--- /dev/null
+#include "common.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <sstream>
+#include <thread>
+#include <mutex>
+#include <vector>
+#include <fstream>
+#include <unordered_map>
+#include <algorithm>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+struct Stats {
+ std::vector<float> values;
+ int ncall = 0;
+};
+
+struct StatParams {
+ std::string ofile = "imatrix.dat";
+ int n_output_frequency = 10;
+ int verbosity = 1;
+ bool collect_output_weight = false;
+};
+
+class IMatrixCollector {
+public:
+ IMatrixCollector() = default;
+ void set_parameters(StatParams&& params) { m_params = std::move(params); }
+ void collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1);
+ void save_imatrix() const;
+private:
+ std::unordered_map<std::string, Stats> m_stats;
+ StatParams m_params;
+ std::mutex m_mutex;
+ int m_last_call = 0;
+};
+
+void IMatrixCollector::collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) {
+ if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return;
+ if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return;
+ std::lock_guard<std::mutex> lock(m_mutex);
+ auto& e = m_stats[src0->name];
+ if (e.values.empty()) {
+ e.values.resize(src1->ne[0], 0);
+ }
+ else if (e.values.size() != (size_t)src1->ne[0]) {
+ fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
+ exit(1); //GGML_ASSERT(false);
+ }
+ ++e.ncall;
+ if (m_params.verbosity > 1) {
+ printf("%s[%d]: %s, %d x %d, %d\n",__func__,m_last_call,src0->name,(int)src1->ne[0],(int)src1->ne[1],(int)src1->type);
+ }
+ for (int row = 0; row < (int)src1->ne[1]; ++row) {
+ const float * x = (const float *)src1->data + row * src1->ne[0];
+ for (int j = 0; j < (int)src1->ne[0]; ++j) {
+ e.values[j] += x[j]*x[j];
+ }
+ }
+ if (e.ncall > m_last_call) {
+ m_last_call = e.ncall;
+ if (m_last_call % m_params.n_output_frequency == 0) {
+ save_imatrix();
+ }
+ }
+}
+
+void IMatrixCollector::save_imatrix() const {
+ const char * fname = m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str();
+ std::ofstream out(fname, std::ios::binary);
+ int n_entries = m_stats.size();
+ out.write((const char*)&n_entries, sizeof(n_entries));
+ for (auto& p : m_stats) {
+ int len = p.first.size();
+ out.write((const char*)&len, sizeof(len));
+ out.write(p.first.c_str(), len);
+ out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
+ int nval = p.second.values.size();
+ out.write((const char*)&nval, sizeof(nval));
+ if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
+ }
+ if (m_params.verbosity > 0) {
+ fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
+ }
+}
+
+static IMatrixCollector g_collector;
+
+static void ik_collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) {
+ g_collector.collect_imatrix(src0, src1);
+}
+
+
+struct results_log_softmax {
+ double log_softmax;
+ float logit;
+ float prob;
+};
+
+static std::vector<float> softmax(const std::vector<float>& logits) {
+ std::vector<float> probs(logits.size());
+ float max_logit = logits[0];
+ for (float v : logits) {
+ max_logit = std::max(max_logit, v);
+ }
+ double sum_exp = 0.0;
+ for (size_t i = 0; i < logits.size(); i++) {
+ // Subtract the maximum logit value from the current logit value for numerical stability
+ const float logit = logits[i] - max_logit;
+ const float exp_logit = expf(logit);
+ sum_exp += exp_logit;
+ probs[i] = exp_logit;
+ }
+ for (size_t i = 0; i < probs.size(); i++) {
+ probs[i] /= sum_exp;
+ }
+ return probs;
+}
+
+static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
+ float max_logit = logits[0];
+ for (int i = 1; i < n_vocab; ++i) {
+ max_logit = std::max(max_logit, logits[i]);
+ }
+ double sum_exp = 0.0;
+ for (int i = 0; i < n_vocab; ++i) {
+ sum_exp += expf(logits[i] - max_logit);
+ }
+ return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
+}
+
+static void process_logits(
+ int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
+ double & nll, double & nll2, float * logit_history, float * prob_history
+) {
+ std::mutex mutex;
+ int counter = 0;
+ auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
+ double local_nll = 0;
+ double local_nll2 = 0;
+ while (true) {
+ std::unique_lock<std::mutex> lock(mutex);
+ int i = counter++;
+ if (i >= n_token) {
+ nll += local_nll; nll2 += local_nll2;
+ break;
+ }
+ lock.unlock();
+ const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
+ const double v = -results.log_softmax;
+ local_nll += v;
+ local_nll2 += v*v;
+
+ logit_history[i] = results.logit;
+ prob_history[i] = results.prob;
+ }
+ };
+ for (auto & w : workers) {
+ w = std::thread(compute);
+ }
+ compute();
+ for (auto & w : workers) {
+ w.join();
+ }
+}
+
+static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
+
+ const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+ const int n_ctx = llama_n_ctx(ctx);
+
+ auto tim1 = std::chrono::high_resolution_clock::now();
+ fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
+
+ std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+
+ auto tim2 = std::chrono::high_resolution_clock::now();
+ fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
+
+ if (int(tokens.size()) < 2*n_ctx) {
+ fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx,
+ n_ctx);
+ fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
+ return false;
+ }
+
+ std::vector<float> logit_history;
+ logit_history.resize(tokens.size());
+
+ std::vector<float> prob_history;
+ prob_history.resize(tokens.size());
+
+ const int n_chunk_max = tokens.size() / n_ctx;
+
+ const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
+ const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+ const int n_batch = params.n_batch;
+
+ int count = 0;
+ double nll = 0.0;
+ double nll2 = 0.0;
+
+ fprintf(stderr, "%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
+
+ std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
+ for (int i = 0; i < n_chunk; ++i) {
+ const int start = i * n_ctx;
+ const int end = start + n_ctx;
+
+ const int num_batches = (n_ctx + n_batch - 1) / n_batch;
+
+ std::vector<float> logits;
+
+ const auto t_start = std::chrono::high_resolution_clock::now();
+
+ // clear the KV cache
+ llama_kv_cache_clear(ctx);
+
+ for (int j = 0; j < num_batches; ++j) {
+ const int batch_start = start + j * n_batch;
+ const int batch_size = std::min(end - batch_start, n_batch);
+
+ // save original token and restore it after eval
+ const auto token_org = tokens[batch_start];
+
+ // add BOS token for the first batch of each chunk
+ if (add_bos && j == 0) {
+ tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+ }
+
+ if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
+ fprintf(stderr, "%s : failed to eval\n", __func__);
+ return false;
+ }
+
+ // restore the original token in case it was set to BOS
+ tokens[batch_start] = token_org;
+
+ const auto * batch_logits = llama_get_logits(ctx);
+ logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+ }
+
+ const auto t_end = std::chrono::high_resolution_clock::now();
+
+ if (i == 0) {
+ const float t_total = std::chrono::duration<float>(t_end - t_start).count();
+ fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
+ int total_seconds = (int)(t_total * n_chunk);
+ if (total_seconds >= 60*60) {
+ fprintf(stderr, "%d hours ", total_seconds / (60*60));
+ total_seconds = total_seconds % (60*60);
+ }
+ fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
+ }
+
+ const int first = n_ctx/2;
+ process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+ workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
+ count += n_ctx - first - 1;
+
+ printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+ fflush(stdout);
+ }
+ printf("\n");
+
+ nll2 /= count;
+ nll /= count;
+ const double ppl = exp(nll);
+ nll2 -= nll * nll;
+ if (nll2 > 0) {
+ nll2 = sqrt(nll2/(count-1));
+ printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
+ } else {
+ printf("Unexpected negative standard deviation of log(prob)\n");
+ }
+
+ return true;
+}
+
+int main(int argc, char ** argv) {
+
+ StatParams sparams;
+ std::vector<char*> args;
+ args.push_back(argv[0]);
+ int iarg = 1;
+ for (; iarg < argc-1; ++iarg) {
+ std::string arg{argv[iarg]};
+ if (arg == "-o" || arg == "--output-file") {
+ sparams.ofile = argv[++iarg];
+ }
+ else if (arg == "-ofreq" || arg == "--output-frequency") {
+ sparams.n_output_frequency = std::stoi(argv[++iarg]);
+ }
+ else if (arg == "-ow" || arg == "--output-weight") {
+ sparams.collect_output_weight = std::stoi(argv[++iarg]);
+ }
+ else if (arg == "--verbosity") {
+ sparams.verbosity = std::stoi(argv[++iarg]);
+ } else {
+ args.push_back(argv[iarg]);
+ }
+ }
+ if (iarg < argc) {
+ args.push_back(argv[iarg]);
+ }
+
+ gpt_params params;
+ params.n_batch = 512;
+ if (!gpt_params_parse(args.size(), args.data(), params)) {
+ return 1;
+ }
+
+ g_collector.set_parameters(std::move(sparams));
+
+ ggml_set_imatrix_collection(ik_collect_imatrix);
+
+ params.logits_all = true;
+ params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+ print_build_info();
+
+ if (params.seed == LLAMA_DEFAULT_SEED) {
+ params.seed = time(NULL);
+ }
+
+ fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
+
+ std::mt19937 rng(params.seed);
+ if (params.random_prompt) {
+ params.prompt = gpt_random_prompt(rng);
+ }
+
+ llama_backend_init(params.numa);
+
+ llama_model * model;
+ llama_context * ctx;
+
+ // load the model and apply lora adapter, if any
+ std::tie(model, ctx) = llama_init_from_gpt_params(params);
+ if (model == NULL) {
+ fprintf(stderr, "%s: error: unable to load model\n", __func__);
+ return 1;
+ }
+
+ const int n_ctx_train = llama_n_ctx_train(model);
+ if (params.n_ctx > n_ctx_train) {
+ fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
+ __func__, n_ctx_train, params.n_ctx);
+ }
+
+ // print system information
+ {
+ fprintf(stderr, "\n");
+ fprintf(stderr, "%s\n", get_system_info(params).c_str());
+ }
+
+ bool OK = compute_imatrix(ctx, params);
+ if (!OK) {
+ return 1;
+ }
+
+ g_collector.save_imatrix();
+
+ llama_print_timings(ctx);
+
+ llama_free(ctx);
+ llama_free_model(model);
+
+ llama_backend_free();
+
+ return 0;
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff