Tool call support (generic + native for Llama, Functionary, Hermes, Mistral, Firefunction, DeepSeek) w/ lazy grammars (#9639)
--------- Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
This commit is contained in:
Olivier Chafik
GitHub
parent
27d135c970
commit
8b576b6c55
48 changed files with 3861 additions and 156 deletions
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@ -113,10 +113,11 @@ struct slot_params {
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struct common_params_speculative speculative;
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// OAI-compat fields
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bool verbose = false;
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oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
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std::string oaicompat_model;
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std::string oaicompat_cmpl_id;
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bool verbose = false;
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oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
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std::string oaicompat_model;
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std::string oaicompat_cmpl_id;
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common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
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json to_json() const {
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std::vector<std::string> samplers;
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@ -164,6 +165,8 @@ struct slot_params {
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{"n_probs", sampling.n_probs},
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{"min_keep", sampling.min_keep},
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{"grammar", sampling.grammar},
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// {"grammar_trigger_words", sampling.grammar_trigger_words},
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{"grammar_trigger_tokens", sampling.grammar_trigger_tokens},
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{"samplers", samplers},
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{"speculative.n_max", speculative.n_max},
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{"speculative.n_min", speculative.n_min},
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@ -325,12 +328,50 @@ struct server_task {
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if (data.contains("json_schema") && !data.contains("grammar")) {
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try {
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auto schema = json_value(data, "json_schema", json::object());
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params.sampling.grammar = json_schema_to_grammar(schema);
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LOG_DBG("JSON schema: %s\n", schema.dump(2).c_str());
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params.sampling.grammar = json_schema_to_grammar(schema);
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LOG_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
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} catch (const std::exception & e) {
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throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
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}
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} else {
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params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
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params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
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LOG_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
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params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
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LOG_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
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}
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{
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auto it = data.find("chat_format");
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if (it != data.end()) {
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params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
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LOG_DBG("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
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} else {
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params.oaicompat_chat_format = defaults.oaicompat_chat_format;
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}
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}
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{
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const auto grammar_triggers = data.find("grammar_triggers");
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if (grammar_triggers != data.end()) {
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for (const auto & t : *grammar_triggers) {
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common_grammar_trigger trigger;
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trigger.word = t.at("word");
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trigger.at_start = t.at("at_start");
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auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true);
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if (ids.size() == 1) {
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LOG_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
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params.sampling.grammar_trigger_tokens.push_back(ids[0]);
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continue;
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}
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LOG_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
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params.sampling.grammar_trigger_words.push_back(trigger);
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}
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}
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if (params.sampling.grammar_lazy) {
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GGML_ASSERT(params.sampling.grammar_trigger_tokens.size() > 0 || params.sampling.grammar_trigger_words.size() > 0);
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}
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}
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{
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@ -382,22 +423,12 @@ struct server_task {
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}
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{
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const auto & samplers = data.find("samplers");
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const auto samplers = data.find("samplers");
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if (samplers != data.end()) {
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if (samplers->is_array()) {
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std::vector<std::string> sampler_names;
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for (const auto & name : *samplers) {
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if (name.is_string()) {
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sampler_names.emplace_back(name);
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}
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}
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params.sampling.samplers = common_sampler_types_from_names(sampler_names, false);
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params.sampling.samplers = common_sampler_types_from_names(*samplers, false);
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} else if (samplers->is_string()){
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std::string sampler_string;
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for (const auto & name : *samplers) {
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sampler_string += name;
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}
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params.sampling.samplers = common_sampler_types_from_chars(sampler_string);
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params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
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}
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} else {
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params.sampling.samplers = defaults.sampling.samplers;
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@ -544,7 +575,7 @@ struct completion_token_output {
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struct server_task_result_cmpl_final : server_task_result {
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int index = 0;
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std::string content;
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std::string content;
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llama_tokens tokens;
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bool stream;
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@ -566,10 +597,11 @@ struct server_task_result_cmpl_final : server_task_result {
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slot_params generation_params;
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// OAI-compat fields
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bool verbose = false;
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oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
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std::string oaicompat_model;
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std::string oaicompat_cmpl_id;
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bool verbose = false;
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oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
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std::string oaicompat_model;
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std::string oaicompat_cmpl_id;
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common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
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virtual int get_index() override {
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return index;
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@ -663,18 +695,38 @@ struct server_task_result_cmpl_final : server_task_result {
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json to_json_oaicompat_chat() {
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std::string finish_reason = "length";
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common_chat_msg message;
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if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
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finish_reason = "stop";
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message = common_chat_parse(content, oaicompat_chat_format);
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finish_reason = message.tool_calls.empty() ? "stop" : "tool_calls";
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} else {
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message.content = content;
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}
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json choice = json{
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json tool_calls;
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if (!message.tool_calls.empty()) {
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tool_calls = json::array();
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for (const auto & tc : message.tool_calls) {
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tool_calls.push_back({
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{"type", "function"},
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{"function", {
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{"name", tc.name},
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{"arguments", tc.arguments},
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}},
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{"id", tc.id.empty() ? json() : json(tc.id)},
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});
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}
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}
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json choice {
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{"finish_reason", finish_reason},
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{"index", 0},
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{"message", json {
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{"content", content},
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{"role", "assistant"}
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}
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}};
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{"content", message.content},
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{"tool_calls", tool_calls},
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{"role", "assistant"},
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}},
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};
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if (!stream && probs_output.size() > 0) {
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choice["logprobs"] = json{
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@ -716,7 +768,7 @@ struct server_task_result_cmpl_final : server_task_result {
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finish_reason = "stop";
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}
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json choice = json{
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json choice = json {
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{"finish_reason", finish_reason},
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{"index", 0},
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{"delta", json::object()}
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@ -1191,6 +1243,8 @@ struct server_slot {
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llama_token sampled;
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common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
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// stats
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size_t n_sent_text = 0; // number of sent text character
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@ -1815,17 +1869,16 @@ struct server_context {
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if (use_jinja) {
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auto templates = common_chat_templates_from_model(model, "");
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common_chat_inputs inputs;
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inputs.messages = json::array({{
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{"role", "user"},
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{"content", "test"},
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}});
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GGML_ASSERT(templates.template_default);
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try {
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templates.template_default->apply({{
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{"role", "user"},
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{"content", "test"},
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}}, json(), true);
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common_chat_params_init(*templates.template_default, inputs);
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if (templates.template_tool_use) {
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templates.template_tool_use->apply({{
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{"role", "user"},
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{"content", "test"},
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}}, json(), true);
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common_chat_params_init(*templates.template_tool_use, inputs);
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}
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return true;
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} catch (const std::exception & e) {
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@ -2275,11 +2328,11 @@ struct server_context {
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res->id_slot = slot.id;
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res->index = slot.index;
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res->content = slot.generated_text;
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res->tokens = slot.generated_tokens;
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res->content = std::move(slot.generated_text);
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res->tokens = std::move(slot.generated_tokens);
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res->timings = slot.get_timings();
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res->prompt = common_detokenize(ctx, slot.prompt_tokens, true);
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res->response_fields = slot.params.response_fields;
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res->response_fields = std::move(slot.params.response_fields);
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res->truncated = slot.truncated;
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res->n_decoded = slot.n_decoded;
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@ -2290,12 +2343,12 @@ struct server_context {
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res->stop = slot.stop;
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res->post_sampling_probs = slot.params.post_sampling_probs;
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res->verbose = slot.params.verbose;
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res->stream = slot.params.stream;
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res->oaicompat = slot.params.oaicompat;
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res->oaicompat_model = slot.params.oaicompat_model;
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res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
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res->verbose = slot.params.verbose;
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res->stream = slot.params.stream;
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res->oaicompat = slot.params.oaicompat;
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res->oaicompat_model = slot.params.oaicompat_model;
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res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
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res->oaicompat_chat_format = slot.params.oaicompat_chat_format;
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// populate res.probs_output
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if (slot.params.sampling.n_probs > 0) {
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if (!slot.params.stream && slot.stop == STOP_TYPE_WORD) {
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@ -2773,6 +2826,11 @@ struct server_context {
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// track if given slot can be batched with slots already in the batch
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server_slot * slot_batched = nullptr;
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auto accept_special_token = [&](server_slot & slot, llama_token token) {
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const auto & trigger_tokens = slot.params.sampling.grammar_trigger_tokens;
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return params_base.special || std::find(trigger_tokens.begin(), trigger_tokens.end(), token) != trigger_tokens.end();
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};
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// frist, add sampled tokens from any ongoing sequences
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for (auto & slot : slots) {
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if (slot.state != SLOT_STATE_GENERATING) {
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@ -3136,7 +3194,7 @@ struct server_context {
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completion_token_output result;
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result.tok = id;
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result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
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result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
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result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
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if (slot.params.sampling.n_probs > 0) {
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@ -3225,7 +3283,7 @@ struct server_context {
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completion_token_output result;
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result.tok = ids[i];
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result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
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result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
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result.prob = 1.0f; // set later
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// TODO: set result.probs
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@ -3722,6 +3780,8 @@ int main(int argc, char ** argv) {
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{ "total_slots", ctx_server.params_base.n_parallel },
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{ "model_path", ctx_server.params_base.model },
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{ "chat_template", ctx_server.chat_templates.template_default->source() },
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{ "bos_token", ctx_server.chat_templates.template_default->bos_token() },
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{ "eos_token", ctx_server.chat_templates.template_default->eos_token() },
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{ "build_info", build_info },
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};
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if (ctx_server.params_base.use_jinja && ctx_server.chat_templates.template_tool_use) {
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@ -3763,7 +3823,9 @@ int main(int argc, char ** argv) {
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std::vector<server_task> tasks;
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try {
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std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, data.at("prompt"), true, true);
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const auto & prompt = data.at("prompt");
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LOG_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
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std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
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tasks.reserve(tokenized_prompts.size());
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for (size_t i = 0; i < tokenized_prompts.size(); i++) {
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server_task task = server_task(type);
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@ -3779,8 +3841,8 @@ int main(int argc, char ** argv) {
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task.id_selected_slot = json_value(data, "id_slot", -1);
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// OAI-compat
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task.params.oaicompat = oaicompat;
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task.params.oaicompat_cmpl_id = completion_id;
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task.params.oaicompat = oaicompat;
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task.params.oaicompat_cmpl_id = completion_id;
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// oaicompat_model is already populated by params_from_json_cmpl
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tasks.push_back(task);
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@ -3949,14 +4011,14 @@ int main(int argc, char ** argv) {
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};
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const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
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LOG_DBG("request: %s\n", req.body.c_str());
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if (ctx_server.params_base.embedding) {
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res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
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return;
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}
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auto body = json::parse(req.body);
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const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
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json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
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json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates);
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return handle_completions_impl(
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SERVER_TASK_TYPE_COMPLETION,
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@ -3966,6 +4028,13 @@ int main(int argc, char ** argv) {
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OAICOMPAT_TYPE_CHAT);
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};
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// same with handle_chat_completions, but without inference part
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const auto handle_apply_template = [&ctx_server, ¶ms, &res_ok](const httplib::Request & req, httplib::Response & res) {
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auto body = json::parse(req.body);
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json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates);
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res_ok(res, {{ "prompt", std::move(data.at("prompt")) }});
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};
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const auto handle_models = [¶ms, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
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json models = {
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{"object", "list"},
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@ -4124,14 +4193,6 @@ int main(int argc, char ** argv) {
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res_ok(res, root);
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};
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const auto handle_apply_template = [&ctx_server, ¶ms, &res_ok](const httplib::Request & req, httplib::Response & res) {
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auto body = json::parse(req.body);
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const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
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json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
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res_ok(res, {{ "prompt", data.at("prompt") }});
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};
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const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
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handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE);
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};
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