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>

1 files changed, 12 insertions, 2 deletions

diff --git a/main.cpp b/main.cpp
index 2f47480..f02b5dd 100644
--- a/main.cpp
+++ b/main.cpp
@@ -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;
                 }