vbatts/llama.cpp
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sampling : refactor + optimize penalties sampler (#10803)

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* sampling : refactor + optimize penalties sampler

ggml-ci

* common : apply ignore_eos as logit bias

ggml-ci

* batched : remove penalties sampler

* params : allow penalty_last_n == -1 to be equal to context size

ggml-ci

* common : by default, move the penalties at the end of the sampling chain

ggml-ci

* common : ignore all EOG tokens

Co-authored-by: Diego Devesa <slarengh@gmail.com>

* common : move back the penalties at the front of the sampling chain

ggml-ci

* readme : restore hint about --ignore-eos flag [no ci]

* llama : minor

ggml-ci

* webui : update

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov 2024-12-16 12:31:14 +02:00 • committed by GitHub
parent 4ddd199f6f
commit 644fd71b44
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GPG key ID: B5690EEEBB952194
17 changed files with 111 additions and 152 deletions
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125
src/llama-sampling.cpp
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@ -1396,19 +1396,15 @@ struct llama_sampler * llama_sampler_init_grammar_impl(const struct llama_vocab
// penalties
struct llama_sampler_penalties {
const int32_t n_vocab;
const llama_token special_eos_id;
const llama_token linefeed_id;
const int32_t penalty_last_n;
const float penalty_repeat;
const float penalty_freq;
const float penalty_present;
const bool penalize_nl;
const bool ignore_eos;
ring_buffer<llama_token> prev;
// a frequency map to count token occurrences
std::unordered_map<llama_token, int> token_count;
};
static const char * llama_sampler_penalties_name(const struct llama_sampler * /*smpl*/) {
@ -1421,76 +1417,50 @@ static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_to
return;
}
ctx->token_count[token]++;
// if the ring buffer is full, remove the oldest token
if (ctx->prev.size() >= (size_t) ctx->penalty_last_n) {
const auto old = ctx->prev.front();
ctx->token_count[old]--;
if (ctx->token_count[old] == 0) {
ctx->token_count.erase(old);
}
}
ctx->prev.push_back(token);
#if 0
// sanity check
std::unordered_map<llama_token, int> tmp;
for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
tmp[ctx->prev.rat(i)]++;
}
assert(ctx->token_count == tmp);
#endif
}
static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (llama_sampler_penalties *) smpl->ctx;
if (ctx->ignore_eos) {
assert(ctx->special_eos_id >= 0);
// optimistically check if the candidates are not yet sorted/shuffled/truncated
if (cur_p->size > (size_t) ctx->special_eos_id && cur_p->data[ctx->special_eos_id].id == ctx->special_eos_id) {
cur_p->data[ctx->special_eos_id].logit = -INFINITY;
} else {
// else, search for the special EOS token
for (size_t i = 0; i < cur_p->size; ++i) {
if (cur_p->data[i].id == ctx->special_eos_id) {
cur_p->data[i].logit = -INFINITY;
break;
}
}
}
}
if ((ctx->penalty_last_n == 0) ||
(ctx->penalty_repeat == 1.0f && ctx->penalty_freq == 0.0f && ctx->penalty_present == 0.0f)) {
return;
}
bool nl_found = false;
size_t nl_idx = 0;
float nl_logit = -INFINITY;
if (!ctx->penalize_nl) {
assert(ctx->linefeed_id >= 0);
// optimistically check if the candidates are not yet sorted/shuffled/truncated
if (cur_p->size > (size_t) ctx->linefeed_id && cur_p->data[ctx->linefeed_id].id == ctx->linefeed_id) {
nl_found = true;
nl_idx = ctx->linefeed_id;
nl_logit = cur_p->data[ctx->linefeed_id].logit;
} else {
// else, search for the linefeed token
for (size_t i = 0; i < cur_p->size; ++i) {
if (cur_p->data[i].id == ctx->linefeed_id) {
nl_found = true;
nl_idx = i;
nl_logit = cur_p->data[i].logit;
break;
}
}
}
}
// Create a frequency map to count occurrences of each token in last_tokens
// TODO: optimize this by maintaining the token count in the sampler context
using llama_token_cnt = std::unordered_map<llama_token, int>;
llama_token_cnt token_count;
for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
token_count[ctx->prev.rat(i)]++;
}
// Apply frequency and presence penalties to the cur_p
for (size_t i = 0; i < cur_p->size; ++i) {
const auto token_iter = token_count.find(cur_p->data[i].id);
if (token_iter == token_count.end()) {
const auto token_iter = ctx->token_count.find(cur_p->data[i].id);
if (token_iter == ctx->token_count.end()) {
continue;
}
const int count = token_iter->second;
assert(count > 0 && count <= ctx->penalty_last_n);
// The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
// This is common fix for this problem, which is to multiply by the penalty instead of dividing.
if (cur_p->data[i].logit <= 0) {
@ -1503,30 +1473,21 @@ static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_tok
}
cur_p->sorted = false;
if (!ctx->penalize_nl && nl_found) {
// restore the logit of the newline token if it was penalized
cur_p->data[nl_idx].logit = nl_logit;
}
}
static void llama_sampler_penalties_reset(struct llama_sampler * smpl) {
auto * ctx = (llama_sampler_penalties *) smpl->ctx;
ctx->prev.clear();
ctx->token_count.clear();
}
static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_sampler * smpl) {
const auto * ctx = (const llama_sampler_penalties *) smpl->ctx;
auto * result = llama_sampler_init_penalties(
ctx->n_vocab,
ctx->special_eos_id,
ctx->linefeed_id,
ctx->penalty_last_n,
ctx->penalty_repeat,
ctx->penalty_freq,
ctx->penalty_present,
ctx->penalize_nl,
ctx->ignore_eos);
ctx->penalty_present);
// copy the state
{
@ -1552,38 +1513,21 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
};
struct llama_sampler * llama_sampler_init_penalties(
int32_t n_vocab,
llama_token special_eos_id,
llama_token linefeed_id,
int32_t penalty_last_n,
float penalty_repeat,
float penalty_freq,
float penalty_present,
bool penalize_nl,
bool ignore_eos) {
if (linefeed_id == LLAMA_TOKEN_NULL) {
penalize_nl = true;
}
if (special_eos_id == LLAMA_TOKEN_NULL) {
ignore_eos = false;
}
float penalty_present) {
penalty_last_n = std::max(penalty_last_n, 0);
return new llama_sampler {
/* .iface = */ &llama_sampler_penalties_i,
/* .ctx = */ new llama_sampler_penalties {
/* .n_vocab = */ n_vocab,
/* .special_eos_id = */ special_eos_id,
/* .linefeed_id = */ linefeed_id,
/* .penalty_last_n = */ penalty_last_n,
/* .penalty_repeat = */ penalty_repeat,
/* .penalty_freq = */ penalty_freq,
/* .penalty_present = */ penalty_present,
/* .penalize_nl = */ penalize_nl,
/* .ignore_eos = */ ignore_eos,
/* .prev = */ ring_buffer<llama_token>(penalty_last_n),
/* .token_count = */ {},
},
};
}
@ -1611,7 +1555,8 @@ static void get_overlapping_token_sequences(const llama_vocab & vocab, const std
if (word.find(str) != std::string::npos) {
token_sequences.emplace(token_id, std::vector<llama_token>());
} else {
size_t word_len = word.size(), str_len = str.size();
size_t word_len = word.size();
size_t str_len = str.size();
size_t pos = -1;
while ((pos = word.find(str[0], pos + 1)) != std::string::npos) {
bool match = true;