mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
a68f31edd7
In `llama-perplexity`, when using `--kl-divergence`, the KL divergence statistics output mistakenly displays the 99th percentile twice. This change fixes that and correctly displays the 90th percentile as originally intended (presumably).
2068 lines
79 KiB
C++
2068 lines
79 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "log.h"
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#include "llama.h"
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#include <chrono>
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#include <algorithm>
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#include <array>
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#include <atomic>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <mutex>
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#include <random>
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#include <sstream>
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#include <thread>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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struct results_perplexity {
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std::vector<llama_token> tokens;
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double ppl_value;
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std::vector<float> logits;
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std::vector<float> probs;
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};
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struct results_log_softmax {
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double log_softmax;
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float logit;
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float prob;
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};
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static std::vector<float> softmax(const std::vector<float>& logits) {
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std::vector<float> probs(logits.size());
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float max_logit = logits[0];
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for (float v : logits) {
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max_logit = std::max(max_logit, v);
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}
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double sum_exp = 0.0;
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for (size_t i = 0; i < logits.size(); i++) {
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// Subtract the maximum logit value from the current logit value for numerical stability
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const float logit = logits[i] - max_logit;
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const float exp_logit = expf(logit);
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sum_exp += exp_logit;
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probs[i] = exp_logit;
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}
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for (size_t i = 0; i < probs.size(); i++) {
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probs[i] /= sum_exp;
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}
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return probs;
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}
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static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
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float max_logit = logits[0];
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for (int i = 1; i < n_vocab; ++i) {
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max_logit = std::max(max_logit, logits[i]);
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}
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double sum_exp = 0.0;
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for (int i = 0; i < n_vocab; ++i) {
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sum_exp += expf(logits[i] - max_logit);
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}
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return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
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}
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static inline int nearest_int(float fval) {
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//assert(fval <= 4194303.f);
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float val = fval + 12582912.f;
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int i; memcpy(&i, &val, sizeof(int));
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return (i & 0x007fffff) - 0x00400000;
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}
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static double log_softmax(int n_vocab, const float * logits, uint16_t * log_prob, int tok) {
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float max_logit = logits[0];
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float min_logit = logits[0];
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for (int i = 1; i < n_vocab; ++i) {
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max_logit = std::max(max_logit, logits[i]);
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min_logit = std::min(min_logit, logits[i]);
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}
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min_logit = std::max(min_logit, max_logit - 16);
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double sum_exp = 0.0;
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for (int i = 0; i < n_vocab; ++i) {
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sum_exp += expf(logits[i] - max_logit);
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}
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const float log_sum_exp = log(sum_exp);
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const float min_log_prob = min_logit - max_logit - log_sum_exp;
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const float scale = (max_logit - min_logit)/65535.f;
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float * d = (float *)log_prob;
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d[0] = scale;
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d[1] = min_log_prob;
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log_prob += 4;
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if (scale) {
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const float inv_scale = 1/scale;
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for (int i = 0; i < n_vocab; ++i) {
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log_prob[i] = logits[i] > min_logit ? nearest_int(inv_scale*(logits[i] - min_logit)) : 0;
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}
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} else {
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std::memset(log_prob, 0, n_vocab*sizeof(uint16_t));
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}
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return max_logit + log_sum_exp - logits[tok];
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}
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static void process_logits(
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int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
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double & nll, double & nll2, float * logit_history, float * prob_history
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) {
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std::mutex mutex;
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int counter = 0;
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auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
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double local_nll = 0;
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double local_nll2 = 0;
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while (true) {
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std::unique_lock<std::mutex> lock(mutex);
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int i = counter++;
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if (i >= n_token) {
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nll += local_nll; nll2 += local_nll2;
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break;
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}
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lock.unlock();
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const results_log_softmax results = log_softmax(n_vocab, logits + size_t(i)*n_vocab, tokens[i+1]);
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const double v = -results.log_softmax;
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local_nll += v;
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local_nll2 += v*v;
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logit_history[i] = results.logit;
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prob_history[i] = results.prob;
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}
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};
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for (auto & w : workers) {
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w = std::thread(compute);
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}
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compute();
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for (auto & w : workers) {
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w.join();
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}
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}
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static void process_logits(std::ostream& out, int n_vocab, const float * logits, const int * tokens, int n_token,
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std::vector<std::thread> & workers, std::vector<uint16_t> & log_probs, double & nll, double & nll2) {
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std::mutex mutex;
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const int nv = 2*((n_vocab + 1)/2) + 4;
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int counter = 0;
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auto compute = [&mutex, &counter, &log_probs, &nll, &nll2, n_vocab, logits, tokens, n_token, nv] () {
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double local_nll = 0;
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double local_nll2 = 0;
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while (true) {
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std::unique_lock<std::mutex> lock(mutex);
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int i = counter++;
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if (i >= n_token) {
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nll += local_nll; nll2 += local_nll2;
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break;
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}
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lock.unlock();
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const double v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, log_probs.data() + i*nv, tokens[i+1]);
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local_nll += v;
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local_nll2 += v*v;
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}
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};
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for (auto & w : workers) {
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w = std::thread(compute);
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}
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compute();
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for (auto & w : workers) {
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w.join();
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}
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out.write((const char *)log_probs.data(), n_token*nv*sizeof(uint16_t));
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}
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struct kl_divergence_result {
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double sum_nll = 0.0;
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double sum_nll2 = 0.0;
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double sum_nll_base = 0.0;
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double sum_nll_base2 = 0.0;
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double sum_nll_nll_base = 0.0;
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double sum_kld = 0.0;
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double sum_kld2 = 0.0;
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double sum_p_diff = 0.0;
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double sum_p_diff2 = 0.0;
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double sum_p_diff4 = 0.0;
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float max_p_diff = 0.0f;
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size_t n_same_top = 0.0;
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size_t count = 0.0;
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};
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static std::pair<double, float> log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
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float max_logit = logits[0];
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int imax = 0;
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for (int i = 1; i < n_vocab; ++i) {
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if (logits[i] > max_logit) {
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max_logit = logits[i];
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imax = i;
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}
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}
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double sum_exp = 0.0;
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for (int i = 0; i < n_vocab; ++i) {
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sum_exp += expf(logits[i] - max_logit);
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}
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const float log_sum_exp = log(sum_exp);
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const float * d = (const float *)base_log_prob;
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const float scale = d[0];
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const float min_log_prob = d[1];
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base_log_prob += 4;
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const float nll = max_logit + log_sum_exp - logits[tok];
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kld.sum_nll += nll;
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kld.sum_nll2 += nll*nll;
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const float nll_base = -(scale*base_log_prob[tok] + min_log_prob);
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kld.sum_nll_base += nll_base;
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kld.sum_nll_base2 += nll_base*nll_base;
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kld.sum_nll_nll_base += nll*nll_base;
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max_logit += log_sum_exp;
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double sum = 0;
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int imax_base = -1;
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float p_log_base_max = 0;
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for (int i = 0; i < n_vocab; ++i) {
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const float p_log_base = scale*base_log_prob[i] + min_log_prob;
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if (i == 0 || p_log_base > p_log_base_max) {
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p_log_base_max = p_log_base;
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imax_base = i;
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}
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if (p_log_base > -16.f) {
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const float p_base = expf(p_log_base);
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sum += p_base * (p_log_base - logits[i] + max_logit);
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}
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}
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kld.sum_kld += sum;
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kld.sum_kld2 += sum*sum;
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++kld.count;
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if (imax == imax_base) {
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++kld.n_same_top;
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}
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const float p_base = expf(-nll_base);
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const float p = expf(-nll);
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const float p_diff = p - p_base;
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kld.sum_p_diff += p_diff;
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const double p_diff2 = p_diff*p_diff;
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kld.sum_p_diff2 += p_diff2;
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kld.sum_p_diff4 += p_diff2*p_diff2;
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kld.max_p_diff = std::max(kld.max_p_diff, std::fabs(p_diff));
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return std::make_pair(sum, p_diff);
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}
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static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token,
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std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld,
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float * kld_values, float * p_diff_values) {
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std::mutex mutex;
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const int nv = 2*((n_vocab + 1)/2) + 4;
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int counter = 0;
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auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values, p_diff_values] () {
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kl_divergence_result local_kld;
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while (true) {
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std::unique_lock<std::mutex> lock(mutex);
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int i = counter++;
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if (i >= n_token) {
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kld.sum_nll += local_kld.sum_nll;
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kld.sum_nll2 += local_kld.sum_nll2;
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kld.sum_nll_base += local_kld.sum_nll_base;
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kld.sum_nll_base2 += local_kld.sum_nll_base2;
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kld.sum_nll_nll_base += local_kld.sum_nll_nll_base;
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kld.sum_kld += local_kld.sum_kld;
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kld.sum_kld2 += local_kld.sum_kld2;
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kld.sum_p_diff += local_kld.sum_p_diff;
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kld.sum_p_diff2 += local_kld.sum_p_diff2;
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kld.sum_p_diff4 += local_kld.sum_p_diff4;
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kld.n_same_top += local_kld.n_same_top;
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kld.max_p_diff = std::max(kld.max_p_diff, local_kld.max_p_diff);
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kld.count += local_kld.count;
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break;
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}
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lock.unlock();
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std::pair<double, float> v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
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kld_values[i] = (float)v.first;
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p_diff_values[i] = v.second;
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}
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};
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for (auto & w : workers) {
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w = std::thread(compute);
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}
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compute();
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for (auto & w : workers) {
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w.join();
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}
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}
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static results_perplexity perplexity_v2(llama_context * ctx, const common_params & params) {
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// Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
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// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
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// Output: `perplexity: 13.5106 [114/114]`
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// BOS tokens will be added for each chunk before eval
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const llama_model * model = llama_get_model(ctx);
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const llama_vocab * vocab = llama_model_get_vocab(model);
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const bool add_bos = llama_vocab_get_add_bos(vocab);
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GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
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LOG_INF("%s: tokenizing the input ..\n", __func__);
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std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
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const int n_ctx = llama_n_ctx(ctx);
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if (int(tokens.size()) < 2*n_ctx) {
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LOG_ERR("%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx,
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n_ctx);
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LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
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return {std::move(tokens), 0., {}, {}};
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}
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std::vector<float> logit_history;
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std::vector<float> prob_history;
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logit_history.resize(tokens.size());
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prob_history.resize(tokens.size());
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if (params.ppl_stride <= 0) {
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LOG_ERR("%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
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return {tokens, -1, logit_history, prob_history};
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}
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const int calc_chunk = n_ctx;
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LOG_INF("%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk);
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if (int(tokens.size()) <= calc_chunk) {
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LOG_ERR("%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
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tokens.size(), n_ctx, params.ppl_stride);
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return {tokens, -1, logit_history, prob_history};
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}
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const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride;
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const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
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const int n_batch = params.n_batch;
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const int n_vocab = llama_vocab_n_tokens(vocab);
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int count = 0;
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double nll = 0.0;
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LOG_INF("%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
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for (int i = 0; i < n_chunk; ++i) {
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const int start = i * params.ppl_stride;
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const int end = start + calc_chunk;
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const int num_batches = (calc_chunk + n_batch - 1) / n_batch;
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//LOG_DBG("%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches);
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std::vector<float> logits;
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const auto t_start = std::chrono::high_resolution_clock::now();
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// clear the KV cache
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llama_memory_clear(llama_get_memory(ctx), true);
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llama_batch batch = llama_batch_init(n_batch, 0, 1);
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for (int j = 0; j < num_batches; ++j) {
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const int batch_start = start + j * n_batch;
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const int batch_size = std::min(end - batch_start, n_batch);
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common_batch_clear(batch);
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for (int i = 0; i < batch_size; i++) {
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common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
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}
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//LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
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if (llama_decode(ctx, batch)) {
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//LOG_ERR("%s : failed to eval\n", __func__);
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llama_batch_free(batch);
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return {tokens, -1, logit_history, prob_history};
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}
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// save original token and restore it after eval
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const auto token_org = tokens[batch_start];
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// add BOS token for the first batch of each chunk
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if (add_bos && j == 0) {
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tokens[batch_start] = llama_vocab_bos(vocab);
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}
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const auto * batch_logits = llama_get_logits(ctx);
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logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab);
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if (j == 0) {
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tokens[batch_start] = token_org;
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}
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}
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llama_batch_free(batch);
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const auto t_end = std::chrono::high_resolution_clock::now();
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if (i == 0) {
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const float t_total = std::chrono::duration<float>(t_end - t_start).count();
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LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
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int total_seconds = (int)(t_total * n_chunk);
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if (total_seconds >= 60*60) {
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LOG("%d hours ", total_seconds / (60*60));
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total_seconds = total_seconds % (60*60);
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}
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LOG("%.2f minutes\n", total_seconds / 60.0);
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}
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//LOG_DBG("%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);
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for (int j = n_ctx - params.ppl_stride - 1; j < n_ctx - 1; ++j) {
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// Calculate probability of next token, given the previous ones.
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const std::vector<float> tok_logits(
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logits.begin() + size_t(j + 0) * n_vocab,
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logits.begin() + size_t(j + 1) * n_vocab);
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const float prob = softmax(tok_logits)[tokens[start + j + 1]];
|
|
logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];
|
|
prob_history[start + j + 1] = prob;
|
|
|
|
nll += -std::log(prob);
|
|
++count;
|
|
}
|
|
// perplexity is e^(average negative log-likelihood)
|
|
if (params.ppl_output_type == 0) {
|
|
LOG("[%d]%.4lf,", i + 1, std::exp(nll / count));
|
|
} else {
|
|
LOG("%8d %.4lf\n", i*params.ppl_stride, std::exp(nll / count));
|
|
}
|
|
}
|
|
LOG("\n");
|
|
|
|
return {tokens, std::exp(nll / count), logit_history, prob_history};
|
|
}
|
|
|
|
static results_perplexity perplexity(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
|
|
if (params.ppl_stride > 0) {
|
|
return perplexity_v2(ctx, params);
|
|
}
|
|
|
|
// Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
|
|
// Run `./llama-perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
|
|
// Output: `perplexity: 13.5106 [114/114]`
|
|
// BOS tokens will be added for each chunk before eval
|
|
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
const bool add_bos = llama_vocab_get_add_bos(vocab);
|
|
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
|
|
|
|
std::ofstream logits_stream;
|
|
if (!params.logits_file.empty()) {
|
|
logits_stream.open(params.logits_file.c_str(), std::ios::binary);
|
|
if (!logits_stream.is_open()) {
|
|
LOG_ERR("%s: failed to open %s for writing\n", __func__, params.logits_file.c_str());
|
|
return {};
|
|
}
|
|
LOG_INF("%s: saving all logits to %s\n", __func__, params.logits_file.c_str());
|
|
logits_stream.write("_logits_", 8);
|
|
logits_stream.write(reinterpret_cast<const char *>(&n_ctx), sizeof(n_ctx));
|
|
}
|
|
|
|
auto tim1 = std::chrono::high_resolution_clock::now();
|
|
LOG_INF("%s: tokenizing the input ..\n", __func__);
|
|
|
|
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
|
|
|
|
auto tim2 = std::chrono::high_resolution_clock::now();
|
|
LOG_INF("%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) {
|
|
LOG_ERR("%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx,
|
|
n_ctx);
|
|
LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
|
|
return {std::move(tokens), 0., {}, {}};
|
|
}
|
|
|
|
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_batch = params.n_batch;
|
|
|
|
const int n_vocab = llama_vocab_n_tokens(vocab);
|
|
|
|
int count = 0;
|
|
double nll = 0.0;
|
|
double nll2 = 0.0;
|
|
|
|
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
|
|
const int n_seq = std::max(1, n_batch / n_ctx);
|
|
|
|
GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
|
|
GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
|
|
|
|
llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
|
|
|
|
std::vector<float> logits;
|
|
if (num_batches > 1) {
|
|
logits.reserve(size_t(n_ctx) * n_vocab);
|
|
}
|
|
|
|
LOG_INF("%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
|
|
|
|
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);
|
|
}
|
|
|
|
// We get the logits for all the tokens in the context window (params.n_ctx)
|
|
// from llama_decode below. Now, based on https://huggingface.co/docs/transformers/perplexity,
|
|
// calculate the perplexity over the last half of the window (so the model always has
|
|
// some context to predict the token).
|
|
//
|
|
// We rely on the fact that attention in the forward pass only looks at previous
|
|
// tokens here, so the logits returned for each token are an accurate representation
|
|
// of what the model would have predicted at that point.
|
|
//
|
|
// Example, we have a context window of 512, we will compute perplexity for each of the
|
|
// last 256 tokens. Then, we split the input up into context window size chunks to
|
|
// process the entire prompt.
|
|
const int first = n_ctx/2;
|
|
|
|
for (int i = 0; i < n_chunk; i += n_seq) {
|
|
const int start = i * n_ctx;
|
|
const int end = start + n_ctx;
|
|
|
|
const int n_seq_batch = std::min(n_seq, n_chunk - i);
|
|
|
|
const auto t_start = std::chrono::high_resolution_clock::now();
|
|
|
|
// clear the KV cache
|
|
llama_memory_clear(llama_get_memory(ctx), true);
|
|
|
|
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);
|
|
|
|
int n_outputs = 0;
|
|
|
|
batch.n_tokens = 0;
|
|
for (int seq = 0; seq < n_seq_batch; seq++) {
|
|
int seq_start = batch_start + seq*n_ctx;
|
|
|
|
// save original token and restore it after decode
|
|
const auto token_org = tokens[seq_start];
|
|
|
|
// add BOS token for the first batch of each chunk
|
|
if (add_bos && j == 0) {
|
|
tokens[seq_start] = llama_vocab_bos(vocab);
|
|
}
|
|
|
|
for (int k = 0; k < batch_size; ++k) {
|
|
const int idx = seq*n_ctx + k;
|
|
batch.token [idx] = tokens[seq_start + k];
|
|
batch.pos [idx] = j*n_batch + k;
|
|
batch.n_seq_id[idx] = 1;
|
|
batch.seq_id [idx][0] = seq;
|
|
batch.logits [idx] = batch.pos[idx] >= first ? 1 : 0;
|
|
|
|
n_outputs += batch.logits[idx] != 0;
|
|
}
|
|
batch.n_tokens += batch_size;
|
|
|
|
// restore the original token in case it was set to BOS
|
|
tokens[seq_start] = token_org;
|
|
}
|
|
|
|
if (llama_decode(ctx, batch)) {
|
|
LOG_INF("%s : failed to decode\n", __func__);
|
|
return {tokens, -1, logit_history, prob_history};
|
|
}
|
|
|
|
if (num_batches > 1 && n_outputs > 0) {
|
|
const auto * batch_logits = llama_get_logits(ctx);
|
|
logits.insert(logits.end(), batch_logits, batch_logits + size_t(n_outputs) * n_vocab);
|
|
}
|
|
}
|
|
|
|
|
|
if (i == 0) {
|
|
llama_synchronize(ctx);
|
|
const auto t_end = std::chrono::high_resolution_clock::now();
|
|
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
|
|
LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
|
|
int total_seconds = (int)(t_total*n_chunk/n_seq);
|
|
if (total_seconds >= 60*60) {
|
|
LOG("%d hours ", total_seconds / (60*60));
|
|
total_seconds = total_seconds % (60*60);
|
|
}
|
|
LOG("%.2f minutes\n", total_seconds / 60.0);
|
|
}
|
|
|
|
for (int seq = 0; seq < n_seq_batch; seq++) {
|
|
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first);
|
|
|
|
llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
|
|
if (!params.logits_file.empty()) {
|
|
process_logits(logits_stream, n_vocab, all_logits,
|
|
tokens_data, n_ctx - 1 - first,
|
|
workers, log_probs, nll, nll2);
|
|
} else {
|
|
process_logits(n_vocab, all_logits,
|
|
tokens_data, n_ctx - 1 - first,
|
|
workers, nll, nll2,
|
|
logit_history.data() + start + seq*n_ctx + first,
|
|
prob_history.data() + start + seq*n_ctx + first);
|
|
}
|
|
count += n_ctx - first - 1;
|
|
|
|
// perplexity is e^(average negative log-likelihood)
|
|
if (params.ppl_output_type == 0) {
|
|
LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
|
|
} else {
|
|
double av = nll/count;
|
|
double av2 = nll2/count - av*av;
|
|
if (av2 > 0) {
|
|
av2 = sqrt(av2/(count-1));
|
|
}
|
|
LOG("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
|
|
}
|
|
}
|
|
|
|
logits.clear();
|
|
}
|
|
LOG("\n");
|
|
|
|
nll2 /= count;
|
|
nll /= count;
|
|
const double ppl = exp(nll);
|
|
nll2 -= nll * nll;
|
|
if (nll2 > 0) {
|
|
nll2 = sqrt(nll2/(count-1));
|
|
LOG_INF("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
|
|
} else {
|
|
LOG_ERR("Unexpected negative standard deviation of log(prob)\n");
|
|
}
|
|
|
|
llama_batch_free(batch);
|
|
|
|
return {tokens, ppl, logit_history, prob_history};
|
|
}
|
|
|
|
static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int n_batch, int n_vocab) {
|
|
int prev_outputs = 0;
|
|
for (int i = 0; i < (int) batch.n_tokens; i += n_batch) {
|
|
const int n_tokens = std::min<int>(n_batch, batch.n_tokens - i);
|
|
|
|
llama_batch batch_view = {
|
|
n_tokens,
|
|
batch.token + i,
|
|
nullptr,
|
|
batch.pos + i,
|
|
batch.n_seq_id + i,
|
|
batch.seq_id + i,
|
|
batch.logits + i,
|
|
};
|
|
|
|
const int ret = llama_decode(ctx, batch_view);
|
|
if (ret != 0) {
|
|
LOG_ERR("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
|
|
return false;
|
|
}
|
|
|
|
int n_outputs = 0;
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
n_outputs += batch_view.logits[i] != 0;
|
|
}
|
|
|
|
memcpy(batch_logits.data() + size_t(prev_outputs)*n_vocab, llama_get_logits(ctx), size_t(n_outputs)*n_vocab*sizeof(float));
|
|
|
|
prev_outputs += n_outputs;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
#define K_TOKEN_CHUNK 4
|
|
|
|
static void compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers,
|
|
const std::vector<std::pair<size_t, llama_token>>& eval_pairs, std::vector<float>& eval_results) {
|
|
if (eval_results.size() != eval_pairs.size()) {
|
|
eval_results.resize(eval_pairs.size());
|
|
}
|
|
if (eval_pairs.empty()) {
|
|
return;
|
|
}
|
|
|
|
size_t max_threads = std::min((eval_pairs.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK, workers.size());
|
|
|
|
std::atomic<int> counter(0);
|
|
auto compute = [&counter, &eval_pairs, &eval_results, batch_logits, n_vocab] () {
|
|
float local_logprobs[K_TOKEN_CHUNK];
|
|
while (true) {
|
|
const size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed);
|
|
if (first >= eval_results.size()) {
|
|
break;
|
|
}
|
|
const size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size());
|
|
for (size_t i = first; i < last; ++i) {
|
|
const auto * logits = batch_logits + eval_pairs[i].first * n_vocab;
|
|
float max_logit = logits[0];
|
|
for (int j = 1; j < n_vocab; ++j) {
|
|
max_logit = std::max(max_logit, logits[j]);
|
|
}
|
|
float sum_p = 0.f;
|
|
for (int j = 0; j < n_vocab; ++j) {
|
|
sum_p += expf(logits[j] - max_logit);
|
|
}
|
|
local_logprobs[i - first] = logits[eval_pairs[i].second] - max_logit - std::log(sum_p);
|
|
}
|
|
std::memcpy(eval_results.data() + first, local_logprobs, (last - first)*sizeof(float));
|
|
}
|
|
};
|
|
|
|
for (size_t it = 0; it < max_threads; ++it) {
|
|
workers[it] = std::thread(compute);
|
|
}
|
|
for (size_t it = 0; it < max_threads; ++it) {
|
|
workers[it].join();
|
|
}
|
|
}
|
|
|
|
static void hellaswag_score(llama_context * ctx, const common_params & params) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
// Calculates hellaswag score (acc_norm) from prompt
|
|
//
|
|
// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
|
|
// All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68
|
|
//
|
|
// All 10042 tasks should be extracted to keep the results standardized like other implementations.
|
|
//
|
|
// Datafile layout:
|
|
// ['??'] denotes json fields
|
|
// 6 lines per task:
|
|
// ['activity_label'] + ": " +['ctx'] - The first part of the query, the context
|
|
// ['label'] - The index the best common sense ending aka gold ending
|
|
// ['endings'][0] - Endings added to the first part of the query
|
|
// ['endings'][1]
|
|
// ['endings'][2]
|
|
// ['endings'][3]
|
|
|
|
std::vector<std::string> prompt_lines;
|
|
std::istringstream strstream(params.prompt);
|
|
std::string line;
|
|
|
|
while (std::getline(strstream,line,'\n')) {
|
|
prompt_lines.push_back(line);
|
|
}
|
|
|
|
if (prompt_lines.size() % 6 != 0) {
|
|
LOG_ERR("%s : number of lines in prompt not a multiple of 6.\n", __func__);
|
|
return;
|
|
}
|
|
|
|
size_t hs_task_count = prompt_lines.size()/6;
|
|
LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
|
|
|
|
const bool is_spm = llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM;
|
|
LOG_INF("================================= is_spm = %d\n", is_spm);
|
|
|
|
// The tasks should be randomized so the score stabilizes quickly.
|
|
bool randomize_tasks = true;
|
|
|
|
// Number of tasks to use when computing the score
|
|
if (params.hellaswag_tasks < hs_task_count) {
|
|
hs_task_count = params.hellaswag_tasks;
|
|
}
|
|
|
|
// The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
|
|
std::mt19937 rng(1);
|
|
|
|
// Dataholder for hellaswag tasks
|
|
struct hs_data_t {
|
|
std::string context;
|
|
size_t gold_ending_idx;
|
|
std::string ending[4];
|
|
size_t ending_logprob_count[4];
|
|
double ending_logprob[4];
|
|
|
|
size_t i_logits; // starting index of logits in the llama_batch
|
|
size_t common_prefix; // max number of initial tokens that are the same in all sentences
|
|
size_t required_tokens; // needed number of tokens to evaluate all 4 endings
|
|
std::vector<llama_token> seq_tokens[4];
|
|
};
|
|
|
|
LOG_INF("%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") );
|
|
|
|
// Select and read data from prompt lines
|
|
std::vector<hs_data_t> hs_data(hs_task_count);
|
|
for (size_t i = 0; i < hs_task_count; i++) {
|
|
size_t idx = i;
|
|
|
|
auto & hs_cur = hs_data[i];
|
|
|
|
// Select a random example of those left in the prompt
|
|
if (randomize_tasks) {
|
|
std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
|
|
idx = dist(rng);
|
|
}
|
|
|
|
hs_cur.context = prompt_lines[idx*6];
|
|
hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
|
|
for (size_t j = 0; j < 4; j++) {
|
|
hs_cur.ending[j] = prompt_lines[idx*6+2+j];
|
|
hs_cur.seq_tokens[j] = common_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
|
|
}
|
|
|
|
// determine the common prefix of the endings
|
|
hs_cur.common_prefix = 0;
|
|
for (size_t k = 0; k < hs_cur.seq_tokens[0].size(); k++) {
|
|
if (hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[1][k] ||
|
|
hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[2][k] ||
|
|
hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[3][k]) {
|
|
break;
|
|
}
|
|
hs_cur.common_prefix++;
|
|
}
|
|
hs_cur.required_tokens = hs_cur.common_prefix +
|
|
hs_cur.seq_tokens[0].size() - hs_cur.common_prefix +
|
|
hs_cur.seq_tokens[1].size() - hs_cur.common_prefix +
|
|
hs_cur.seq_tokens[2].size() - hs_cur.common_prefix +
|
|
hs_cur.seq_tokens[3].size() - hs_cur.common_prefix;
|
|
|
|
//GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, true).size());
|
|
|
|
// Delete the selected random example from the prompt
|
|
if (randomize_tasks) {
|
|
prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) );
|
|
}
|
|
}
|
|
|
|
LOG_INF("%s : calculating hellaswag score over selected tasks.\n", __func__);
|
|
|
|
LOG("\ntask\tacc_norm\t95%% confidence interval\n");
|
|
|
|
double acc = 0.0f;
|
|
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
const int n_batch = params.n_batch;
|
|
|
|
const int n_vocab = llama_vocab_n_tokens(vocab);
|
|
|
|
const int max_tasks_per_batch = 32;
|
|
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
|
|
|
|
llama_batch batch = llama_batch_init(n_ctx, 0, 4);
|
|
|
|
std::vector<float> tok_logits(n_vocab);
|
|
// TODO: this could be made smaller; it's currently the worst-case size
|
|
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
|
|
|
|
std::vector<std::pair<size_t, llama_token>> eval_pairs;
|
|
std::vector<float> eval_results;
|
|
std::vector<std::thread> workers(std::thread::hardware_concurrency());
|
|
|
|
for (size_t i0 = 0; i0 < hs_task_count; i0++) {
|
|
int n_cur = 0;
|
|
|
|
size_t i1 = i0;
|
|
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
|
|
|
|
common_batch_clear(batch);
|
|
|
|
// batch as much tasks as possible into the available context
|
|
// each task has 4 unique sequence ids - one for each ending
|
|
// the common prefix is shared among the 4 sequences to save tokens
|
|
// we extract logits only from the last common token and from all ending tokens of each sequence
|
|
while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
|
|
auto & hs_cur = hs_data[i1];
|
|
int n_logits = 0;
|
|
|
|
const int s0 = 4*(i1 - i0);
|
|
if (s0 + 4 > max_seq) {
|
|
break;
|
|
}
|
|
|
|
for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
|
|
common_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
|
|
}
|
|
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
|
|
n_logits += 1;
|
|
|
|
for (int s = 0; s < 4; ++s) {
|
|
const size_t seq_tokens_size = hs_cur.seq_tokens[s].size();
|
|
// TODO: don't evaluate the last token of each sequence
|
|
for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
|
|
const bool needs_logits = i < seq_tokens_size - 1;
|
|
common_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
|
|
n_logits += needs_logits;
|
|
}
|
|
}
|
|
|
|
hs_cur.i_logits = i_logits;
|
|
i_logits += n_logits;
|
|
|
|
n_cur += hs_data[i1].required_tokens;
|
|
if (++i1 == hs_task_count) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (i0 == i1) {
|
|
LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, hs_data[i0].required_tokens);
|
|
return;
|
|
}
|
|
|
|
llama_memory_clear(llama_get_memory(ctx), true);
|
|
|
|
// decode all tasks [i0, i1)
|
|
if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
|
|
LOG_ERR("%s: llama_decode() failed\n", __func__);
|
|
return;
|
|
}
|
|
|
|
// Compute log-probs in parallel
|
|
// First we collect all tasks
|
|
eval_pairs.clear();
|
|
for (size_t i = i0; i < i1; ++i) {
|
|
auto & hs_cur = hs_data[i];
|
|
size_t li = 1; // skip the last logit of the common prefix (computed separately below)
|
|
for (int s = 0; s < 4; ++s) {
|
|
for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
|
|
eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]);
|
|
}
|
|
}
|
|
}
|
|
// Then we do the actual calculation
|
|
compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
|
|
|
|
size_t ir = 0;
|
|
|
|
// compute the logprobs for each ending of the decoded tasks
|
|
for (size_t i = i0; i < i1; ++i) {
|
|
auto & hs_cur = hs_data[i];
|
|
|
|
// get the logits of the last token of the common prefix
|
|
std::memcpy(tok_logits.data(), batch_logits.data() + hs_cur.i_logits*n_vocab, n_vocab*sizeof(float));
|
|
|
|
const auto first_probs = softmax(tok_logits);
|
|
|
|
for (int s = 0; s < 4; ++s) {
|
|
hs_cur.ending_logprob_count[s] = 1;
|
|
hs_cur.ending_logprob[s] = std::log(first_probs[hs_cur.seq_tokens[s][hs_cur.common_prefix]]);
|
|
for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
|
|
hs_cur.ending_logprob[s] += eval_results[ir++];
|
|
hs_cur.ending_logprob_count[s]++;
|
|
}
|
|
hs_cur.ending_logprob[s] /= hs_cur.ending_logprob_count[s];
|
|
}
|
|
|
|
// Find the ending with maximum logprob
|
|
size_t ending_logprob_max_idx = 0;
|
|
double ending_logprob_max_val = hs_cur.ending_logprob[0];
|
|
for (size_t s = 1; s < 4; s++) {
|
|
if (hs_cur.ending_logprob[s] > ending_logprob_max_val) {
|
|
ending_logprob_max_idx = s;
|
|
ending_logprob_max_val = hs_cur.ending_logprob[s];
|
|
}
|
|
}
|
|
|
|
//LOG("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_cur.gold_ending_idx);
|
|
|
|
// If the gold ending got the maximum logprobe add one accuracy point
|
|
if (ending_logprob_max_idx == hs_cur.gold_ending_idx) {
|
|
acc += 1.0;
|
|
}
|
|
|
|
double freq = acc / double(i + 1);
|
|
|
|
const double za = 1.95996398454;
|
|
|
|
// // Wald normal approx
|
|
// double conf =za*sqrt(freq*(1-freq)/double(i + 1));
|
|
// LOG("%zu\t%.8lf +/- %.8lf\n", i + 1, freq*100.0, conf*100.0);
|
|
|
|
// Wilson score interval, more accurate
|
|
double z = za * za / double(i + 1);
|
|
double cnf = z * sqrt(double(i + 1) * (4.0 * freq * (1 - freq) + z)) / (za + za);
|
|
double a = (freq + z * 0.5 - cnf) / (1.0 + z);
|
|
double b = (freq + z * 0.5 + cnf) / (1.0 + z);
|
|
|
|
// Print the accumulated accuracy mean x 100 and confidence interval
|
|
LOG("%zu\t%3.8lf%%\t[%3.4lf%%, %3.4lf%%]\n", i + 1, freq * 100.0, a * 100.0, b * 100.0);
|
|
}
|
|
|
|
i0 = i1 - 1;
|
|
}
|
|
|
|
llama_batch_free(batch);
|
|
|
|
LOG("\n");
|
|
}
|
|
|
|
struct winogrande_entry {
|
|
std::string first;
|
|
std::string second;
|
|
std::array<std::string, 2> choices;
|
|
int answer;
|
|
|
|
size_t i_logits;
|
|
size_t common_prefix;
|
|
size_t required_tokens;
|
|
size_t n_base1; // number of tokens for context + choice 1
|
|
size_t n_base2; // number of tokens for context + choice 2
|
|
std::vector<llama_token> seq_tokens[2];
|
|
};
|
|
|
|
static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string & prompt) {
|
|
std::vector<winogrande_entry> result;
|
|
std::istringstream in(prompt);
|
|
std::string line;
|
|
std::array<int, 4> comma_pos;
|
|
while (true) {
|
|
std::getline(in, line);
|
|
if (in.fail() || in.eof()) break;
|
|
int ipos = 0;
|
|
bool quote_open = false;
|
|
for (int i = 0; i < int(line.size()); ++i) {
|
|
if (!quote_open) {
|
|
if (line[i] == ',') {
|
|
comma_pos[ipos++] = i;
|
|
if (ipos == 4) break;
|
|
}
|
|
else if (line[i] == '"') {
|
|
quote_open = true;
|
|
}
|
|
}
|
|
else {
|
|
if (line[i] == '"') {
|
|
quote_open = false;
|
|
}
|
|
}
|
|
}
|
|
if (ipos != 4) {
|
|
LOG_ERR("%s: failed to find comma separators in <%s>\n", __func__, line.c_str());
|
|
continue;
|
|
}
|
|
auto sentence = line[comma_pos[0]+1] == '"' ? line.substr(comma_pos[0]+2, comma_pos[1] - comma_pos[0] - 3)
|
|
: line.substr(comma_pos[0]+1, comma_pos[1] - comma_pos[0] - 1);
|
|
auto choice1 = line.substr(comma_pos[1]+1, comma_pos[2] - comma_pos[1] - 1);
|
|
auto choice2 = line.substr(comma_pos[2]+1, comma_pos[3] - comma_pos[2] - 1);
|
|
auto answer = line.substr(comma_pos[3]+1, line.size() - comma_pos[3] - 1);
|
|
auto index = line.substr(0, comma_pos[0]);
|
|
int where = 0;
|
|
for ( ; where < int(sentence.size()); ++where) {
|
|
if (sentence[where] == '_') break;
|
|
}
|
|
if (where == int(sentence.size())) {
|
|
LOG_ERR("%s: no _ in <%s>\n", __func__, sentence.c_str());
|
|
continue;
|
|
}
|
|
std::istringstream stream(answer.c_str());
|
|
int i_answer; stream >> i_answer;
|
|
if (stream.fail() || i_answer < 1 || i_answer > 2) {
|
|
LOG_ERR("%s: failed to parse answer <%s>\n", __func__, answer.c_str());
|
|
continue;
|
|
}
|
|
result.emplace_back();
|
|
auto& wg = result.back();
|
|
wg.first = sentence.substr(0, where);
|
|
wg.second = sentence.substr(where + 1, sentence.size() - where - 1);
|
|
wg.choices[0] = std::move(choice1);
|
|
wg.choices[1] = std::move(choice2);
|
|
wg.answer = i_answer;
|
|
}
|
|
return result;
|
|
}
|
|
|
|
/*
|
|
* Evaluates the Winogrande score.
|
|
* Uses a CSV containing task index, dentence, choice 1, choice 2, answer (1 or 2)
|
|
* You can get one such dataset from e.g. https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp
|
|
* As an example, the 1st row in the above dataset is
|
|
*
|
|
* 0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2
|
|
*
|
|
*/
|
|
static void winogrande_score(llama_context * ctx, const common_params & params) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
constexpr int k_min_trailing_ctx = 3;
|
|
|
|
auto data = load_winogrande_from_csv(params.prompt);
|
|
if (data.empty()) {
|
|
LOG_ERR("%s: no tasks\n", __func__);
|
|
return;
|
|
}
|
|
|
|
LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, data.size());
|
|
|
|
if (params.winogrande_tasks > 0 && params.winogrande_tasks < data.size()) {
|
|
LOG_INF("%s : selecting %zu random tasks\n", __func__, params.winogrande_tasks);
|
|
std::mt19937 rng(1);
|
|
std::vector<int> aux(data.size());
|
|
for (int i = 0; i < int(data.size()); ++i) {
|
|
aux[i] = i;
|
|
}
|
|
float scale = 1/(1.f + (float)rng.max());
|
|
std::vector<winogrande_entry> selected;
|
|
selected.resize(params.winogrande_tasks);
|
|
for (int i = 0; i < int(params.winogrande_tasks); ++i) {
|
|
int j = int(scale*rng()*aux.size());
|
|
selected[i] = std::move(data[aux[j]]);
|
|
aux[j] = aux.back();
|
|
aux.pop_back();
|
|
}
|
|
data = std::move(selected);
|
|
}
|
|
|
|
LOG_INF("%s : tokenizing selected tasks\n", __func__);
|
|
|
|
for (auto & task : data) {
|
|
task.seq_tokens[0] = common_tokenize(ctx, task.first + task.choices[0] + task.second, true);
|
|
task.seq_tokens[1] = common_tokenize(ctx, task.first + task.choices[1] + task.second, true);
|
|
|
|
task.common_prefix = 0;
|
|
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
|
|
if (task.seq_tokens[0][k] != task.seq_tokens[1][k]) {
|
|
break;
|
|
}
|
|
task.common_prefix++;
|
|
}
|
|
|
|
// TODO: the last token of each of the sequences don't need to be evaluated
|
|
task.required_tokens = task.common_prefix +
|
|
task.seq_tokens[0].size() - task.common_prefix +
|
|
task.seq_tokens[1].size() - task.common_prefix;
|
|
|
|
task.n_base1 = common_tokenize(ctx, task.first + task.choices[0], true).size();
|
|
task.n_base2 = common_tokenize(ctx, task.first + task.choices[1], true).size();
|
|
}
|
|
|
|
LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__);
|
|
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
const int n_batch = params.n_batch;
|
|
|
|
const int n_vocab = llama_vocab_n_tokens(vocab);
|
|
|
|
const int max_tasks_per_batch = 128;
|
|
const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
|
|
|
|
llama_batch batch = llama_batch_init(n_ctx, 0, 2);
|
|
|
|
std::vector<float> tok_logits(n_vocab);
|
|
// TODO: this could be made smaller; it's currently the worst-case size
|
|
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
|
|
|
|
std::vector<std::pair<size_t, llama_token>> eval_pairs;
|
|
std::vector<float> eval_results;
|
|
std::vector<std::thread> workers(std::thread::hardware_concurrency());
|
|
|
|
int n_correct = 0;
|
|
int n_done = 0;
|
|
|
|
for (size_t i0 = 0; i0 < data.size(); i0++) {
|
|
int n_cur = 0;
|
|
|
|
size_t i1 = i0;
|
|
size_t i_logits = 0;
|
|
|
|
common_batch_clear(batch);
|
|
|
|
while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
|
|
int n_logits = 0;
|
|
const int s0 = 2*(i1 - i0);
|
|
if (s0 + 2 > max_seq) {
|
|
break;
|
|
}
|
|
|
|
for (size_t i = 0; i < data[i1].common_prefix; ++i) {
|
|
common_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
|
|
}
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
n_logits += 1;
|
|
|
|
for (int s = 0; s < 2; ++s) {
|
|
// TODO: end before the last token, no need to predict past the end of the sequences
|
|
for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
|
|
common_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
|
|
n_logits += 1;
|
|
}
|
|
}
|
|
|
|
data[i1].i_logits = i_logits;
|
|
i_logits += n_logits;
|
|
|
|
n_cur += data[i1].required_tokens;
|
|
if (++i1 == data.size()) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (i0 == i1) {
|
|
LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, data[i0].required_tokens);
|
|
return;
|
|
}
|
|
|
|
llama_memory_clear(llama_get_memory(ctx), true);
|
|
|
|
// decode all tasks [i0, i1)
|
|
if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
|
|
LOG_ERR("%s: llama_decode() failed\n", __func__);
|
|
return;
|
|
}
|
|
|
|
eval_pairs.clear();
|
|
for (size_t i = i0; i < i1; ++i) {
|
|
auto & task = data[i];
|
|
|
|
const bool skip_choice =
|
|
task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
|
|
task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
|
|
|
|
const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
|
|
const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
|
|
size_t li = n_base1 - task.common_prefix;
|
|
for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
|
|
eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]);
|
|
}
|
|
const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
|
|
const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
|
|
// FIXME: this uses the wrong first logits when not skipping the choice word
|
|
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix;
|
|
for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
|
|
eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]);
|
|
}
|
|
}
|
|
compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
|
|
|
|
size_t ir = 0;
|
|
for (size_t i = i0; i < i1; ++i) {
|
|
auto & task = data[i];
|
|
|
|
const bool skip_choice =
|
|
task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
|
|
task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
|
|
|
|
float score_1st = 0;
|
|
const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
|
|
const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
|
|
for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
|
|
score_1st += eval_results[ir++];
|
|
}
|
|
score_1st /= (task.seq_tokens[0].size() - n_base1 - last_1st);
|
|
|
|
float score_2nd = 0;
|
|
const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
|
|
const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
|
|
for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
|
|
score_2nd += eval_results[ir++];
|
|
}
|
|
score_2nd /= (task.seq_tokens[1].size() - n_base2 - last_2nd);
|
|
|
|
int result = score_1st > score_2nd ? 1 : 2;
|
|
|
|
if (result == task.answer) {
|
|
++n_correct;
|
|
}
|
|
++n_done;
|
|
|
|
// print the accumulated accuracy mean x 100
|
|
LOG("%zu\t%.4lf\t%10.6f %10.6f %d %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer);
|
|
}
|
|
|
|
i0 = i1 - 1;
|
|
}
|
|
|
|
LOG("\n");
|
|
|
|
if (n_done < 100) return;
|
|
|
|
const float p = 1.f*n_correct/n_done;
|
|
const float sigma = 100.f*sqrt(p*(1-p)/(n_done-1));
|
|
|
|
LOG_INF("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma);
|
|
}
|
|
|
|
static bool deserialize_string(std::istream & in, std::string & str) {
|
|
uint32_t size;
|
|
if (!in.read((char *)&size, sizeof(size)).fail()) {
|
|
str.resize(size);
|
|
if (!in.read((char *)&str[0], size).fail()) return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
struct multiple_choice_answers {
|
|
std::vector<std::string> answers;
|
|
std::vector<int> labels;
|
|
bool deserialize(std::istream& in) {
|
|
uint32_t n;
|
|
in.read((char *)&n, sizeof(n));
|
|
if (in.fail() || n > 100) return false; // 100 as max. number of answers should be good enough for any practical purpose
|
|
answers.resize(n);
|
|
labels.resize(n);
|
|
for (auto& a : answers) {
|
|
if (!deserialize_string(in, a)) return false;
|
|
}
|
|
in.read((char *)labels.data(), n*sizeof(int));
|
|
return !in.fail();
|
|
}
|
|
};
|
|
|
|
struct multiple_choice_task {
|
|
std::string question; // the question (or context that needs to be continued)
|
|
multiple_choice_answers mc1; // possible answers (continuations) with a single correct answer
|
|
multiple_choice_answers mc2; // possible answers (continuations) with multiple correct answers - not handled yet
|
|
bool deserialize(std::istream& in) {
|
|
if (!deserialize_string(in, question)) return false;
|
|
return mc1.deserialize(in) && mc2.deserialize(in);
|
|
}
|
|
|
|
// For evaluation
|
|
size_t i_logits; // starting index of logits in the llama_batch
|
|
size_t common_prefix; // max number of initial tokens that are the same in all sentences
|
|
size_t required_tokens; // needed number of tokens to evaluate all answers
|
|
std::vector<std::vector<llama_token>> seq_tokens;
|
|
std::vector<float> log_probs;
|
|
};
|
|
|
|
static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choice_task& task, bool log_error) {
|
|
if (task.question.empty() || task.mc1.answers.empty()) {
|
|
if (log_error) {
|
|
LOG_ERR("%s: found bad task with empty question and/or answers\n", __func__);
|
|
}
|
|
return false;
|
|
}
|
|
task.seq_tokens.reserve(task.mc1.answers.size());
|
|
for (auto& answer : task.mc1.answers) {
|
|
if (answer.empty()) {
|
|
if (log_error) {
|
|
LOG_ERR("%s: found empty answer\n", __func__);
|
|
}
|
|
return false;
|
|
}
|
|
task.seq_tokens.emplace_back(::common_tokenize(ctx, task.question + " " + answer, true));
|
|
}
|
|
auto min_len = task.seq_tokens.front().size();
|
|
for (auto& seq : task.seq_tokens) {
|
|
min_len = std::min(min_len, seq.size());
|
|
}
|
|
task.common_prefix = 0;
|
|
for (size_t k = 0; k < min_len; ++k) {
|
|
auto token = task.seq_tokens[0][k];
|
|
bool all_same = true;
|
|
for (size_t i = 1; i < task.seq_tokens.size(); ++i) {
|
|
if (task.seq_tokens[i][k] != token) {
|
|
all_same = false;
|
|
break;
|
|
}
|
|
}
|
|
if (!all_same) {
|
|
break;
|
|
}
|
|
++task.common_prefix;
|
|
}
|
|
task.required_tokens = task.common_prefix;
|
|
for (auto& seq : task.seq_tokens) {
|
|
task.required_tokens += seq.size() - task.common_prefix;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// Calculates score for multiple choice tasks with single correct answer from prompt.
|
|
// Commonly used LLM evaluation metrics of this type are
|
|
// * ARC
|
|
// * HellaSwag
|
|
// * MMLU
|
|
// * TruthfulQA
|
|
//
|
|
// Validation datasets for these 4 tests can be found at
|
|
// https://huggingface.co/datasets/ikawrakow/validation-datasets-for-llama.cpp
|
|
// The data for these datasets was extracted from
|
|
// git@hf.co:datasets/allenai/ai2_arc
|
|
// https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
|
|
// git@hf.co:datasets/Stevross/mmlu
|
|
// https://huggingface.co/datasets/truthful_qa
|
|
//
|
|
static void multiple_choice_score(llama_context * ctx, const common_params & params) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
std::istringstream strstream(params.prompt);
|
|
uint32_t n_task;
|
|
strstream.read((char *)&n_task, sizeof(n_task));
|
|
if (strstream.fail() || n_task == 0) {
|
|
LOG_ERR("%s: no tasks\n", __func__);
|
|
return;
|
|
}
|
|
LOG_INF("%s: there are %u tasks in prompt\n", __func__, n_task);
|
|
std::vector<uint32_t> task_pos(n_task);
|
|
strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
|
|
if (strstream.fail()) {
|
|
LOG_ERR("%s: failed to read task positions from prompt\n", __func__);
|
|
return;
|
|
}
|
|
|
|
std::vector<multiple_choice_task> tasks;
|
|
if (params.multiple_choice_tasks == 0 || params.multiple_choice_tasks >= (size_t)n_task) {
|
|
// Use all tasks
|
|
tasks.resize(n_task);
|
|
LOG_INF("%s: reading tasks", __func__);
|
|
int n_dot = std::max((int) n_task/100, 1);
|
|
int i = 0;
|
|
for (auto& task : tasks) {
|
|
++i;
|
|
if (!task.deserialize(strstream)) {
|
|
LOG_ERR("%s: failed to read task %d of %u\n", __func__, i, n_task);
|
|
return;
|
|
}
|
|
if (i%n_dot == 0) LOG(".");
|
|
}
|
|
LOG("done\n");
|
|
}
|
|
else {
|
|
LOG_INF("%s: selecting %zu random tasks from %u tasks available\n", __func__, params.multiple_choice_tasks, n_task);
|
|
std::mt19937 rng(1);
|
|
std::vector<int> aux(n_task);
|
|
for (uint32_t i = 0; i < n_task; ++i) aux[i] = i;
|
|
float scale = 1.f/(1.f + (float)std::mt19937::max());
|
|
tasks.resize(params.multiple_choice_tasks);
|
|
for (auto& task : tasks) {
|
|
int j = (int)(scale * rng() * aux.size());
|
|
int idx = aux[j];
|
|
aux[j] = aux.back();
|
|
aux.pop_back();
|
|
strstream.seekg(task_pos[idx], std::ios::beg);
|
|
if (!task.deserialize(strstream)) {
|
|
LOG_ERR("%s: failed to read task %d at position %u\n", __func__, idx, task_pos[idx]);
|
|
return;
|
|
}
|
|
}
|
|
n_task = params.multiple_choice_tasks;
|
|
}
|
|
|
|
LOG_INF("%s: preparing task data", __func__);
|
|
if (n_task > 500) {
|
|
LOG("...");
|
|
std::atomic<int> counter(0);
|
|
std::atomic<int> n_bad(0);
|
|
auto prepare = [&counter, &n_bad, &tasks, ctx] () {
|
|
int num_tasks = tasks.size();
|
|
int n_bad_local = 0;
|
|
while (true) {
|
|
int first = counter.fetch_add(K_TOKEN_CHUNK);
|
|
if (first >= num_tasks) {
|
|
if (n_bad_local > 0) n_bad += n_bad_local;
|
|
break;
|
|
}
|
|
int last = std::min(first + K_TOKEN_CHUNK, num_tasks);
|
|
for (int i = first; i < last; ++i) {
|
|
if (!multiple_choice_prepare_one_task(ctx, tasks[i], false)) ++n_bad_local;
|
|
}
|
|
}
|
|
};
|
|
size_t max_thread = std::thread::hardware_concurrency();
|
|
max_thread = std::min(max_thread, (tasks.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK);
|
|
std::vector<std::thread> workers(max_thread-1);
|
|
for (auto& w : workers) w = std::thread(prepare);
|
|
prepare();
|
|
for (auto& w : workers) w.join();
|
|
LOG("done\n");
|
|
int nbad = n_bad;
|
|
if (nbad > 0) {
|
|
LOG_ERR("%s: found %d malformed tasks\n", __func__, nbad);
|
|
return;
|
|
}
|
|
} else {
|
|
int n_dot = std::max((int) n_task/100, 1);
|
|
int i_task = 0;
|
|
for (auto& task : tasks) {
|
|
++i_task;
|
|
if (!multiple_choice_prepare_one_task(ctx, task, true)) {
|
|
return;
|
|
}
|
|
if (i_task%n_dot == 0) {
|
|
LOG(".");
|
|
}
|
|
}
|
|
LOG("done\n");
|
|
}
|
|
|
|
LOG_INF("%s : calculating TruthfulQA score over %zu tasks.\n", __func__, tasks.size());
|
|
|
|
LOG("\ntask\tacc_norm\n");
|
|
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
const int n_batch = params.n_batch;
|
|
|
|
const int n_vocab = llama_vocab_n_tokens(vocab);
|
|
|
|
const int max_tasks_per_batch = 32;
|
|
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
|
|
|
|
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
|
|
|
|
std::vector<float> tok_logits(n_vocab);
|
|
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
|
|
|
|
std::vector<std::pair<size_t, llama_token>> eval_pairs;
|
|
std::vector<float> eval_results;
|
|
std::vector<std::thread> workers(std::thread::hardware_concurrency());
|
|
std::vector<int> batch_indeces;
|
|
|
|
int n_done = 0;
|
|
int n_correct = 0;
|
|
int n_tot_answers = 0;
|
|
|
|
for (size_t i0 = 0; i0 < tasks.size(); i0++) {
|
|
int n_cur = 0;
|
|
|
|
size_t i1 = i0;
|
|
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
|
|
|
|
common_batch_clear(batch);
|
|
|
|
// batch as much tasks as possible into the available context
|
|
// each task has 4 unique sequence ids - one for each ending
|
|
// the common prefix is shared among the 4 sequences to save tokens
|
|
// we extract logits only from the last common token and from all ending tokens of each sequence
|
|
int s0 = 0;
|
|
while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
|
|
auto& cur_task = tasks[i1];
|
|
int n_logits = 0;
|
|
|
|
int num_answers = cur_task.seq_tokens.size();
|
|
if (s0 + num_answers > max_seq) {
|
|
if (s0 == 0) {
|
|
LOG_ERR("%s : task %zu requires a higher -np|--parallel value (at least %d)\n", __func__, i0, num_answers);
|
|
return;
|
|
}
|
|
break;
|
|
}
|
|
|
|
if (int(batch_indeces.size()) != num_answers) {
|
|
batch_indeces.resize(num_answers);
|
|
}
|
|
|
|
for (int s = 0; s < num_answers; ++s) {
|
|
batch_indeces[s] = s0 + s;
|
|
}
|
|
|
|
for (size_t i = 0; i < cur_task.common_prefix; ++i) {
|
|
//llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
|
|
common_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
|
|
}
|
|
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
|
|
n_logits += 1;
|
|
|
|
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
|
|
const size_t seq_tokens_size = cur_task.seq_tokens[s].size();
|
|
// TODO: don't evaluate the last token of each sequence
|
|
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
|
|
const bool needs_logits = i < seq_tokens_size - 1;
|
|
common_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
|
|
n_logits += needs_logits;
|
|
}
|
|
}
|
|
|
|
s0 += num_answers;
|
|
|
|
cur_task.i_logits = i_logits;
|
|
i_logits += n_logits;
|
|
|
|
n_cur += cur_task.required_tokens;
|
|
if (++i1 == tasks.size()) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (i0 == i1) {
|
|
LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, tasks[i0].required_tokens);
|
|
return;
|
|
}
|
|
|
|
llama_memory_clear(llama_get_memory(ctx), true);
|
|
|
|
// decode all tasks [i0, i1)
|
|
if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
|
|
LOG_ERR("%s: llama_decode() failed\n", __func__);
|
|
return;
|
|
}
|
|
|
|
// Compute log-probs in parallel
|
|
// First we collect all tasks
|
|
eval_pairs.clear();
|
|
for (size_t i = i0; i < i1; ++i) {
|
|
auto& cur_task = tasks[i];
|
|
size_t li = 1; // skip the last logit of the common prefix (computed separately below)
|
|
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
|
|
for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
|
|
eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]);
|
|
}
|
|
}
|
|
}
|
|
// Then we do the actual calculation
|
|
compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
|
|
|
|
size_t ir = 0;
|
|
|
|
// compute the logprobs for each ending of the decoded tasks
|
|
for (size_t i = i0; i < i1; ++i) {
|
|
auto & cur_task = tasks[i];
|
|
//LOG("==== Evaluating <%s> with correct answer ", cur_task.question.c_str());
|
|
//for (int j = 0; j < int(cur_task.mc1.labels.size()); ++j) {
|
|
// if (cur_task.mc1.labels[j] == 1) {
|
|
// LOG("%d", j+1);
|
|
// }
|
|
//}
|
|
//LOG("\n common_prefix: %zu\n", cur_task.common_prefix);
|
|
|
|
// get the logits of the last token of the common prefix
|
|
std::memcpy(tok_logits.data(), batch_logits.data() + cur_task.i_logits*n_vocab, n_vocab*sizeof(float));
|
|
|
|
const auto first_probs = softmax(tok_logits);
|
|
|
|
cur_task.log_probs.resize(cur_task.seq_tokens.size());
|
|
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
|
|
size_t count = 1;
|
|
float log_prob = std::log(first_probs[cur_task.seq_tokens[s][cur_task.common_prefix]]);
|
|
for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
|
|
//LOG(" %zu %g\n", ir, eval_results[ir]);
|
|
++count;
|
|
log_prob += eval_results[ir++];
|
|
}
|
|
cur_task.log_probs[s] = log_prob / count;
|
|
//LOG(" Final: %g\n", log_prob / count);
|
|
//LOG(" <%s> : %g\n", cur_task.mc1.answers[s].c_str(), log_prob/count);
|
|
}
|
|
|
|
// Find the ending with maximum logprob
|
|
size_t logprob_max_idx = 0;
|
|
float logprob_max_val = cur_task.log_probs[0];
|
|
for (size_t s = 1; s < cur_task.log_probs.size(); s++) {
|
|
if (cur_task.log_probs[s] > logprob_max_val) {
|
|
logprob_max_val = cur_task.log_probs[s];
|
|
logprob_max_idx = s;
|
|
}
|
|
}
|
|
|
|
n_tot_answers += cur_task.log_probs.size();
|
|
if (cur_task.mc1.labels[logprob_max_idx] == 1) {
|
|
++n_correct;
|
|
}
|
|
++n_done;
|
|
|
|
// Print the accumulated accuracy mean x 100
|
|
LOG("%d\t%.8lf\n", n_done, 100.*n_correct/n_done);
|
|
}
|
|
|
|
i0 = i1 - 1;
|
|
}
|
|
|
|
llama_batch_free(batch);
|
|
|
|
if (n_done < 100 && (params.multiple_choice_tasks != 0 && params.multiple_choice_tasks < (size_t)n_task)) return;
|
|
|
|
float p = 1.f*n_correct/n_done;
|
|
float sigma = sqrt(p*(1-p)/(n_done-1));
|
|
LOG("\n");
|
|
LOG_INF("Final result: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma);
|
|
p = 1.f*n_done/n_tot_answers;
|
|
sigma = sqrt(p*(1-p)/(n_done-1));
|
|
LOG_INF("Random chance: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma);
|
|
|
|
LOG_INF("\n");
|
|
}
|
|
|
|
static void kl_divergence(llama_context * ctx, const common_params & params) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
if (params.logits_file.empty()) {
|
|
LOG_ERR("%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) {
|
|
LOG_ERR("%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) {
|
|
LOG_ERR("%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)) {
|
|
LOG_ERR("%s: %s has been computed with %u, 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;
|
|
int n_chunk;
|
|
in.read((char *)&n_vocab, sizeof(n_vocab));
|
|
in.read((char *)&n_chunk, sizeof(n_chunk));
|
|
if (in.fail()) {
|
|
LOG_ERR("%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str());
|
|
return;
|
|
}
|
|
if (n_vocab != llama_vocab_n_tokens(vocab)) {
|
|
LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_vocab_n_tokens(vocab));
|
|
}
|
|
|
|
std::vector<llama_token> tokens(size_t(n_ctx) * n_chunk);
|
|
if (in.read((char *)tokens.data(), tokens.size()*sizeof(tokens[0])).fail()) {
|
|
LOG_ERR("%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_vocab_get_add_bos(vocab);
|
|
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
|
|
|
|
std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
|
|
std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
|
|
std::vector<float> p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
|
|
std::vector<float> logits;
|
|
if (num_batches > 1) {
|
|
logits.reserve(size_t(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);
|
|
};
|
|
auto covariance = [] (double suma, double sumb, double sumab, size_t count) {
|
|
if (count < 10) {
|
|
return 0.0;
|
|
}
|
|
double var = sumab/count - (suma/count)*(sumb/count);
|
|
var /= count - 1;
|
|
return var;
|
|
};
|
|
|
|
kl_divergence_result kld;
|
|
auto kld_ptr = kld_values.data();
|
|
auto p_diff_ptr = p_diff_values.data();
|
|
|
|
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()) {
|
|
LOG_ERR("%s: failed reading log-probs for chunk %d\n", __func__, i);
|
|
return;
|
|
}
|
|
|
|
// clear the KV cache
|
|
llama_memory_clear(llama_get_memory(ctx), true);
|
|
|
|
llama_batch batch = llama_batch_init(n_batch, 0, 1);
|
|
|
|
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_vocab_bos(vocab);
|
|
}
|
|
|
|
common_batch_clear(batch);
|
|
for (int i = 0; i < batch_size; i++) {
|
|
common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
|
|
}
|
|
|
|
if (llama_decode(ctx, batch)) {
|
|
LOG_ERR("%s : failed to eval\n", __func__);
|
|
llama_batch_free(batch);
|
|
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 + size_t(batch_size) * n_vocab);
|
|
}
|
|
}
|
|
|
|
llama_batch_free(batch);
|
|
|
|
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();
|
|
LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
|
|
int total_seconds = (int)(t_total * n_chunk);
|
|
if (total_seconds >= 60*60) {
|
|
LOG("%d hours ", total_seconds / (60*60));
|
|
total_seconds = total_seconds % (60*60);
|
|
}
|
|
LOG("%.2f minutes\n", total_seconds / 60.0);
|
|
}
|
|
LOG("\n");
|
|
LOG("chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p\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 + size_t(first)*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
|
|
workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr);
|
|
p_diff_ptr += n_ctx - 1 - first;
|
|
kld_ptr += n_ctx - 1 - first;
|
|
|
|
LOG("%4d", i+1);
|
|
|
|
auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
|
|
const double ppl_val = exp(log_ppl.first);
|
|
const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
|
|
LOG(" %9.4lf ± %9.4lf", ppl_val, ppl_unc);
|
|
|
|
auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
|
|
const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
|
|
const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
|
|
const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
|
|
LOG(" %10.5lf ± %10.5lf", log_ppl_ratio_val, log_ppl_ratio_unc);
|
|
|
|
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
|
|
LOG(" %10.5lf ± %10.5lf", kl_div.first, kl_div.second);
|
|
|
|
auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
|
|
const double p_diff_rms_val = sqrt(p_diff_mse.first);
|
|
const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
|
|
LOG(" %6.3lf ± %6.3lf %%", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
|
|
|
|
double p_top_val = 1.*kld.n_same_top/kld.count;
|
|
double p_top_unc = sqrt(p_top_val*(1 - p_top_val)/(kld.count - 1));
|
|
LOG(" %6.3lf ± %6.3lf %%", 100.0*p_top_val, 100.0*p_top_unc);
|
|
|
|
LOG("\n");
|
|
|
|
logits.clear();
|
|
}
|
|
LOG("\n");
|
|
|
|
if (kld.count < 100) return; // we do not wish to do statistics on so few values
|
|
|
|
std::sort(kld_values.begin(), kld_values.end());
|
|
std::sort(p_diff_values.begin(), p_diff_values.end());
|
|
|
|
LOG("====== Perplexity statistics ======\n");
|
|
|
|
auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
|
|
const double ppl_val = exp(log_ppl.first);
|
|
const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
|
|
LOG("Mean PPL(Q) : %10.6lf ± %10.6lf\n", ppl_val, ppl_unc);
|
|
|
|
auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
|
|
const double ppl_base_val = exp(log_ppl_base.first);
|
|
const double ppl_base_unc = ppl_base_val * log_ppl_base.second; // ppl_base_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_base.second ** 2 )
|
|
LOG("Mean PPL(base) : %10.6lf ± %10.6lf\n", ppl_base_val, ppl_base_unc);
|
|
|
|
const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
|
|
// LOG("Cov(ln(PPL(Q)), ln(PPL(base))): %10.6lf\n", log_ppl_cov);
|
|
const double log_ppl_cor = log_ppl_cov / (log_ppl.second*log_ppl_base.second);
|
|
LOG("Cor(ln(PPL(Q)), ln(PPL(base))): %6.2lf%%\n", 100.0*log_ppl_cor);
|
|
|
|
const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
|
|
const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
|
|
LOG("Mean ln(PPL(Q)/PPL(base)) : %10.6lf ± %10.6lf\n", log_ppl_ratio_val, log_ppl_ratio_unc);
|
|
|
|
const double ppl_ratio_val = exp(log_ppl_ratio_val);
|
|
const double ppl_ratio_unc = ppl_ratio_val * log_ppl_ratio_unc; // ppl_ratio_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_ratio.second ** 2 )
|
|
LOG("Mean PPL(Q)/PPL(base) : %10.6lf ± %10.6lf\n", ppl_ratio_val, ppl_ratio_unc);
|
|
|
|
const double ppl_cov = ppl_val * ppl_base_val * log_ppl_cov;
|
|
const double ppl_diff_val = ppl_val - ppl_base_val;
|
|
const double ppl_diff_unc = sqrt(ppl_unc*ppl_unc + ppl_base_unc*ppl_base_unc - 2.0*ppl_cov);
|
|
LOG("Mean PPL(Q)-PPL(base) : %10.6lf ± %10.6lf\n", ppl_diff_val, ppl_diff_unc);
|
|
|
|
LOG("\n");
|
|
|
|
LOG("====== KL divergence statistics ======\n");
|
|
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
|
|
LOG("Mean KLD: %10.6lf ± %10.6lf\n", kl_div.first, kl_div.second);
|
|
auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1])
|
|
: kld_values[kld_values.size()/2];
|
|
|
|
auto percentile = [] (std::vector<float> values, float fraction) {
|
|
if (fraction <= 0) return values.front();
|
|
if (fraction >= 1) return values.back();
|
|
float p = fraction*(values.size() - 1);
|
|
size_t ip = size_t(p); p -= ip;
|
|
return (1 - p)*values[ip] + p*values[std::min(ip+1, values.size()-1)];
|
|
};
|
|
|
|
LOG("Maximum KLD: %10.6f\n", kld_values.back());
|
|
LOG("99.9%% KLD: %10.6f\n", percentile(kld_values, 0.999f));
|
|
LOG("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f));
|
|
LOG("90.0%% KLD: %10.6f\n", percentile(kld_values, 0.900f));
|
|
LOG("Median KLD: %10.6f\n", kld_median);
|
|
LOG("10.0%% KLD: %10.6f\n", percentile(kld_values, 0.100f));
|
|
LOG(" 5.0%% KLD: %10.6f\n", percentile(kld_values, 0.050f));
|
|
LOG(" 1.0%% KLD: %10.6f\n", percentile(kld_values, 0.010f));
|
|
LOG("Minimum KLD: %10.6f\n", kld_values.front());
|
|
|
|
LOG("\n");
|
|
|
|
LOG("====== Token probability statistics ======\n");
|
|
|
|
auto p_diff = mean_and_uncertainty(kld.sum_p_diff, kld.sum_p_diff2, kld.count);
|
|
LOG("Mean Δp: %6.3lf ± %5.3lf %%\n", 100.0*p_diff.first, 100.0*p_diff.second);
|
|
|
|
auto p_diff_median = p_diff_values.size()%2 == 0 ? 0.5f*(p_diff_values[p_diff_values.size()/2] + p_diff_values[p_diff_values.size()/2-1])
|
|
: p_diff_values[p_diff_values.size()/2];
|
|
|
|
LOG("Maximum Δp: %6.3lf%%\n", 100.0*p_diff_values.back());
|
|
LOG("99.9%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.999f));
|
|
LOG("99.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.990f));
|
|
LOG("95.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.950f));
|
|
LOG("90.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.900f));
|
|
LOG("75.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.750f));
|
|
LOG("Median Δp: %6.3lf%%\n", 100.0*p_diff_median);
|
|
LOG("25.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.250f));
|
|
LOG("10.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.100f));
|
|
LOG(" 5.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.050f));
|
|
LOG(" 1.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.010f));
|
|
LOG(" 0.1%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.001f));
|
|
LOG("Minimum Δp: %6.3lf%%\n", 100.0*p_diff_values.front());
|
|
|
|
auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
|
|
// LOG("MSE Δp : %10.6lf ± %10.6lf\n", p_diff_mse.first, p_diff_mse.second);
|
|
|
|
const double p_diff_rms_val = sqrt(p_diff_mse.first);
|
|
const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
|
|
LOG("RMS Δp : %6.3lf ± %5.3lf %%\n", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
|
|
|
|
const double same_top_p = 1.0*kld.n_same_top/kld.count;
|
|
LOG("Same top p: %6.3lf ± %5.3lf %%\n", 100.0*same_top_p, 100.0*sqrt(same_top_p*(1.0 - same_top_p)/(kld.count - 1)));
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
common_params params;
|
|
|
|
params.n_ctx = 512;
|
|
params.escape = false;
|
|
|
|
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
|
|
return 1;
|
|
}
|
|
|
|
common_init();
|
|
|
|
const int32_t n_ctx = params.n_ctx;
|
|
|
|
if (n_ctx <= 0) {
|
|
LOG_ERR("%s: perplexity tool requires '--ctx-size' > 0\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence;
|
|
|
|
if (ppl) {
|
|
const int32_t n_seq = std::max(1, params.n_batch / n_ctx);
|
|
const int32_t n_kv = n_seq * n_ctx;
|
|
|
|
params.n_parallel = n_seq;
|
|
params.n_ctx = n_kv;
|
|
|
|
params.n_batch = std::min(params.n_batch, n_kv);
|
|
} else {
|
|
params.n_batch = std::min(params.n_batch, params.n_ctx);
|
|
if (params.kl_divergence) {
|
|
params.n_parallel = 1;
|
|
} else {
|
|
// ensure there's at least enough seq_ids for HellaSwag
|
|
params.n_parallel = std::max(4, params.n_parallel);
|
|
}
|
|
}
|
|
|
|
if (params.ppl_stride > 0) {
|
|
LOG_INF("Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
|
|
params.n_ctx, params.n_ctx + params.ppl_stride/2);
|
|
params.n_ctx += params.ppl_stride/2;
|
|
}
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
// load the model and apply lora adapter, if any
|
|
common_init_result llama_init = common_init_from_params(params);
|
|
|
|
llama_model * model = llama_init.model.get();
|
|
llama_context * ctx = llama_init.context.get();
|
|
|
|
if (model == NULL) {
|
|
LOG_ERR("%s: unable to load model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
const int n_ctx_train = llama_model_n_ctx_train(model);
|
|
|
|
if (params.n_ctx > n_ctx_train) {
|
|
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n",
|
|
__func__, n_ctx_train, params.n_ctx);
|
|
}
|
|
|
|
// print system information
|
|
{
|
|
LOG_INF("\n");
|
|
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
|
|
}
|
|
|
|
struct results_perplexity results;
|
|
if (params.hellaswag) {
|
|
hellaswag_score(ctx, params);
|
|
} else if (params.winogrande) {
|
|
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, n_ctx);
|
|
}
|
|
|
|
LOG("\n");
|
|
llama_perf_context_print(ctx);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|