vbatts/llama.cpp
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Add repetition penalty (#20)

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* Adding repeat penalization

* Update utils.h

* Update utils.cpp

* Numeric fix

Should probably still scale by temp even if penalized

* Update comments, more proper application

I see that numbers can go negative so a fix from a referenced commit

* Minor formatting

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
beiller 2023-03-12 05:27:42 -04:00 • committed by GitHub
parent 702fddf5c5
commit 129c7d1ea8
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3 changed files with 36 additions and 3 deletions
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21
utils.cpp
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@ -23,6 +23,10 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.top_p = std::stof(argv[++i]);
} else if (arg == "--temp") {
params.temp = std::stof(argv[++i]);
} else if (arg == "--repeat_last_n") {
params.repeat_last_n = std::stoi(argv[++i]);
} else if (arg == "--repeat_penalty") {
params.repeat_penalty = std::stof(argv[++i]);
} else if (arg == "-b" || arg == "--batch_size") {
params.n_batch = std::stoi(argv[++i]);
} else if (arg == "-m" || arg == "--model") {
@ -52,6 +56,8 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " -m FNAME, --model FNAME\n");
@ -372,6 +378,8 @@ gpt_vocab::id gpt_sample_top_k_top_p(
gpt_vocab::id llama_sample_top_p(
const gpt_vocab & vocab,
const float * logits,
std::vector<gpt_vocab::id> & last_n_tokens,
double repeat_penalty,
double top_p,
double temp,
std::mt19937 & rng) {
@ -383,7 +391,18 @@ gpt_vocab::id llama_sample_top_p(
{
const double scale = 1.0/temp;
for (int i = 0; i < n_logits; ++i) {
logits_id.push_back(std::make_pair(logits[i]*scale, i));
// repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if (logits[i] < 0.0) {
logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
} else {
logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
}
} else {
logits_id.push_back(std::make_pair(logits[i]*scale, i));
}
}
}