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`
This commit is contained in:
Georgi Gerganov
GitHub
parent
947538acb8
commit
df270ef745
48 changed files with 3497 additions and 2914 deletions
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@ -1,5 +1,6 @@
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#include "ggml.h"
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#include "llama.h"
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#include "llama-sampling.h"
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#ifdef NDEBUG
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#undef NDEBUG
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@ -10,181 +11,197 @@
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#include <string>
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#include <vector>
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static void dump(const llama_token_data_array * candidates) {
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for (size_t i = 0; i < candidates->size; i++) {
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printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit);
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static void dump(const llama_token_data_array * cur_p) {
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for (size_t i = 0; i < cur_p->size; i++) {
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printf("%d: %f (%f)\n", cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
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}
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}
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#define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
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#define DUMP(__cur_p) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__cur_p)); printf("-\n"); } while(0)
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#define APPLY(__cnstr, __cur_p) do { \
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auto * cnstr = (__cnstr); \
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llama_sampler_apply(cnstr, (__cur_p)); \
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llama_sampler_free(cnstr); \
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} while(0)
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static void test_top_k(const std::vector<float> & probs, const std::vector<float> & expected_probs, int k) {
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const size_t n_vocab = probs.size();
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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std::vector<llama_token_data> cur;
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cur.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
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const float logit = logf(probs[token_id]);
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candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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llama_sample_softmax(nullptr, &candidates_p);
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DUMP(&candidates_p);
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llama_sample_top_k(nullptr, &candidates_p, k, 1);
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DUMP(&candidates_p);
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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APPLY(llama_sampler_init_top_k(k), &cur_p);
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DUMP(&cur_p);
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GGML_ASSERT(candidates_p.size == expected_probs.size());
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for (size_t i = 0; i < candidates_p.size; i++) {
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GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-5);
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GGML_ASSERT(cur_p.size == expected_probs.size());
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for (size_t i = 0; i < cur_p.size; i++) {
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GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-5);
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}
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}
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static void test_top_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
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const size_t n_vocab = probs.size();
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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std::vector<llama_token_data> cur;
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cur.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
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const float logit = logf(probs[token_id]);
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candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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llama_sample_softmax(nullptr, &candidates_p);
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DUMP(&candidates_p);
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llama_sample_top_p(nullptr, &candidates_p, p, 1);
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DUMP(&candidates_p);
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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APPLY(llama_sampler_init_top_p(p, 1), &cur_p);
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DUMP(&cur_p);
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GGML_ASSERT(candidates_p.size == expected_probs.size());
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for (size_t i = 0; i < candidates_p.size; i++) {
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GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
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GGML_ASSERT(cur_p.size == expected_probs.size());
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for (size_t i = 0; i < cur_p.size; i++) {
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GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
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}
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}
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static void test_tfs(const std::vector<float> & probs, const std::vector<float> & expected_probs, float z) {
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const size_t n_vocab = probs.size();
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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std::vector<llama_token_data> cur;
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cur.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
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const float logit = logf(probs[token_id]);
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candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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DUMP(&candidates_p);
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llama_sample_tail_free(nullptr, &candidates_p, z, 1);
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DUMP(&candidates_p);
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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DUMP(&cur_p);
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APPLY(llama_sampler_init_tail_free(z, 1), &cur_p);
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DUMP(&cur_p);
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GGML_ASSERT(candidates_p.size == expected_probs.size());
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for (size_t i = 0; i < candidates_p.size; i++) {
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GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
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GGML_ASSERT(cur_p.size == expected_probs.size());
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for (size_t i = 0; i < cur_p.size; i++) {
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GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
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}
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}
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static void test_min_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
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const size_t n_vocab = probs.size();
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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std::vector<llama_token_data> cur;
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cur.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
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const float logit = logf(probs[token_id]);
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candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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DUMP(&candidates_p);
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llama_sample_min_p(nullptr, &candidates_p, p, 1);
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DUMP(&candidates_p);
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llama_sample_softmax(nullptr, &candidates_p);
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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DUMP(&cur_p);
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APPLY(llama_sampler_init_min_p(p, 1), &cur_p);
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DUMP(&cur_p);
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APPLY(llama_sampler_init_softmax(), &cur_p);
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GGML_ASSERT(candidates_p.size == expected_probs.size());
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for (size_t i = 0; i < candidates_p.size; i++) {
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GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
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GGML_ASSERT(cur_p.size == expected_probs.size());
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for (size_t i = 0; i < cur_p.size; i++) {
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GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
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}
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}
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static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
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const size_t n_vocab = probs.size();
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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std::vector<llama_token_data> cur;
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cur.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
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const float logit = logf(probs[token_id]);
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candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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DUMP(&candidates_p);
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llama_sample_typical(nullptr, &candidates_p, p, 1);
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DUMP(&candidates_p);
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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DUMP(&cur_p);
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APPLY(llama_sampler_init_typical(p, 1), &cur_p);
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DUMP(&cur_p);
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GGML_ASSERT(candidates_p.size == expected_probs.size());
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for (size_t i = 0; i < candidates_p.size; i++) {
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GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
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GGML_ASSERT(cur_p.size == expected_probs.size());
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for (size_t i = 0; i < cur_p.size; i++) {
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GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
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}
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}
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static void test_repetition_penalties(
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static void test_penalties(
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const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
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const std::vector<float> & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence
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) {
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GGML_ASSERT(probs.size() == expected_probs.size());
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const size_t n_vocab = probs.size();
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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std::vector<llama_token_data> cur;
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cur.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
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const float logit = logf(probs[token_id]);
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candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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llama_sample_softmax(nullptr, &candidates_p);
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DUMP(&candidates_p);
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llama_sample_repetition_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
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llama_sample_softmax(nullptr, &candidates_p);
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DUMP(&candidates_p);
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llama_token_cnt token_count;
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for (size_t i = 0; i < last_tokens.size(); i++) {
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token_count[last_tokens[i]]++;
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}
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GGML_ASSERT(candidates_p.size == expected_probs.size());
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for (size_t i = 0; i < candidates_p.size; i++) {
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GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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llama_sampler_penalties_impl(&cur_p, token_count, repeat_penalty, alpha_frequency, alpha_presence); // TODO: avoid
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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GGML_ASSERT(cur_p.size == expected_probs.size());
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for (size_t i = 0; i < cur_p.size; i++) {
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GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3);
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}
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}
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static void test_sampler_queue(
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const size_t n_vocab, const std::string samplers_sequence, const int top_k, const float top_p, const float min_p
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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
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) {
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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std::vector<llama_token_data> cur;
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cur.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
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const float logit = logf(token_id);
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candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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llama_token min_token_id = 0;
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const llama_token max_token_id = n_vocab-1;
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for (auto s : samplers_sequence) {
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switch (s){
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case 'k': llama_sample_top_k (nullptr, &candidates_p, top_k, 1); break;
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case 'k': APPLY(llama_sampler_init_top_k(top_k), &cur_p); break;
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case 'f': GGML_ABORT("tail_free test not implemented");
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case 'y': GGML_ABORT("typical test not implemented");
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case 'p': llama_sample_top_p (nullptr, &candidates_p, top_p, 1); break;
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case 'm': llama_sample_min_p (nullptr, &candidates_p, min_p, 1); break;
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case 'p': APPLY(llama_sampler_init_top_p(top_p, 1), &cur_p); break;
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case 'm': APPLY(llama_sampler_init_min_p(min_p, 1), &cur_p); break;
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case 't': GGML_ABORT("temperature test not implemented");
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default : GGML_ABORT("Unknown sampler");
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}
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llama_sample_softmax(nullptr, &candidates_p); // make sure tokens are sorted for tests
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APPLY(llama_sampler_init_softmax(), &cur_p); // make sure tokens are sorted for tests
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const int size = candidates_p.size;
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const int size = cur_p.size;
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if (s == 'k') {
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const int expected_size = std::min(size, top_k);
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min_token_id = std::max(min_token_id, (llama_token)(n_vocab - top_k));
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GGML_ASSERT(size == expected_size);
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GGML_ASSERT(candidates_p.data[0].id == max_token_id);
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GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
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GGML_ASSERT(cur_p.data[0].id == max_token_id);
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GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
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} else if (s == 'p') {
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const int softmax_divisor = n_vocab * (n_vocab-1) / 2 - min_token_id * (min_token_id-1) / 2;
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const int softmax_numerator_target = ceilf(top_p * softmax_divisor);
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@ -206,8 +223,8 @@ static void test_sampler_queue(
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}
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GGML_ASSERT(size == expected_size);
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GGML_ASSERT(candidates_p.data[0].id == max_token_id);
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GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
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GGML_ASSERT(cur_p.data[0].id == max_token_id);
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GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
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} else if (s == 'm') {
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int expected_size = ceilf((1.0f-min_p) * n_vocab);
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expected_size = std::max(expected_size, 1);
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@ -219,8 +236,8 @@ static void test_sampler_queue(
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min_token_id = std::min(min_token_id, (llama_token)(n_vocab - 1));
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GGML_ASSERT(size == expected_size);
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GGML_ASSERT(candidates_p.data[0].id == max_token_id);
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GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
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GGML_ASSERT(cur_p.data[0].id == max_token_id);
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GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
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} else {
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GGML_ABORT("fatal error");
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}
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@ -259,13 +276,13 @@ int main(void) {
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test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
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test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
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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);
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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);
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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);
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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);
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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);
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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);
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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);
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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);
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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);
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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);
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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);
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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);
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test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
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test_sampler_queue(10000, "k", 1, 1.0f, 1.0f);
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|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue