return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
}
+static inline int nearest_int(float fval) {
+ //assert(fval <= 4194303.f);
+ float val = fval + 12582912.f;
+ int i; memcpy(&i, &val, sizeof(int));
+ return (i & 0x007fffff) - 0x00400000;
+}
+
+static double log_softmax(int n_vocab, const float * logits, uint16_t * log_prob, int tok) {
+ float max_logit = logits[0];
+ float min_logit = logits[0];
+ for (int i = 1; i < n_vocab; ++i) {
+ max_logit = std::max(max_logit, logits[i]);
+ min_logit = std::min(min_logit, logits[i]);
+ }
+ min_logit = std::max(min_logit, max_logit - 16);
+ double sum_exp = 0.0;
+ for (int i = 0; i < n_vocab; ++i) {
+ sum_exp += expf(logits[i] - max_logit);
+ }
+ const float log_sum_exp = log(sum_exp);
+ const float min_log_prob = min_logit - max_logit - log_sum_exp;
+ const float scale = (max_logit - min_logit)/65535.f;
+ float * d = (float *)log_prob;
+ d[0] = scale;
+ d[1] = min_log_prob;
+ log_prob += 4;
+ if (scale) {
+ const float inv_scale = 1/scale;
+ for (int i = 0; i < n_vocab; ++i) {
+ log_prob[i] = logits[i] > min_logit ? nearest_int(inv_scale*(logits[i] - min_logit)) : 0;
+ }
+ } else {
+ std::memset(log_prob, 0, n_vocab*sizeof(uint16_t));
+ }
+ return max_logit + log_sum_exp - logits[tok];
+}
+
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
}
}
+static void process_logits(std::ostream& out, int n_vocab, const float * logits, const int * tokens, int n_token,
+ std::vector<std::thread> & workers, std::vector<uint16_t> & log_probs, double & nll, double & nll2) {
+ std::mutex mutex;
+ const int nv = 2*((n_vocab + 1)/2) + 4;
+ int counter = 0;
+ auto compute = [&mutex, &counter, &log_probs, &nll, &nll2, n_vocab, logits, tokens, n_token, nv] () {
+ 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 double v = log_softmax(n_vocab, logits + i*n_vocab, log_probs.data() + i*nv, tokens[i+1]);
+ local_nll += v;
+ local_nll2 += v*v;
+ }
+ };
+ for (auto & w : workers) {
+ w = std::thread(compute);
+ }
+ compute();
+ for (auto & w : workers) {
+ w.join();
+ }
+ out.write((const char *)log_probs.data(), n_token*nv*sizeof(uint16_t));
+}
+
+struct kl_divergence_result {
+ double sum_nll = 0;
+ double sum_nll2 = 0;
+ double sum_kld = 0;
+ double sum_kld2 = 0;
+ double sum_nll_diff = 0;
+ double sum_nll_diff2 = 0;
+ size_t count = 0;
+};
+
+static void log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
+ 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);
+ }
+ const float log_sum_exp = log(sum_exp);
+ const float * d = (const float *)base_log_prob;
+ const float scale = d[0];
+ const float min_log_prob = d[1];
+ base_log_prob += 4;
+ float nll = max_logit + log_sum_exp - logits[tok];
+ kld.sum_nll += nll;
+ kld.sum_nll2 += nll*nll;
+ nll += (scale*base_log_prob[tok] + min_log_prob);
+ kld.sum_nll_diff += nll;
+ kld.sum_nll_diff2 += nll*nll;
+ max_logit += log_sum_exp;
+ double sum = 0;
+ for (int i = 0; i < n_vocab; ++i) {
+ const float p_log_base = scale*base_log_prob[i] + min_log_prob;
+ if (p_log_base > -16.f) {
+ const float p_base = expf(p_log_base);
+ sum += p_base * (p_log_base - logits[i] + max_logit);
+ }
+ }
+ kld.sum_kld += sum;
+ kld.sum_kld2 += sum*sum;
+ ++kld.count;
+}
+
+static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token,
+ std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld) {
+ std::mutex mutex;
+ const int nv = 2*((n_vocab + 1)/2) + 4;
+ int counter = 0;
+ auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv] () {
+ kl_divergence_result local_kld;
+ while (true) {
+ std::unique_lock<std::mutex> lock(mutex);
+ int i = counter++;
+ if (i >= n_token) {
+ kld.sum_nll += local_kld.sum_nll;
+ kld.sum_nll2 += local_kld.sum_nll2;
+ kld.sum_kld += local_kld.sum_kld;
+ kld.sum_kld2 += local_kld.sum_kld2;
+ kld.sum_nll_diff += local_kld.sum_nll_diff;
+ kld.sum_nll_diff2 += local_kld.sum_nll_diff2;
+ kld.count += local_kld.count;
+ break;
+ }
+ lock.unlock();
+ log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
+ }
+ };
+ for (auto & w : workers) {
+ w = std::thread(compute);
+ }
+ compute();
+ for (auto & w : workers) {
+ w.join();
+ }
+}
+
static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
+ std::ofstream logits_stream;
+ if (!params.logits_file.empty()) {
+ logits_stream.open(params.logits_file.c_str());
+ if (!logits_stream.is_open()) {
+ fprintf(stderr, "%s: failed to open %s for writing\n", __func__, params.logits_file.c_str());
+ return {};
+ }
+ fprintf(stderr, "%s: saving all logits to %s\n", __func__, params.logits_file.c_str());
+ logits_stream.write("_logits_", 8);
+ logits_stream.write((const char *)&n_ctx, sizeof(n_ctx));
+ }
+
auto tim1 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+ std::vector<uint16_t> log_probs;
+ if (!params.logits_file.empty()) {
+ logits_stream.write((const char *)&n_vocab, sizeof(n_vocab));
+ logits_stream.write((const char *)&n_chunk, sizeof(n_chunk));
+ logits_stream.write((const char *)tokens.data(), n_chunk*n_ctx*sizeof(tokens[0]));
+ const int nv = 2*((n_vocab + 1)/2) + 4;
+ log_probs.resize(n_ctx * nv);
+ }
+
for (int i = 0; i < n_chunk; ++i) {
const int start = i * n_ctx;
const int end = start + n_ctx;
// process the entire prompt.
const int first = n_ctx/2;
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
- process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
- workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
+ if (!params.logits_file.empty()) {
+ process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+ workers, log_probs, nll, nll2);
+ } else {
+ process_logits(n_vocab, all_logits + 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;
// perplexity is e^(average negative log-likelihood)
printf("\n");
}
+static void kl_divergence(llama_context * ctx, const gpt_params & params) {
+ if (params.logits_file.empty()) {
+ fprintf(stderr, "%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__);
+ return;
+ }
+ std::ifstream in(params.logits_file.c_str(), std::ios::binary);
+ if (!in) {
+ fprintf(stderr, "%s: failed to open %s\n", __func__, params.logits_file.c_str());
+ return;
+ }
+ {
+ char check[9]; check[8] = 0;
+ in.read(check, 8);
+ if (in.fail() || strncmp("_logits_", check, 8) != 0) {
+ fprintf(stderr, "%s: %s does not look like a file containing log-probabilities\n", __func__, params.logits_file.c_str());
+ return;
+ }
+ }
+
+ uint32_t n_ctx;
+ in.read((char *)&n_ctx, sizeof(n_ctx));
+ if (n_ctx > llama_n_ctx(ctx)) {
+ fprintf(stderr, "%s: %s has been computed with %d, while the current context is %d. Increase it with -c and retry\n",
+ __func__, params.logits_file.c_str(), n_ctx, params.n_ctx);
+ }
+
+ int n_vocab, n_chunk;
+ in.read((char *)&n_vocab, sizeof(n_vocab));
+ in.read((char *)&n_chunk, sizeof(n_chunk));
+ if (in.fail()) {
+ fprintf(stderr, "%s: failed rwading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str());
+ return;
+ }
+ if (n_vocab != llama_n_vocab(llama_get_model(ctx))) {
+ fprintf(stderr, "%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx)));
+ }
+
+ std::vector<llama_token> tokens(n_ctx * n_chunk);
+ if (in.read((char *)tokens.data(), tokens.size()*sizeof(tokens[0])).fail()) {
+ fprintf(stderr, "%s: failed reading evaluation tokens from %s\n", __func__, params.logits_file.c_str());
+ return;
+ }
+
+ const int n_batch = params.n_batch;
+ const int num_batches = (n_ctx + n_batch - 1)/n_batch;
+ const int nv = 2*((n_vocab + 1)/2) + 4;
+ const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+
+ std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
+ std::vector<float> logits;
+ if (num_batches > 1) {
+ logits.reserve(n_ctx * n_vocab);
+ }
+
+ std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
+ auto mean_and_uncertainty = [] (double sum, double sum2, size_t count) {
+ if (count < 1) {
+ return std::make_pair(0., 0.);
+ }
+ double f = sum/count;
+ double df = sum2/count - f*f;
+ df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.;
+ return std::make_pair(f, df);
+ };
+
+ kl_divergence_result kld;
+
+ for (int i = 0; i < n_chunk; ++i) {
+ const int start = i * n_ctx;
+ const int end = start + n_ctx;
+
+ const auto t_start = std::chrono::high_resolution_clock::now();
+
+ if (in.read((char *)log_probs_uint16.data(), log_probs_uint16.size()*sizeof(uint16_t)).fail()) {
+ fprintf(stderr, "%s: failed reading log-probs for chunk %d\n", __func__, i);
+ return;
+ }
+
+ // 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;
+ }
+
+ // restore the original token in case it was set to BOS
+ tokens[batch_start] = token_org;
+
+ if (num_batches > 1) {
+ 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);
+
+ printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL-Divergence\n");
+ }
+
+ const int first = n_ctx/2;
+ const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
+ process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+ workers, log_probs_uint16, kld);
+
+ auto ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
+ auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count);
+ auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
+
+ printf("%4d %10.4lf %10.5lf ± %10.5f %10.5f ± %10.5lf\n", i+1, exp(ppl.first),
+ log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second);
+
+ fflush(stdout);
+
+ logits.clear();
+ }
+ printf("\n");
+
+}
int main(int argc, char ** argv) {
gpt_params params;
winogrande_score(ctx, params);
} else if (params.multiple_choice) {
multiple_choice_score(ctx, params);
+ } else if (params.kl_divergence) {
+ kl_divergence(ctx, params);
} else {
results = perplexity(ctx, params);
}
overview / pkg / ggml / sources / llama.cpp / commitdiff