diff options
author | beiller <beiller@gmail.com> | 2023-03-12 05:27:42 -0400 |
---|---|---|
committer | GitHub <noreply@github.com> | 2023-03-12 11:27:42 +0200 |
commit | 129c7d1ea886e52ac1b87ff6184310bab3158806 (patch) | |
tree | fe55fbb47ffb6bd7676890bf342c5b8c7f8c6c52 | |
parent | 702fddf5c5c3c1377e169ba9ecdfed4cb16c268b (diff) |
Add repetition penalty (#20)
* 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>
-rw-r--r-- | main.cpp | 14 | ||||
-rw-r--r-- | utils.cpp | 21 | ||||
-rw-r--r-- | utils.h | 4 |
3 files changed, 36 insertions, 3 deletions
@@ -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; } @@ -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)); + } } } @@ -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); |