diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/server/README.md | 37 | ||||
| -rw-r--r-- | examples/server/completion.js.hpp | 548 | ||||
| -rwxr-xr-x | examples/server/deps.sh | 4 | ||||
| -rw-r--r-- | examples/server/index.html.hpp | 1579 | ||||
| -rw-r--r-- | examples/server/public/completion.js | 119 | ||||
| -rw-r--r-- | examples/server/public/index.html | 135 | ||||
| -rw-r--r-- | examples/server/server.cpp | 825 |
7 files changed, 1889 insertions, 1358 deletions
diff --git a/examples/server/README.md b/examples/server/README.md index 037412d..c5139c1 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -26,20 +26,17 @@ Command line options: ## Build -Build llama.cpp with server from repository root with either make or CMake. +server is build alongside everything else from the root of the project - Using `make`: ```bash - LLAMA_BUILD_SERVER=1 make + make ``` - Using `CMake`: ```bash - mkdir build-server - cd build-server - cmake -DLLAMA_BUILD_SERVER=ON .. cmake --build . --config Release ``` @@ -208,24 +205,30 @@ openai.api_base = "http://<Your api-server IP>:port" Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API -### Extending the Web Front End +### Extending or building alternative Web Front End -The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. A simple example is below: +The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. -``` +Read the documentation in `/completion.js` to see convenient ways to access llama. + +A simple example is below: + +```html <html> <body> <pre> <script type="module"> - import { llamaComplete } from '/completion.js' - - llamaComplete({ - prompt: "### Instruction:\nWrite dad jokes, each one paragraph. You can use html formatting if needed.\n\n### Response:", - n_predict: 1024, - }, - null, - (chunk) => document.write(chunk.data.content) - ) + import { llama } from '/completion.js' + + const prompt = `### Instruction: +Write dad jokes, each one paragraph. +You can use html formatting if needed. + +### Response:` + + for await (const chunk of llama(prompt)) { + document.write(chunk.data.content) + } </script> </pre> </body> diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 002830c..f399fb1 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -7,187 +7,369 @@ unsigned char completion_js[] = { 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x5d, 0x0a, 0x7d, - 0x3b, 0x0a, 0x0a, 0x2f, 0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x54, 0x68, - 0x69, 0x73, 0x20, 0x66, 0x75, 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0x20, 0x72, + 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, + 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, + 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a }; -unsigned int completion_js_len = 2275; +unsigned int completion_js_len = 4462; diff --git a/examples/server/deps.sh b/examples/server/deps.sh index cf99516..1e9fe96 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -4,10 +4,6 @@ # get the directory of this script file DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" PUBLIC=$DIR/public -OUTPUT=$DIR/templats.hpp - -echo "// Generated file, do not edit" > $OUTPUT -echo "" > $OUTPUT echo "download js bundle files" curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 832e9a3..42707fa 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -13,138 +13,143 @@ unsigned char index_html[] = { 0x3c, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x20, 0x2d, 0x20, 0x63, 0x68, 0x61, 0x74, 0x3c, 0x2f, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x3c, - 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x62, 0x6f, 0x64, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, - 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x66, 0x66, 0x66, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, - 0x3a, 0x20, 0x23, 0x30, 0x30, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, - 0x79, 0x3a, 0x20, 0x73, 0x79, 0x73, 0x74, 0x65, 0x6d, 0x2d, 0x75, 0x69, - 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@param {object} controller - an instance of AbortController if you need one, or null. - * @param {function} callback - The callback function to call when the completion is done. - * @returns {string} the completed text as a string. Ideally ignored, and you get at it via the callback. - */ -export const llamaComplete = async (params, controller, callback) => { +let generation_settings = null; + + +// Completes the prompt as a generator. Recommended for most use cases. +// +// Example: +// +// import { llama } from '/completion.js' +// +// const request = llama("Tell me a joke", {n_predict: 800}) +// for await (const chunk of request) { +// document.write(chunk.data.content) +// } +// +export async function* llama(prompt, params = {}, config = {}) { + let controller = config.controller; + if (!controller) { controller = new AbortController(); } - const completionParams = { ...paramDefaults, ...params }; - // we use fetch directly here becasue the built in fetchEventSource does not support POST + const completionParams = { ...paramDefaults, ...params, prompt }; + const response = await fetch("/completion", { method: 'POST', body: JSON.stringify(completionParams), @@ -36,7 +45,6 @@ export const llamaComplete = async (params, controller, callback) => { let content = ""; try { - let cont = true; while (cont) { @@ -59,18 +67,21 @@ export const llamaComplete = async (params, controller, callback) => { result.data = JSON.parse(result.data); content += result.data.content; - // callack - if (callback) { - cont = callback(result) != false; - } + // yield + yield result; // if we got a stop token from server, we will break here if (result.data.stop) { + if (result.data.generation_settings) { + generation_settings = result.data.generation_settings; + } break; } } } catch (e) { - console.error("llama error: ", e); + if (e.name !== 'AbortError') { + console.error("llama error: ", e); + } throw e; } finally { @@ -79,3 +90,79 @@ export const llamaComplete = async (params, controller, callback) => { return content; } + +// Call llama, return an event target that you can subcribe to +// +// Example: +// +// import { llamaEventTarget } from '/completion.js' +// +// const conn = llamaEventTarget(prompt) +// conn.addEventListener("message", (chunk) => { +// document.write(chunk.detail.content) +// }) +// +export const llamaEventTarget = (prompt, params = {}, config = {}) => { + const eventTarget = new EventTarget(); + (async () => { + let content = ""; + for await (const chunk of llama(prompt, params, config)) { + if (chunk.data) { + content += chunk.data.content; + eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); + } + if (chunk.data.generation_settings) { + eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); + } + if (chunk.data.timings) { + eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); + } + } + eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); + })(); + return eventTarget; +} + +// Call llama, return a promise that resolves to the completed text. This does not support streaming +// +// Example: +// +// llamaPromise(prompt).then((content) => { +// document.write(content) +// }) +// +// or +// +// const content = await llamaPromise(prompt) +// document.write(content) +// +export const llamaPromise = (prompt, params = {}, config = {}) => { + return new Promise(async (resolve, reject) => { + let content = ""; + try { + for await (const chunk of llama(prompt, params, config)) { + content += chunk.data.content; + } + resolve(content); + } catch (error) { + reject(error); + } + }); +}; + +/** + * (deprecated) + */ +export const llamaComplete = async (params, controller, callback) => { + for await (const chunk of llama(params.prompt, params, { controller })) { + callback(chunk); + } +} + +// Get the model info from the server. This is useful for getting the context window and so on. +export const llamaModelInfo = async () => { + if (!generation_settings) { + generation_settings = await fetch("/model.json").then(r => r.json()); + } + return generation_settings; +} diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 6393e2e..8ace0b0 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -6,7 +6,6 @@ <title>llama.cpp - chat</title> <style> - body { background-color: #fff; color: #000; @@ -22,10 +21,6 @@ height: 100%; } - header, footer { - text-align: center; - } - main { margin: 3px; display: flex; @@ -99,6 +94,15 @@ margin: 0.5em 0; display: block; } + + header, footer { + text-align: center; + } + + footer { + font-size: 80%; + color: #888; + } </style> <script type="module"> @@ -106,10 +110,10 @@ html, h, signal, effect, computed, render, useSignal, useEffect, useRef } from '/index.js'; - import { llamaComplete } from '/completion.js'; + import { llama } from '/completion.js'; const session = signal({ - prompt: "This is a conversation between user and llama, a friendly chatbot. respond in markdown.", + prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.", template: "{{prompt}}\n\n{{history}}\n{{char}}:", historyTemplate: "{{name}}: {{message}}", transcript: [], @@ -118,15 +122,6 @@ user: "User", }) - const transcriptUpdate = (transcript) => { - session.value = { - ...session.value, - transcript - } - } - - const chatStarted = computed(() => session.value.transcript.length > 0) - const params = signal({ n_predict: 400, temperature: 0.7, @@ -136,8 +131,18 @@ top_p: 0.5, }) + const llamaStats = signal(null) const controller = signal(null) + const generating = computed(() => controller.value == null ) + const chatStarted = computed(() => session.value.transcript.length > 0) + + const transcriptUpdate = (transcript) => { + session.value = { + ...session.value, + transcript + } + } // simple template replace const template = (str, extraSettings) => { @@ -158,7 +163,7 @@ transcriptUpdate([...session.value.transcript, ["{{user}}", msg]]) - const payload = template(session.value.template, { + const prompt = template(session.value.template, { message: msg, history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"), }); @@ -168,22 +173,26 @@ const llamaParams = { ...params.value, - prompt: payload, stop: ["</s>", template("{{char}}:"), template("{{user}}:")], } - await llamaComplete(llamaParams, controller.value, (message) => { - const data = message.data; + for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) { + const data = chunk.data; currentMessage += data.content; + // remove leading whitespace currentMessage = currentMessage.replace(/^\s+/, "") transcriptUpdate([...history, ["{{char}}", currentMessage]]) if (data.stop) { - console.log("-->", data, ' response was:', currentMessage, 'transcript state:', session.value.transcript); + console.log("Completion finished: '", currentMessage, "', summary: ", data); } - }) + + if (data.timings) { + llamaStats.value = data.timings; + } + } controller.value = null; } @@ -219,13 +228,12 @@ return html` <form onsubmit=${submit}> <div> - <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/> - + <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/> </div> <div class="right"> - <button type="submit" disabled=${!generating.value} >Send</button> - <button onclick=${stop} disabled=${generating}>Stop</button> - <button onclick=${reset}>Reset</button> + <button type="submit" disabled=${!generating.value} >Send</button> + <button onclick=${stop} disabled=${generating}>Stop</button> + <button onclick=${reset}>Reset</button> </div> </form> ` @@ -243,7 +251,7 @@ }, [messages]) const chatLine = ([user, msg]) => { - return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdown} text=${template(msg)} /></p>` + return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>` }; return html` @@ -313,39 +321,52 @@ </form> ` } -const Markdown = (params) => { - const md = params.text - .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>') - .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>') - .replace(/__(.*?)__/g, '<strong>$1</strong>') - .replace(/\*(.*?)\*/g, '<em>$1</em>') - .replace(/_(.*?)_/g, '<em>$1</em>') - .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>') - .replace(/`(.*?)`/g, '<code>$1</code>') - .replace(/\n/gim, '<br />'); - return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`; -}; + // poor mans markdown replacement + const Markdownish = (params) => { + const md = params.text + .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>') + .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>') + .replace(/__(.*?)__/g, '<strong>$1</strong>') + .replace(/\*(.*?)\*/g, '<em>$1</em>') + .replace(/_(.*?)_/g, '<em>$1</em>') + .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>') + .replace(/`(.*?)`/g, '<code>$1</code>') + .replace(/\n/gim, '<br />'); + return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`; + }; + + const ModelGenerationInfo = (params) => { + if (!llamaStats.value) { + return html`<span/>` + } + return html` + <span> + ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second + </span> + ` + } function App(props) { return html` - <div id="container"> - <header> - <h1>llama.cpp</h1> - </header> - - <main id="content"> - <${chatStarted.value ? ChatLog : ConfigForm} /> - </main> - - <footer id="write"> - <${MessageInput} /> - </footer> - - <footer> - <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a></p> - </footer> - </div> + <div id="container"> + <header> + <h1>llama.cpp</h1> + </header> + + <main id="content"> + <${chatStarted.value ? ChatLog : ConfigForm} /> + </main> + + <section id="write"> + <${MessageInput} /> + </section> + + <footer> + <p><${ModelGenerationInfo} /></p> + <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p> + </footer> + </div> `; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a835c39..2cbfc00 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -22,7 +22,8 @@ using namespace httplib; using json = nlohmann::json; -struct server_params { +struct server_params +{ std::string hostname = "127.0.0.1"; std::string public_path = "examples/server/public"; int32_t port = 8080; @@ -31,8 +32,10 @@ struct server_params { }; // completion token output with probabilities -struct completion_token_output { - struct token_prob { +struct completion_token_output +{ + struct token_prob + { llama_token tok; float prob; }; @@ -41,30 +44,40 @@ struct completion_token_output { llama_token tok; }; -static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) { +static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b) +{ size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) + { + } return i; } -enum stop_type { +enum stop_type +{ STOP_FULL, STOP_PARTIAL, }; -static bool ends_with(const std::string & str, const std::string & suffix) { +static bool ends_with(const std::string &str, const std::string &suffix) +{ return str.size() >= suffix.size() && - 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); + 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } -static size_t find_partial_stop_string(const std::string & stop, - const std::string & text) { - if (!text.empty() && !stop.empty()) { +static size_t find_partial_stop_string(const std::string &stop, + const std::string &text) +{ + if (!text.empty() && !stop.empty()) + { const char text_last_char = text.back(); - for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) { - if (stop[char_index] == text_last_char) { + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) + { + if (stop[char_index] == text_last_char) + { const std::string current_partial = stop.substr(0, char_index + 1); - if (ends_with(text, current_partial)) { + if (ends_with(text, current_partial)) + { return text.size() - char_index - 1; } } @@ -73,26 +86,30 @@ static size_t find_partial_stop_string(const std::string & stop, return std::string::npos; } -template<class Iter> -static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { +template <class Iter> +static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) +{ std::string ret; - for (; begin != end; ++begin) { + for (; begin != end; ++begin) + { ret += llama_token_to_str(ctx, *begin); } return ret; } -static void server_log(const char * level, const char * function, int line, - const char * message, const nlohmann::ordered_json & extra) { - nlohmann::ordered_json log { - { "timestamp", time(nullptr) }, - { "level", level }, - { "function", function }, - { "line", line }, - { "message", message }, +static void server_log(const char *level, const char *function, int line, + const char *message, const nlohmann::ordered_json &extra) +{ + nlohmann::ordered_json log{ + {"timestamp", time(nullptr)}, + {"level", level}, + {"function", function}, + {"line", line}, + {"message", message}, }; - if (!extra.empty()) { + if (!extra.empty()) + { log.merge_patch(extra); } @@ -102,32 +119,37 @@ static void server_log(const char * level, const char * function, int line, } // format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { +static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) +{ std::string out = token == -1 ? "" : llama_token_to_str(ctx, token); // if first bit is 1, meaning it's a partial character - if (out.size() > 0 && (out[0] & 0x80) == 0x80) { + if (out.size() > 0 && (out[0] & 0x80) == 0x80) + { std::stringstream ss; - ss<< std::hex << (out[0] & 0xff); - std::string res ( ss.str() ); + ss << std::hex << (out[0] & 0xff); + std::string res(ss.str()); out = "byte: \\x" + res; } return out; } // convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> probs) { +static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> probs) +{ json out = json::array(); - for (const auto & prob : probs) { + for (const auto &prob : probs) + { json probs_for_token = json::array(); - for (const auto & p : prob.probs) { + for (const auto &p : prob.probs) + { std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json { - { "tok_str", tok_str }, - { "prob", p.prob }, + probs_for_token.push_back(json{ + {"tok_str", tok_str}, + {"prob", p.prob}, }); } std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json { + out.push_back(json{ {"content", tok_str}, {"probs", probs_for_token}, }); @@ -138,21 +160,24 @@ static json probs_vector_to_json(const llama_context * ctx, const std::vector<co static bool server_verbose = false; #if SERVER_VERBOSE != 1 -# define LOG_VERBOSE(MSG, ...) +#define LOG_VERBOSE(MSG, ...) #else -# define LOG_VERBOSE(MSG, ...) \ - do { \ - if (server_verbose) { \ +#define LOG_VERBOSE(MSG, ...) \ + do \ + { \ + if (server_verbose) \ + { \ server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ } \ - } while(0) + } while (0) #endif #define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -struct llama_server_context { +struct llama_server_context +{ bool stream = false; bool has_next_token = false; std::string generated_text; @@ -166,8 +191,8 @@ struct llama_server_context { std::vector<llama_token> embd; std::vector<llama_token> last_n_tokens; - llama_model * model = nullptr; - llama_context * ctx = nullptr; + llama_model *model = nullptr; + llama_context *ctx = nullptr; gpt_params params; bool truncated = false; @@ -179,22 +204,27 @@ struct llama_server_context { std::mutex mutex; - std::unique_lock<std::mutex> lock() { + std::unique_lock<std::mutex> lock() + { return std::unique_lock<std::mutex>(mutex); } - ~llama_server_context() { - if (ctx) { + ~llama_server_context() + { + if (ctx) + { llama_free(ctx); ctx = nullptr; } - if (model) { + if (model) + { llama_free_model(model); model = nullptr; } } - void rewind() { + void rewind() + { params.antiprompt.clear(); num_prompt_tokens = 0; num_tokens_predicted = 0; @@ -212,11 +242,13 @@ struct llama_server_context { n_past = 0; } - bool loadModel(const gpt_params & params_) { + bool loadModel(const gpt_params ¶ms_) + { params = params_; std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (model == nullptr) { - LOG_ERROR("unable to load model", { { "model", params_.model } }); + if (model == nullptr) + { + LOG_ERROR("unable to load model", {{"model", params_.model}}); return false; } @@ -225,18 +257,21 @@ struct llama_server_context { return true; } - void loadPrompt() { + void loadPrompt() + { params.prompt.insert(0, 1, ' '); // always add a first space std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true); num_prompt_tokens = prompt_tokens.size(); - if (params.n_keep < 0) { + if (params.n_keep < 0) + { params.n_keep = (int)num_prompt_tokens; } params.n_keep = std::min(params.n_ctx - 4, params.n_keep); // if input prompt is too big, truncate like normal - if (num_prompt_tokens>= (size_t)params.n_ctx) { + if (num_prompt_tokens >= (size_t)params.n_ctx) + { const int n_left = (params.n_ctx - params.n_keep) / 2; std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; @@ -244,15 +279,17 @@ struct llama_server_context { std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); LOG_VERBOSE("input truncated", { - { "n_ctx", params.n_ctx }, - { "n_keep", params.n_keep }, - { "n_left", n_left }, - { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) }, - }); + {"n_ctx", params.n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); truncated = true; prompt_tokens = new_tokens; - } else { + } + else + { const size_t ps = num_prompt_tokens; std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0); std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps); @@ -261,31 +298,35 @@ struct llama_server_context { // compare the evaluated prompt with the new prompt n_past = common_part(embd, prompt_tokens); embd = prompt_tokens; - if (n_past == num_prompt_tokens) { + if (n_past == num_prompt_tokens) + { // we have to evaluate at least 1 token to generate logits. n_past--; } LOG_VERBOSE("prompt ingested", { - { "n_past", n_past }, - { "cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past) }, - { "to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) }, - }); + {"n_past", n_past}, + {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)}, + {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, + }); has_next_token = true; } - void beginCompletion() { + void beginCompletion() + { // number of tokens to keep when resetting context n_remain = params.n_predict; llama_set_rng_seed(ctx, params.seed); } - completion_token_output nextToken() { + completion_token_output nextToken() + { completion_token_output result; result.tok = -1; - if (embd.size() >= (size_t)params.n_ctx) { + if (embd.size() >= (size_t)params.n_ctx) + { // Reset context const int n_left = (params.n_ctx - params.n_keep) / 2; @@ -295,32 +336,36 @@ struct llama_server_context { n_past = params.n_keep; truncated = true; LOG_VERBOSE("input truncated", { - { "n_ctx", params.n_ctx }, - { "n_keep", params.n_keep }, - { "n_left", n_left }, - { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) }, - }); + {"n_ctx", params.n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); } - while (n_past < embd.size()) { + while (n_past < embd.size()) + { int n_eval = (int)embd.size() - n_past; - if (n_eval > params.n_batch) { + if (n_eval > params.n_batch) + { n_eval = params.n_batch; } - if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) { + if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) + { LOG_ERROR("failed to eval", { - { "n_eval", n_eval }, - { "n_past", n_past }, - { "n_threads", params.n_threads }, - { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) }, - }); + {"n_eval", n_eval}, + {"n_past", n_past}, + {"n_threads", params.n_threads}, + {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, + }); has_next_token = false; return result; } n_past += n_eval; } - if (params.n_predict == 0) { + if (params.n_predict == 0) + { has_next_token = false; result.tok = llama_token_eos(); return result; @@ -343,52 +388,64 @@ struct llama_server_context { const int32_t n_probs = params.n_probs; { - auto * logits = llama_get_logits(ctx); + auto *logits = llama_get_logits(ctx); auto n_vocab = llama_n_vocab(ctx); // Apply params.logit_bias map - for (const auto & it : params.logit_bias) { + for (const auto &it : params.logit_bias) + { logits[it.first] += it.second; } std::vector<llama_token_data> candidates; candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) + { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); } - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false}; // Apply penalties float nl_logit = logits[llama_token_nl()]; auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx); llama_sample_repetition_penalty(ctx, &candidates_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, repeat_penalty); + last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, + last_n_repeat, repeat_penalty); llama_sample_frequency_and_presence_penalties(ctx, &candidates_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, alpha_frequency, alpha_presence); - if (!penalize_nl) { + last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, + last_n_repeat, alpha_frequency, alpha_presence); + if (!penalize_nl) + { logits[llama_token_nl()] = nl_logit; } - if (temp <= 0) { + if (temp <= 0) + { // Greedy sampling result.tok = llama_sample_token_greedy(ctx, &candidates_p); - if (n_probs > 0) { + if (n_probs > 0) + { llama_sample_softmax(ctx, &candidates_p); } - } else { - if (mirostat == 1) { + } + else + { + if (mirostat == 1) + { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; llama_sample_temperature(ctx, &candidates_p, temp); result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { + } + else if (mirostat == 2) + { static float mirostat_mu = 2.0f * mirostat_tau; llama_sample_temperature(ctx, &candidates_p, temp); result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { + } + else + { // Temperature sampling size_t min_keep = std::max(1, n_probs); llama_sample_top_k(ctx, &candidates_p, top_k, min_keep); @@ -400,7 +457,8 @@ struct llama_server_context { } } - for (size_t i = 0; i < std::min(candidates_p.size, (size_t) n_probs); ++i) { + for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i) + { result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p}); } last_n_tokens.erase(last_n_tokens.begin()); @@ -413,8 +471,9 @@ struct llama_server_context { // decrement remaining sampling budget --n_remain; - if (!embd.empty() && embd.back() == llama_token_eos()) { - //stopping_word = llama_token_to_str(ctx, embd.back()); + if (!embd.empty() && embd.back() == llama_token_eos()) + { + // stopping_word = llama_token_to_str(ctx, embd.back()); has_next_token = false; stopped_eos = true; LOG_VERBOSE("eos token found", {}); @@ -425,22 +484,28 @@ struct llama_server_context { return result; } - size_t findStoppingStrings(const std::string & text, const size_t last_token_size, - const stop_type type) { + size_t findStoppingStrings(const std::string &text, const size_t last_token_size, + const stop_type type) + { size_t stop_pos = std::string::npos; - for (const std::string & word : params.antiprompt) { + for (const std::string &word : params.antiprompt) + { size_t pos; - if (type == STOP_FULL) { + if (type == STOP_FULL) + { const size_t tmp = word.size() + last_token_size; const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; pos = text.find(word, from_pos); } - else { + else + { pos = find_partial_stop_string(word, text); } if (pos != std::string::npos && - (stop_pos == std::string::npos || pos < stop_pos)) { - if (type == STOP_FULL) { + (stop_pos == std::string::npos || pos < stop_pos)) + { + if (type == STOP_FULL) + { stopping_word = word; stopped_word = true; has_next_token = false; @@ -451,74 +516,91 @@ struct llama_server_context { return stop_pos; } - completion_token_output doCompletion() { + completion_token_output doCompletion() + { const completion_token_output token_with_probs = nextToken(); const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok); generated_text += token_text; - if (params.n_probs > 0) { + if (params.n_probs > 0) + { generated_token_probs.push_back(token_with_probs); } - if (multibyte_pending > 0) { + if (multibyte_pending > 0) + { multibyte_pending -= token_text.size(); - } else if (token_text.size() == 1) { + } + else if (token_text.size() == 1) + { const char c = token_text[0]; // 2-byte characters: 110xxxxx 10xxxxxx - if ((c & 0xE0) == 0xC0) { + if ((c & 0xE0) == 0xC0) + { multibyte_pending = 1; - // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx - } else if ((c & 0xF0) == 0xE0) { + // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx + } + else if ((c & 0xF0) == 0xE0) + { multibyte_pending = 2; - // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx - } else if ((c & 0xF8) == 0xF0) { + // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx + } + else if ((c & 0xF8) == 0xF0) + { multibyte_pending = 3; - } else { + } + else + { multibyte_pending = 0; } } - if (multibyte_pending > 0 && !has_next_token) { + if (multibyte_pending > 0 && !has_next_token) + { has_next_token = true; n_remain++; } - if (!has_next_token && n_remain == 0) { + if (!has_next_token && n_remain == 0) + { stopped_limit = true; } LOG_VERBOSE("next token", { - { "token", token_with_probs.tok }, - { "token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok) }, - { "has_next_token", has_next_token }, - { "n_remain", n_remain }, - { "num_tokens_predicted", num_tokens_predicted }, - { "stopped_eos", stopped_eos }, - { "stopped_word", stopped_word }, - { "stopped_limit", stopped_limit }, - { "stopping_word", stopping_word }, - }); + {"token", token_with_probs.tok}, + {"token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok)}, + {"has_next_token", has_next_token}, + {"n_remain", n_remain}, + {"num_tokens_predicted", num_tokens_predicted}, + {"stopped_eos", stopped_eos}, + {"stopped_word", stopped_word}, + {"stopped_limit", stopped_limit}, + {"stopping_word", stopping_word}, + }); return token_with_probs; } - std::vector<float> getEmbedding() { + std::vector<float> getEmbedding() + { static const int n_embd = llama_n_embd(ctx); - if (!params.embedding) { + if (!params.embedding) + { LOG_WARNING("embedding disabled", { - { "params.embedding", params.embedding }, - }); + {"params.embedding", params.embedding}, + }); return std::vector<float>(n_embd, 0.0f); } - const float * data = llama_get_embeddings(ctx); + const float *data = llama_get_embeddings(ctx); std::vector<float> embedding(data, data + n_embd); return embedding; } }; -static void server_print_usage(const char * argv0, const gpt_params & params, - const server_params & sparams) { +static void server_print_usage(const char *argv0, const gpt_params ¶ms, + const server_params &sparams) +{ fprintf(stderr, "usage: %s [options]\n", argv0); fprintf(stderr, "\n"); fprintf(stderr, "options:\n"); @@ -529,10 +611,12 @@ static void server_print_usage(const char * argv0, const gpt_params & params, fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n"); - if (llama_mlock_supported()) { + if (llama_mlock_supported()) + { fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_mmap_supported()) { + if (llama_mmap_supported()) + { fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD @@ -558,78 +642,113 @@ static void server_print_usage(const char * argv0, const gpt_params & params, fprintf(stderr, "\n"); } -static void server_params_parse(int argc, char ** argv, server_params & sparams, - gpt_params & params) { +static void server_params_parse(int argc, char **argv, server_params &sparams, + gpt_params ¶ms) +{ gpt_params default_params; server_params default_sparams; std::string arg; bool invalid_param = false; - for (int i = 1; i < argc; i++) { + for (int i = 1; i < argc; i++) + { arg = argv[i]; - if (arg == "--port") { - if (++i >= argc) { + if (arg == "--port") + { + if (++i >= argc) + { invalid_param = true; break; } sparams.port = std::stoi(argv[i]); - } else if (arg == "--host") { - if (++i >= argc) { + } + else if (arg == "--host") + { + if (++i >= argc) + { invalid_param = true; break; } sparams.hostname = argv[i]; - } else if (arg == "--path") { - if (++i >= argc) { + } + else if (arg == "--path") + { + if (++i >= argc) + { invalid_param = true; break; } sparams.public_path = argv[i]; - } else if (arg == "--timeout" || arg == "-to") { - if (++i >= argc) { + } + else if (arg == "--timeout" || arg == "-to") + { + if (++i >= argc) + { invalid_param = true; break; } sparams.read_timeout = std::stoi(argv[i]); sparams.write_timeout = std::stoi(argv[i]); - } else if (arg == "-m" || arg == "--model") { - if (++i >= argc) { + } + else if (arg == "-m" || arg == "--model") + { + if (++i >= argc) + { invalid_param = true; break; } params.model = argv[i]; - } else if (arg == "-a" || arg == "--alias") { - if (++i >= argc) { + } + else if (arg == "-a" || arg == "--alias") + { + if (++i >= argc) + { invalid_param = true; break; } params.model_alias = argv[i]; - } else if (arg == "-h" || arg == "--help") { + } + else if (arg == "-h" || arg == "--help") + { server_print_usage(argv[0], default_params, default_sparams); exit(0); - } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") { - if (++i >= argc) { + } + else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") + { + if (++i >= argc) + { invalid_param = true; break; } params.n_ctx = std::stoi(argv[i]); - } else if (arg == "--memory-f32" || arg == "--memory_f32") { + } + else if (arg == "--memory-f32" || arg == "--memory_f32") + { params.memory_f16 = false; - } else if (arg == "--threads" || arg == "-t") { - if (++i >= argc) { + } + else if (arg == "--threads" || arg == "-t") + { + if (++i >= argc) + { invalid_param = true; break; } params.n_threads = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { - if (++i >= argc) { + } + else if (arg == "-b" || arg == "--batch-size") + { + if (++i >= argc) + { invalid_param = true; break; } params.n_batch = std::stoi(argv[i]); params.n_batch = std::min(512, params.n_batch); - } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { + } + else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") + { + if (++i >= argc) + { invalid_param = true; break; } @@ -637,11 +756,14 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, params.n_gpu_layers = std::stoi(argv[i]); #else LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " - "See main README.md for information on enabling GPU BLAS support", { { "n_gpu_layers", params.n_gpu_layers } }); + "See main README.md for information on enabling GPU BLAS support", + {{"n_gpu_layers", params.n_gpu_layers}}); #endif } - else if (arg == "--tensor-split" || arg == "-ts") { - if (++i >= argc) { + else if (arg == "--tensor-split" || arg == "-ts") + { + if (++i >= argc) + { invalid_param = true; break; } @@ -649,16 +771,19 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, std::string arg_next = argv[i]; // split string by , and / - const std::regex regex{ R"([,/]+)" }; - std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 }; - std::vector<std::string> split_arg{ it, {} }; + const std::regex regex{R"([,/]+)"}; + std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; + std::vector<std::string> split_arg{it, {}}; GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); - for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) { - if (i_device < split_arg.size()) { + for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) + { + if (i_device < split_arg.size()) + { params.tensor_split[i_device] = std::stof(split_arg[i_device]); } - else { + else + { params.tensor_split[i_device] = 0.0f; } } @@ -674,8 +799,10 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n"); #endif // GGML_USE_CUBLAS } - else if (arg == "--main-gpu" || arg == "-mg") { - if (++i >= argc) { + else if (arg == "--main-gpu" || arg == "-mg") + { + if (++i >= argc) + { invalid_param = true; break; } @@ -684,125 +811,173 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); #endif - } else if (arg == "--lora") { - if (++i >= argc) { + } + else if (arg == "--lora") + { + if (++i >= argc) + { invalid_param = true; break; } params.lora_adapter = argv[i]; params.use_mmap = false; - } else if (arg == "--lora-base") { - if (++i >= argc) { + } + else if (arg == "--lora-base") + { + if (++i >= argc) + { invalid_param = true; break; } params.lora_base = argv[i]; - } else if (arg == "-v" || arg == "--verbose") { + } + else if (arg == "-v" || arg == "--verbose") + { #if SERVER_VERBOSE != 1 LOG_WARNING("server.cpp is not built with verbose logging.", {}); #else server_verbose = true; #endif - } else if (arg == "--mlock") { + } + else if (arg == "--mlock") + { params.use_mlock = true; - } else if (arg == "--no-mmap") { + } + else if (arg == "--no-mmap") + { params.use_mmap = false; - } else if (arg == "--embedding") { + } + else if (arg == "--embedding") + { params.embedding = true; - } else { + } + else + { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } - if (invalid_param) { + if (invalid_param) + { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } -static json format_generation_settings(llama_server_context & llama) { +static json format_generation_settings(llama_server_context &llama) +{ const auto eos_bias = llama.params.logit_bias.find(llama_token_eos()); const bool ignore_eos = eos_bias != llama.params.logit_bias.end() && - eos_bias->second < 0.0f && std::isinf(eos_bias->second); - - return json { - { "seed", llama.params.seed }, - { "temp", llama.params.temp }, - { "top_k", llama.params.top_k }, - { "top_p", llama.params.top_p }, - { "tfs_z", llama.params.tfs_z }, - { "typical_p", llama.params.typical_p }, - { "repeat_last_n", llama.params.repeat_last_n }, - { "repeat_penalty", llama.params.repeat_penalty }, - { "presence_penalty", llama.params.presence_penalty }, - { "frequency_penalty", llama.params.frequency_penalty }, - { "mirostat", llama.params.mirostat }, - { "mirostat_tau", llama.params.mirostat_tau }, - { "mirostat_eta", llama.params.mirostat_eta }, - { "penalize_nl", llama.params.penalize_nl }, - { "stop", llama.params.antiprompt }, - { "n_predict", llama.params.n_predict }, - { "n_keep", llama.params.n_keep }, - { "ignore_eos", ignore_eos }, - { "stream", llama.stream }, - { "logit_bias", llama.params.logit_bias }, - { "n_probs", llama.params.n_probs }, + eos_bias->second < 0.0f && std::isinf(eos_bias->second); + + return json{ + {"n_ctx", llama.params.n_ctx}, + {"model", llama.params.model_alias}, + {"seed", llama.params.seed}, + {"temp", llama.params.temp}, + {"top_k", llama.params.top_k}, + {"top_p", llama.params.top_p}, + {"tfs_z", llama.params.tfs_z}, + {"typical_p", llama.params.typical_p}, + {"repeat_last_n", llama.params.repeat_last_n}, + {"repeat_penalty", llama.params.repeat_penalty}, + {"presence_penalty", llama.params.presence_penalty}, + {"frequency_penalty", llama.params.frequency_penalty}, + {"mirostat", llama.params.mirostat}, + {"mirostat_tau", llama.params.mirostat_tau}, + {"mirostat_eta", llama.params.mirostat_eta}, + {"penalize_nl", llama.params.penalize_nl}, + {"stop", llama.params.antiprompt}, + {"n_predict", llama.params.n_predict}, + {"n_keep", llama.params.n_keep}, + {"ignore_eos", ignore_eos}, + {"stream", llama.stream}, + {"logit_bias", llama.params.logit_bias}, + {"n_probs", llama.params.n_probs}, + }; +} + +static json format_embedding_response(llama_server_context &llama) +{ + return json{ + {"embedding", llama.getEmbedding()}, }; } -static json format_embedding_response(llama_server_context & llama) { - return json { - { "embedding", llama.getEmbedding() }, +static json format_timings(llama_server_context &llama) +{ + const auto timings = llama_get_timings(llama.ctx); + + assert(timings.n_eval == llama.num_tokens_predicted); + + return json{ + {"prompt_n", timings.n_eval}, + {"prompt_ms", timings.t_p_eval_ms}, + {"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval}, + {"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval}, + + {"predicted_n", timings.n_eval}, + {"predicted_ms", timings.t_eval_ms}, + {"predicted_per_token_ms", timings.t_eval_ms / timings.n_eval}, + {"predicted_per_second", 1e3 / timings.t_eval_ms * timings.n_eval}, }; } -static json format_final_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) { - - json res = json { - { "content", content }, - { "stop", true }, - { "model", llama.params.model_alias }, - { "tokens_predicted", llama.num_tokens_predicted }, - { "tokens_evaluated", llama.num_prompt_tokens }, - { "generation_settings", format_generation_settings(llama) }, - { "prompt", llama.params.prompt }, - { "truncated", llama.truncated }, - { "stopped_eos", llama.stopped_eos }, - { "stopped_word", llama.stopped_word }, - { "stopped_limit", llama.stopped_limit }, - { "stopping_word", llama.stopping_word }, +static json format_final_response(llama_server_context &llama, const std::string &content, const std::vector<completion_token_output> &probs) +{ + + json res = json{ + {"content", content}, + {"stop", true}, + {"model", llama.params.model_alias}, + {"tokens_predicted", llama.num_tokens_predicted}, + {"tokens_evaluated", llama.num_prompt_tokens}, + {"generation_settings", format_generation_settings(llama)}, + {"prompt", llama.params.prompt}, + {"truncated", llama.truncated}, + {"stopped_eos", llama.stopped_eos}, + {"stopped_word", llama.stopped_word}, + {"stopped_limit", llama.stopped_limit}, + {"stopping_word", llama.stopping_word}, + {"tokens_cached", llama.n_past}, + {"tokens_predicted", llama.num_tokens_predicted}, + {"timings", format_timings(llama)}, }; - if (llama.params.n_probs > 0) { + if (llama.params.n_probs > 0) + { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } return res; } -static json format_partial_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) { - json res = json { - { "content", content }, - { "stop", false }, +static json format_partial_response(llama_server_context &llama, const std::string &content, const std::vector<completion_token_output> &probs) +{ + json res = json{ + {"content", content}, + {"stop", false}, }; - if (llama.params.n_probs > 0) { + if (llama.params.n_probs > 0) + { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } return res; } -static json format_tokenizer_response(const std::vector<llama_token> & tokens) { - return json { - { "tokens", tokens } - }; +static json format_tokenizer_response(const std::vector<llama_token> &tokens) +{ + return json{ + {"tokens", tokens}}; } -static void parse_options_completion(const json & body, llama_server_context & llama) { +static void parse_options_completion(const json &body, llama_server_context &llama) +{ gpt_params default_params; llama.stream = body.value("stream", false); @@ -826,20 +1001,28 @@ static void parse_options_completion(const json & body, llama_server_context & l llama.params.n_probs = body.value("n_probs", default_params.n_probs); llama.params.logit_bias.clear(); - if (body.value("ignore_eos", false)) { + if (body.value("ignore_eos", false)) + { llama.params.logit_bias[llama_token_eos()] = -INFINITY; } - const auto & logit_bias = body.find("logit_bias"); - if (logit_bias != body.end() && logit_bias->is_array()) { + const auto &logit_bias = body.find("logit_bias"); + if (logit_bias != body.end() && logit_bias->is_array()) + { const int n_vocab = llama_n_vocab(llama.ctx); - for (const auto & el : *logit_bias) { - if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) { + for (const auto &el : *logit_bias) + { + if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) + { llama_token tok = el[0].get<llama_token>(); - if (tok >= 0 && tok < n_vocab) { - if (el[1].is_number()) { + if (tok >= 0 && tok < n_vocab) + { + if (el[1].is_number()) + { llama.params.logit_bias[tok] = el[1].get<float>(); - } else if (el[1].is_boolean() && !el[1].get<bool>()) { + } + else if (el[1].is_boolean() && !el[1].get<bool>()) + { llama.params.logit_bias[tok] = -INFINITY; } } @@ -848,10 +1031,13 @@ static void parse_options_completion(const json & body, llama_server_context & l } llama.params.antiprompt.clear(); - const auto & stop = body.find("stop"); - if (stop != body.end() && stop->is_array()) { - for (const auto & word : *stop) { - if (!word.empty()) { + const auto &stop = body.find("stop"); + if (stop != body.end() && stop->is_array()) + { + for (const auto &word : *stop) + { + if (!word.empty()) + { llama.params.antiprompt.push_back(word); } } @@ -860,25 +1046,25 @@ static void parse_options_completion(const json & body, llama_server_context & l LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); } - -static void log_server_request(const Request & req, const Response & res) { +static void log_server_request(const Request &req, const Response &res) +{ LOG_INFO("request", { - { "remote_addr", req.remote_addr }, - { "remote_port", req.remote_port }, - { "status", res.status }, - { "method", req.method }, - { "path", req.path }, - { "params", req.params }, - }); + {"remote_addr", req.remote_addr}, + {"remote_port", req.remote_port}, + {"status", res.status}, + {"method", req.method}, + {"path", req.path}, + {"params", req.params}, + }); LOG_VERBOSE("request", { - { "request", req.body }, - { "response", res.body }, - }); + {"request", req.body}, + {"response", res.body}, + }); } - -int main(int argc, char ** argv) { +int main(int argc, char **argv) +{ // own arguments required by this example gpt_params params; server_params sparams; @@ -888,54 +1074,53 @@ int main(int argc, char ** argv) { server_params_parse(argc, argv, sparams, params); - if (params.model_alias == "unknown") { + if (params.model_alias == "unknown") + { params.model_alias = params.model; } llama_init_backend(params.numa); - LOG_INFO("build info", { - { "build", BUILD_NUMBER }, - { "commit", BUILD_COMMIT } - }); + LOG_INFO("build info", {{"build", BUILD_NUMBER}, + {"commit", BUILD_COMMIT}}); LOG_INFO("system info", { - { "n_threads", params.n_threads }, - { "total_threads", std::thread::hardware_concurrency() }, - { "system_info", llama_print_system_info() }, - }); + {"n_threads", params.n_threads}, + {"total_threads", std::thread::hardware_concurrency()}, + {"system_info", llama_print_system_info()}, + }); // load the model - if (!llama.loadModel(params)) { + if (!llama.loadModel(params)) + { return 1; } Server svr; - svr.set_default_headers({ - { "Server", "llama.cpp" }, - { "Access-Control-Allow-Origin", "*" }, - { "Access-Control-Allow-Headers", "content-type" } - }); - - // this is only called if no index.js is found in the public --path - svr.Get("/index.js", [](const Request &, Response & res) { - res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript"); - return false; - }); + svr.set_default_headers({{"Server", "llama.cpp"}, + {"Access-Control-Allow-Origin", "*"}, + {"Access-Control-Allow-Headers", "content-type"}}); // this is only called if no index.html is found in the public --path - svr.Get("/", [](const Request &, Response & res) { + svr.Get("/", [](const Request &, Response &res) + { res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html"); - return false; - }); + return false; }); + + // this is only called if no index.js is found in the public --path + svr.Get("/index.js", [](const Request &, Response &res) + { + res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript"); + return false; }); // this is only called if no index.html is found in the public --path - svr.Get("/completion.js", [](const Request &, Response & res) { + svr.Get("/completion.js", [](const Request &, Response &res) + { res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript"); - return false; - }); + return false; }); - svr.Post("/completion", [&llama](const Request & req, Response & res) { + svr.Post("/completion", [&llama](const Request &req, Response &res) + { auto lock = llama.lock(); llama.rewind(); @@ -1040,24 +1225,28 @@ int main(int argc, char ** argv) { return true; }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider); - } - }); + } }); + + svr.Get("/model.json", [&llama](const Request &, Response &res) + { + const json data = format_generation_settings(llama); + return res.set_content(data.dump(), "application/json"); }); - svr.Options(R"(/.*)", [](const Request &, Response & res) { - return res.set_content("", "application/json"); - }); + svr.Options(R"(/.*)", [](const Request &, Response &res) + { return res.set_content("", "application/json"); }); - svr.Post("/tokenize", [&llama](const Request & req, Response & res) { + svr.Post("/tokenize", [&llama](const Request &req, Response &res) + { auto lock = llama.lock(); const json body = json::parse(req.body); const std::string content = body.value("content", ""); const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false); const json data = format_tokenizer_response(tokens); - return res.set_content(data.dump(), "application/json"); - }); + return res.set_content(data.dump(), "application/json"); }); - svr.Post("/embedding", [&llama](const Request & req, Response & res) { + svr.Post("/embedding", [&llama](const Request &req, Response &res) + { auto lock = llama.lock(); const json body = json::parse(req.body); @@ -1071,12 +1260,12 @@ int main(int argc, char ** argv) { llama.doCompletion(); const json data = format_embedding_response(llama); - return res.set_content(data.dump(), "application/json"); - }); + return res.set_content(data.dump(), "application/json"); }); svr.set_logger(log_server_request); - svr.set_exception_handler([](const Request &, Response & res, std::exception_ptr ep) { + svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep) + { const auto * fmt = "500 Internal Server Error\n%s"; char buf[BUFSIZ]; try { @@ -1087,20 +1276,19 @@ int main(int argc, char ** argv) { snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); } res.set_content(buf, "text/plain"); - res.status = 500; - }); + res.status = 500; }); - svr.set_error_handler([](const Request &, Response & res) { + svr.set_error_handler([](const Request &, Response &res) + { res.set_content("File Not Found", "text/plain"); - res.status = 404; - }); - + res.status = 404; }); // set timeouts and change hostname and port svr.set_read_timeout(sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); - if (!svr.bind_to_port(sparams.hostname, sparams.port)) { + if (!svr.bind_to_port(sparams.hostname, sparams.port)) + { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } @@ -1112,11 +1300,12 @@ int main(int argc, char ** argv) { fprintf(stdout, "\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); LOG_INFO("HTTP server listening", { - { "hostname", sparams.hostname }, - { "port", sparams.port }, - }); + {"hostname", sparams.hostname}, + {"port", sparams.port}, + }); - if (!svr.listen_after_bind()) { + if (!svr.listen_after_bind()) + { return 1; } |