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llama.cpp/tests/test-sampling.cpp
Georgi Gerganov df270ef745
llama : refactor sampling v2 (#9294) 
- Add `struct llama_sampler` and `struct llama_sampler_i`
- Add `llama_sampler_` API
- Add `llama_sampler_chain_` API for chaining multiple samplers
- Remove `LLAMA_API_INTERNAL`
- Add `llama_perf_` API and remove old `llama_print_timings` and `llama_reset_timings`
2024-09-07 15:16:19 +03:00

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#include "ggml.h"
#include "llama.h"
#include "llama-sampling.h"
#ifdef NDEBUG
#undef NDEBUG
#endif
#include <algorithm>
#include <cmath>
#include <string>
#include <vector>
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)
#define APPLY(__cnstr, __cur_p) do { \
auto * cnstr = (__cnstr); \
llama_sampler_apply(cnstr, (__cur_p)); \
llama_sampler_free(cnstr); \
} while(0)
static void test_top_k(const std::vector<float> & probs, const std::vector<float> & expected_probs, int k) {
const size_t n_vocab = probs.size();
std::vector<llama_token_data> cur;
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
APPLY(llama_sampler_init_softmax(), &cur_p);
DUMP(&cur_p);
APPLY(llama_sampler_init_top_k(k), &cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-5);
}
}
static void test_top_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
const size_t n_vocab = probs.size();
std::vector<llama_token_data> cur;
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
APPLY(llama_sampler_init_softmax(), &cur_p);
DUMP(&cur_p);
APPLY(llama_sampler_init_top_p(p, 1), &cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
static void test_tfs(const std::vector<float> & probs, const std::vector<float> & expected_probs, float z) {
const size_t n_vocab = probs.size();
std::vector<llama_token_data> cur;
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
DUMP(&cur_p);
APPLY(llama_sampler_init_tail_free(z, 1), &cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
static void test_min_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
const size_t n_vocab = probs.size();
std::vector<llama_token_data> cur;
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
DUMP(&cur_p);
APPLY(llama_sampler_init_min_p(p, 1), &cur_p);
DUMP(&cur_p);
APPLY(llama_sampler_init_softmax(), &cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
const size_t n_vocab = probs.size();
std::vector<llama_token_data> cur;
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
DUMP(&cur_p);
APPLY(llama_sampler_init_typical(p, 1), &cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
static void test_penalties(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence
) {
GGML_ASSERT(probs.size() == expected_probs.size());
const size_t n_vocab = probs.size();
std::vector<llama_token_data> cur;
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
llama_token_cnt token_count;
for (size_t i = 0; i < last_tokens.size(); i++) {
token_count[last_tokens[i]]++;
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
APPLY(llama_sampler_init_softmax(), &cur_p);
DUMP(&cur_p);
llama_sampler_penalties_impl(&cur_p, token_count, repeat_penalty, alpha_frequency, alpha_presence); // TODO: avoid
APPLY(llama_sampler_init_softmax(), &cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
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
) {
std::vector<llama_token_data> cur;
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});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_token min_token_id = 0;
const llama_token max_token_id = n_vocab-1;
for (auto s : samplers_sequence) {
switch (s){
case 'k': APPLY(llama_sampler_init_top_k(top_k), &cur_p); break;
case 'f': GGML_ABORT("tail_free test not implemented");
case 'y': GGML_ABORT("typical test not implemented");
case 'p': APPLY(llama_sampler_init_top_p(top_p, 1), &cur_p); break;
case 'm': APPLY(llama_sampler_init_min_p(min_p, 1), &cur_p); break;
case 't': GGML_ABORT("temperature test not implemented");
default : GGML_ABORT("Unknown sampler");
}
APPLY(llama_sampler_init_softmax(), &cur_p); // make sure tokens are sorted for tests
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.data[0].id == max_token_id);
GGML_ASSERT(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.data[0].id == max_token_id);
GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
} else {
GGML_ABORT("fatal error");
}
}
printf("Sampler queue %3s OK with n_vocab=%05ld top_k=%05d top_p=%f min_p=%f\n",
samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
}
int main(void) {
ggml_time_init();
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 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}, {0.4f, 0.3f, 0.2f, 0.1f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 0.8f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.00f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.24f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f}, 0.26f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f}, 0.49f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f}, 0.51f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/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_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
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}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f);
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, 0.0002f, 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");
return 0;
}
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