mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-06-28 04:15:21 +00:00
tool-call: refactor common chat / tool-call api (+ tests / fixes) (#11900)
* tool-call refactoring: moved common_chat_* to chat.h, common_chat_templates_init return a unique_ptr to opaque type * addressed clang-tidy lints in [test-]chat.* * rm minja deps from util & common & move it to common/minja/ * add name & tool_call_id to common_chat_msg * add common_chat_tool * added json <-> tools, msgs conversions to chat.h * fix double bos/eos jinja avoidance hack (was preventing inner bos/eos tokens) * fix deepseek r1 slow test (no longer <think> opening w/ new template) * allow empty tools w/ auto + grammar * fix & test server grammar & json_schema params w/ & w/o --jinja
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@ -329,9 +329,6 @@ struct server_task {
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}
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// process "json_schema" and "grammar"
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if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) {
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throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both");
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}
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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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@ -1807,7 +1804,7 @@ struct server_context {
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// Necessary similarity of prompt for slot selection
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float slot_prompt_similarity = 0.0f;
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common_chat_templates chat_templates;
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common_chat_templates_ptr chat_templates;
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~server_context() {
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// Clear any sampling context
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@ -1891,45 +1888,17 @@ struct server_context {
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llama_init_dft.context.reset();
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}
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if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) {
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chat_templates = common_chat_templates_init(model, params_base.chat_template);
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try {
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common_chat_format_example(chat_templates.get(), params.use_jinja);
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} catch (const std::exception & e) {
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SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
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chat_templates = common_chat_templates_from_model(model, "chatml");
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} else {
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chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
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chat_templates = common_chat_templates_init(model, "chatml");
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}
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GGML_ASSERT(chat_templates.template_default.get() != nullptr);
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return true;
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}
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bool validate_builtin_chat_template(bool use_jinja) const {
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llama_chat_message chat[] = {{"user", "test"}};
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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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common_chat_params_init(*templates.template_default, inputs);
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if (templates.template_tool_use) {
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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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SRV_ERR("failed to apply template: %s\n", e.what());
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return false;
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}
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} else {
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const char * tmpl = llama_model_chat_template(model, /* name */ nullptr);
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const int32_t chat_res = llama_chat_apply_template(tmpl, chat, 1, true, nullptr, 0);
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return chat_res > 0;
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}
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}
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void init() {
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const int32_t n_ctx_slot = n_ctx / params_base.n_parallel;
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@ -3822,13 +3791,15 @@ int main(int argc, char ** argv) {
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{ "default_generation_settings", ctx_server.default_generation_settings_for_props },
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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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{ "chat_template", common_chat_templates_source(ctx_server.chat_templates.get()) },
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{ "bos_token", common_token_to_piece(ctx_server.ctx, llama_vocab_bos(ctx_server.vocab), /* special= */ true)},
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{ "eos_token", common_token_to_piece(ctx_server.ctx, llama_vocab_eos(ctx_server.vocab), /* special= */ true)},
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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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data["chat_template_tool_use"] = ctx_server.chat_templates.template_tool_use->source();
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if (ctx_server.params_base.use_jinja) {
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if (auto tool_use_src = common_chat_templates_source(ctx_server.chat_templates.get(), "tool_use")) {
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data["chat_template_tool_use"] = tool_use_src;
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}
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}
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res_ok(res, data);
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@ -4063,7 +4034,7 @@ int main(int argc, char ** argv) {
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}
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auto body = json::parse(req.body);
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json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates);
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json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates.get());
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return handle_completions_impl(
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SERVER_TASK_TYPE_COMPLETION,
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@ -4076,7 +4047,7 @@ int main(int argc, char ** argv) {
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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, params.reasoning_format, ctx_server.chat_templates);
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json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates.get());
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res_ok(res, {{ "prompt", std::move(data.at("prompt")) }});
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};
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@ -4493,8 +4464,8 @@ 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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LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
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ctx_server.chat_templates.template_default->source().c_str(),
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common_chat_format_example(*ctx_server.chat_templates.template_default, ctx_server.params_base.use_jinja).c_str());
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common_chat_templates_source(ctx_server.chat_templates.get()),
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common_chat_format_example(ctx_server.chat_templates.get(), ctx_server.params_base.use_jinja).c_str());
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ctx_server.queue_tasks.on_new_task([&ctx_server](const server_task & task) {
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ctx_server.process_single_task(task);
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