr, "You must provide at least two comma separated files to use --combine\n");
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return 1;
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}
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printf("Combining the following %d files\n", int(files.size()));
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for (auto& file : files) {
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printf(" %s\n", file.c_str());
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if (!g_collector.load_imatrix(file.c_str(), true)) {
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fprintf(stderr, "Failed to load %s\n", file.c_str());
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return 1;
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}
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}
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g_collector.save_imatrix();
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return 0;
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}
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if (!prev_result_file.empty()) {
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if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) {
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fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str());
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return 1;
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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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g_collector.set_parameters(std::move(sparams));
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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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@ -495,7 +603,7 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "%s\n", get_system_info(params).c_str());
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}
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bool OK = compute_imatrix(ctx, params, compute_ppl);
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bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk);
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if (!OK) {
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return 1;
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}
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