aditya/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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14
main.cpp
View file

@ -792,7 +792,7 @@ int main(int argc, char ** argv) {
printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
}
printf("\n");
printf("sampling parameters: temp = %f, top_k = %d, top_p = %f\n", params.temp, params.top_k, params.top_p);
printf("sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
printf("\n\n");
std::vector<gpt_vocab::id> embd;
@ -801,6 +801,10 @@ int main(int argc, char ** argv) {
size_t mem_per_token = 0;
llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
int last_n_size = params.repeat_last_n;
std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
// predict
if (embd.size() > 0) {
@ -821,6 +825,7 @@ int main(int argc, char ** argv) {
// sample next token
const float top_p = params.top_p;
const float temp = params.temp;
const float repeat_penalty = params.repeat_penalty;
const int n_vocab = model.hparams.n_vocab;
@ -829,7 +834,10 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_sample_us = ggml_time_us();
id = llama_sample_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_p, temp, rng);
id = llama_sample_top_p(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_p, temp, rng);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
t_sample_us += ggml_time_us() - t_start_sample_us;
}
@ -840,6 +848,8 @@ int main(int argc, char ** argv) {
// if here, it means we are still processing the input prompt
for (int k = i; k < embd_inp.size(); k++) {
embd.push_back(embd_inp[k]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[k]);
if (embd.size() > params.n_batch) {
break;
}

21
utils.cpp
View file

@ -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));
}
}
}

4
utils.h
View file

@ -16,11 +16,13 @@ struct gpt_params {
int32_t seed = -1; // RNG seed
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_predict = 128; // new tokens to predict
int32_t repeat_last_n = 64; // last n tokens to penalize
// sampling parameters
int32_t top_k = 40; // unused
float top_p = 0.95f;
float temp = 0.80f;
float repeat_penalty = 1.30f;
int32_t n_batch = 8; // batch size for prompt processing
@ -89,6 +91,8 @@ 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);