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https://github.com/ggerganov/llama.cpp.git
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multiple client support
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FSSRepo
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81484805f0
commit
5b8e29de53
@ -76,7 +76,7 @@ struct slot_params {
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uint32_t seed = -1; // RNG seed
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int32_t n_predict = 128; // new tokens to predict
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std::string grammar = ""; // optional BNF-like grammar to constrain sampling
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bool remember_generation = false; // remember a part of the prompt to avoid reprocessing all prompt
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bool remember_generation = false; // remember a the prompt to avoid reprocessing all prompt
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std::vector<std::string> antiprompt;
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};
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@ -256,6 +256,7 @@ struct llama_client_slot
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stopping_word = "";
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multibyte_pending = 0;
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n_past = 0;
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sent_count = 0;
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if (grammar != nullptr) {
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llama_grammar_free(grammar);
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@ -299,8 +300,7 @@ struct llama_client_slot
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}
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bool available() {
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return state == IDLE &&
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command == NONE && !params.remember_generation;
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return state == IDLE && command == NONE;
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}
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bool isProcessing() {
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@ -354,12 +354,6 @@ struct llama_server_context
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int n_ctx;
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int n_vocab;
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bool clean_kv_cache = true;
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std::mutex mutex;
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std::unique_lock<std::mutex> lock()
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{
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return std::unique_lock<std::mutex>(mutex);
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}
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~llama_server_context()
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{
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@ -406,7 +400,7 @@ struct llama_server_context
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slot.last_n_tokens.resize(params.n_predict); // max prediction per slot
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slot.reset();
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std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0);
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LOG_TEE(" - slot %i\n", slot.id);
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LOG_TEE(" -> Slot %i\n", slot.id);
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slots.push_back(slot);
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}
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LOG_TEE("Context Size: %i\n", params.n_ctx);
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@ -716,7 +710,6 @@ struct llama_server_context
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slot.generated_text.begin() + pos + stop_pos,
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slot.generated_text.end());
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pos = std::min(slot.sent_count, slot.generated_text.size());
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result.tok = -1;
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} else {
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is_stop_full = false;
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stop_pos = findStoppingStrings(str_test, token_str.size(),
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@ -737,7 +730,6 @@ struct llama_server_context
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{
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slot.generated_token_probs.push_back(result);
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}
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if (slot.multibyte_pending > 0)
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{
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slot.multibyte_pending -= token_str.size();
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@ -780,7 +772,6 @@ struct llama_server_context
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slot.stopped_eos = true;
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LOG_VERBOSE("eos token found", {});
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}
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LOG_VERBOSE("next token", {
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{"token", result.tok},
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{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
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@ -818,18 +809,11 @@ struct llama_server_context
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if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken())
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{
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LOG_TEE("slot %i released\n", slot.id);
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if(!slot.params.remember_generation) {
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llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system, n_ctx);
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slot.state = IDLE;
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slot.command = NONE;
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slot.generated_text.clear();
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return true;
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} else {
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slot.state = SLEEPING;
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slot.command = NONE;
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}
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slot.state = slot.params.remember_generation ? SLEEPING : IDLE;
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slot.command = NONE;
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continue;
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}
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kv_cache_free -= slot.num_prompt_tokens;
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if (slot.state == IDLE || slot.command == RELEASE) {
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@ -858,23 +842,28 @@ struct llama_server_context
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auto prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
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slot.num_prompt_tokens = prompt_tokens.size();
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slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0;
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slot.context_tokens = prompt_tokens;
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if (slot.n_past == slot.num_prompt_tokens) {
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// we have to evaluate at least 1 token to generate logits.
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printf("we have to evaluate at least 1 token to generate logits\n");
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slot.n_past--;
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}
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llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1);
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LOG_VERBOSE("prompt ingested", {
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{"n_past", slot.n_past},
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{"cached", tokens_to_str(ctx, slot.context_tokens.cbegin(), slot.context_tokens.cbegin() + slot.n_past)},
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{"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())},
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});
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if(system_prompt.empty()) {
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llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1);
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}
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std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0);
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for (; slot.n_past < prompt_tokens.size(); ++slot.n_past) {
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printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str());
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//printf(llama_token_to_piece(ctx, prompt_tokens[slot.n_past]).c_str());
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batch.token [batch.n_tokens] = prompt_tokens[slot.n_past];
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batch.pos [batch.n_tokens] = slot.n_past + num_tokens_system;
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batch.seq_id[batch.n_tokens] = slot.id;
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@ -1693,7 +1682,6 @@ int main(int argc, char **argv)
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svr.Post("/completion", [&llama](const Request &req, Response &res)
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{
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auto lock = llama.lock();
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json data = json::parse(req.body);
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@ -1763,13 +1751,12 @@ int main(int argc, char **argv)
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// "application/json");
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} else {
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const auto chunked_content_provider = [slot, &llama](size_t, DataSink & sink) {
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size_t sent_count = 0;
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size_t sent_token_probs_index = 0;
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while(slot->isProcessing()) {
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if(slot->hasNewToken()) { // new token notification
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const completion_token_output token = slot->next();
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std::vector<completion_token_output> probs_output = {};
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if (slot->sparams.n_probs > 0) {
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if (slot->sparams.n_probs > 0) {
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const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, token.text_to_send, false);
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size_t probs_pos = std::min(sent_token_probs_index, slot->generated_token_probs.size());
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size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), slot->generated_token_probs.size());
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@ -1816,12 +1803,10 @@ int main(int argc, char **argv)
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return true;
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};
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auto on_complete = [slot, &llama] (bool) {
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llama.mutex.unlock();
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slot->sent_tokens = 0;
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slot->generated_token_probs.clear();
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slot->release();
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};
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lock.release();
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res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
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} });
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@ -1956,7 +1941,6 @@ int main(int argc, char **argv)
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svr.Post("/tokenize", [&llama](const Request &req, Response &res)
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{
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auto lock = llama.lock();
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const json body = json::parse(req.body);
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std::vector<llama_token> tokens;
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@ -1969,7 +1953,6 @@ int main(int argc, char **argv)
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svr.Post("/detokenize", [&llama](const Request &req, Response &res)
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{
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auto lock = llama.lock();
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const json body = json::parse(req.body);
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std::string content;
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