vbatts/llama.cpp
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sampling : refactor init to use llama_sampling_params (#3696)

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* sampling : refactor init to use llama_sampling_params

* llama : combine repetition, frequency and presence penalties in 1 call

* examples : remove embd-input and gptneox-wip

* sampling : rename penalty params + reduce size of "prev" vector

* sampling : add llama_sampling_print helper

* sampling : hide prev behind API and apply #3661

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This commit is contained in:
Georgi Gerganov 2023-10-20 21:07:23 +03:00 • committed by GitHub
parent 8cf19d60dc
commit d1031cf49c
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30 changed files with 365 additions and 4502 deletions
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75
tests/test-sampling.cpp
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@ -8,11 +8,9 @@
#include <cmath>
#include <numeric>
#include <cassert>
#include <iostream>
#include <vector>
#include <algorithm>
static void dump(const llama_token_data_array * candidates) {
for (size_t i = 0; i < candidates->size; i++) {
printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit);
@ -21,7 +19,6 @@ static void dump(const llama_token_data_array * candidates) {
#define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
static void test_top_k(const std::vector<float> & probs, const std::vector<float> & expected_probs, int k) {
size_t n_vocab = probs.size();
std::vector<llama_token_data> candidates;
@ -37,13 +34,12 @@ static void test_top_k(const std::vector<float> & probs, const std::vector<float
llama_sample_top_k(nullptr, &candidates_p, k, 1);
DUMP(&candidates_p);
assert(candidates_p.size == expected_probs.size());
GGML_ASSERT(candidates_p.size == expected_probs.size());
for (size_t i = 0; i < candidates_p.size; i++) {
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-5);
GGML_ASSERT(fabs(candidates_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) {
size_t n_vocab = probs.size();
std::vector<llama_token_data> candidates;
@ -59,13 +55,12 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
llama_sample_top_p(nullptr, &candidates_p, p, 1);
DUMP(&candidates_p);
assert(candidates_p.size == expected_probs.size());
GGML_ASSERT(candidates_p.size == expected_probs.size());
for (size_t i = 0; i < candidates_p.size; i++) {
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
GGML_ASSERT(fabs(candidates_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) {
size_t n_vocab = probs.size();
std::vector<llama_token_data> candidates;
@ -80,13 +75,12 @@ static void test_tfs(const std::vector<float> & probs, const std::vector<float>
llama_sample_tail_free(nullptr, &candidates_p, z, 1);
DUMP(&candidates_p);
assert(candidates_p.size == expected_probs.size());
GGML_ASSERT(candidates_p.size == expected_probs.size());
for (size_t i = 0; i < candidates_p.size; i++) {
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
GGML_ASSERT(fabs(candidates_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) {
size_t n_vocab = probs.size();
std::vector<llama_token_data> candidates;
@ -101,18 +95,17 @@ static void test_typical(const std::vector<float> & probs, const std::vector<flo
llama_sample_typical(nullptr, &candidates_p, p, 1);
DUMP(&candidates_p);
assert(candidates_p.size == expected_probs.size());
GGML_ASSERT(candidates_p.size == expected_probs.size());
for (size_t i = 0; i < candidates_p.size; i++) {
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
static void test_repetition_penalty(
static void test_repetition_penalties(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & expected_probs, float penalty
const std::vector<float> & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence
) {
assert(probs.size() == expected_probs.size());
GGML_ASSERT(probs.size() == expected_probs.size());
size_t n_vocab = probs.size();
std::vector<llama_token_data> candidates;
@ -125,41 +118,13 @@ static void test_repetition_penalty(
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
llama_sample_softmax(nullptr, &candidates_p);
DUMP(&candidates_p);
llama_sample_repetition_penalty(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), penalty);
llama_sample_repetition_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
llama_sample_softmax(nullptr, &candidates_p);
DUMP(&candidates_p);
assert(candidates_p.size == expected_probs.size());
GGML_ASSERT(candidates_p.size == expected_probs.size());
for (size_t i = 0; i < candidates_p.size; i++) {
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-6);
}
}
static void test_frequency_presence_penalty(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & expected_probs, float alpha_frequency, float alpha_presence
) {
assert(probs.size() == expected_probs.size());
size_t n_vocab = probs.size();
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
float logit = log(probs[token_id]);
candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
llama_sample_softmax(nullptr, &candidates_p);
// DUMP(&candidates_p);
llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), alpha_frequency, alpha_presence);
llama_sample_softmax(nullptr, &candidates_p);
// DUMP(&candidates_p);
assert(candidates_p.size == expected_probs.size());
for (size_t i = 0; i < candidates_p.size; i++) {
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
@ -181,13 +146,13 @@ int main(void) {
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_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f);
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
test_repetition_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_repetition_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_repetition_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_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 5.0f, 5.0f);
test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 5.0f, 5.0f);
test_frequency_presence_penalty({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}, 5.0f, 5.0f);
test_repetition_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_repetition_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_repetition_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);
printf("OK\n");