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llama : deprecate softmax sampler + fix dist sampler

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-10-15 14:24:05 +03:00
parent 3752217ed5
commit e31c8790ff
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GPG Key ID: 449E073F9DC10735
7 changed files with 35 additions and 88 deletions
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2
common/sampling.cpp
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@ -203,7 +203,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
GGML_ASSERT(false && "unknown sampler type");
}
}
llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
@ -222,7 +221,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
// the following will not produce exactly the same probs as applyging softmax to the full vocabulary, but
// it is much faster, since we avoid sorting all tokens and should give a good approximation
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k(params.n_probs));
llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_greedy());
}

1
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
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@ -46,7 +46,6 @@ actor LlamaContext {
let sparams = llama_sampler_chain_default_params()
self.sampling = llama_sampler_chain_init(sparams)
llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4))
llama_sampler_chain_add(self.sampling, llama_sampler_init_softmax())
llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234))
}

3
examples/save-load-state/save-load-state.cpp
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@ -42,7 +42,6 @@ int main(int argc, char ** argv) {
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed));
// tokenize prompt
@ -96,7 +95,6 @@ int main(int argc, char ** argv) {
llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl2, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed));
printf("\nsecond run: %s", params.prompt.c_str());
@ -156,7 +154,6 @@ int main(int argc, char ** argv) {
llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl3, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed));
printf("\nsingle seq run: %s", params.prompt.c_str());

3
examples/speculative/speculative.cpp
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@ -180,8 +180,6 @@ int main(int argc, char ** argv) {
// target model sampling context (reuse the llama_context's sampling instance)
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams);
struct llama_sampler * softmax = llama_sampler_init_softmax();
// draft sequence data
std::vector<seq_draft> drafts(n_seq_dft);
@ -624,7 +622,6 @@ int main(int argc, char ** argv) {
common_sampler_free(drafts[s].smpl);
}
llama_sampler_free(softmax);
llama_batch_free(batch_dft);
llama_free(ctx_tgt);

4
include/llama.h
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@ -217,6 +217,7 @@ extern "C" {
typedef struct llama_token_data_array {
// TODO: consider SoA
// NOTE: this pointer can be modified by the samplers
llama_token_data * data;
size_t size;
int64_t selected; // this is the index in the data array (i.e. not the token id)
@ -1086,7 +1087,8 @@ extern "C" {
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
/// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first.
LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void);
DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void),
"will be removed in the future (see https://github.com/ggerganov/llama.cpp/pull/9896#discussion_r1800920915)");
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k);

3
src/llama-sampling.cpp
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@ -427,6 +427,9 @@ static const char * llama_sampler_dist_name(const struct llama_sampler * /*smpl*
static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (llama_sampler_dist *) smpl->ctx;
llama_sampler_softmax_impl(cur_p);
cur_p->selected = llama_sample_dist(cur_p, ctx->rng);
}

107
tests/test-sampling.cpp
View File

@ -24,20 +24,22 @@ static void dump(const llama_token_data_array * cur_p) {
llama_sampler_free(cnstr); \
} while(0)
#define CUR_P_FROM_PROBS() \
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 }
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();
CUR_P_FROM_PROBS();
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);
APPLY(llama_sampler_init_dist (0), &cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
@ -47,19 +49,12 @@ static void test_top_k(const std::vector<float> & probs, const std::vector<float
}
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();
CUR_P_FROM_PROBS();
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);
APPLY(llama_sampler_init_dist (0), &cur_p);
DUMP(&cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
@ -69,16 +64,8 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
}
static void test_tfs(const std::vector<float> & probs, const std::vector<float> & expected_probs, float z) {
const size_t n_vocab = probs.size();
CUR_P_FROM_PROBS();
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);
@ -90,20 +77,12 @@ static void test_tfs(const std::vector<float> & probs, const std::vector<float>
}
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();
CUR_P_FROM_PROBS();
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);
APPLY(llama_sampler_init_dist (0), &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++) {
@ -112,17 +91,8 @@ static void test_min_p(const std::vector<float> & probs, const std::vector<float
}
static void test_xtc(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p, float t) {
const size_t n_vocab = probs.size();
CUR_P_FROM_PROBS();
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_xtc(p, t, 0, 0), &cur_p);
DUMP(&cur_p);
@ -134,16 +104,8 @@ static void test_xtc(const std::vector<float> & probs, const std::vector<float>
}
static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
const size_t n_vocab = probs.size();
CUR_P_FROM_PROBS();
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);
@ -160,16 +122,7 @@ static void test_penalties(
) {
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_data_array cur_p = { cur.data(), cur.size(), -1, false };
CUR_P_FROM_PROBS();
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);
@ -177,10 +130,9 @@ static void test_penalties(
llama_sampler_accept(sampler, last_tokens[i]);
}
APPLY(llama_sampler_init_softmax(), &cur_p);
DUMP(&cur_p);
APPLY(sampler, &cur_p);
APPLY(llama_sampler_init_softmax(), &cur_p);
APPLY(llama_sampler_init_dist(0), &cur_p);
DUMP(&cur_p);
GGML_ASSERT(cur_p.size == expected_probs.size());
@ -214,7 +166,7 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler
default : GGML_ABORT("Unknown sampler");
}
APPLY(llama_sampler_init_softmax(), &cur_p); // make sure tokens are sorted for tests
APPLY(llama_sampler_init_dist(0), &cur_p);
const int size = cur_p.size;
@ -307,21 +259,20 @@ static void test_perf() {
BENCH(llama_sampler_init_tail_free(0.5f, 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);
BENCH(llama_sampler_init_softmax (), data, 32);
}
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}, {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}, {0.4f, 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_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);