diff options
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | Makefile | 4 | ||||
| -rw-r--r-- | build.zig | 3 | ||||
| -rw-r--r-- | examples/llava/CMakeLists.txt | 2 | ||||
| -rw-r--r-- | examples/llava/clip.cpp | 4 | ||||
| -rw-r--r-- | examples/server/CMakeLists.txt | 2 | ||||
| -rw-r--r-- | examples/server/README.md | 36 | ||||
| -rwxr-xr-x | examples/server/api_like_OAI.py | 5 | ||||
| -rw-r--r-- | examples/server/chat.mjs | 11 | ||||
| -rw-r--r-- | examples/server/index.html.hpp | 3990 | ||||
| -rw-r--r-- | examples/server/public/index.html | 115 | ||||
| -rw-r--r-- | examples/server/server.cpp | 2637 |
12 files changed, 3920 insertions, 2890 deletions
@@ -10,6 +10,7 @@ *.gcno *.gcda *.dot +*.bat *.metallib .DS_Store .build/ @@ -605,8 +605,8 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml. save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) +server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) + $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) @@ -131,6 +131,7 @@ pub fn build(b: *std.build.Builder) !void { const sampling = make.obj("sampling", "common/sampling.cpp"); const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); const train = make.obj("train", "common/train.cpp"); + const clip = make.obj("clip", "examples/llava/clip.cpp"); _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, console, grammar_parser }); _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common }); @@ -139,7 +140,7 @@ pub fn build(b: *std.build.Builder) !void { _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser, clip }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index d02e6ab4..2d7979ec 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -1,7 +1,7 @@ set(TARGET clip) add_library(${TARGET} clip.cpp clip.h) install(TARGETS ${TARGET} LIBRARY) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if (NOT MSVC) target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 1ae9077b..61932e65 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -610,8 +610,8 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); for (int i = 0; i < 3; ++i) { - new_clip->image_mean[i] = *((float *)gguf_get_arr_data(ctx, idx_mean)); - new_clip->image_std[i] = *((float *)gguf_get_arr_data(ctx, idx_std)); + new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean)); + new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std)); } if (verbosity >= 2) { diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 3782f9b8..a23ddcc5 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}> ) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/examples/server/README.md b/examples/server/README.md index 9f0ace3d..71500773 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -24,6 +24,10 @@ Command line options: - `--port`: Set the port to listen. Default: `8080`. - `--path`: path from which to serve static files (default examples/server/public) - `--embedding`: Enable embedding extraction, Default: disabled. +- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) +- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) +- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) +- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. ## Build @@ -158,6 +162,8 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + *Result JSON:* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. @@ -188,6 +194,12 @@ node index.js `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) + `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) + + `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false) + + `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) + - **POST** `/tokenize`: Tokenize a given text. *Options:* @@ -218,8 +230,32 @@ node index.js It also accepts all the options of `/completion` except `stream` and `prompt`. +- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots. + ## More examples +### Change system prompt on runtime + +To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it. + +`prompt`: Specify a context that you want all connecting clients to respect. + +`anti_prompt`: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the `/props` endpoint. + +`assistant_name`: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the `/props` endpoint. + +```json +{ + "system_prompt": { + "prompt": "Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:", + "anti_prompt": "User:", + "assistant_name": "Assistant:" + } +} +``` + +**NOTE**: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option `-spf FNAME` or `--system-prompt-file FNAME`. + ### Interactive mode Check the sample in [chat.mjs](chat.mjs). diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py index 14d2dcf6..313e1a96 100755 --- a/examples/server/api_like_OAI.py +++ b/examples/server/api_like_OAI.py @@ -8,6 +8,7 @@ import json app = Flask(__name__) +slot_id = -1 parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.") parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n') @@ -77,7 +78,8 @@ def make_postData(body, chat=False, stream=False): if(is_present(body, "stop")): postData["stop"] += body["stop"] postData["n_keep"] = -1 postData["stream"] = stream - + postData["cache_prompt"] = True + postData["slot_id"] = slot_id return postData def make_resData(data, chat=False, promptToken=[]): @@ -128,6 +130,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False): } ] } + slot_id = data["slot_id"] if (chat): if (start): resData["choices"][0]["delta"] = { diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index 87f4d292..219ebb51 100644 --- a/examples/server/chat.mjs +++ b/examples/server/chat.mjs @@ -7,6 +7,11 @@ const args = process.argv.slice(2); const grammarJsonSchemaFile = args.find( (_, index) => args[index - 1] === "--grammar-json-schema" ); + +const no_cached_prompt = args.find( + (_, index) => args[index - 1] === "--no-cache-prompt" +) ?? "false"; + const grammarFile = args.find((_, index) => args[index - 1] === "--grammar"); // Example usage: function,arguments @@ -30,6 +35,9 @@ if (grammarFile) { grammar = readFileSync(grammarFile, 'utf-8') } +// for cached prompt +let slot_id = -1; + const API_URL = 'http://127.0.0.1:8080' const chat = [ @@ -76,6 +84,8 @@ async function chat_completion(question) { top_p: 0.9, n_keep: n_keep, n_predict: 256, + cache_prompt: no_cached_prompt === "false", + slot_id: slot_id, stop: ["\n### Human:"], // stop completion after generating this grammar, stream: true, @@ -92,6 +102,7 @@ async function chat_completion(question) { const t = Buffer.from(chunk).toString('utf8') if (t.startsWith('data: ')) { const message = JSON.parse(t.substring(6)) + slot_id = message.slot_id answer += message.content process.stdout.write(message.content) if (message.stop) { diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 58e3387d..5d3bdfbd 100644 --- 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0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, + 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, + 0x61, 0x6c, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, + 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, + 0x6d, 0x6c, 0x3e, 0x0a, 0x0a }; -unsigned int index_html_len = 30190; +unsigned int index_html_len = 32105; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index f539884e..39d7bb93 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -125,6 +125,7 @@ background-color: #222; color: #ddd; } + code { font-family: monospace; padding: 0.1em 0.3em; @@ -141,7 +142,8 @@ display: inline; } - header, footer { + header, + footer { text-align: center; } @@ -163,6 +165,7 @@ 0% { background-position: 0%; } + 100% { background-position: 100%; } @@ -181,6 +184,7 @@ --loading-color-1: #22222200; --loading-color-2: #222222ff; } + .popover-content { background-color: black; } @@ -194,6 +198,8 @@ import { llama } from '/completion.js'; import { SchemaConverter } from '/json-schema-to-grammar.mjs'; + let selected_image = false; + var slot_id = -1; const session = signal({ prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.", @@ -203,6 +209,7 @@ type: "chat", // "chat" | "completion" char: "Llama", user: "User", + image_selected: '' }) const params = signal({ @@ -220,7 +227,9 @@ mirostat_tau: 5, // target entropy mirostat_eta: 0.1, // learning rate grammar: '', - n_probs: 0, // no completion_probabilities + n_probs: 0, // no completion_probabilities, + image_data: [], + cache_prompt: true }) /* START: Support for storing prompt templates and parameters in borwser LocalStorage */ @@ -270,6 +279,7 @@ // saved templates were successfuly imported. console.log('Processing saved templates and updating default template') + params.value = { ...params.value, image_data: [] }; //console.log(importedTemplates); savedUserTemplates.value = importedTemplates; @@ -294,7 +304,9 @@ function userTemplateApply(t) { session.value = t.data.session; + session.value = { ...session.value, image_selected: '' }; params.value = t.data.params; + params.value = { ...params.value, image_data: [] }; } function userTemplateResetToDefaultAndApply() { @@ -385,20 +397,25 @@ throw new Error("already running"); } controller.value = new AbortController(); - for await (const chunk of llama(prompt, llamaParams, {controller: controller.value})) { + for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) { const data = chunk.data; if (data.stop) { while ( currentMessages.length > 0 && currentMessages[currentMessages.length - 1].content.match(/\n$/) != null - ) { + ) { currentMessages.pop(); } transcriptUpdate([...history, [char, currentMessages]]) console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data); } else { currentMessages.push(data); + slot_id = data.slot_id; + if (selected_image && !data.multimodal) { + alert("The server was not compiled for multimodal or the model projector can't be loaded."); + return; + } transcriptUpdate([...history, [char, currentMessages]]) } @@ -419,7 +436,7 @@ transcriptUpdate([...session.value.transcript, ["{{user}}", msg]]) - const prompt = template(session.value.template, { + let prompt = template(session.value.template, { message: msg, history: session.value.transcript.flatMap( ([name, data]) => @@ -434,9 +451,12 @@ ) ).join("\n"), }); - + if (selected_image) { + prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`; + } await runLlama(prompt, { ...params.value, + slot_id: slot_id, stop: ["</s>", template("{{char}}:"), template("{{user}}:")], }, "{{char}}"); } @@ -446,10 +466,11 @@ console.log('already running...'); return; } - const {prompt} = session.value; + const { prompt } = session.value; transcriptUpdate([...session.value.transcript, ["", prompt]]); await runLlama(prompt, { ...params.value, + slot_id: slot_id, stop: [], }, ""); } @@ -467,6 +488,27 @@ transcriptUpdate([]); } + const uploadImage = (e) => { + e.preventDefault(); + document.getElementById("fileInput").click(); + document.getElementById("fileInput").addEventListener("change", function (event) { + const selectedFile = event.target.files[0]; + if (selectedFile) { + const reader = new FileReader(); + reader.onload = function () { + const image_data = reader.result; + session.value = { ...session.value, image_selected: image_data }; + params.value = { + ...params.value, image_data: [ + { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }] + } + }; + selected_image = true; + reader.readAsDataURL(selectedFile); + } + }); + } + function MessageInput() { const message = useSignal("") @@ -497,6 +539,7 @@ </div> <div class="right"> <button type="submit" disabled=${generating.value}>Send</button> + <button onclick=${uploadImage}>Upload Image</button> <button onclick=${stop} disabled=${!generating.value}>Stop</button> <button onclick=${reset}>Reset</button> </div> @@ -540,7 +583,7 @@ data; message = html`<${Markdownish} text=${template(text)} />` } - if(user) { + if (user) { return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>` } else { return html`<p key=${index}>${message}</p>` @@ -549,6 +592,7 @@ return html` <section id="chat" ref=${container}> + <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/> ${messages.flatMap(chatLine)} </section>`; }; @@ -567,7 +611,7 @@ const converter = new SchemaConverter( grammarJsonSchemaPropOrder.value .split(',') - .reduce((acc, cur, i) => ({...acc, [cur.trim()]: i}), {}) + .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}) ) converter.visit(schema, '') params.value = { @@ -579,7 +623,7 @@ } } - const FloatField = ({label, max, min, name, step, value}) => { + const FloatField = ({ label, max, min, name, step, value }) => { return html` <div> <label for="${name}">${label}</label> @@ -589,7 +633,7 @@ ` }; - const IntField = ({label, max, min, name, value}) => { + const IntField = ({ label, max, min, name, value }) => { return html` <div> <label for="${name}">${label}</label> @@ -672,7 +716,7 @@ ${GrammarControl()} </fieldset> ` - ); + ); const CompletionConfigForm = () => ( html` @@ -694,20 +738,20 @@ ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()} <fieldset class="two"> - ${IntField({label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict})} - ${FloatField({label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature})} - ${FloatField({label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty})} - ${IntField({label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n})} - ${IntField({label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k})} - ${FloatField({label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p})} + ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })} + ${FloatField({ label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} + ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} + ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} + ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} + ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} </fieldset> <details> <summary>More options</summary> <fieldset class="two"> - ${FloatField({label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z})} - ${FloatField({label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p})} - ${FloatField({label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty})} - ${FloatField({label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty})} + ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} + ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} + ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} + ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} </fieldset> <hr /> <fieldset class="three"> @@ -716,11 +760,11 @@ <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label> <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label> </div> - ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})} - ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})} + ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })} + ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })} </fieldset> <fieldset> - ${IntField({label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs})} + ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })} </fieldset> </details> </form> @@ -759,20 +803,20 @@ const popoverChildren = html` <div class="prob-set"> ${probs.map((p, index) => { - return html` + return html` <div key=${index} title=${`prob: ${p.prob}`} style=${{ - padding: '0.3em', - backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent' - }} + padding: '0.3em', + backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent' + }} > <span>${p.tok_str}: </span> <span>${Math.floor(p.prob * 100)}%</span> </div> ` - })} + })} </div> ` @@ -851,9 +895,9 @@ ref=${popoverRef} class="popover-content" style=${{ - top: position.value.top, - left: position.value.left, - }} + top: position.value.top, + left: position.value.left, + }} > ${props.popoverChildren} </div> @@ -952,8 +996,11 @@ </head> <body> - <div id="container"></div> + <div id="container"> + <input type="file" id="fileInput" accept="image/*" style="display: none;"> + </div> <div id="portal"></div> </body> </html> + diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b5ad3cc9..c3279dbc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,6 +1,11 @@ #include "common.h" #include "llama.h" #include "build-info.h" +#include "grammar-parser.h" + +#include "../llava/clip.h" + +#include "stb_image.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -17,12 +22,14 @@ #include "json-schema-to-grammar.mjs.hpp" #include <cstddef> +#include <thread> +#include <mutex> +#include <chrono> #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif -using namespace httplib; using json = nlohmann::json; struct server_params @@ -34,6 +41,165 @@ struct server_params int32_t write_timeout = 600; }; +static bool server_verbose = false; + +#if SERVER_VERBOSE != 1 +#define LOG_VERBOSE(MSG, ...) +#else +#define LOG_VERBOSE(MSG, ...) \ + do \ + { \ + if (server_verbose) \ + { \ + server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ + } \ + } 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__) + +// +// base64 utils (TODO: move to common in the future) +// + +static const std::string base64_chars = + "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "abcdefghijklmnopqrstuvwxyz" + "0123456789+/"; + +static inline bool is_base64(uint8_t c) +{ + return (isalnum(c) || (c == '+') || (c == '/')); +} + +static std::vector<uint8_t> base64_decode(std::string const &encoded_string) +{ + int i = 0; + int j = 0; + int in_ = 0; + + int in_len = encoded_string.size(); + + uint8_t char_array_4[4]; + uint8_t char_array_3[3]; + + std::vector<uint8_t> ret; + + while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) + { + char_array_4[i++] = encoded_string[in_]; in_++; + if (i == 4) + { + for (i = 0; i <4; i++) + { + char_array_4[i] = base64_chars.find(char_array_4[i]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (i = 0; (i < 3); i++) + { + ret.push_back(char_array_3[i]); + } + i = 0; + } + } + + if (i) + { + for (j = i; j <4; j++) + { + char_array_4[j] = 0; + } + + for (j = 0; j <4; j++) + { + char_array_4[j] = base64_chars.find(char_array_4[j]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (j = 0; (j < i - 1); j++) + { + ret.push_back(char_array_3[j]); + } + } + + return ret; +} + +// +// parallel +// + +enum task_type { + COMPLETION_TASK, + CANCEL_TASK +}; + +struct task_server { + int id; + int target_id; + task_type type; + json data; + bool infill_mode = false; +}; + +struct task_result { + int id; + bool stop; + bool error; + json result_json; +}; + +// TODO: can become bool if we can't find use of more states +enum slot_state +{ + IDLE, + PROCESSING, +}; + +enum slot_command +{ + NONE, + LOAD_PROMPT, + RELEASE, +}; + +struct slot_params +{ + bool stream = true; + bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt + + uint32_t seed = -1; // RNG seed + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_predict = -1; // new tokens to predict + + std::vector<std::string> antiprompt; + + json input_prefix; + json input_suffix; +}; + +struct slot_image +{ + int32_t id; + + bool request_encode_image = false; + float* image_embedding = nullptr; + int32_t image_tokens = 0; + + clip_image_u8 img_data; + + std::string prefix_prompt; // before of this image +}; + // completion token output with probabilities struct completion_token_output { @@ -45,6 +211,7 @@ struct completion_token_output std::vector<token_prob> probs; llama_token tok; + std::string text_to_send; }; static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b) @@ -89,6 +256,7 @@ static size_t find_partial_stop_string(const std::string &stop, return std::string::npos; } +// TODO: reuse llama_detokenize template <class Iter> static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) { @@ -103,12 +271,13 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) static void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { - nlohmann::ordered_json log{ + nlohmann::ordered_json log + { {"timestamp", time(nullptr)}, - {"level", level}, - {"function", function}, - {"line", line}, - {"message", message}, + {"level", level}, + {"function", function}, + {"line", line}, + {"message", message}, }; if (!extra.empty()) @@ -138,7 +307,7 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c } // 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) @@ -147,76 +316,211 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector<com for (const auto &p : prob.probs) { std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json{ + probs_for_token.push_back(json + { {"tok_str", tok_str}, - {"prob", p.prob}, + {"prob", p.prob}, }); } std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); out.push_back(json{ {"content", tok_str}, - {"probs", probs_for_token}, + {"probs", probs_for_token}, }); } return out; } -static bool server_verbose = false; - -#if SERVER_VERBOSE != 1 -#define LOG_VERBOSE(MSG, ...) -#else -#define LOG_VERBOSE(MSG, ...) \ - do \ - { \ - if (server_verbose) \ - { \ - server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ - } \ - } 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__) +template <typename T> +static T json_value(const json &body, const std::string &key, const T &default_value) +{ + // Fallback null to default value + return body.contains(key) && !body.at(key).is_null() + ? body.value(key, default_value) + : default_value; +} -struct llama_server_context +struct llama_client_slot { - bool stream = false; - bool has_next_token = false; - std::string generated_text; - std::vector<completion_token_output> generated_token_probs; + int id; + int task_id = -1; - size_t num_prompt_tokens = 0; - size_t num_tokens_predicted = 0; - size_t n_past = 0; - size_t n_remain = 0; + struct slot_params params; - json prompt; - std::vector<llama_token> embd; + slot_state state = IDLE; + slot_command command = NONE; - gpt_params params; + // used to determine the slot that has been used the longest + int64_t t_last_used = -1; - llama_model *model = nullptr; - llama_context *ctx = nullptr; - llama_sampling_context *ctx_sampling = nullptr; + // generation props + int32_t n_ctx = 0; // context size per slot + int32_t n_past = 0; + int32_t n_decoded = 0; + int32_t n_remaining = -1; + int32_t i_batch = -1; - int n_ctx; + int32_t num_prompt_tokens = 0; + int32_t num_prompt_tokens_processed = 0; + int32_t multibyte_pending = 0; + + json prompt; + std::string generated_text; + llama_token sampled; + std::vector<llama_token> cache_tokens; + std::vector<completion_token_output> generated_token_probs; + bool infill = false; + bool has_next_token = true; bool truncated = false; bool stopped_eos = false; bool stopped_word = false; bool stopped_limit = false; + std::string stopping_word; - int32_t multibyte_pending = 0; - std::mutex mutex; + // sampling + struct llama_sampling_params sparams; + llama_sampling_context *ctx_sampling = nullptr; - std::unique_lock<std::mutex> lock() - { - return std::unique_lock<std::mutex>(mutex); + // multimodal + std::vector<slot_image> images; + + // stats + size_t sent_count = 0; + size_t sent_token_probs_index = 0; + + int64_t t_start_process_prompt; + int64_t t_start_genereration; + + double t_prompt_processing; // ms + double t_token_generation; // ms + + void reset() { + num_prompt_tokens = 0; + generated_text = ""; + truncated = false; + stopped_eos = false; + stopped_word = false; + stopped_limit = false; + stopping_word = ""; + multibyte_pending = 0; + n_past = 0; + sent_count = 0; + sent_token_probs_index = 0; + infill = false; + + generated_token_probs.clear(); + + for (slot_image &img : images) + { + free(img.image_embedding); + delete[] img.img_data.data; + img.prefix_prompt = ""; + } + + images.clear(); + // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? + } + + bool has_budget(gpt_params &global_params) { + n_remaining = -1; + if(params.n_predict != -1) + { + n_remaining = params.n_predict - n_decoded; + } + else if (global_params.n_predict != -1) + { + n_remaining = global_params.n_predict - n_decoded; + } + return n_remaining > 0 || n_remaining == -1; // no budget || limitless + } + + bool available() const { + return state == IDLE && command == NONE; + } + + bool is_processing() const { + return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; + } + + void add_token_string(const completion_token_output &token) { + if (command == RELEASE) + { + return; + } + cache_tokens.push_back(token.tok); + generated_token_probs.push_back(token); + } + + void release() { + if (state == PROCESSING) + { + t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; + command = RELEASE; + } } + json get_formated_timings() { + return json + { + {"prompt_n", num_prompt_tokens_processed}, + {"prompt_ms", t_prompt_processing}, + {"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed}, + + {"predicted_n", n_decoded}, + {"predicted_ms", t_token_generation}, + {"predicted_per_token_ms", t_token_generation / n_decoded}, + {"predicted_per_second", 1e3 / t_token_generation * n_decoded}, + }; + } + + void print_timings() { + LOG_TEE("\n"); + LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed); + LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded); + LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation); + } +}; + +struct llama_server_context +{ + llama_model *model = nullptr; + llama_context *ctx = nullptr; + + clip_ctx *clp_ctx = nullptr; + + gpt_params params; + + llama_batch batch; + + bool multimodal = false; + bool clean_kv_cache = true; + bool all_slots_are_idle = false; + + int32_t id_gen; + int32_t n_ctx; // total context for all clients / slots + + // system prompt + bool system_need_update = false; + + std::string system_prompt; + std::vector<llama_token> system_tokens; + + std::string name_user; // this should be the antiprompt + std::string name_assistant; + + // slots / clients + std::vector<llama_client_slot> slots; + + std::vector<task_server> queue_tasks; + std::vector<task_result> queue_results; + std::mutex mutex_tasks; + std::mutex mutex_results; + ~llama_server_context() { if (ctx) @@ -231,46 +535,74 @@ struct llama_server_context } } - void rewind() + bool load_model(const gpt_params ¶ms_) { - params.antiprompt.clear(); - params.sparams.grammar.clear(); - num_prompt_tokens = 0; - num_tokens_predicted = 0; - generated_text = ""; - generated_text.reserve(n_ctx); - generated_token_probs.clear(); - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - multibyte_pending = 0; - n_remain = 0; - n_past = 0; - params.sparams.n_prev = n_ctx; - } + params = params_; + if (!params.mmproj.empty()) { + multimodal = true; + LOG_TEE("Multi Modal Mode Enabled"); + clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); + if(clp_ctx == nullptr) { + LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); + return false; + } - void initSampling() { - if (ctx_sampling != nullptr) { - llama_sampling_free(ctx_sampling); + if (params.n_ctx < 2048) { // request larger context for the image embedding + params.n_ctx = 2048; + } } - ctx_sampling = llama_sampling_init(params.sparams); - } - 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}}); + LOG_ERROR("unable to load model", {{"model", params.model}}); return false; } + + if (multimodal) { + const int n_embd_clip = clip_n_mmproj_embd(clp_ctx); + const int n_embd_llm = llama_n_embd(model); + if (n_embd_clip != n_embd_llm) { + LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm); + llama_free(ctx); + llama_free_model(model); + return false; + } + } + n_ctx = llama_n_ctx(ctx); + return true; } + void initialize() { + id_gen = 0; + + // create slots + all_slots_are_idle = true; + + const int32_t n_ctx_slot = n_ctx / params.n_parallel; + + LOG_TEE("Available slots:\n"); + for (int i = 0; i < params.n_parallel; i++) + { + llama_client_slot slot; + + slot.id = i; + slot.n_ctx = n_ctx_slot; + slot.reset(); + + LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot); + slots.push_back(slot); + } + + batch = llama_batch_init(n_ctx, 0, params.n_parallel); + + // empty system prompt + system_prompt = ""; + system_tokens.clear(); + } + std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const { // If `add_bos` is true, we only add BOS, when json_prompt is a string, @@ -316,366 +648,1115 @@ struct llama_server_context return prompt_tokens; } - void truncatePrompt(std::vector<llama_token> &prompt_tokens) { - const int n_left = n_ctx - params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_block_size) / n_block_size; + llama_client_slot* get_slot(int id) { + int64_t t_last = ggml_time_us(); + llama_client_slot *last_used = nullptr; - // Keep n_keep tokens at start of prompt (at most n_ctx - 4) - std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + for (llama_client_slot & slot : slots) + { + if (slot.id == id && slot.available()) + { + return &slot; + } - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - {"num_prompt_tokens", new_tokens.size()} - }); + if (slot.available() && slot.t_last_used < t_last) + { + last_used = &slot; + t_last = slot.t_last_used; + } + } - truncated = true; - prompt_tokens = new_tokens; + return last_used; } - void loadInfill() - { - bool suff_rm_leading_spc = true; - if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { - params.input_suffix.erase(0, 1); - suff_rm_leading_spc = false; + bool launch_slot_with_data(llama_client_slot* &slot, json data) { + slot_params default_params; + llama_sampling_params default_sparams; + + slot->params.stream = json_value(data, "stream", false); + slot->params.cache_prompt = json_value(data, "cache_prompt", false); + slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); + slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); + slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); + slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); + slot->params.seed = json_value(data, "seed", default_params.seed); + slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + + // infill + if (data.count("input_prefix") != 0) + { + slot->params.input_prefix = data["input_prefix"]; + } + else + { + slot->params.input_prefix = ""; } - auto prefix_tokens = tokenize(params.input_prefix, false); - auto suffix_tokens = tokenize(params.input_suffix, false); - const int space_token = 29871; - if (suff_rm_leading_spc && suffix_tokens[0] == space_token) { - suffix_tokens.erase(suffix_tokens.begin()); + if (data.count("input_suffix") != 0) + { + slot->params.input_suffix = data["input_suffix"]; + } + else + { + slot->params.input_suffix = ""; } - prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); - prefix_tokens.push_back(llama_token_middle(ctx)); - auto prompt_tokens = prefix_tokens; + if (data.count("prompt") != 0) + { + slot->prompt = data["prompt"]; + } + else + { + slot->prompt = ""; + } - num_prompt_tokens = prompt_tokens.size(); + slot->sparams.logit_bias.clear(); - if (params.n_keep < 0) + if (json_value(data, "ignore_eos", false)) { - params.n_keep = (int)num_prompt_tokens; + slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY; } - 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) n_ctx) + const auto &logit_bias = data.find("logit_bias"); + if (logit_bias != data.end() && logit_bias->is_array()) { - truncatePrompt(prompt_tokens); - num_prompt_tokens = prompt_tokens.size(); - - GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx); + const int n_vocab = llama_n_vocab(model); + 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()) + { + slot->sparams.logit_bias[tok] = el[1].get<float>(); + } + else if (el[1].is_boolean() && !el[1].get<bool>()) + { + slot->sparams.logit_bias[tok] = -INFINITY; + } + } + } + } } - // push the prompt into the sampling context (do not apply grammar) - for (auto & token : prompt_tokens) + slot->params.antiprompt.clear(); + const auto &stop = data.find("stop"); + if (stop != data.end() && stop->is_array()) { - llama_sampling_accept(ctx_sampling, ctx, token, false); + for (const auto &word : *stop) + { + if (!word.empty()) + { + slot->params.antiprompt.push_back(word); + } + } } - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); - embd = prompt_tokens; + if (multimodal) + { + const auto &images_data = data.find("image_data"); + if (images_data != data.end() && images_data->is_array()) + { + for (const auto &img : *images_data) + { + std::string data_b64 = img["data"].get<std::string>(); + slot_image img_sl; + img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size(); + int width, height, channels; + std::vector<uint8_t> image_buffer = base64_decode(data_b64); + data_b64.clear(); + auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); + if (!data) { + LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id); + return false; + } + LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height); + img_sl.img_data.nx = width; + img_sl.img_data.ny = height; + img_sl.img_data.size = width * height * 3; + img_sl.img_data.data = new uint8_t[width * height * 3](); + memcpy(img_sl.img_data.data, data, width * height * 3); + stbi_image_free(data); + img_sl.request_encode_image = true; + slot->images.push_back(img_sl); + } + // process prompt + // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} + if (slot->images.size() > 0 && !slot->prompt.is_array()) + { + std::string prompt = slot->prompt.get<std::string>(); + size_t pos = 0, begin_prefix = 0; + std::string pattern = "[img-"; + while ((pos = prompt.find(pattern, pos)) != std::string::npos) { + size_t end_prefix = pos; + pos += pattern.length(); + size_t end_pos = prompt.find("]", pos); + if (end_pos != std::string::npos) + { + std::string image_id = prompt.substr(pos, end_pos - pos); + try + { + int img_id = std::stoi(image_id); + bool found = false; + for (slot_image &img : slot->images) + { + if (img.id == img_id) { + found = true; + img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); + begin_prefix = end_pos + 1; + break; + } + } + if (!found) { + LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id); + slot->images.clear(); + return false; + } + } catch (const std::invalid_argument& e) { + LOG_TEE("Invalid image number id in prompt\n"); + slot->images.clear(); + return false; + } + } + } + slot->prompt = ""; + slot->params.input_suffix = prompt.substr(begin_prefix); + slot->params.cache_prompt = false; // multimodal doesn't support cache prompt + } + } + } - if (n_past == num_prompt_tokens) + if (slot->ctx_sampling != nullptr) { - // we have to evaluate at least 1 token to generate logits. - printf("we have to evaluate at least 1 token to generate logits\n"); - n_past--; + llama_sampling_free(slot->ctx_sampling); } + slot->ctx_sampling = llama_sampling_init(slot->sparams); + slot->command = LOAD_PROMPT; - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + all_slots_are_idle = false; - 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())}, - }); + LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id); - has_next_token = true; + return true; } - void loadPrompt() - { - auto prompt_tokens = tokenize(prompt, true); // always add BOS - num_prompt_tokens = prompt_tokens.size(); + void kv_cache_clear() { + // clear the entire KV cache + llama_kv_cache_tokens_rm(ctx, -1, -1); + clean_kv_cache = false; + } + + void update_system_prompt() { + system_tokens = ::llama_tokenize(ctx, system_prompt, true); + + llama_batch_clear(batch); + + kv_cache_clear(); - if (params.n_keep < 0) + for (int32_t i = 0; i < batch.n_tokens; ++i) { - params.n_keep = (int)num_prompt_tokens; + llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } - params.n_keep = std::min(n_ctx - 4, params.n_keep); - // if input prompt is too big, truncate like normal - if (num_prompt_tokens >= (size_t) n_ctx) + if (llama_decode(ctx, batch) != 0) { - truncatePrompt(prompt_tokens); - num_prompt_tokens = prompt_tokens.size(); - - GGML_ASSERT(num_prompt_tokens < (size_t)n_ctx); + LOG_TEE("%s: llama_decode() failed\n", __func__); + return; } - // push the prompt into the sampling context (do not apply grammar) - for (auto & token : prompt_tokens) + // assign the system KV cache to all parallel sequences + for (int32_t i = 1; i < params.n_parallel; ++i) { - llama_sampling_accept(ctx_sampling, ctx, token, false); + llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); } - // compare the evaluated prompt with the new prompt - n_past = common_part(embd, prompt_tokens); + LOG_TEE("system prompt updated\n"); + system_need_update = false; + } - embd = prompt_tokens; - if (n_past == num_prompt_tokens) + void notify_system_prompt_changed() { + // release all slots + for (llama_client_slot &slot : slots) { - // we have to evaluate at least 1 token to generate logits. - n_past--; + slot.release(); } + wait_all_are_idle(); + all_slots_are_idle = true; - // since #3228 we now have to manually manage the KV cache - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + // wait until system prompt load + system_need_update = true; + while (system_need_update) + { + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + // system prompt loaded, continue + } - 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())}, - }); + void process_system_prompt_data(const json &sys_props) { + system_prompt = sys_props.value("prompt", ""); + name_user = sys_props.value("anti_prompt", ""); + name_assistant = sys_props.value("assistant_name", ""); - has_next_token = true; + if (slots.size() > 0) + { + notify_system_prompt_changed(); + } + else + { + system_need_update = true; + } } - void beginCompletion() - { - // number of tokens to keep when resetting context - n_remain = params.n_predict; - llama_set_rng_seed(ctx, params.seed); + void wait_all_are_idle() { + bool wait = true; + while (wait) + { + wait = false; + for (auto &slot : slots) + { + if (!slot.available()) + { + wait = true; + break; + } + } + } } - completion_token_output nextToken() + static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, + const stop_type type, llama_client_slot &slot) { - completion_token_output result; - result.tok = -1; + size_t stop_pos = std::string::npos; - if (embd.size() >= (size_t)n_ctx) + for (const std::string &word : slot.params.antiprompt) { - // Shift context + size_t pos; + 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 + { + pos = find_partial_stop_string(word, text); + } + if (pos != std::string::npos && + (stop_pos == std::string::npos || pos < stop_pos)) + { + if (type == STOP_FULL) + { + slot.stopped_word = true; + slot.stopping_word = word; + slot.has_next_token = false; + } + stop_pos = pos; + + } + } - const int n_left = n_past - params.n_keep - 1; - const int n_discard = n_left/2; + return stop_pos; + } - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); + bool process_token(completion_token_output &result, llama_client_slot &slot) { + // remember which tokens were sampled - used for repetition penalties during sampling + const std::string token_str = llama_token_to_piece(ctx, result.tok); + slot.sampled = result.tok; - for (size_t i = params.n_keep + 1 + n_discard; i < embd.size(); i++) + // search stop word and delete it + slot.generated_text += token_str; + slot.has_next_token = true; + + if (slot.multibyte_pending > 0) + { + slot.multibyte_pending -= token_str.size(); + } + else if (token_str.size() == 1) + { + const char c = token_str[0]; + // 2-byte characters: 110xxxxx 10xxxxxx + if ((c & 0xE0) == 0xC0) { - embd[i - n_discard] = embd[i]; + slot.multibyte_pending = 1; + // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx + } + else if ((c & 0xF0) == 0xE0) + { + slot.multibyte_pending = 2; + // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx + } + else if ((c & 0xF8) == 0xF0) + { + slot.multibyte_pending = 3; + } + else + { + slot.multibyte_pending = 0; } - embd.resize(embd.size() - n_discard); - - n_past -= n_discard; - - truncated = true; - LOG_VERBOSE("input truncated", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - }); } - bool tg = true; - while (n_past < embd.size()) + if (slot.multibyte_pending == 0) { - int n_eval = (int)embd.size() - n_past; - tg = n_eval == 1; - if (n_eval > params.n_batch) + size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + const std::string str_test = slot.generated_text.substr(pos); + bool is_stop_full = false; + size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); + if (stop_pos != std::string::npos) + { + is_stop_full = true; + slot.generated_text.erase( + slot.generated_text.begin() + pos + stop_pos, + slot.generated_text.end()); + pos = std::min(slot.sent_count, slot.generated_text.size()); + } + else { - n_eval = params.n_batch; + is_stop_full = false; + stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); } - if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0))) + // check if there is any token to predict + if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { - LOG_ERROR("failed to eval", { - {"n_eval", n_eval}, - {"n_past", n_past}, - {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())}, - }); - has_next_token = false; - return result; + // no send the stop word in the response + result.text_to_send = slot.generated_text.substr(pos, std::string::npos); + slot.sent_count += result.text_to_send.size(); + // add the token to slot queue and cache + } + slot.add_token_string(result); + if (slot.params.stream) + { + send_partial_response(slot, result); } - n_past += n_eval; } - if (params.n_predict == 0) + if (slot.multibyte_pending > 0 && !slot.has_next_token) { - has_next_token = false; - result.tok = llama_token_eos(ctx); - return result; + slot.has_next_token = true; } + // check the limits + if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params)) { - // out of user input, sample next token - result.tok = llama_sampling_sample(ctx_sampling, ctx, NULL); + slot.stopped_limit = true; + slot.has_next_token = false; + } - llama_token_data_array cur_p = { ctx_sampling->cur.data(), ctx_sampling->cur.size(), false }; + if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) + { + slot.stopped_eos = true; + slot.has_next_token = false; + LOG_VERBOSE("eos token found", {}); + } - const int32_t n_probs = params.sparams.n_probs; - if (params.sparams.temp <= 0 && n_probs > 0) + LOG_VERBOSE("next token", { + {"token", result.tok}, + {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, + {"num_tokens_predicted", slot.n_decoded}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + }); + + return slot.has_next_token; // continue + } + + bool process_images(llama_client_slot &slot) const + { + for (slot_image &img : slot.images) + { + if (!img.request_encode_image) + { + continue; + } + clip_image_f32 img_res; + if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) + { + LOG_TEE("Error processing the given image"); + clip_free(clp_ctx); + return false; + } + img.image_tokens = clip_n_patches(clp_ctx); + img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); + if (!img.image_embedding) { - // For llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &cur_p); + LOG_TEE("Unable to allocate memory for image embeddings\n"); + clip_free(clp_ctx); + return false; } + LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id); + if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) + { + LOG_TEE("Unable to encode image\n"); + return false; + } + img.request_encode_image = false; + } + + return slot.images.size() > 0; + } + + void send_error(int id, std::string error) + { + std::lock_guard<std::mutex> lock(mutex_results); + task_result res; + res.id = id; + res.error = true; + res.result_json = { { "content", error } }; + queue_results.push_back(res); + } + + json get_model_props() + { + return get_formated_generation(slots[0]); + } + + json get_formated_generation(llama_client_slot &slot) + { + const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx)); + const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && + eos_bias->second < 0.0f && std::isinf(eos_bias->second); + return json { + {"n_ctx", slot.n_ctx}, + {"model", params.model_alias}, + {"seed", slot.params.seed}, + {"temp", slot.sparams.temp}, + {"top_k", slot.sparams.top_k}, + {"top_p", slot.sparams.top_p}, + {"tfs_z", slot.sparams.tfs_z}, + {"typical_p", slot.sparams.typical_p}, + {"repeat_last_n", slot.sparams.penalty_last_n}, + {"repeat_penalty", slot.sparams.penalty_repeat}, + {"presence_penalty", slot.sparams.penalty_present}, + {"frequency_penalty", slot.sparams.penalty_freq}, + {"mirostat", slot.sparams.mirostat}, + {"mirostat_tau", slot.sparams.mirostat_tau}, + {"mirostat_eta", slot.sparams.mirostat_eta}, + {"penalize_nl", slot.sparams.penalize_nl}, + {"stop", slot.params.antiprompt}, + {"n_predict", slot.params.n_predict}, + {"n_keep", params.n_keep}, + {"ignore_eos", ignore_eos}, + {"stream", slot.params.stream}, + {"logit_bias", slot.sparams.logit_bias}, + {"n_probs", slot.sparams.n_probs}, + {"grammar", slot.sparams.grammar}, + }; + } + + void send_partial_response(llama_client_slot &slot, completion_token_output tkn) + { + std::lock_guard<std::mutex> lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = false; - for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) + res.result_json = json + { + {"content", tkn.text_to_send}, + {"stop", false}, + {"slot_id", slot.id}, + {"multimodal", multimodal} + }; + + if (slot.sparams.n_probs > 0) + { + std::vector<completion_token_output> probs_output = {}; + const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); + size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size()); + size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size()); + if (probs_pos < probs_stop_pos) { - result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); + probs_output = std::vector<completion_token_output>(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); } + slot.sent_token_probs_index = probs_stop_pos; + res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); + } + + queue_results.push_back(res); + } - llama_sampling_accept(ctx_sampling, ctx, result.tok, true); + void send_final_response(llama_client_slot &slot) + { + std::lock_guard<std::mutex> lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = true; - if (tg) { - num_tokens_predicted++; + res.result_json = json + { + {"content", !slot.params.stream ? slot.generated_text : ""}, + {"slot_id", slot.id}, + {"stop", true}, + {"model", params.model_alias}, + {"tokens_predicted", slot.n_decoded}, + {"tokens_evaluated", slot.num_prompt_tokens}, + {"generation_settings", get_formated_generation(slot)}, + {"prompt", slot.prompt}, + {"truncated", slot.truncated}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, + {"tokens_cached", slot.n_past}, + {"timings", slot.get_formated_timings()} + }; + + if (slot.sparams.n_probs > 0) + { + std::vector<completion_token_output> probs = {}; + if (!slot.params.stream && slot.stopped_word) + { + const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); + probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size()); } + else + { + probs = std::vector<completion_token_output>( + slot.generated_token_probs.begin(), + slot.generated_token_probs.begin() + slot.sent_token_probs_index); + } + res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); } - // add it to the context - embd.push_back(result.tok); - // decrement remaining sampling budget - --n_remain; + queue_results.push_back(res); + } + + void send_embedding(llama_client_slot &slot) + { + std::lock_guard<std::mutex> lock(mutex_results); + task_result res; + res.id = slot.task_id; + res.error = false; + res.stop = true; - if (!embd.empty() && embd.back() == llama_token_eos(ctx)) + const int n_embd = llama_n_embd(model); + if (!params.embedding) { - // stopping_word = llama_token_to_piece(ctx, embd.back()); - has_next_token = false; - stopped_eos = true; - LOG_VERBOSE("eos token found", {}); - return result; + LOG_WARNING("embedding disabled", { + {"params.embedding", params.embedding}, + }); + res.result_json = json + { + {"embedding", std::vector<float>(n_embd, 0.0f)}, + }; } + else + { + const float *data = llama_get_embeddings(ctx); + std::vector<float> embedding(data, data + n_embd); + res.result_json = json + { + {"embedding", embedding }, + }; + } + queue_results.push_back(res); + } - has_next_token = params.n_predict == -1 || n_remain != 0; - return result; + int request_completion(json data, bool infill) + { + std::lock_guard<std::mutex> lock(mutex_tasks); + task_server task; + task.id = id_gen++; + task.data = data; + task.infill_mode = infill; + task.type = COMPLETION_TASK; + queue_tasks.push_back(task); + return task.id; } - size_t findStoppingStrings(const std::string &text, const size_t last_token_size, - const stop_type type) + task_result next_result(int task_id) { - size_t stop_pos = std::string::npos; - for (const std::string &word : params.antiprompt) + while (true) { - size_t pos; - if (type == STOP_FULL) + std::this_thread::sleep_for(std::chrono::microseconds(5)); + std::lock_guard<std::mutex> lock(mutex_results); + + if (queue_results.empty()) { - 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); + continue; } - else + + for (int i = 0; i < (int) queue_results.size(); i++) { - pos = find_partial_stop_string(word, text); + if (queue_results[i].id == task_id) + { + task_result res = queue_results[i]; + queue_results.erase(queue_results.begin() + i); + return res; + } } - if (pos != std::string::npos && - (stop_pos == std::string::npos || pos < stop_pos)) + } + + // never reached + //return task_result{-1, false, false, {}}; + } + + // for multiple images processing + bool ingest_images(llama_client_slot &slot, int n_batch) + { + int image_idx = 0; + + while (image_idx < (int) slot.images.size()) + { + slot_image &img = slot.images[image_idx]; + + // process prefix prompt + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { - if (type == STOP_FULL) + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + if (llama_decode(ctx, batch_view)) { - stopping_word = word; - stopped_word = true; - has_next_token = false; + LOG_TEE("%s : failed to eval\n", __func__); + return false; } - stop_pos = pos; + } + + // process image with llm + for (int i = 0; i < img.image_tokens; i += n_batch) + { + int n_eval = img.image_tokens - i; + if (n_eval > n_batch) + { + n_eval = n_batch; + } + + const int n_embd = llama_n_embd(model); + llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + if (llama_decode(ctx, batch_img)) + { + LOG_TEE("%s : failed to eval image\n", __func__); + return false; + } + slot.n_past += n_eval; + } + image_idx++; + + llama_batch_clear(batch); + + // append prefix of next image + const auto json_prompt = (image_idx >= (int) slot.images.size()) ? + slot.params.input_suffix : // no more images, then process suffix prompt + (json)(slot.images[image_idx].prefix_prompt); + + std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image + for (int i = 0; i < (int) append_tokens.size(); ++i) + { + llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); + slot.n_past += 1; } } - return stop_pos; + + return true; } - completion_token_output doCompletion() + void request_cancel(int task_id) { - auto token_with_probs = nextToken(); - - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok); - generated_text += token_text; + std::lock_guard<std::mutex> lock(mutex_tasks); + task_server task; + task.id = id_gen++; + task.type = CANCEL_TASK; + task.target_id = task_id; + queue_tasks.push_back(task); + } - if (params.sparams.n_probs > 0) + void process_tasks() + { + std::lock_guard<std::mutex> lock(mutex_tasks); + while (!queue_tasks.empty()) { - generated_token_probs.push_back(token_with_probs); + task_server task = queue_tasks.front(); + queue_tasks.erase(queue_tasks.begin()); + switch (task.type) + { + case COMPLETION_TASK: { + llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); + if (slot == nullptr) + { + LOG_TEE("slot unavailable\n"); + // send error result + send_error(task.id, "slot unavaliable"); + return; + } + + if (task.data.contains("system_prompt")) + { + process_system_prompt_data(task.data["system_prompt"]); + } + + slot->reset(); + + slot->infill = task.infill_mode; + slot->task_id = task.id; + + if (!launch_slot_with_data(slot, task.data)) + { + // send error result + send_error(task.id, "internal_error"); + break; + } + } break; + case CANCEL_TASK: { // release slot linked with the task id + for (auto & slot : slots) + { + if (slot.task_id == task.target_id) + { + slot.release(); + break; + } + } + } break; + } } + } + + bool update_slots() { + // attend tasks + process_tasks(); - if (multibyte_pending > 0) + // update the system prompt wait until all slots are idle state + if (system_need_update) { - multibyte_pending -= token_text.size(); + LOG_TEE("updating system prompt\n"); + update_system_prompt(); } - else if (token_text.size() == 1) + + llama_batch_clear(batch); + + if (all_slots_are_idle) { - const char c = token_text[0]; - // 2-byte characters: 110xxxxx 10xxxxxx - if ((c & 0xE0) == 0xC0) + if (system_prompt.empty() && clean_kv_cache) { - multibyte_pending = 1; - // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx + LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n"); + kv_cache_clear(); } - else if ((c & 0xF0) == 0xE0) + // avoid 100% usage of cpu all time + std::this_thread::sleep_for(std::chrono::milliseconds(5)); + } + + for (llama_client_slot &slot : slots) + { + if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) { - multibyte_pending = 2; - // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx + // Shift context + const int n_left = slot.n_past - slot.params.n_keep - 1; + const int n_discard = n_left / 2; + + LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); + llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + + for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + { + slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; + } + + slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + + slot.n_past -= n_discard; + + slot.truncated = true; + + LOG_VERBOSE("context shift", { + {"n_ctx", n_ctx}, + {"n_keep", params.n_keep}, + {"n_left", n_left}, + }); } - else if ((c & 0xF8) == 0xF0) + } + + // decode any currently ongoing sequences + for (auto & slot : slots) + { + // release the slot + if (slot.state == PROCESSING && slot.command == RELEASE) { - multibyte_pending = 3; + slot.state = IDLE; + slot.command = NONE; + slot.t_last_used = ggml_time_us(); + + LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size()); + + continue; } - else + + if (slot.state == IDLE || slot.command == RELEASE) { - multibyte_pending = 0; + continue; } + + slot.i_batch = batch.n_tokens; + + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true); + + slot.n_decoded += 1; + slot.n_past += 1; } - if (multibyte_pending > 0 && !has_next_token) + // process in chunks of params.n_batch + int32_t n_batch = params.n_batch; + + // assign workload to the slots + if (params.cont_batching || batch.n_tokens == 0) { - has_next_token = true; - n_remain++; + for (auto & slot : slots) + { + // need process the prompt + if (slot.state == IDLE && slot.command == LOAD_PROMPT) + { + slot.state = PROCESSING; + slot.command = NONE; + std::vector<llama_token> prompt_tokens; + slot.t_start_process_prompt = ggml_time_us(); + slot.t_start_genereration = 0; + + if (slot.infill) + { + bool suff_rm_leading_spc = true; + if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) + { + params.input_suffix.erase(0, 1); + suff_rm_leading_spc = false; + } + auto prefix_tokens = tokenize(slot.params.input_prefix, false); + auto suffix_tokens = tokenize(slot.params.input_suffix, false); + + const int space_token = 29871; // TODO: this should not be hardcoded + if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { + suffix_tokens.erase(suffix_tokens.begin()); + } + + prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx)); + prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.push_back(llama_token_middle(ctx)); + prompt_tokens = prefix_tokens; + } + else + { + prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt + } + + slot.num_prompt_tokens = prompt_tokens.size(); + + if (!slot.params.cache_prompt) + { + llama_sampling_reset(slot.ctx_sampling); + + slot.n_past = 0; + slot.num_prompt_tokens_processed = slot.num_prompt_tokens; + } + else + { + if (slot.params.n_keep < 0) + { + slot.params.n_keep = slot.num_prompt_tokens; + } + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); + + // if input prompt is too big, truncate it + if (slot.num_prompt_tokens >= slot.n_ctx) + { + const int n_left = slot.n_ctx - slot.params.n_keep; + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + + std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + + LOG_VERBOSE("input truncated", { + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); + slot.truncated = true; + prompt_tokens = new_tokens; + + slot.num_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); + } + + // push the prompt into the sampling context (do not apply grammar) + for (auto &token : prompt_tokens) + { + llama_sampling_accept(slot.ctx_sampling, ctx, token, false); + } + + slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + + LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); + } + + LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); + + llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); + + slot.cache_tokens = prompt_tokens; + + if (slot.n_past == slot.num_prompt_tokens) + { + // we have to evaluate at least 1 token to generate logits. + LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); + slot.n_past--; + } + + LOG_VERBOSE("prompt ingested", { + {"n_past", slot.n_past}, + {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, + {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, + }); + + const bool has_images = process_images(slot); + + // process the prefix of first image + std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; + for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) + { + llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false); + } + + if (has_images && !ingest_images(slot, n_batch)) + { + LOG_TEE("failed processing images\n"); + return false; + } + + // extract the logits only for the last token + if (batch.n_tokens > 0) + { + batch.logits[batch.n_tokens - 1] = true; + } + + slot.n_decoded = 0; + slot.i_batch = batch.n_tokens - 1; + } + } } - if (!has_next_token && n_remain == 0) + if (batch.n_tokens == 0) { - stopped_limit = true; + all_slots_are_idle = true; + return 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}, - }); + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) + { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + llama_batch batch_view = + { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; - return token_with_probs; - } + const int ret = llama_decode(ctx, batch_view); + if (ret != 0) + { + if (n_batch == 1 || ret < 0) + { + // if you get here, it means the KV cache is full - try increasing it via the context size + LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); + return false; + } - std::vector<float> getEmbedding() - { - static const int n_embd = llama_n_embd(model); - if (!params.embedding) - { - LOG_WARNING("embedding disabled", { - {"params.embedding", params.embedding}, - }); - return std::vector<float>(n_embd, 0.0f); + LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2); + + // retry with half the batch size to try to find a free slot in the KV cache + n_batch /= 2; + i -= n_batch; + continue; + } + + for (auto & slot : slots) + { + if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) + { + continue; + } + + // prompt evaluated for embedding + if (params.embedding) + { + send_embedding(slot); + slot.release(); + slot.i_batch = -1; + return true; + } + + completion_token_output result; + const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i); + + llama_sampling_accept(slot.ctx_sampling, ctx, id, true); + + if (slot.n_decoded == 1) + { + slot.t_start_genereration = ggml_time_us(); + slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; + } + + llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; + result.tok = id; + + const int32_t n_probs = slot.sparams.n_probs; + if (slot.sparams.temp <= 0 && n_probs > 0) + { + // for llama_sample_token_greedy we need to sort candidates + llama_sample_softmax(ctx, &cur_p); + } + + for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) + { + result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); + } + + if (!process_token(result, slot)) + { + slot.release(); + send_final_response(slot); + slot.print_timings(); + } + + slot.i_batch = -1; + } } - const float *data = llama_get_embeddings(ctx); - std::vector<float> embedding(data, data + n_embd); - return embedding; + return true; } }; @@ -685,16 +1766,15 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("usage: %s [options]\n", argv0); printf("\n"); printf("options:\n"); - printf(" -h, --help show this help message and exit\n"); - printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n"); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); + printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); + printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n"); + printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); + printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -725,11 +1805,16 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); + printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); + printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); + printf(" -spf FNAME, --system-prompt-file FNAME\n"); + printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); + printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf("\n"); } static void server_params_parse(int argc, char **argv, server_params &sparams, - gpt_params ¶ms) + gpt_params ¶ms, llama_server_context& llama) { gpt_params default_params; server_params default_sparams; @@ -839,15 +1924,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads = std::stoi(argv[i]); } - else if (arg == "--threads-batch" || arg == "-tb") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) @@ -984,6 +2060,56 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, { params.embedding = true; } + else if (arg == "-cb" || arg == "--cont-batching") + { + params.cont_batching = true; + } + else if (arg == "-np" || arg == "--parallel") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_parallel = std::stoi(argv[i]); + } else if (arg == "-n" || arg == "--n-predict") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_predict = std::stoi(argv[i]); + } else if (arg == "-spf" || arg == "--system-prompt-file") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + break; + } + std::string systm_content; + std::copy( + std::istreambuf_iterator<char>(file), + std::istreambuf_iterator<char>(), + std::back_inserter(systm_content) + ); + llama.process_system_prompt_data(json::parse(systm_content)); + } + else if(arg == "--mmproj") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.mmproj = argv[i]; + } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); @@ -1000,102 +2126,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } -static json format_generation_settings(llama_server_context &llama) -{ - const auto & sparams = llama.params.sparams; - const auto eos_bias = sparams.logit_bias.find(llama_token_eos(llama.ctx)); - const bool ignore_eos = eos_bias != sparams.logit_bias.end() && - eos_bias->second < 0.0f && std::isinf(eos_bias->second); - - return json{ - {"n_ctx", llama.n_ctx}, - {"model", llama.params.model_alias}, - {"seed", llama.params.seed}, - {"temp", sparams.temp}, - {"top_k", sparams.top_k}, - {"top_p", sparams.top_p}, - {"tfs_z", sparams.tfs_z}, - {"typical_p", sparams.typical_p}, - {"repeat_last_n", sparams.penalty_last_n}, - {"repeat_penalty", sparams.penalty_repeat}, - {"frequency_penalty", sparams.penalty_freq}, - {"presence_penalty", sparams.penalty_present}, - {"mirostat", sparams.mirostat}, - {"mirostat_tau", sparams.mirostat_tau}, - {"mirostat_eta", sparams.mirostat_eta}, - {"penalize_nl", sparams.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", sparams.logit_bias}, - {"n_probs", sparams.n_probs}, - {"grammar", llama.params.sparams.grammar}, - }; -} - -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); - - return json{ - {"prompt_n", timings.n_p_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.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}, - {"timings", format_timings(llama)}, - }; - - if (llama.params.sparams.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 + llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs ) { - json res = json{ - {"content", content}, - {"stop", false}, + json res = json + { + {"content", content }, + {"stop", false}, + {"slot_id", slot->id }, + {"multimodal", llama.multimodal } }; - if (llama.params.sparams.n_probs > 0) + if (slot->sparams.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } @@ -1115,120 +2157,8 @@ static json format_detokenized_response(std::string content) {"content", content}}; } -template <typename T> -static T json_value(const json &body, const std::string &key, const T &default_value) -{ - // Fallback null to default value - return body.contains(key) && !body.at(key).is_null() - ? body.value(key, default_value) - : default_value; -} - -static void parse_options_completion(const json &body, llama_server_context &llama) -{ - gpt_params default_params; - const auto & default_sparams = default_params.sparams; - - auto & params = llama.params; - auto & sparams = llama.params.sparams; - - llama.stream = json_value(body, "stream", false); - params.n_predict = json_value(body, "n_predict", default_params.n_predict); - sparams.top_k = json_value(body, "top_k", default_sparams.top_k); - sparams.top_p = json_value(body, "top_p", default_sparams.top_p); - sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z); - sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p); - sparams.temp = json_value(body, "temperature", default_sparams.temp); - sparams.penalty_last_n = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); - sparams.penalty_repeat = json_value(body, "repeat_penalty", default_sparams.penalty_repeat); - sparams.penalty_freq = json_value(body, "frequency_penalty", default_sparams.penalty_freq); - sparams.penalty_present = json_value(body, "presence_penalty", default_sparams.penalty_present); - sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat); - sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl); - params.n_keep = json_value(body, "n_keep", default_params.n_keep); - params.seed = json_value(body, "seed", default_params.seed); - sparams.grammar = json_value(body, "grammar", default_sparams.grammar); - sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs); - - if (body.count("prompt") != 0) - { - llama.prompt = body["prompt"]; - } - else - { - llama.prompt = ""; - } - - sparams.logit_bias.clear(); - if (json_value(body, "ignore_eos", false)) - { - sparams.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY; - } - - 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.model); - 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()) - { - sparams.logit_bias[tok] = el[1].get<float>(); - } - else if (el[1].is_boolean() && !el[1].get<bool>()) - { - sparams.logit_bias[tok] = -INFINITY; - } - } - } - } - } - - 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()) - { - llama.params.antiprompt.push_back(word); - } - } - } - - LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); -} - -static void parse_options_infill(const json &body, llama_server_context &llama) -{ - if (body.count("input_prefix") != 0) - { - llama.params.input_prefix = body["input_prefix"]; - } - else - { - llama.params.input_prefix = ""; - } - if (body.count("input_suffix") != 0) - { - llama.params.input_suffix = body["input_suffix"]; - } - else - { - llama.params.input_suffix = ""; - } - parse_options_completion(body, llama); -} -static void log_server_request(const Request &req, const Response &res) +static void log_server_request(const httplib::Request &req, const httplib::Response &res) { LOG_INFO("request", { {"remote_addr", req.remote_addr}, @@ -1245,60 +2175,26 @@ static void log_server_request(const Request &req, const Response &res) }); } -static bool is_at_eob(llama_server_context &server_context, const llama_token *tokens, const size_t n_tokens) { - return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx); -} - -// Function matching type llama_beam_search_callback_fn_t. -// Custom callback example is called each time the beams lengths increase: -// * Show progress by printing ',' following by number of convergent beam tokens if any. -// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. -// This is also called when the stop condition is met. -// Collect tokens into std::vector<llama_token> response which is pointed to by callback_data. -static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { - auto & llama = *static_cast<llama_server_context*>(callback_data); - // Mark beams as EOS as needed. - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - llama_beam_view& beam_view = beams_state.beam_views[i]; - if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { - beam_view.eob = true; - } - } - printf(","); // Show progress - if (const size_t n = beams_state.common_prefix_length) { - llama.generated_token_probs.resize(llama.generated_token_probs.size() + n); - assert(0u < beams_state.n_beams); - const llama_token * tokens = beams_state.beam_views[0].tokens; - const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; - std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); - printf("%zu", n); - } - fflush(stdout); -#if 0 // DEBUG: print current beams for this iteration - std::cout << "\n\nCurrent beams:\n"; - for (size_t i=0 ; i < beams_state.n_beams ; ++i) { - std::cout << "beams["<<i<<"]: " << ostream_beam_view{state.ctx,beams_state.beam_views[i]} << std::endl; - } -#endif -} - -struct token_translator { +struct token_translator +{ llama_context * ctx; - std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } - std::string operator()(const completion_token_output & cto) const { return (*this)(cto.tok); } + std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } + std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } }; -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama) +static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot) { - auto & gtps = llama.generated_token_probs; + auto & gtps = slot->generated_token_probs; auto translator = token_translator{llama.ctx}; auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen); - if (llama.generated_text.capacity() < llama.generated_text.size() + len) { - llama.generated_text.reserve(llama.generated_text.size() + len); + if (slot->generated_text.capacity() < slot->generated_text.size() + len) + { + slot->generated_text.reserve(slot->generated_text.size() + len); } - for (const completion_token_output & cto : gtps) { - llama.generated_text += translator(cto); + for (const completion_token_output & cto : gtps) + { + slot->generated_text += translator(cto); } } @@ -1311,7 +2207,7 @@ int main(int argc, char **argv) // struct that contains llama context and inference llama_server_context llama; - server_params_parse(argc, argv, sparams, params); + server_params_parse(argc, argv, sparams, params, llama); if (params.model_alias == "unknown") { @@ -1322,6 +2218,7 @@ int main(int argc, char **argv) LOG_INFO("build info", {{"build", BUILD_NUMBER}, {"commit", BUILD_COMMIT}}); + LOG_INFO("system info", { {"n_threads", params.n_threads}, {"n_threads_batch", params.n_threads_batch}, @@ -1330,405 +2227,261 @@ int main(int argc, char **argv) }); // load the model - if (!llama.loadModel(params)) + if (!llama.load_model(params)) { return 1; } - Server svr; + llama.initialize(); + + httplib::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.html is found in the public --path - svr.Get("/", [](const Request &, Response &res) + svr.Get("/", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html"); - return false; }); + res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html"); + return false; + }); // this is only called if no index.js is found in the public --path - svr.Get("/index.js", [](const Request &, Response &res) + svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript"); - return false; }); + 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 httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript"); - return false; }); + res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript"); + return false; + }); // this is only called if no index.html is found in the public --path - svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res) + svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res) { - res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); - return false; }); - - svr.Post("/completion", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - llama.rewind(); - - llama_reset_timings(llama.ctx); - parse_options_completion(json::parse(req.body), llama); - - llama.initSampling(); - llama.loadPrompt(); - llama.beginCompletion(); - - if (!llama.stream) { - if (llama.params.n_beams) { - // Fill llama.generated_token_probs vector with final beam. - llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams, - llama.n_past, llama.n_remain); - // Translate llama.generated_token_probs to llama.generated_text. - append_to_generated_text_from_generated_token_probs(llama); - } else { - size_t stop_pos = std::string::npos; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok); - - stop_pos = llama.findStoppingStrings(llama.generated_text, - token_text.size(), STOP_FULL); - } - - if (stop_pos == std::string::npos) { - stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL); - } - if (stop_pos != std::string::npos) { - llama.generated_text.erase(llama.generated_text.begin() + stop_pos, - llama.generated_text.end()); - } - } - - auto probs = llama.generated_token_probs; - if (llama.params.sparams.n_probs > 0 && llama.stopped_word) { - const std::vector<llama_token> stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); - probs = std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); - } - - const json data = format_final_response(llama, llama.generated_text, probs); - - llama_print_timings(llama.ctx); + res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript"); + return false; + }); - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), - "application/json"); - } else { - const auto chunked_content_provider = [&](size_t, DataSink & sink) { - size_t sent_count = 0; - size_t sent_token_probs_index = 0; + svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res) + { + res.set_header("Access-Control-Allow-Origin", "*"); + json data = { + { "user_name", llama.name_user.c_str() }, + { "assistant_name", llama.name_assistant.c_str() } + }; + res.set_content(data.dump(), "application/json"); + }); - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { - continue; + svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res) + { + json data = json::parse(req.body); + const int task_id = llama.request_completion(data, false); + if (!json_value(data, "stream", false)) { + std::string completion_text; + task_result result = llama.next_result(task_id); + if(!result.error && result.stop) { + res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } - const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); - - size_t pos = std::min(sent_count, llama.generated_text.size()); - - const std::string str_test = llama.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = - llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - if (stop_pos != std::string::npos) { - is_stop_full = true; - llama.generated_text.erase( - llama.generated_text.begin() + pos + stop_pos, - llama.generated_text.end()); - pos = std::min(sent_count, llama.generated_text.size()); - } else { - is_stop_full = false; - stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - STOP_PARTIAL); + else + { + res.status = 404; + res.set_content(result.result_json["content"], "text/plain"); + return; } - - if ( - stop_pos == std::string::npos || - // Send rest of the text if we are at the end of the generation - (!llama.has_next_token && !is_stop_full && stop_pos > 0) - ) { - const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - sent_count += to_send.size(); - - std::vector<completion_token_output> probs_output = {}; - - if (llama.params.sparams.n_probs > 0) { - const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + } else { + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) + { + while (true) + { + task_result result = llama.next_result(task_id); + if (!result.error) { + const std::string str = + "data: " + + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.c_str(), str.size())) + { + return false; + } + if(result.stop) { + break; + } + } else { + break; } - sent_token_probs_index = probs_stop_pos; } + sink.done(); + return true; + }; - const json data = format_partial_response(llama, to_send, probs_output); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; + auto on_complete = [task_id, &llama] (bool) + { + // cancel + llama.request_cancel(task_id); + }; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }); - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } + svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res) + { + json data = json::parse(req.body); + const int task_id = llama.request_completion(data, true); + if (!json_value(data, "stream", false)) { + std::string completion_text; + task_result result = llama.next_result(task_id); + if (!result.error && result.stop) + { + res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json"); } - - if (!llama.has_next_token) { - // Generation is done, send extra information. - const json data = format_final_response( - llama, - "", - std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) - ); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } + else + { + res.status = 404; + res.set_content(result.result_json["content"], "text/plain"); + return; } - } - - llama_print_timings(llama.ctx); - sink.done(); - return true; - }; - const auto on_complete = [&](bool) { - llama.mutex.unlock(); - }; - lock.release(); - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } }); - - svr.Post("/infill", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - llama.rewind(); - - llama_reset_timings(llama.ctx); - parse_options_infill(json::parse(req.body), llama); - - llama.initSampling(); - llama.loadInfill(); - llama.beginCompletion(); - const auto chunked_content_provider = [&](size_t, DataSink & sink) { - size_t sent_count = 0; - size_t sent_token_probs_index = 0; - - while (llama.has_next_token) { - const completion_token_output token_with_probs = llama.doCompletion(); - if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { - continue; - } - const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); - - size_t pos = std::min(sent_count, llama.generated_text.size()); - - const std::string str_test = llama.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = - llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); - if (stop_pos != std::string::npos) { - is_stop_full = true; - llama.generated_text.erase( - llama.generated_text.begin() + pos + stop_pos, - llama.generated_text.end()); - pos = std::min(sent_count, llama.generated_text.size()); } else { - is_stop_full = false; - stop_pos = llama.findStoppingStrings(str_test, token_text.size(), - STOP_PARTIAL); - } - - if ( - stop_pos == std::string::npos || - // Send rest of the text if we are at the end of the generation - (!llama.has_next_token && !is_stop_full && stop_pos > 0) - ) { - const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - - sent_count += to_send.size(); - - std::vector<completion_token_output> probs_output = {}; - - if (llama.params.sparams.n_probs > 0) { - const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { + while (true) + { + task_result result = llama.next_result(task_id); + if (!result.error) { + const std::string str = + "data: " + + result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + if (!sink.write(str.c_str(), str.size())) + { + return false; + } + if (result.stop) + { + break; + } + } + else + { + break; + } } - sent_token_probs_index = probs_stop_pos; - } - const json data = format_partial_response(llama, to_send, probs_output); + sink.done(); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; + return true; + }; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } - } + auto on_complete = [task_id, &llama] (bool) + { + // cancel + llama.request_cancel(task_id); + }; - if (!llama.has_next_token) { - // Generation is done, send extra information. - const json data = format_final_response( - llama, - "", - std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) - ); - - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; - } + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } - } - - llama_print_timings(llama.ctx); - sink.done(); - return true; - }; - const auto on_complete = [&](bool) { - llama.mutex.unlock(); - }; - lock.release(); - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - }); + }); - svr.Get("/model.json", [&llama](const Request &, Response &res) + svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) { - const json data = format_generation_settings(llama); - return res.set_content(data.dump(), "application/json"); }); + const json data = llama.get_model_props(); + return res.set_content(data.dump(), "application/json"); + }); - svr.Options(R"(/.*)", [](const Request &, Response &res) + svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json"); }); - svr.Post("/tokenize", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - const json body = json::parse(req.body); - std::vector<llama_token> tokens; - if (body.count("content") != 0) - { - tokens = llama.tokenize(body["content"], false); - } - const json data = format_tokenizer_response(tokens); - return res.set_content(data.dump(), "application/json"); }); - - svr.Post("/detokenize", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - const json body = json::parse(req.body); - std::string content; - if (body.count("tokens") != 0) - { - const std::vector<llama_token> tokens = body["tokens"]; - content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); - } - - const json data = format_detokenized_response(content); - return res.set_content(data.dump(), "application/json"); }); - - svr.Post("/embedding", [&llama](const Request &req, Response &res) - { - auto lock = llama.lock(); - - const json body = json::parse(req.body); - - llama.rewind(); - - llama_reset_timings(llama.ctx); + svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) + { + const json body = json::parse(req.body); + std::vector<llama_token> tokens; + if (body.count("content") != 0) + { + tokens = llama.tokenize(body["content"], false); + } + const json data = format_tokenizer_response(tokens); + return res.set_content(data.dump(), "application/json"); + }); - if (body.count("content") != 0) - { - llama.prompt = body["content"]; - } - else - { - llama.prompt = ""; - } - llama.params.n_predict = 0; + svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) + { + const json body = json::parse(req.body); + std::string content; + if (body.count("tokens") != 0) + { + const std::vector<llama_token> tokens = body["tokens"]; + content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); + } - llama.initSampling(); - llama.loadPrompt(); - llama.beginCompletion(); - llama.doCompletion(); + const json data = format_detokenized_response(content); + return res.set_content(data.dump(), "application/json"); + }); - const json data = format_embedding_response(llama); - return res.set_content(data.dump(), "application/json"); }); + svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) + { + const json body = json::parse(req.body); + json prompt; + if (body.count("content") != 0) + { + prompt = body["content"]; + } + else + { + prompt = ""; + } + const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false); + task_result result = llama.next_result(task_id); + return res.set_content(result.result_json.dump(), "application/json"); + }); svr.set_logger(log_server_request); - svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep) - { - const char fmt[] = "500 Internal Server Error\n%s"; - char buf[BUFSIZ]; - try { - std::rethrow_exception(std::move(ep)); - } catch (std::exception & e) { - snprintf(buf, sizeof(buf), fmt, e.what()); - } catch (...) { - snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); - } - res.set_content(buf, "text/plain"); - res.status = 500; }); - - svr.set_error_handler([](const Request &, Response &res) - { - if (res.status == 400) { - res.set_content("Invalid request", "text/plain"); - } else if (res.status != 500) { - res.set_content("File Not Found", "text/plain"); - res.status = 404; - } }); + svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) + { + const char fmt[] = "500 Internal Server Error\n%s"; + char buf[BUFSIZ]; + try + { + std::rethrow_exception(std::move(ep)); + } + catch (std::exception &e) + { + snprintf(buf, sizeof(buf), fmt, e.what()); + } + catch (...) + { + snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); + } + res.set_content(buf, "text/plain"); + res.status = 500; + }); + + svr.set_error_handler([](const httplib::Request &, httplib::Response &res) + { + if (res.status == 400) + { + res.set_content("Invalid request", "text/plain"); + } + else if (res.status != 500) + { + res.set_content("File Not Found", "text/plain"); + res.status = 404; + } + }); // set timeouts and change hostname and port - svr.set_read_timeout(sparams.read_timeout); + svr.set_read_timeout (sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); if (!svr.bind_to_port(sparams.hostname, sparams.port)) @@ -1741,20 +2494,38 @@ int main(int argc, char **argv) svr.set_base_dir(sparams.public_path); // to make it ctrl+clickable: - printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); + LOG_TEE("\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}, }); - if (!svr.listen_after_bind()) + // run the HTTP server in a thread - see comment below + std::thread t([&]() + { + if (!svr.listen_after_bind()) + { + return 1; + } + + return 0; + }); + + // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? + // "Bus error: 10" - this is on macOS, it does not crash on Linux + //std::thread t2([&]() { - return 1; + bool running = true; + while (running) + { + running = llama.update_slots(); + } } + //); - llama_sampling_free(llama.ctx_sampling); - llama_backend_free(); + t.join(); + llama_backend_free(); return 0; } |