server : add "/chat/completions" alias for "/v1/...` (#5722)
* Add "/chat/completions" as alias for "/v1/chat/completions" * merge to upstream master * minor : fix trailing whitespace --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Jorge A
GitHub
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@ -3211,87 +3211,88 @@ int main(int argc, char **argv)
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res.set_content(models.dump(), "application/json; charset=utf-8");
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});
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const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res)
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{
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res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
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if (!validate_api_key(req, res)) {
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return;
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}
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json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template);
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// TODO: add mount point without "/v1" prefix -- how?
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svr.Post("/v1/chat/completions", [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res)
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{
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res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
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if (!validate_api_key(req, res)) {
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return;
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}
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json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template);
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const int task_id = llama.queue_tasks.get_new_id();
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llama.queue_results.add_waiting_task_id(task_id);
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llama.request_completion(task_id, data, false, false, -1);
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const int task_id = llama.queue_tasks.get_new_id();
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llama.queue_results.add_waiting_task_id(task_id);
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llama.request_completion(task_id, data, false, false, -1);
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if (!json_value(data, "stream", false)) {
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std::string completion_text;
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task_result result = llama.queue_results.recv(task_id);
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if (!json_value(data, "stream", false)) {
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std::string completion_text;
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task_result result = llama.queue_results.recv(task_id);
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if (!result.error && result.stop) {
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json oaicompat_result = format_final_response_oaicompat(data, result);
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if (!result.error && result.stop) {
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json oaicompat_result = format_final_response_oaicompat(data, result);
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res.set_content(oaicompat_result.dump(-1, ' ', false,
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json::error_handler_t::replace),
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"application/json; charset=utf-8");
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} else {
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res.status = 500;
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res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
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}
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llama.queue_results.remove_waiting_task_id(task_id);
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} else {
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const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
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while (true) {
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task_result llama_result = llama.queue_results.recv(task_id);
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if (!llama_result.error) {
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std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
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res.set_content(oaicompat_result.dump(-1, ' ', false,
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json::error_handler_t::replace),
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"application/json; charset=utf-8");
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} else {
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res.status = 500;
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res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
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}
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llama.queue_results.remove_waiting_task_id(task_id);
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} else {
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const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
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while (true) {
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task_result llama_result = llama.queue_results.recv(task_id);
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if (!llama_result.error) {
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std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
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for (auto it = result_array.begin(); it != result_array.end(); ++it)
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{
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if (!it->empty()) {
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const std::string str =
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"data: " +
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it->dump(-1, ' ', false, json::error_handler_t::replace) +
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"\n\n";
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LOG_VERBOSE("data stream", {{"to_send", str}});
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if (!sink.write(str.c_str(), str.size())) {
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llama.queue_results.remove_waiting_task_id(task_id);
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return false;
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}
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}
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}
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if (llama_result.stop) {
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break;
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}
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} else {
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for (auto it = result_array.begin(); it != result_array.end(); ++it)
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{
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if (!it->empty()) {
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const std::string str =
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"error: " +
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llama_result.result_json.dump(-1, ' ', false,
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json::error_handler_t::replace) +
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"data: " +
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it->dump(-1, ' ', false, json::error_handler_t::replace) +
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"\n\n";
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LOG_VERBOSE("data stream", {{"to_send", str}});
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if (!sink.write(str.c_str(), str.size())) {
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llama.queue_results.remove_waiting_task_id(task_id);
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return false;
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}
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break;
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}
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}
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sink.done();
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llama.queue_results.remove_waiting_task_id(task_id);
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return true;
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};
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auto on_complete = [task_id, &llama](bool) {
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// cancel request
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llama.request_cancel(task_id);
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llama.queue_results.remove_waiting_task_id(task_id);
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};
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res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
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if (llama_result.stop) {
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break;
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}
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} else {
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const std::string str =
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"error: " +
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llama_result.result_json.dump(-1, ' ', false,
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json::error_handler_t::replace) +
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"\n\n";
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LOG_VERBOSE("data stream", {{"to_send", str}});
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if (!sink.write(str.c_str(), str.size())) {
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llama.queue_results.remove_waiting_task_id(task_id);
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return false;
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}
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break;
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}
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}
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});
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sink.done();
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llama.queue_results.remove_waiting_task_id(task_id);
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return true;
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};
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auto on_complete = [task_id, &llama](bool) {
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// cancel request
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llama.request_cancel(task_id);
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llama.queue_results.remove_waiting_task_id(task_id);
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};
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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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};
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svr.Post("/chat/completions", chat_completions);
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svr.Post("/v1/chat/completions", chat_completions);
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svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
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{
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@ -54,6 +54,28 @@ Feature: Parallel
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| disabled | 128 |
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| enabled | 64 |
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Scenario Outline: Multi users OAI completions compatibility no v1
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Given a system prompt You are a writer.
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And a model tinyllama-2
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Given a prompt:
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"""
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Write a very long book.
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"""
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And a prompt:
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"""
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Write another a poem.
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"""
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And <n_predict> max tokens to predict
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And streaming is <streaming>
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Given concurrent OAI completions requests no v1
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Then the server is busy
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Then the server is idle
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Then all prompts are predicted with <n_predict> tokens
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Examples:
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| streaming | n_predict |
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| disabled | 128 |
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| enabled | 64 |
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Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969
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Given a prompt:
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"""
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@ -231,6 +231,7 @@ async def step_oai_chat_completions(context, api_error):
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completion = await oai_chat_completions(context.prompts.pop(),
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context.system_prompt,
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context.base_url,
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'/v1/chat',
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False,
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model=context.model if hasattr(context, 'model') else None,
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@ -288,6 +289,28 @@ async def step_oai_chat_completions(context):
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# user_prompt is inserted automatically
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context.system_prompt,
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context.base_url,
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'/v1/chat/completions',
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True, # async_client
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model=context.model
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if hasattr(context, 'model') else None,
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n_predict=context.n_predict
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if hasattr(context, 'n_predict') else None,
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enable_streaming=context.enable_streaming
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if hasattr(context, 'enable_streaming') else None,
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server_seed=context.server_seed
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if hasattr(context, 'server_seed') else None,
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user_api_key=context.user_api_key
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if hasattr(context, 'user_api_key') else None)
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@step(u'concurrent OAI completions requests no v1')
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@async_run_until_complete
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async def step_oai_chat_completions(context):
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await concurrent_requests(context, oai_chat_completions,
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# user_prompt is inserted automatically
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context.system_prompt,
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context.base_url,
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'/chat/completions',
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True, # async_client
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model=context.model
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if hasattr(context, 'model') else None,
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@ -497,6 +520,7 @@ async def request_completion(prompt,
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async def oai_chat_completions(user_prompt,
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system_prompt,
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base_url,
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base_path,
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async_client,
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debug=False,
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model=None,
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@ -537,7 +561,7 @@ async def oai_chat_completions(user_prompt,
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origin = 'llama.cpp'
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headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
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async with aiohttp.ClientSession() as session:
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async with session.post(f'{base_url}/v1/chat/completions',
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async with session.post(f'{base_url}{base_path}',
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json=payload,
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headers=headers) as response:
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if enable_streaming:
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@ -579,7 +603,7 @@ async def oai_chat_completions(user_prompt,
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else:
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try:
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openai.api_key = user_api_key
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openai.api_base = f'{base_url}/v1/chat'
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openai.api_base = f'{base_url}{base_path}'
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chat_completion = openai.Completion.create(
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messages=payload['messages'],
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model=model,
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