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https://github.com/ggerganov/llama.cpp.git
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Add chat template support for llama-cli (#8068)
* add chat template support for llama-cli * add help message * server: simplify format_chat * more consistent naming * improve * add llama_chat_format_example * fix server * code style * code style * Update examples/main/main.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Xuan Son Nguyen
GitHub
parent
3791ad2193
commit
48e6b92cc3
@ -1444,7 +1444,10 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
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options.push_back({ "main", " --cfg-negative-prompt-file FNAME",
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"negative prompt file to use for guidance" });
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options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
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options.push_back({ "main", " --chat-template JINJA_TEMPLATE",
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"set custom jinja chat template (default: template taken from model's metadata)\n"
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"only commonly used templates are accepted:\n"
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"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
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options.push_back({ "grammar" });
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options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() });
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options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" });
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@ -2604,12 +2607,67 @@ bool llama_should_add_bos_token(const llama_model * model) {
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return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
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}
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//
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// Chat template utils
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//
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bool llama_chat_verify_template(const std::string & tmpl) {
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llama_chat_message chat[] = {{"user", "test"}};
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int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
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return res >= 0;
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}
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std::string llama_chat_apply_template(const struct llama_model * model,
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const std::string & tmpl,
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const std::vector<llama_chat_msg> & msgs,
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bool add_ass) {
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int alloc_size = 0;
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std::vector<llama_chat_message> chat;
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for (auto & msg : msgs) {
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chat.push_back({msg.role.c_str(), msg.content.c_str()});
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alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
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}
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const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
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std::vector<char> buf(alloc_size);
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// run the first time to get the total output length
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int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
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// if it turns out that our buffer is too small, we resize it
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if ((size_t) res > buf.size()) {
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buf.resize(res);
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res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
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}
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std::string formatted_chat(buf.data(), res);
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return formatted_chat;
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}
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std::string llama_chat_format_single(const struct llama_model * model,
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const std::string & tmpl,
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const std::vector<llama_chat_msg> & past_msg,
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const llama_chat_msg & new_msg,
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bool add_ass) {
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auto fmt_past_msg = llama_chat_apply_template(model, tmpl, past_msg, false);
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std::vector<llama_chat_msg> chat_new(past_msg);
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chat_new.push_back(new_msg);
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auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
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auto formatted = fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
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return formatted;
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}
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std::string llama_chat_format_example(const struct llama_model * model,
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const std::string & tmpl) {
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std::vector<llama_chat_msg> msgs = {
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{"system", "You are a helpful assistant"},
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{"user", "Hello"},
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{"assistant", "Hi there"},
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{"user", "How are you?"},
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};
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return llama_chat_apply_template(model, tmpl, msgs, true);
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}
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//
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// KV cache utils
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//
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@ -365,9 +365,32 @@ bool llama_should_add_bos_token(const llama_model * model);
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// Chat template utils
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//
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// same with llama_chat_message, but uses std::string
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struct llama_chat_msg {
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std::string role;
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std::string content;
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};
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// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
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bool llama_chat_verify_template(const std::string & tmpl);
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// CPP wrapper for llama_chat_apply_template
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std::string llama_chat_apply_template(const struct llama_model * model,
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const std::string & tmpl,
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const std::vector<llama_chat_msg> & chat,
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bool add_ass);
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// Format single message, while taking into account the position of that message in chat history
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std::string llama_chat_format_single(const struct llama_model * model,
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const std::string & tmpl,
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const std::vector<llama_chat_msg> & past_msg,
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const llama_chat_msg & new_msg,
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bool add_ass);
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// Returns an example of formatted chat
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std::string llama_chat_format_example(const struct llama_model * model,
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const std::string & tmpl);
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//
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// KV cache utils
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//
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@ -39,12 +39,12 @@ static std::ostringstream * g_output_ss;
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static std::vector<llama_token> * g_output_tokens;
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static bool is_interacting = false;
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static bool file_exists(const std::string &path) {
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static bool file_exists(const std::string & path) {
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std::ifstream f(path.c_str());
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return f.good();
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}
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static bool file_is_empty(const std::string &path) {
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static bool file_is_empty(const std::string & path) {
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std::ifstream f;
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f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
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f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
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@ -117,6 +117,14 @@ static void llama_log_callback_logTee(ggml_log_level level, const char * text, v
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LOG_TEE("%s", text);
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}
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static std::string chat_add_and_format(struct llama_model * model, std::vector<llama_chat_msg> & chat_msgs, std::string role, std::string content) {
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llama_chat_msg new_msg{role, content};
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auto formatted = llama_chat_format_single(
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model, g_params->chat_template, chat_msgs, new_msg, role == "user");
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chat_msgs.push_back({role, content});
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return formatted;
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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g_params = ¶ms;
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@ -190,6 +198,7 @@ int main(int argc, char ** argv) {
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llama_model * model;
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llama_context * ctx;
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llama_context * ctx_guidance = NULL;
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std::vector<llama_chat_msg> chat_msgs;
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g_model = &model;
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g_ctx = &ctx;
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@ -215,6 +224,8 @@ int main(int argc, char ** argv) {
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__func__, n_ctx_train, n_ctx);
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}
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LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str());
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// print system information
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{
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LOG_TEE("\n");
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@ -249,16 +260,21 @@ int main(int argc, char ** argv) {
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std::vector<llama_token> embd_inp;
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if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
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LOG("tokenize the prompt\n");
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embd_inp = ::llama_tokenize(ctx, params.prompt, true, true);
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} else {
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LOG("use session tokens\n");
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embd_inp = session_tokens;
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}
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{
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auto prompt = params.conversation
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? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode
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: params.prompt;
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if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
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LOG("tokenize the prompt\n");
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embd_inp = ::llama_tokenize(ctx, prompt, true, true);
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} else {
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LOG("use session tokens\n");
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embd_inp = session_tokens;
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}
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LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
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LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
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LOG("prompt: \"%s\"\n", log_tostr(prompt));
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LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
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}
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// Should not run without any tokens
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if (embd_inp.empty()) {
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@ -478,6 +494,7 @@ int main(int argc, char ** argv) {
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std::vector<int> input_tokens; g_input_tokens = &input_tokens;
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std::vector<int> output_tokens; g_output_tokens = &output_tokens;
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std::ostringstream output_ss; g_output_ss = &output_ss;
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std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode
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// the first thing we will do is to output the prompt, so set color accordingly
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console::set_display(console::prompt);
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@ -793,11 +810,18 @@ int main(int argc, char ** argv) {
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is_antiprompt = true;
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}
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chat_add_and_format(model, chat_msgs, "system", assistant_ss.str());
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is_interacting = true;
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printf("\n");
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}
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}
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// if current token is not EOG, we add it to current assistant message
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if (params.conversation) {
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auto id = llama_sampling_last(ctx_sampling);
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assistant_ss << llama_token_to_piece(ctx, id, false);
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}
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if (n_past > 0 && is_interacting) {
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LOG("waiting for user input\n");
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@ -848,8 +872,12 @@ int main(int argc, char ** argv) {
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string_process_escapes(buffer);
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}
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std::string user_inp = params.conversation
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? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
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: std::move(buffer);
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// TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
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const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
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const auto line_inp = ::llama_tokenize(ctx, buffer, false, false);
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const auto line_inp = ::llama_tokenize(ctx, user_inp, false, params.conversation);
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const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
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LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
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@ -864,6 +892,9 @@ int main(int argc, char ** argv) {
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output_ss << llama_token_to_piece(ctx, token);
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}
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// reset assistant message
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assistant_ss.str("");
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n_remain -= line_inp.size();
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LOG("n_remain: %d\n", n_remain);
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} else {
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@ -2606,17 +2606,9 @@ int main(int argc, char ** argv) {
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// print sample chat example to make it clear which template is used
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{
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json chat;
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chat.push_back({{"role", "system"}, {"content", "You are a helpful assistant"}});
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chat.push_back({{"role", "user"}, {"content", "Hello"}});
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chat.push_back({{"role", "assistant"}, {"content", "Hi there"}});
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chat.push_back({{"role", "user"}, {"content", "How are you?"}});
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const std::string chat_example = format_chat(ctx_server.model, params.chat_template, chat);
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LOG_INFO("chat template", {
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{"chat_example", chat_example},
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{"built_in", params.chat_template.empty()},
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{"chat_example", llama_chat_format_example(ctx_server.model, params.chat_template)},
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{"built_in", params.chat_template.empty()},
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});
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}
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@ -118,36 +118,17 @@ static inline void server_log(const char * level, const char * function, int lin
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// Format given chat. If tmpl is empty, we take the template from model metadata
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inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
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size_t alloc_size = 0;
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// vector holding all allocated string to be passed to llama_chat_apply_template
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std::vector<std::string> str(messages.size() * 2);
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std::vector<llama_chat_message> chat(messages.size());
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std::vector<llama_chat_msg> chat;
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for (size_t i = 0; i < messages.size(); ++i) {
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const auto & curr_msg = messages[i];
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str[i*2 + 0] = json_value(curr_msg, "role", std::string(""));
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str[i*2 + 1] = json_value(curr_msg, "content", std::string(""));
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alloc_size += str[i*2 + 1].length();
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chat[i].role = str[i*2 + 0].c_str();
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chat[i].content = str[i*2 + 1].c_str();
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std::string role = json_value(curr_msg, "role", std::string(""));
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std::string content = json_value(curr_msg, "content", std::string(""));
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chat.push_back({role, content});
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}
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const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
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std::vector<char> buf(alloc_size * 2);
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// run the first time to get the total output length
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int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
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// if it turns out that our buffer is too small, we resize it
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if ((size_t) res > buf.size()) {
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buf.resize(res);
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res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
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}
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const std::string formatted_chat(buf.data(), res);
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auto formatted_chat = llama_chat_apply_template(model, tmpl, chat, true);
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LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
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return formatted_chat;
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}
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@ -18818,10 +18818,10 @@ static int32_t llama_chat_apply_template_internal(
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if (add_ass) {
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ss << "<|im_start|>assistant\n";
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}
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} else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
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} else if (tmpl == "llama2" || tmpl == "mistral" || tmpl.find("[INST]") != std::string::npos) {
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// llama2 template and its variants
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// [variant] support system message
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bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
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bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos || tmpl == "mistral";
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// [variant] space before + after response
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bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos;
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// [variant] add BOS inside history
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@ -7,6 +7,7 @@
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#include <cassert>
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#include "llama.h"
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#include "common.h"
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int main(void) {
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llama_chat_message conversation[] = {
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@ -119,5 +120,24 @@ int main(void) {
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std::cout << output << "\n-------------------------\n";
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assert(output == expected);
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}
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// test llama_chat_format_single
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std::cout << "\n\n=== llama_chat_format_single ===\n\n";
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std::vector<llama_chat_msg> chat2;
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chat2.push_back({"system", "You are a helpful assistant"});
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chat2.push_back({"user", "Hello"});
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chat2.push_back({"assistant", "I am assistant"});
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llama_chat_msg new_msg{"user", "How are you"};
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auto fmt_single = [&](std::string tmpl) {
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auto output = llama_chat_format_single(nullptr, tmpl, chat2, new_msg, true);
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std::cout << "fmt_single(" << tmpl << ")\n" << output << "\n-------------------------\n";
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return output;
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};
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assert(fmt_single("chatml") == "<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n");
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assert(fmt_single("llama2") == "[INST] How are you [/INST]");
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assert(fmt_single("gemma") == "<start_of_turn>user\nHow are you<end_of_turn>\n<start_of_turn>model\n");
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assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n");
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return 0;
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}
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