llama : default sampling changes + greedy update (#9897)
* llama : deprecate softmax sampler + fix dist sampler ggml-ci * tests : replace macros with functions ggml-ci * sampling : change temperature sampler logic For t <= 0.0f, keep the max logit intact and set the rest to -inf * cont : no need for special "greedy" logic top-k == 1 is the same * tests : init prob correctly * llama : handle temp <= 0.0 in the temp_ext sampler too ggml-ci * cont : avoid extra loop in temperature sampler for sub-zero temp ggml-ci
This commit is contained in:
Georgi Gerganov
GitHub
parent
bc21975084
commit
55e47786e3
7 changed files with 202 additions and 218 deletions
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@ -18,203 +18,176 @@ static void dump(const llama_token_data_array * cur_p) {
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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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struct sampler_tester {
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sampler_tester(size_t n_vocab) {
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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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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cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false };
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}
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sampler_tester(const std::vector<float> & probs, const std::vector<float> & probs_expected) : probs_expected(probs_expected) {
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cur.reserve(probs.size());
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for (llama_token token_id = 0; token_id < (llama_token)probs.size(); token_id++) {
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const float logit = logf(probs[token_id]);
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cur.emplace_back(llama_token_data{token_id, logit, probs[token_id]});
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}
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cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false };
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}
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void apply(llama_sampler * sampler) {
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llama_sampler_apply(sampler, &cur_p);
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llama_sampler_free(sampler);
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}
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void check() {
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GGML_ASSERT(cur_p.size == probs_expected.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 - probs_expected[i]) < 1e-5);
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}
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}
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llama_token_data_array cur_p;
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private:
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const std::vector<float> probs_expected;
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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};
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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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static void test_temp(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp) {
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sampler_tester tester(probs, probs_expected);
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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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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_temp(temp));
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tester.apply(llama_sampler_init_dist(0));
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DUMP(&tester.cur_p);
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tester.check();
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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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static void test_temp_ext(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp, float delta, float exponent) {
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sampler_tester tester(probs, probs_expected);
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_temp_ext(temp, delta, exponent));
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tester.apply(llama_sampler_init_dist (0));
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DUMP(&tester.cur_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(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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tester.check();
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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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static void test_top_k(const std::vector<float> & probs, const std::vector<float> & probs_expected, int k) {
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sampler_tester tester(probs, probs_expected);
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_top_k(k));
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tester.apply(llama_sampler_init_dist (0));
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DUMP(&tester.cur_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(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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tester.check();
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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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static void test_top_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
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sampler_tester tester(probs, probs_expected);
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_top_p(p, 1));
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tester.apply(llama_sampler_init_dist (0));
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DUMP(&tester.cur_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(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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tester.check();
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}
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static void test_xtc(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p, float t) {
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const size_t n_vocab = probs.size();
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static void test_tfs(const std::vector<float> & probs, const std::vector<float> & probs_expected, float z) {
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sampler_tester tester(probs, probs_expected);
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_tail_free(z, 1));
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DUMP(&tester.cur_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_xtc(p, t, 0, 0), &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-5);
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}
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tester.check();
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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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static void test_min_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
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sampler_tester tester(probs, probs_expected);
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_min_p(p, 1));
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tester.apply(llama_sampler_init_dist (0));
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DUMP(&tester.cur_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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tester.check();
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}
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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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static void test_xtc(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p, float t) {
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sampler_tester tester(probs, probs_expected);
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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_xtc(p, t, 0, 0));
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DUMP(&tester.cur_p);
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tester.check();
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}
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static void test_typical(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
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sampler_tester tester(probs, probs_expected);
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DUMP(&tester.cur_p);
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tester.apply(llama_sampler_init_typical(p, 1));
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DUMP(&tester.cur_p);
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tester.check();
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}
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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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const std::vector<float> & probs_expected, 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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GGML_ASSERT(probs.size() == probs_expected.size());
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sampler_tester tester(probs, probs_expected);
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const size_t n_vocab = probs.size();
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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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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auto * sampler = llama_sampler_init_penalties(n_vocab, LLAMA_TOKEN_NULL, LLAMA_TOKEN_NULL, last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence, false, false);
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for (size_t i = 0; i < last_tokens.size(); i++) {
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llama_sampler_accept(sampler, last_tokens[i]);
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}
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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APPLY(sampler, &cur_p);
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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DUMP(&tester.cur_p);
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tester.apply(sampler);
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tester.apply(llama_sampler_init_dist(0));
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DUMP(&tester.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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tester.check();
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}
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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> 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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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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sampler_tester tester(n_vocab);
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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': APPLY(llama_sampler_init_top_k(top_k), &cur_p); break;
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case 'k': tester.apply(llama_sampler_init_top_k(top_k)); 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': 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 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break;
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case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); 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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APPLY(llama_sampler_init_softmax(), &cur_p); // make sure tokens are sorted for tests
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tester.apply(llama_sampler_init_dist(0));
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auto & cur_p = tester.cur_p;
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const int size = cur_p.size;
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@ -307,21 +280,26 @@ static void test_perf() {
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BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32);
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BENCH(llama_sampler_init_typical (0.5f, 1), data, 32);
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BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32);
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BENCH(llama_sampler_init_softmax (), data, 32);
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}
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int main(void) {
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ggml_time_init();
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3);
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test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f);
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test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f);
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test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f, 0.0f, 1.0f);
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test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f, 0.0f, 1.0f);
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 1);
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 3);
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4);
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test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0);
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test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
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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_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}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f);
|
||||
|
||||
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);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue