bugfixes and support for persistent states

This commit is contained in:
Concedo 2023-03-20 00:59:45 +08:00
parent f952b7c613
commit 356c1b87ba
3 changed files with 66 additions and 22 deletions

View file

@ -28,7 +28,8 @@ extern "C" {
const int top_k;
const float top_p;
const float rep_pen;
const int rep_pen_range;
const int rep_pen_range;
const bool reset_state = true; //determines if we can continue off the previous prompt state
};
struct generation_outputs
{
@ -40,7 +41,10 @@ extern "C" {
gpt_vocab api_vocab;
llama_model api_model;
int api_n_past = 0;
gpt_vocab::id old_embd_id = -1;
std::vector<float> api_logits;
std::vector<gpt_vocab::id> last_n_tokens;
size_t mem_per_token = 0;
bool load_model(const load_model_inputs inputs)
{
@ -69,6 +73,12 @@ extern "C" {
api_params.temp = inputs.temperature;
api_params.repeat_last_n = inputs.rep_pen_range;
api_params.repeat_penalty = inputs.rep_pen;
bool reset_state = inputs.reset_state;
if(api_n_past==0)
{
reset_state = true;
}
if(api_params.repeat_last_n<1)
{
@ -88,42 +98,61 @@ extern "C" {
// char * tst2 = (char*)tst.c_str();
// gpt_print_usage(1,&tst2,api_params);
api_params.prompt.insert(0, 1, ' ');
if(reset_state)
{
api_params.prompt.insert(0, 1, ' ');
mem_per_token = 0;
}
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(api_vocab, api_params.prompt, true);
api_params.n_predict = std::min(api_params.n_predict, api_model.hparams.n_ctx - (int)embd_inp.size());
std::vector<gpt_vocab::id> embd;
size_t mem_per_token = 0;
llama_eval(api_model, api_params.n_threads, 0, {0, 1, 2, 3}, api_logits, mem_per_token);
int last_n_size = api_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);
last_n_tokens.resize(last_n_size);
if(reset_state)
{
llama_eval(api_model, api_params.n_threads, 0, {0, 1, 2, 3}, api_logits, mem_per_token);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
api_n_past = 0;
}else{
//strip out the reset token (1) at the start of the embedding
if(embd_inp.size()>0)
{
embd_inp.erase(embd_inp.begin());
}
if(old_embd_id!=-1)
{
embd.push_back(old_embd_id);
}
}
int remaining_tokens = api_params.n_predict;
int input_consumed = 0;
std::mt19937 api_rng(api_params.seed);
std::string concat_output = "";
std::string concat_output = "";
while (remaining_tokens > 0)
{
gpt_vocab::id id = 0;
gpt_vocab::id id = 0;
// predict
if (embd.size() > 0)
{
// for (auto i: embd) {
// std::cout << i << ',';
// }
//printf("\nnp:%d embd:%d mem:%d",api_n_past,embd.size(),mem_per_token);
if (!llama_eval(api_model, api_params.n_threads, api_n_past, embd, api_logits, mem_per_token))
{
fprintf(stderr, "Failed to predict\n");
_snprintf_s(output.text,sizeof(output.text),_TRUNCATE,"%s","");
snprintf(output.text, sizeof(output.text), "%s", "");
output.status = 0;
return output;
}
}
api_n_past += embd.size();
embd.clear();
embd.clear();
if (embd_inp.size() <= input_consumed)
{
// out of user input, sample next token
@ -148,11 +177,12 @@ extern "C" {
}
// add it to the context
old_embd_id = id;
embd.push_back(id);
// decrement remaining sampling budget
--remaining_tokens;
//printf("\nid:%d word:%s\n",id,api_vocab.id_to_token[id].c_str());
concat_output += api_vocab.id_to_token[id].c_str();
}
else
@ -160,6 +190,7 @@ extern "C" {
// some user input remains from prompt or interaction, forward it to processing
while (embd_inp.size() > input_consumed)
{
old_embd_id = embd_inp[input_consumed];
embd.push_back(embd_inp[input_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[input_consumed]);
@ -175,7 +206,7 @@ extern "C" {
//printf("output: %s",concat_output.c_str());
output.status = 1;
_snprintf_s(output.text,sizeof(output.text),_TRUNCATE,"%s",concat_output.c_str());
snprintf(output.text, sizeof(output.text), "%s", concat_output.c_str());
return output;
}
}