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llama : minor sampling refactor (2) (#9386)

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slaren 2024-09-09 17:10:46 +02:00 committed by GitHub
parent 38ca6f644b
commit 5fb5e24811
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12 changed files with 115 additions and 113 deletions
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2
examples/batched.swift/Sources/main.swift
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@ -140,8 +140,6 @@ while n_cur <= n_len {
let new_token_id = llama_sampler_sample(smpl, context, i_batch[i])
llama_sampler_accept(smpl, new_token_id)
// is it an end of stream? -> mark the stream as finished
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
i_batch[i] = -1

2
examples/batched/batched.cpp
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@ -172,8 +172,6 @@ int main(int argc, char ** argv) {
const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
llama_sampler_accept(smpl, new_token_id);
// is it an end of generation? -> mark the stream as finished
if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
i_batch[i] = -1;

1
examples/gritlm/gritlm.cpp
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@ -121,7 +121,6 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
llama_decode(ctx, bat);
llama_token token = llama_sampler_sample(smpl, ctx, bat.n_tokens - 1);
llama_sampler_accept(smpl, token);
if (token == eos_token) {
break;

2
examples/llama.android/llama/src/main/cpp/llama-android.cpp
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@ -414,8 +414,6 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
// sample the most likely token
const auto new_token_id = llama_sampler_sample(sampler, context, -1);
llama_sampler_accept(sampler, new_token_id);
const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
return nullptr;

2
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
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@ -152,8 +152,6 @@ actor LlamaContext {
new_token_id = llama_sampler_sample(sampling, context, batch.n_tokens - 1)
llama_sampler_accept(sampling, new_token_id)
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
print("\n")
is_done = true

2
examples/passkey/passkey.cpp
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@ -220,8 +220,6 @@ int main(int argc, char ** argv) {
{
const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1);
llama_sampler_accept(smpl, new_token_id);
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
LOG_TEE("\n");

6
examples/save-load-state/save-load-state.cpp
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@ -74,8 +74,6 @@ int main(int argc, char ** argv) {
auto next_token = llama_sampler_sample(smpl, ctx, -1);
auto next_token_str = llama_token_to_piece(ctx, next_token);
llama_sampler_accept(smpl, next_token);
printf("%s", next_token_str.c_str());
result0 += next_token_str;
@ -132,8 +130,6 @@ int main(int argc, char ** argv) {
auto next_token = llama_sampler_sample(smpl2, ctx2, -1);
auto next_token_str = llama_token_to_piece(ctx2, next_token);
llama_sampler_accept(smpl2, next_token);
printf("%s", next_token_str.c_str());
result1 += next_token_str;
@ -222,8 +218,6 @@ int main(int argc, char ** argv) {
auto next_token = llama_sampler_sample(smpl3, ctx3, -1);
auto next_token_str = llama_token_to_piece(ctx3, next_token);
llama_sampler_accept(smpl3, next_token);
printf("%s", next_token_str.c_str());
result2 += next_token_str;

2
examples/server/server.cpp
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@ -613,7 +613,7 @@ struct server_context {
gpt_params params;
llama_batch batch;
llama_batch batch = {};
bool clean_kv_cache = true;
bool add_bos_token = true;

2
examples/simple/simple.cpp
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@ -118,8 +118,6 @@ int main(int argc, char ** argv) {
{
const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1);
llama_sampler_accept(smpl, new_token_id);
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
LOG_TEE("\n");

11
include/llama.h
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@ -1127,15 +1127,16 @@ extern "C" {
int32_t n_logit_bias,
const llama_logit_bias * logit_bias);
// Shorthand for:
/// @details Sample and accept a token from the idx-th output of the last evaluation
//
// Shorthand for:
// const auto * logits = llama_get_logits_ith(ctx, idx);
// llama_token_data_array cur_p = { ... init from logits ... };
// llama_sampler_apply(smpl, &cur_p);
// return cur_p.data[cur_p.selected].id;
//
// At this point, this is mostly a convenience function.
//
// auto token = cur_p.data[cur_p.selected].id;
// llama_sampler_accept(smpl, token);
// return token;
// Returns the sampled token
LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx);
// TODO: extend in the future

194
src/llama-sampling.cpp
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@ -8,49 +8,44 @@
#include <cstring>
#include <ctime>
#include <cfloat>
#include <cmath>
#include <numeric>
#include <random>
#include <unordered_map>
static int llama_sample_dist(llama_token_data_array * cur_p, std::mt19937 & rng, std::vector<float> & probs) {
#if 1
probs.resize(cur_p->size);
for (size_t i = 0; i < cur_p->size; ++i) {
probs[i] = cur_p->data[i].p;
}
std::discrete_distribution<size_t> dist(probs.begin(), probs.end());
#else
// avoid the copy with a custom iterator
static int llama_sample_dist(llama_token_data_array * cur_p, std::mt19937 & rng) {
// iterator for the probabilities
#ifdef __GNUC__
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-local-typedefs"
#endif
struct probs_iterator {
typedef std::input_iterator_tag iterator_category;
typedef float value_type;
typedef float * pointer;
typedef float & reference;
typedef size_t difference_type;
typedef ptrdiff_t difference_type;
const llama_token_data_array * data;
size_t i;
const llama_token_data * data;
bool operator==(const probs_iterator & other) const { return data + i == other.data + other.i; }
bool operator!=(const probs_iterator & other) const { return data + i != other.data + other.i; }
float operator*() const { return data->data[i].p; }
probs_iterator & operator++() { ++i; return *this; }
probs_iterator operator++(int) { probs_iterator tmp = *this; ++i; return tmp; }
bool operator==(const probs_iterator & other) const { return data == other.data; }
bool operator!=(const probs_iterator & other) const { return data != other.data; }
const float & operator*() const { return data->p; }
probs_iterator & operator++() { ++data; return *this; }
probs_iterator operator++(int) { probs_iterator tmp = *this; ++data; return tmp; }
};
#ifdef __GNUC__
#pragma GCC diagnostic pop
std::discrete_distribution<size_t> dist(probs_iterator{cur_p, 0}, probs_iterator{cur_p, cur_p->size});
GGML_UNUSED(probs);
#endif
std::discrete_distribution<int> dist(probs_iterator{cur_p->data}, probs_iterator{cur_p->data + cur_p->size});
return dist(rng);
}
/*
static void llama_log_softmax(float * array, size_t size) {
float max_l = *std::max_element(array, array + size);
float sum = 0.f;
@ -64,6 +59,7 @@ static void llama_log_softmax(float * array, size_t size) {
array[i] = logf(array[i] / sum);
}
}
*/
static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) {
GGML_ASSERT(cur_p->size > 0);
@ -231,67 +227,92 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_token_data_array cur_p = {
/* .data = */ cur.data(),
/* .size = */ cur.size(),
/* .selected = */ -1,
/* .sorted = */ false,
};
llama_sampler_apply(smpl, &cur_p);
return cur_p.data[cur_p.selected].id;
GGML_ASSERT(cur_p.selected >= 0 && cur_p.selected < (int32_t) cur_p.size);
auto token = cur_p.data[cur_p.selected].id;
llama_sampler_accept(smpl, token);
return token;
}
// sampler chain
static const char * llama_sampler_chain_name(const struct llama_sampler * /*smpl*/) {
return "chain";
}
static void llama_sampler_chain_accept(struct llama_sampler * smpl, llama_token token) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
time_meas tm(chain->t_sample_us, chain->params.no_perf);
for (auto * smpl : chain->samplers) {
llama_sampler_accept(smpl, token);
}
chain->n_sample++;
}
static void llama_sampler_chain_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
time_meas tm(chain->t_sample_us, chain->params.no_perf);
for (auto * smpl : chain->samplers) {
llama_sampler_apply(smpl, cur_p);
}
}
static void llama_sampler_chain_reset(struct llama_sampler * smpl) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
for (auto * smpl : chain->samplers) {
llama_sampler_reset(smpl);
}
chain->t_sample_us = 0;
chain->n_sample = 0;
}
static struct llama_sampler * llama_sampler_chain_clone(const struct llama_sampler * smpl) {
const auto * chain_src = (const llama_sampler_chain *) smpl->ctx;
auto * result = llama_sampler_chain_init(chain_src->params);
for (auto * smpl : chain_src->samplers) {
llama_sampler_chain_add(result, llama_sampler_clone(smpl));
}
return result;
}
static void llama_sampler_chain_free(struct llama_sampler * smpl) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
for (auto * smpl : chain->samplers) {
llama_sampler_free(smpl);
}
delete chain;
}
static struct llama_sampler_i llama_sampler_chain_i = {
/* .name = */ [](const struct llama_sampler * /*smpl*/) { return "chain"; },
/* .accept = */ [](struct llama_sampler * smpl, llama_token token) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
time_meas tm(chain->t_sample_us, chain->params.no_perf);
for (auto * smpl : chain->samplers) {
llama_sampler_accept(smpl, token);
}
chain->n_sample++;
},
/* .apply = */ [](struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
time_meas tm(chain->t_sample_us, chain->params.no_perf);
for (auto * smpl : chain->samplers) {
llama_sampler_apply(smpl, cur_p);
}
},
/* .reset = */ [](struct llama_sampler * smpl) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
for (auto * smpl : chain->samplers) {
llama_sampler_reset(smpl);
}
chain->t_sample_us = 0;
chain->n_sample = 0;
},
/* .clone = */ [](const struct llama_sampler * smpl) {
const auto * chain_src = (const llama_sampler_chain *) smpl->ctx;
auto * result = llama_sampler_chain_init(chain_src->params);
for (auto * smpl : chain_src->samplers) {
llama_sampler_chain_add(result, llama_sampler_clone(smpl));
}
return result;
},
/* .free = */ [](struct llama_sampler * smpl) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
for (auto * smpl : chain->samplers) {
llama_sampler_free(smpl);
}
delete chain;
},
/* .name = */ llama_sampler_chain_name,
/* .accept = */ llama_sampler_chain_accept,
/* .apply = */ llama_sampler_chain_apply,
/* .reset = */ llama_sampler_chain_reset,
/* .clone = */ llama_sampler_chain_clone,
/* .free = */ llama_sampler_chain_free,
};
struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params) {
@ -368,8 +389,6 @@ struct llama_sampler_dist {
const uint32_t seed;
std::mt19937 rng;
std::vector<float> probs; // work array
};
static const char * llama_sampler_dist_name(const struct llama_sampler * /*smpl*/) {
@ -378,7 +397,7 @@ 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;
cur_p->selected = llama_sample_dist(cur_p, ctx->rng, ctx->probs);
cur_p->selected = llama_sample_dist(cur_p, ctx->rng);
}
static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sampler * smpl) {
@ -419,7 +438,6 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
/* .ctx = */ new llama_sampler_dist {
/* .seed = */ seed,
/* .rng = */ std::mt19937(seed),
/* .probs = */ {},
},
};
}
@ -1023,8 +1041,6 @@ struct llama_sampler_mirostat {
float mu;
std::mt19937 rng;
std::vector<float> probs;
};
static const char * llama_sampler_mirostat_name(const struct llama_sampler * /*smpl*/) {
@ -1055,7 +1071,7 @@ static void llama_sampler_mirostat_apply(struct llama_sampler * smpl, llama_toke
llama_sampler_top_k_impl(cur_p, std::max(int(k), 1));
llama_sampler_softmax_impl(cur_p);
const int idx = llama_sample_dist(cur_p, ctx->rng, ctx->probs);
const int idx = llama_sample_dist(cur_p, ctx->rng);
cur_p->selected = idx;
@ -1111,7 +1127,6 @@ struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t see
/* .m = */ m,
/* .mu = */ 2.0f*tau,
/* .rng = */ std::mt19937(seed),
/* .probs = */ {},
},
};
}
@ -1127,8 +1142,6 @@ struct llama_sampler_mirostat_v2 {
float mu;
std::mt19937 rng;
std::vector<float> probs;
};
static const char * llama_sampler_mirostat_v2_name(const struct llama_sampler * /*smpl*/) {
@ -1152,7 +1165,7 @@ static void llama_sampler_mirostat_v2_apply(struct llama_sampler * smpl, llama_t
// Normalize the probabilities of the remaining words
llama_sampler_softmax_impl(cur_p);
const int idx = llama_sample_dist(cur_p, ctx->rng, ctx->probs);
const int idx = llama_sample_dist(cur_p, ctx->rng);
cur_p->selected = idx;
@ -1207,7 +1220,6 @@ struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau,
/* .eta = */ eta,
/* .mu = */ 2.0f*tau,
/* .rng = */ std::mt19937(seed),
/* .probs = */ {},
},
};
}
@ -1527,6 +1539,10 @@ static const char * llama_sampler_logit_bias_name(const struct llama_sampler * /
static void llama_sampler_logit_bias_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (llama_sampler_logit_bias *) smpl->ctx;
if (ctx->logit_bias.empty()) {
return;
}
ctx->to_search.clear();
// update the candidates that have not been shuffled in the vocabulary (i.e. idx == id)
@ -1538,6 +1554,10 @@ static void llama_sampler_logit_bias_apply(struct llama_sampler * smpl, llama_to
}
}
if (ctx->to_search.empty()) {
return;
}
// search for the remaining candidates that were not found in the previous step
for (size_t i = 0; i < cur_p->size; ++i) {
for (const auto & lb : ctx->to_search) {

2
tests/test-sampling.cpp
View File

@ -245,7 +245,7 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler
}
}
printf("Sampler queue %3s OK with n_vocab=%05ld top_k=%05d top_p=%f min_p=%f\n",
printf("Sampler queue %3s OK with n_vocab=%05zu top_k=%05d top_p=%f min_p=%f\n",
samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
}