sampling : refactor init to use llama_sampling_params (#3696)

* sampling : refactor init to use llama_sampling_params

* llama : combine repetition, frequency and presence penalties in 1 call

* examples : remove embd-input and gptneox-wip

* sampling : rename penalty params + reduce size of "prev" vector

* sampling : add llama_sampling_print helper

* sampling : hide prev behind API and apply #3661

ggml-ci

1 files changed, 11 insertions, 17 deletions

diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 1a5911c5..db5309af 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -108,7 +108,7 @@ int main(int argc, char ** argv) {
     if (!gpt_params_parse(argc, argv, params)) {
         return 1;
     }
-    llama_sampling_params & sparams = params.sampling_params;
+    llama_sampling_params & sparams = params.sparams;
 
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("main", "log"));
@@ -415,8 +415,7 @@ int main(int argc, char ** argv) {
             }
         }
     }
-    LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
-            sparams.repeat_last_n, sparams.repeat_penalty, sparams.presence_penalty, sparams.frequency_penalty, sparams.top_k, sparams.tfs_z, sparams.top_p, sparams.typical_p, sparams.temp, sparams.mirostat, sparams.mirostat_eta, sparams.mirostat_tau);
+    LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
     LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
     LOG_TEE("\n\n");
 
@@ -459,7 +458,7 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
-    struct llama_sampling_context * ctx_sampling = llama_sampling_init(params);
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
 
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
         // predict
@@ -612,7 +611,7 @@ int main(int argc, char ** argv) {
 
             const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
 
-            llama_sampling_accept(ctx_sampling, ctx, id);
+            llama_sampling_accept(ctx_sampling, ctx, id, true);
 
             LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());
 
@@ -631,12 +630,9 @@ int main(int argc, char ** argv) {
             while ((int) embd_inp.size() > n_consumed) {
                 embd.push_back(embd_inp[n_consumed]);
 
-                // GG: I'm not sure it's a good idea to push the prompt tokens into the sampling context
-                //     Most likely will remove this in the future to avoid exposing "prev"
-                //     Same thing is done in "server". If we stop pushing the prompt tokens, then the repetition
-                //     penalty will be applied only based on the tokens generated by the model.
-                ctx_sampling->prev.erase(ctx_sampling->prev.begin());
-                ctx_sampling->prev.push_back(embd_inp[n_consumed]);
+                // push the prompt in the sampling context in order to apply repetition penalties later
+                // for the prompt, we don't apply grammar rules
+                llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);
 
                 ++n_consumed;
                 if ((int) embd.size() >= params.n_batch) {
@@ -667,12 +663,10 @@ int main(int argc, char ** argv) {
 
         // if not currently processing queued inputs;
         if ((int) embd_inp.size() <= n_consumed) {
-            // check for reverse prompt
+            // check for reverse prompt in the last n_prev tokens
             if (!params.antiprompt.empty()) {
-                std::string last_output;
-                for (auto id : ctx_sampling->prev) {
-                    last_output += llama_token_to_piece(ctx, id);
-                }
+                const int n_prev = 32;
+                const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev);
 
                 is_antiprompt = false;
                 // Check if each of the reverse prompts appears at the end of the output.
@@ -699,7 +693,7 @@ int main(int argc, char ** argv) {
             }
 
             // deal with end of text token in interactive mode
-            if (ctx_sampling->prev.back() == llama_token_eos(ctx)) {
+            if (llama_sampling_last(ctx_sampling) == llama_token_eos(ctx)) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {