#include "ggml.h"
#include "llama.h"
#ifdef NDEBUG
#undef NDEBUG
#endif
#include <algorithm>
#include <cmath>
#include <string>
#include <vector>
extern struct llama_sampler * llama_sampler_init_dry_testing(float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers);
static void dump(const llama_token_data_array * cur_p) {
for (size_t i = 0; i < cur_p->size; i++) {
printf("%d: %f (%f)\n", cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
}
}
#define DUMP(__cur_p) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__cur_p)); printf("-\n"); } while(0)
struct sampler_tester {
sampler_tester(size_t n_vocab) {
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(token_id);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false };
}
sampler_tester(const std::vector<float> & probs, const std::vector<float> & probs_expected) : probs_expected(probs_expected) {
cur.reserve(probs.size());
for (llama_token token_id = 0; token_id < (llama_token)probs.size(); token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, probs[token_id]});
}
cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false };
}
void apply(llama_sampler * sampler) {
llama_sampler_apply(sampler, &cur_p);
llama_sampler_free(sampler);
}
void check() {
GGML_ASSERT(cur_p.size == probs_expected.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - probs_expected[i]) < 1e-5);
}
}
llama_token_data_array cur_p;
private:
const std::vector<float> probs_expected;
std::vector<llama_token_data> cur;
};
static llama_token sample_dist(llama_sampler * sampler, const std::vector<float> & logits) {
std::vector<llama_token_data> cur;
for (llama_token token_id = 0; token_id < (llama_token) logits.size(); ++token_id) {
cur.push_back({ token_id, logits[token_id], 0.0f });
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_sampler_apply(sampler, &cur_p);
GGML_ASSERT(cur_p.selected >= 0);
GGML_ASSERT((size_t) cur_p.selected < cur_p.size);
return cur_p.data[cur_p.selected].id;
}
static void test_dist_singleton_rng() {
llama_sampler * singleton = llama_sampler_init_dist(4242);
llama_sampler * control = llama_sampler_init_dist(4242);
sample_dist(singleton, { 0.0f });
sample_dist(control, { 0.0f, 0.0f });
const std::vector<float> logits(256, 0.0f);
for (int i = 0; i < 4; ++i) {
GGML_ASSERT(sample_dist(singleton, logits) == sample_dist(control, logits));
}
llama_sampler_free(singleton);
llama_sampler_free(control);
}
static void test_temp(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_temp(temp));
tester.apply(llama_sampler_init_dist(0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_temp_ext(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp, float delta, float exponent) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_temp_ext(temp, delta, exponent));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_top_k(const std::vector<float> & probs, const std::vector<float> & probs_expected, int k) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_top_k(k));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_top_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_top_p(p, 0));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_min_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_min_p(p, 0));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_xtc(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p, float t) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_xtc(p, t, 0, 0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_typical(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_typical(p, 0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_penalties(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & probs_expected, float repeat_penalty, float alpha_frequency, float alpha_presence
) {
GGML_ASSERT(probs.size() == probs_expected.size());
sampler_tester tester(probs, probs_expected);
auto * sampler = llama_sampler_init_penalties((int32_t) probs.size(), (int32_t) last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
for (size_t i = 0; i < last_tokens.size(); i++) {
llama_sampler_accept(sampler, last_tokens[i]);
}
DUMP(&tester.cur_p);
tester.apply(sampler);
tester.apply(llama_sampler_init_dist(0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_dry(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & expected_probs, float dry_multiplier, float dry_base,
int dry_allowed_length, int dry_penalty_last_n,
const std::vector<std::vector<llama_token>> & seq_breakers
) {
GGML_ASSERT(probs.size() == expected_probs.size());
sampler_tester tester(probs, expected_probs);
auto * sampler = llama_sampler_init_dry_testing(dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers);
for (size_t i = 0; i < last_tokens.size(); i++) {
llama_sampler_accept(sampler, last_tokens[i]);
}
DUMP(&tester.cur_p);
tester.apply(sampler);
tester.apply(llama_sampler_init_dist(0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_top_n_sigma(const std::vector<float> & probs, const std::vector<float> & probs_expected, int n) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_top_n_sigma(n));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p
) {
sampler_tester tester(n_vocab);
llama_token min_token_id = 0;
const llama_token max_token_id = n_vocab - 1;
for (auto s : samplers_sequence) {
switch (s) {
case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break;
case 'y': GGML_ABORT("typical test not implemented");
case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break;
case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); break;
case 't': GGML_ABORT("temperature test not implemented");
default : GGML_ABORT("Unknown sampler");
}
tester.apply(llama_sampler_init_dist(0));
auto & cur_p = tester.cur_p;
const int size = cur_p.size;
if (s == 'k') {
const int expected_size = std::min(size, top_k);
min_token_id = std::max(min_token_id, (llama_token)(n_vocab - top_k));
GGML_ASSERT(size == expected_size);
GGML_ASSERT(cur_p.data[0].id == max_token_id);
GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
} else if (s == 'p') {
const int softmax_divisor = n_vocab * (n_vocab-1) / 2 - min_token_id * (min_token_id-1) / 2;
const int softmax_numerator_target = ceilf(top_p * softmax_divisor);
min_token_id = n_vocab;
int expected_size = 0;
int cumsum = 0;
do { // do-while because always at least one token is sampled
min_token_id--;
expected_size++;
cumsum += min_token_id;
} while (cumsum < softmax_numerator_target);
// token 0 has p == 0, need special consideration for cumsum because top_p immediately returns
if (min_token_id == 1) {
min_token_id--;
expected_size += 1;
}
GGML_ASSERT(size == expected_size);
GGML_ASSERT(!cur_p.sorted || cur_p.data[0].id == max_token_id);
GGML_ASSERT(!cur_p.sorted || cur_p.data[expected_size-1].id == min_token_id);
} else if (s == 'm') {
int expected_size = ceilf((1.0f - min_p) * n_vocab);
expected_size = std::max(expected_size, 1);
expected_size = std::min(expected_size, size);
min_token_id = floorf(min_p * n_vocab);
min_token_id = std::max(min_token_id, 1);
min_token_id = std::max(min_token_id, (llama_token)(n_vocab - size));
min_token_id = std::min(min_token_id, (llama_token)(n_vocab - 1));
GGML_ASSERT(size == expected_size);
GGML_ASSERT(!cur_p.sorted || cur_p.data[0].id == max_token_id);
GGML_ASSERT(!cur_p.sorted || cur_p.data[expected_size-1].id == min_token_id);
} else {
GGML_ABORT("fatal error");
}
}
printf("Sampler queue %3s OK with n_vocab=%05zu top_k=%5d top_p=%f min_p=%f\n",
samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
}
static void bench(llama_sampler * cnstr, const char * cnstr_name, const std::vector<llama_token_data> & data, int n_iter) {
std::vector<llama_token_data> cur(data.size());
std::copy(data.begin(), data.end(), cur.begin());
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_sampler_apply(cnstr, &cur_p);
llama_sampler_reset(cnstr);
const int64_t t_start = ggml_time_us();
for (int i = 0; i < n_iter; i++) {
std::copy(data.begin(), data.end(), cur.begin());
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_sampler_apply(cnstr, &cur_p);
llama_sampler_reset(cnstr);
}
const int64_t t_end = ggml_time_us();
llama_sampler_free(cnstr);
printf("%-43s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter);
}
#define BENCH(__cnstr, __data, __n_iter) bench((__cnstr), #__cnstr, (__data), (__n_iter))
static void test_perf() {
const int n_vocab = 1 << 17;
std::vector<llama_token_data> data;
data.reserve(n_vocab);
for (int i = 0; i < n_vocab; i++) {
const float logit = 2.0f*((double)(rand())/RAND_MAX - 0.5);
data.emplace_back(llama_token_data{i, logit, 0.0f});
}
BENCH(llama_sampler_init_top_k (40), data, 32);
BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32);
BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32);
BENCH(llama_sampler_init_typical(0.5f, 1), data, 32);
BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32);
}
int main(void) {
ggml_time_init();
test_dist_singleton_rng();
test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f);
test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f, 0.0f, 1.0f);
test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f, 0.0f, 1.0f);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 1);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 3);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f}, 0.7f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 0.8f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f/1.0f, 0.2f/1.0f, 0.3f/1.0f, 0.4f/1.0f}, 0.00f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f/1.0f, 0.2f/1.0f, 0.3f/1.0f, 0.4f/1.0f}, 0.24f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.2f/0.9f, 0.3f/0.9f, 0.4f/0.9f}, 0.26f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.2f/0.9f, 0.3f/0.9f, 0.4f/0.9f}, 0.49f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.3f/0.7f, 0.4f/0.7f}, 0.51f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.3f/0.7f, 0.4f/0.7f}, 0.74f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 0.76f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.00f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.05f);
printf("XTC should:\n");
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.1f}, 0.99f, 0.09f);
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.2f, 0.1f}, 0.99f, 0.19f);
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.3f, 0.2f, 0.1f}, 0.99f, 0.29f);
printf("XTC should not:\n");
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.99f, 0.39f);
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0, 0.25f, 0.25f, 0.25f, 0.25f}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0, 0, 0, 0.5f, 0.5f}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0, 0, 0, 0.5f, 0.5f}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.000011f, 0.249997f, 0.249997f, 0.249997f, 0.249997f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.000023f, 0.000023f, 0.000023f, 0.499966f, 0.499966f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.000000f, 0.000023f, 0.000023f, 0.499977f, 0.499977f}, 1.0f, 5.0f, 5.0f);
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1}, {0.25f, 0.25f, 0.25f, 0.25f}, 1.0f, 1.1f, 2, 4, {});
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.109232f, 0.296923f}, 1.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 2, 6, {{3}});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.032727f, 0.241818f, 0.241818f}, 2.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {});
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.428571f, 0.571429f}, 1.00f);
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0.00f); // top_n_sigma == 0 now represents a no-op rather than greedy decoding as of PR#13345
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 3.00f);
test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
test_sampler_queue(10000, "k", 1, 1.0f, 1.0f);
test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f);
test_sampler_queue(10000, "p", 10000, 0.0f, 1.0f);
test_sampler_queue(10000, "m", 10000, 1.0f, 1.0f);
test_sampler_queue(10000, "m", 10000, 1.0f, 1e-12);
test_sampler_queue(10000, "k", 100, 1.0000f, 1.0f);
test_sampler_queue(10000, "p", 10000, 0.0003f, 1.0f);
test_sampler_queue(10000, "p", 10000, 0.8000f, 1.0f);
test_sampler_queue(10000, "m", 10000, 1.0000f, 9997.9f/9999.0f);
test_sampler_queue(10000, "m", 10000, 1.0000f, 0.1f);
test_sampler_queue(10000, "kp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "km", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pk", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pm", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mk", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mp", 100, 0.8f, 9997.9f/9999.0f);
test_sampler_queue(10000, "mp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "kpm", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "kmp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pkm", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pmk", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mkp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mpk", 100, 0.8f, 0.1f);
printf("OK\n");
test_perf();
return 0;
}