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Merge branch 'master' into HEAD

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This commit is contained in:
Georgi Gerganov
2023-07-04 22:02:38 +03:00
parent 81f28f2539 ed9a54e512
commit 8e9af803ba
20 changed files with 4383 additions and 1048 deletions
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2
.github/workflows/build.yml vendored
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@@ -111,6 +111,7 @@ jobs:
- name: Dependencies - name: Dependencies
id: depends id: depends
continue-on-error: true
run: | run: |
brew update brew update
@@ -129,6 +130,7 @@ jobs:
- name: Dependencies - name: Dependencies
id: depends id: depends
continue-on-error: true
run: | run: |
brew update brew update

2
CMakeLists.txt
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@@ -79,7 +79,7 @@ option(LLAMA_QKK_64 "llama: use super-block size of 64
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" OFF) option(LLAMA_BUILD_SERVER "llama: build server example" ON)
# #
# Build info header # Build info header

14
Makefile
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@@ -1,11 +1,5 @@
# Define the default target now so that it is always the first target # Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test
ifdef LLAMA_BUILD_SERVER
BUILD_TARGETS += server
LLAMA_SERVER_VERBOSE ?= 1
server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
endif
default: $(BUILD_TARGETS) default: $(BUILD_TARGETS)
@@ -61,6 +55,10 @@ else
CXXFLAGS += -DNDEBUG CXXFLAGS += -DNDEBUG
endif endif
ifdef LLAMA_SERVER_VERBOSE
CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
endif
# warnings # warnings
CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
@@ -272,7 +270,7 @@ libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
clean: clean:
rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch embd-input-test build-info.h rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h
# #
# Examples # Examples

3
examples/common.h
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@@ -31,7 +31,7 @@ struct gpt_params {
int32_t n_gpu_layers = 0; // number of layers to store in VRAM int32_t n_gpu_layers = 0; // number of layers to store in VRAM
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
bool low_vram = 0; // if true, reduce VRAM usage at the cost of performance int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
// sampling parameters // sampling parameters
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
@@ -59,6 +59,7 @@ struct gpt_params {
std::string lora_adapter = ""; // lora adapter path std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter std::string lora_base = ""; // base model path for the lora adapter
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
bool memory_f16 = true; // use f16 instead of f32 for memory kv bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs bool use_color = false; // use color to distinguish generations and inputs

16
examples/server/README.md
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@@ -190,3 +190,19 @@ Run with bash:
```sh ```sh
bash chat.sh bash chat.sh
``` ```
### API like OAI
API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
This example must be used with server.cpp
```sh
python api_like_OAI.py
```
After running the API server, you can use it in Python by setting the API base URL.
```python
openai.api_base = "http://<Your api-server IP>:port"
```
Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API

219
examples/server/api_like_OAI.py Executable file
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@@ -0,0 +1,219 @@
import argparse
from flask import Flask, jsonify, request, Response
import urllib.parse
import requests
import time
import json
app = Flask(__name__)
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')
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
args = parser.parse_args()
def is_present(json, key):
try:
buf = json[key]
except KeyError:
return False
return True
#convert chat to prompt
def convert_chat(messages):
prompt = "" + args.chat_prompt.replace("\\n", "\n")
system_n = args.system_name.replace("\\n", "\n")
user_n = args.user_name.replace("\\n", "\n")
ai_n = args.ai_name.replace("\\n", "\n")
stop = args.stop.replace("\\n", "\n")
for line in messages:
if (line["role"] == "system"):
prompt += f"{system_n}{line['content']}"
if (line["role"] == "user"):
prompt += f"{user_n}{line['content']}"
if (line["role"] == "assistant"):
prompt += f"{ai_n}{line['content']}{stop}"
prompt += ai_n.rstrip()
return prompt
def make_postData(body, chat=False, stream=False):
postData = {}
if (chat):
postData["prompt"] = convert_chat(body["messages"])
else:
postData["prompt"] = body["prompt"]
if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
if(is_present(body, "seed")): postData["seed"] = body["seed"]
if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
if (args.stop != ""):
postData["stop"] = [args.stop]
else:
postData["stop"] = []
if(is_present(body, "stop")): postData["stop"] += body["stop"]
postData["n_keep"] = -1
postData["stream"] = stream
return postData
def make_resData(data, chat=False, promptToken=[]):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion" if (chat) else "text_completion",
"created": int(time.time()),
"truncated": data["truncated"],
"model": "LLaMA_CPP",
"usage": {
"prompt_tokens": data["tokens_evaluated"],
"completion_tokens": data["tokens_predicted"],
"total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
}
}
if (len(promptToken) != 0):
resData["promptToken"] = promptToken
if (chat):
#only one choice is supported
resData["choices"] = [{
"index": 0,
"message": {
"role": "assistant",
"content": data["content"],
},
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
else:
#only one choice is supported
resData["choices"] = [{
"text": data["content"],
"index": 0,
"logprobs": None,
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
return resData
def make_resData_stream(data, chat=False, time_now = 0, start=False):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
"created": time_now,
"model": "LLaMA_CPP",
"choices": [
{
"finish_reason": None,
"index": 0
}
]
}
if (chat):
if (start):
resData["choices"][0]["delta"] = {
"role": "assistant"
}
else:
resData["choices"][0]["delta"] = {
"content": data["content"]
}
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
else:
resData["choices"][0]["text"] = data["content"]
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
return resData
@app.route('/chat/completions', methods=['POST'])
@app.route('/v1/chat/completions', methods=['POST'])
def chat_completions():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=True, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=True, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
yield 'data: {}\n'.format(json.dumps(resData))
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
@app.route('/completions', methods=['POST'])
@app.route('/v1/completions', methods=['POST'])
def completion():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=False, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=False, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
if __name__ == '__main__':
app.run(args.host, port=args.port)

193
examples/server/completion.js.hpp Normal file
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@@ -0,0 +1,193 @@
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};
unsigned int completion_js_len = 2275;

22
examples/server/deps.sh Executable file
View File

@@ -0,0 +1,22 @@
#!/bin/bash
# Download and update deps for binary
# get the directory of this script file
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
PUBLIC=$DIR/public
OUTPUT=$DIR/templats.hpp
echo "// Generated file, do not edit" > $OUTPUT
echo "" > $OUTPUT
echo "download js bundle files"
curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
echo >> $PUBLIC/index.js # add newline
FILES=$(ls $PUBLIC)
for FILE in $FILES; do
func=$(echo $FILE | tr '.' '_')
echo "generate $FILE.hpp ($func)"
xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp
done

846
examples/server/index.html.hpp Normal file
View File

@@ -0,0 +1,846 @@
unsigned char index_html[] = {
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};
unsigned int index_html_len = 10108;

1851
examples/server/index.js.hpp Normal file
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81
examples/server/public/completion.js Normal file
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const paramDefaults = {
stream: true,
n_predict: 500,
temperature: 0.2,
stop: ["</s>"]
};
/**
* This function completes the input text using a llama dictionary.
* @param {object} params - The parameters for the completion request.
* @param {object} controller - an instance of AbortController if you need one, or null.
* @param {function} callback - The callback function to call when the completion is done.
* @returns {string} the completed text as a string. Ideally ignored, and you get at it via the callback.
*/
export const llamaComplete = async (params, controller, callback) => {
if (!controller) {
controller = new AbortController();
}
const completionParams = { ...paramDefaults, ...params };
// we use fetch directly here becasue the built in fetchEventSource does not support POST
const response = await fetch("/completion", {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream'
},
signal: controller.signal,
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let content = "";
try {
let cont = true;
while (cont) {
const result = await reader.read();
if (result.done) {
break;
}
// sse answers in the form multiple lines of: value\n with data always present as a key. in our case we
// mainly care about the data: key here, which we expect as json
const text = decoder.decode(result.value);
// parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const match of text.matchAll(regex)) {
result[match[1]] = match[2]
}
// since we know this is llama.cpp, let's just decode the json in data
result.data = JSON.parse(result.data);
content += result.data.content;
// callack
if (callback) {
cont = callback(result) != false;
}
// if we got a stop token from server, we will break here
if (result.data.stop) {
break;
}
}
} catch (e) {
console.error("llama error: ", e);
throw e;
}
finally {
controller.abort();
}
return content;
}

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examples/server/public/index.html Normal file
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<html>
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
<title>llama.cpp - chat</title>
<style>
body {
background-color: #fff;
color: #000;
font-family: system-ui;
font-size: 90%;
}
#container {
margin: 0em auto;
display: flex;
flex-direction: column;
justify-content: space-between;
height: 100%;
}
header, footer {
text-align: center;
}
main {
margin: 3px;
display: flex;
flex-direction: column;
justify-content: space-between;
gap: 1em;
flex-grow: 1;
overflow-y: auto;
border: 1px solid #ccc;
border-radius: 5px;
padding: 0.5em;
}
body {
max-width: 600px;
min-width: 300px;
line-height: 1.2;
margin: 0 auto;
padding: 0 0.5em;
}
p {
overflow-wrap: break-word;
word-wrap: break-word;
hyphens: auto;
margin-top: 0.5em;
margin-bottom: 0.5em;
}
#write form {
margin: 1em 0 0 0;
display: flex;
flex-direction: column;
gap: 0.5em;
align-items: stretch;
}
.right {
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
}
fieldset {
border: none;
padding: 0;
margin: 0;
}
textarea {
padding: 5px;
flex-grow: 1;
width: 100%;
}
pre code {
display: block;
background-color: #222;
color: #ddd;
}
code {
font-family: monospace;
padding: 0.1em 0.3em;
border-radius: 3px;
}
fieldset label {
margin: 0.5em 0;
display: block;
}
</style>
<script type="module">
import {
html, h, signal, effect, computed, render, useSignal, useEffect, useRef
} from '/index.js';
import { llamaComplete } from '/completion.js';
const session = signal({
prompt: "This is a conversation between user and llama, a friendly chatbot. respond in markdown.",
template: "{{prompt}}\n\n{{history}}\n{{char}}:",
historyTemplate: "{{name}}: {{message}}",
transcript: [],
type: "chat",
char: "llama",
user: "User",
})
const transcriptUpdate = (transcript) => {
session.value = {
...session.value,
transcript
}
}
const chatStarted = computed(() => session.value.transcript.length > 0)
const params = signal({
n_predict: 400,
temperature: 0.7,
repeat_last_n: 256,
repeat_penalty: 1.18,
top_k: 40,
top_p: 0.5,
})
const controller = signal(null)
const generating = computed(() => controller.value == null )
// simple template replace
const template = (str, extraSettings) => {
let settings = session.value;
if (extraSettings) {
settings = { ...settings, ...extraSettings };
}
return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
}
// send message to server
const chat = async (msg) => {
if (controller.value) {
console.log('already running...');
return;
}
controller.value = new AbortController();
transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
const payload = template(session.value.template, {
message: msg,
history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
});
let currentMessage = '';
const history = session.value.transcript
const llamaParams = {
...params.value,
prompt: payload,
stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
}
await llamaComplete(llamaParams, controller.value, (message) => {
const data = message.data;
currentMessage += data.content;
// remove leading whitespace
currentMessage = currentMessage.replace(/^\s+/, "")
transcriptUpdate([...history, ["{{char}}", currentMessage]])
if (data.stop) {
console.log("-->", data, ' response was:', currentMessage, 'transcript state:', session.value.transcript);
}
})
controller.value = null;
}
function MessageInput() {
const message = useSignal("")
const stop = (e) => {
e.preventDefault();
if (controller.value) {
controller.value.abort();
controller.value = null;
}
}
const reset = (e) => {
stop(e);
transcriptUpdate([]);
}
const submit = (e) => {
stop(e);
chat(message.value);
message.value = "";
}
const enterSubmits = (event) => {
if (event.which === 13 && !event.shiftKey) {
submit(event);
}
}
return html`
<form onsubmit=${submit}>
<div>
<textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
</div>
<div class="right">
<button type="submit" disabled=${!generating.value} >Send</button>
<button onclick=${stop} disabled=${generating}>Stop</button>
<button onclick=${reset}>Reset</button>
</div>
</form>
`
}
const ChatLog = (props) => {
const messages = session.value.transcript;
const container = useRef(null)
useEffect(() => {
// scroll to bottom (if needed)
if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
container.current.scrollTo(0, container.current.scrollHeight)
}
}, [messages])
const chatLine = ([user, msg]) => {
return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdown} text=${template(msg)} /></p>`
};
return html`
<section id="chat" ref=${container}>
${messages.flatMap(chatLine)}
</section>`;
};
const ConfigForm = (props) => {
const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
return html`
<form>
<fieldset>
<div>
<label for="prompt">Prompt</label>
<textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
</div>
<div>
<label for="user">User name</label>
<input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
</div>
<div>
<label for="bot">Bot name</label>
<input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
</div>
<div>
<label for="template">Prompt template</label>
<textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
</div>
<div>
<label for="template">Chat history template</label>
<textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
</div>
<div>
<label for="temperature">Temperature</label>
<input type="range" id="temperature" min="0.0" max="1.0" step="0.01" name="temperature" value="${params.value.temperature}" oninput=${updateParamsFloat} />
<span>${params.value.temperature}</span>
</div>
<div>
<label for="nPredict">Predictions</label>
<input type="range" id="nPredict" min="1" max="2048" step="1" name="n_predict" value="${params.value.n_predict}" oninput=${updateParamsFloat} />
<span>${params.value.n_predict}</span>
</div>
<div>
<label for="repeat_penalty">Penalize repeat sequence</label>
<input type="range" id="repeat_penalty" min="0.0" max="2.0" step="0.01" name="repeat_penalty" value="${params.value.repeat_penalty}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_penalty}</span>
</div>
<div>
<label for="repeat_last_n">Consider N tokens for penalize</label>
<input type="range" id="repeat_last_n" min="0.0" max="2048" name="repeat_last_n" value="${params.value.repeat_last_n}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_last_n}</span>
</div>
</fieldset>
</form>
`
}
const Markdown = (params) => {
const md = params.text
.replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
.replace(/__(.*?)__/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*/g, '<em>$1</em>')
.replace(/_(.*?)_/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');
return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
};
function App(props) {
return html`
<div id="container">
<header>
<h1>llama.cpp</h1>
</header>
<main id="content">
<${chatStarted.value ? ChatLog : ConfigForm} />
</main>
<footer id="write">
<${MessageInput} />
</footer>
<footer>
<p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a></p>
</footer>
</div>
`;
}
render(h(App), document.body);
</script>
</head>
<body>
</body>
</html>

1
examples/server/public/index.js Normal file
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233
examples/server/server.cpp
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@@ -2,8 +2,6 @@
#include "llama.h" #include "llama.h"
#include "build-info.h" #include "build-info.h"
// single thread
#define CPPHTTPLIB_THREAD_POOL_COUNT 1
#ifndef NDEBUG #ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error // crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1 #define CPPHTTPLIB_NO_EXCEPTIONS 1
@@ -12,6 +10,11 @@
#include "httplib.h" #include "httplib.h"
#include "json.hpp" #include "json.hpp"
// auto generated files (update with ./deps.sh)
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#ifndef SERVER_VERBOSE #ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1 #define SERVER_VERBOSE 1
#endif #endif
@@ -21,11 +24,23 @@ using json = nlohmann::json;
struct server_params { struct server_params {
std::string hostname = "127.0.0.1"; std::string hostname = "127.0.0.1";
std::string public_path = "examples/server/public";
int32_t port = 8080; int32_t port = 8080;
int32_t read_timeout = 600; int32_t read_timeout = 600;
int32_t write_timeout = 600; int32_t write_timeout = 600;
}; };
// completion token output with probabilities
struct completion_token_output {
struct token_prob {
llama_token tok;
float prob;
};
std::vector<token_prob> probs;
llama_token tok;
};
static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) { static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
size_t i; size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
@@ -86,6 +101,40 @@ static void server_log(const char * level, const char * function, int line,
fflush(stdout); fflush(stdout);
} }
// format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
// if first bit is 1, meaning it's a partial character
if (out.size() > 0 && (out[0] & 0x80) == 0x80) {
std::stringstream ss;
ss<< std::hex << (out[0] & 0xff);
std::string res ( ss.str() );
out = "byte: \\x" + res;
}
return out;
}
// convert a vector of completion_token_output to json
static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> probs) {
json out = json::array();
for (const auto & prob : probs) {
json probs_for_token = json::array();
for (const auto & p : prob.probs) {
std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
probs_for_token.push_back(json {
{ "tok_str", tok_str },
{ "prob", p.prob },
});
}
std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
out.push_back(json {
{"content", tok_str},
{"probs", probs_for_token},
});
}
return out;
}
static bool server_verbose = false; static bool server_verbose = false;
#if SERVER_VERBOSE != 1 #if SERVER_VERBOSE != 1
@@ -107,7 +156,9 @@ struct llama_server_context {
bool stream = false; bool stream = false;
bool has_next_token = false; bool has_next_token = false;
std::string generated_text; std::string generated_text;
std::vector<completion_token_output> generated_token_probs;
size_t num_prompt_tokens = 0;
size_t num_tokens_predicted = 0; size_t num_tokens_predicted = 0;
size_t n_past = 0; size_t n_past = 0;
size_t n_remain = 0; size_t n_remain = 0;
@@ -126,6 +177,12 @@ struct llama_server_context {
std::string stopping_word; std::string stopping_word;
int32_t multibyte_pending = 0; int32_t multibyte_pending = 0;
std::mutex mutex;
std::unique_lock<std::mutex> lock() {
return std::unique_lock<std::mutex>(mutex);
}
~llama_server_context() { ~llama_server_context() {
if (ctx) { if (ctx) {
llama_free(ctx); llama_free(ctx);
@@ -139,9 +196,11 @@ struct llama_server_context {
void rewind() { void rewind() {
params.antiprompt.clear(); params.antiprompt.clear();
num_prompt_tokens = 0;
num_tokens_predicted = 0; num_tokens_predicted = 0;
generated_text = ""; generated_text = "";
generated_text.reserve(params.n_ctx); generated_text.reserve(params.n_ctx);
generated_token_probs.clear();
truncated = false; truncated = false;
stopped_eos = false; stopped_eos = false;
stopped_word = false; stopped_word = false;
@@ -169,17 +228,18 @@ struct llama_server_context {
void loadPrompt() { void loadPrompt() {
params.prompt.insert(0, 1, ' '); // always add a first space params.prompt.insert(0, 1, ' '); // always add a first space
std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true); std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
num_prompt_tokens = prompt_tokens.size();
if (params.n_keep < 0) { if (params.n_keep < 0) {
params.n_keep = (int)prompt_tokens.size(); params.n_keep = (int)num_prompt_tokens;
} }
params.n_keep = std::min(params.n_ctx - 4, params.n_keep); params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
// if input prompt is too big, truncate like normal // if input prompt is too big, truncate like normal
if (prompt_tokens.size() >= (size_t)params.n_ctx) { if (num_prompt_tokens>= (size_t)params.n_ctx) {
const int n_left = (params.n_ctx - params.n_keep) / 2; const int n_left = (params.n_ctx - params.n_keep) / 2;
std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_left - 1) / n_left; const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
@@ -193,7 +253,7 @@ struct llama_server_context {
truncated = true; truncated = true;
prompt_tokens = new_tokens; prompt_tokens = new_tokens;
} else { } else {
const size_t ps = prompt_tokens.size(); const size_t ps = num_prompt_tokens;
std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0); std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps); std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
} }
@@ -201,7 +261,7 @@ struct llama_server_context {
// compare the evaluated prompt with the new prompt // compare the evaluated prompt with the new prompt
n_past = common_part(embd, prompt_tokens); n_past = common_part(embd, prompt_tokens);
embd = prompt_tokens; embd = prompt_tokens;
if (n_past == prompt_tokens.size()) { if (n_past == num_prompt_tokens) {
// we have to evaluate at least 1 token to generate logits. // we have to evaluate at least 1 token to generate logits.
n_past--; n_past--;
} }
@@ -221,8 +281,9 @@ struct llama_server_context {
llama_set_rng_seed(ctx, params.seed); llama_set_rng_seed(ctx, params.seed);
} }
llama_token nextToken() { completion_token_output nextToken() {
llama_token result = -1; completion_token_output result;
result.tok = -1;
if (embd.size() >= (size_t)params.n_ctx) { if (embd.size() >= (size_t)params.n_ctx) {
// Reset context // Reset context
@@ -261,7 +322,8 @@ struct llama_server_context {
if (params.n_predict == 0) { if (params.n_predict == 0) {
has_next_token = false; has_next_token = false;
return llama_token_eos(); result.tok = llama_token_eos();
return result;
} }
// out of user input, sample next token // out of user input, sample next token
@@ -278,7 +340,7 @@ struct llama_server_context {
const float mirostat_tau = params.mirostat_tau; const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta; const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl; const bool penalize_nl = params.penalize_nl;
llama_token id = 0; const int32_t n_probs = params.n_probs;
{ {
auto * logits = llama_get_logits(ctx); auto * logits = llama_get_logits(ctx);
@@ -312,35 +374,42 @@ struct llama_server_context {
if (temp <= 0) { if (temp <= 0) {
// Greedy sampling // Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p); result.tok = llama_sample_token_greedy(ctx, &candidates_p);
if (n_probs > 0) {
llama_sample_softmax(ctx, &candidates_p);
}
} else { } else {
if (mirostat == 1) { if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau; static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100; const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp); llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) { } else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau; static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp); llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else { } else {
// Temperature sampling // Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1); size_t min_keep = std::max(1, n_probs);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); llama_sample_top_k(ctx, &candidates_p, top_k, min_keep);
llama_sample_typical(ctx, &candidates_p, typical_p, 1); llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep);
llama_sample_top_p(ctx, &candidates_p, top_p, 1); llama_sample_typical(ctx, &candidates_p, typical_p, min_keep);
llama_sample_top_p(ctx, &candidates_p, top_p, min_keep);
llama_sample_temperature(ctx, &candidates_p, temp); llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p); result.tok = llama_sample_token(ctx, &candidates_p);
} }
} }
for (size_t i = 0; i < std::min(candidates_p.size, (size_t) n_probs); ++i) {
result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
}
last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id); last_n_tokens.push_back(result.tok);
num_tokens_predicted++; num_tokens_predicted++;
} }
// add it to the context // add it to the context
embd.push_back(id); embd.push_back(result.tok);
result = id;
// decrement remaining sampling budget // decrement remaining sampling budget
--n_remain; --n_remain;
@@ -382,12 +451,16 @@ struct llama_server_context {
return stop_pos; return stop_pos;
} }
std::string doCompletion() { completion_token_output doCompletion() {
const llama_token token = nextToken(); const completion_token_output token_with_probs = nextToken();
const std::string token_text = token == -1 ? "" : llama_token_to_str(ctx, token); const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
generated_text += token_text; generated_text += token_text;
if (params.n_probs > 0) {
generated_token_probs.push_back(token_with_probs);
}
if (multibyte_pending > 0) { if (multibyte_pending > 0) {
multibyte_pending -= token_text.size(); multibyte_pending -= token_text.size();
} else if (token_text.size() == 1) { } else if (token_text.size() == 1) {
@@ -416,8 +489,8 @@ struct llama_server_context {
} }
LOG_VERBOSE("next token", { LOG_VERBOSE("next token", {
{ "token", token }, { "token", token_with_probs.tok },
{ "token_text", llama_token_to_str(ctx, token) }, { "token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok) },
{ "has_next_token", has_next_token }, { "has_next_token", has_next_token },
{ "n_remain", n_remain }, { "n_remain", n_remain },
{ "num_tokens_predicted", num_tokens_predicted }, { "num_tokens_predicted", num_tokens_predicted },
@@ -427,7 +500,7 @@ struct llama_server_context {
{ "stopping_word", stopping_word }, { "stopping_word", stopping_word },
}); });
return token_text; return token_with_probs;
} }
std::vector<float> getEmbedding() { std::vector<float> getEmbedding() {
@@ -479,6 +552,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params,
fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port); fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port);
fprintf(stderr, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
fprintf(stderr, "\n"); fprintf(stderr, "\n");
@@ -505,6 +579,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
break; break;
} }
sparams.hostname = argv[i]; sparams.hostname = argv[i];
} else if (arg == "--path") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.public_path = argv[i];
} else if (arg == "--timeout" || arg == "-to") { } else if (arg == "--timeout" || arg == "-to") {
if (++i >= argc) { if (++i >= argc) {
invalid_param = true; invalid_param = true;
@@ -669,6 +749,7 @@ static json format_generation_settings(llama_server_context & llama) {
{ "ignore_eos", ignore_eos }, { "ignore_eos", ignore_eos },
{ "stream", llama.stream }, { "stream", llama.stream },
{ "logit_bias", llama.params.logit_bias }, { "logit_bias", llama.params.logit_bias },
{ "n_probs", llama.params.n_probs },
}; };
} }
@@ -678,12 +759,14 @@ static json format_embedding_response(llama_server_context & llama) {
}; };
} }
static json format_final_response(llama_server_context & llama, const std::string & content) { static json format_final_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
return json {
json res = json {
{ "content", content }, { "content", content },
{ "stop", true }, { "stop", true },
{ "model", llama.params.model_alias }, { "model", llama.params.model_alias },
{ "tokens_predicted", llama.num_tokens_predicted }, { "tokens_predicted", llama.num_tokens_predicted },
{ "tokens_evaluated", llama.num_prompt_tokens },
{ "generation_settings", format_generation_settings(llama) }, { "generation_settings", format_generation_settings(llama) },
{ "prompt", llama.params.prompt }, { "prompt", llama.params.prompt },
{ "truncated", llama.truncated }, { "truncated", llama.truncated },
@@ -692,13 +775,25 @@ static json format_final_response(llama_server_context & llama, const std::strin
{ "stopped_limit", llama.stopped_limit }, { "stopped_limit", llama.stopped_limit },
{ "stopping_word", llama.stopping_word }, { "stopping_word", llama.stopping_word },
}; };
if (llama.params.n_probs > 0) {
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
}
return res;
} }
static json format_partial_response(const std::string & content) { static json format_partial_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
return json { json res = json {
{ "content", content }, { "content", content },
{ "stop", false }, { "stop", false },
}; };
if (llama.params.n_probs > 0) {
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
}
return res;
} }
static json format_tokenizer_response(const std::vector<llama_token> & tokens) { static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
@@ -728,6 +823,7 @@ static void parse_options_completion(const json & body, llama_server_context & l
llama.params.n_keep = body.value("n_keep", default_params.n_keep); llama.params.n_keep = body.value("n_keep", default_params.n_keep);
llama.params.seed = body.value("seed", default_params.seed); llama.params.seed = body.value("seed", default_params.seed);
llama.params.prompt = body.value("prompt", default_params.prompt); llama.params.prompt = body.value("prompt", default_params.prompt);
llama.params.n_probs = body.value("n_probs", default_params.n_probs);
llama.params.logit_bias.clear(); llama.params.logit_bias.clear();
if (body.value("ignore_eos", false)) { if (body.value("ignore_eos", false)) {
@@ -764,17 +860,24 @@ static void parse_options_completion(const json & body, llama_server_context & l
LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama));
} }
static void log_server_request(const Request & req, const Response & res) { static void log_server_request(const Request & req, const Response & res) {
LOG_INFO("request", { LOG_INFO("request", {
{ "remote_addr", req.remote_addr }, { "remote_addr", req.remote_addr },
{ "remote_port", req.remote_port }, { "remote_port", req.remote_port },
{ "status", res.status }, { "status", res.status },
{ "method", req.method },
{ "path", req.path }, { "path", req.path },
{ "params", req.params },
});
LOG_VERBOSE("request", {
{ "request", req.body }, { "request", req.body },
{ "response", res.body }, { "response", res.body },
}); });
} }
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
// own arguments required by this example // own arguments required by this example
gpt_params params; gpt_params params;
@@ -809,16 +912,34 @@ int main(int argc, char ** argv) {
Server svr; Server svr;
svr.set_default_headers({ svr.set_default_headers({
{ "Server", "llama.cpp" },
{ "Access-Control-Allow-Origin", "*" }, { "Access-Control-Allow-Origin", "*" },
{ "Access-Control-Allow-Headers", "content-type" } { "Access-Control-Allow-Headers", "content-type" }
}); });
// this is only called if no index.js is found in the public --path
svr.Get("/index.js", [](const Request &, Response & res) {
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
return false;
});
// this is only called if no index.html is found in the public --path
svr.Get("/", [](const Request &, Response & res) { svr.Get("/", [](const Request &, Response & res) {
res.set_content("<h1>llama.cpp server works</h1>", "text/html"); res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
return false;
});
// this is only called if no index.html is found in the public --path
svr.Get("/completion.js", [](const Request &, Response & res) {
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
return false;
}); });
svr.Post("/completion", [&llama](const Request & req, Response & res) { svr.Post("/completion", [&llama](const Request & req, Response & res) {
auto lock = llama.lock();
llama.rewind(); llama.rewind();
llama_reset_timings(llama.ctx); llama_reset_timings(llama.ctx);
parse_options_completion(json::parse(req.body), llama); parse_options_completion(json::parse(req.body), llama);
@@ -830,7 +951,8 @@ int main(int argc, char ** argv) {
size_t stop_pos = std::string::npos; size_t stop_pos = std::string::npos;
while (llama.has_next_token) { while (llama.has_next_token) {
const std::string token_text = llama.doCompletion(); const completion_token_output token_with_probs = llama.doCompletion();
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
stop_pos = llama.findStoppingStrings(llama.generated_text, stop_pos = llama.findStoppingStrings(llama.generated_text,
token_text.size(), STOP_FULL); token_text.size(), STOP_FULL);
@@ -844,7 +966,7 @@ int main(int argc, char ** argv) {
llama.generated_text.end()); llama.generated_text.end());
} }
const json data = format_final_response(llama, llama.generated_text); const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs);
llama_print_timings(llama.ctx); llama_print_timings(llama.ctx);
@@ -853,9 +975,11 @@ int main(int argc, char ** argv) {
} else { } else {
const auto chunked_content_provider = [&](size_t, DataSink & sink) { const auto chunked_content_provider = [&](size_t, DataSink & sink) {
size_t sent_count = 0; size_t sent_count = 0;
size_t sent_token_probs_index = 0;
while (llama.has_next_token) { while (llama.has_next_token) {
const std::string token_text = llama.doCompletion(); const completion_token_output token_with_probs = llama.doCompletion();
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
if (llama.multibyte_pending > 0) { if (llama.multibyte_pending > 0) {
continue; continue;
} }
@@ -878,10 +1002,22 @@ int main(int argc, char ** argv) {
const std::string to_send = llama.generated_text.substr(pos, stop_pos); const std::string to_send = llama.generated_text.substr(pos, stop_pos);
sent_count += to_send.size(); sent_count += to_send.size();
std::vector<completion_token_output> probs_output = {};
if (llama.params.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);
}
sent_token_probs_index = probs_stop_pos;
}
const json data = llama.has_next_token const json data = llama.has_next_token
? format_partial_response(to_send) ? format_partial_response(llama, to_send, probs_output)
// Generation is done, send extra information. // Generation is done, send extra information.
: format_final_response(llama, to_send); : format_final_response(llama, to_send, llama.generated_token_probs);
const std::string str = const std::string str =
"data: " + "data: " +
@@ -912,6 +1048,8 @@ int main(int argc, char ** argv) {
}); });
svr.Post("/tokenize", [&llama](const Request & req, Response & res) { svr.Post("/tokenize", [&llama](const Request & req, Response & res) {
auto lock = llama.lock();
const json body = json::parse(req.body); const json body = json::parse(req.body);
const std::string content = body.value("content", ""); const std::string content = body.value("content", "");
const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false); const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
@@ -920,6 +1058,8 @@ int main(int argc, char ** argv) {
}); });
svr.Post("/embedding", [&llama](const Request & req, Response & res) { svr.Post("/embedding", [&llama](const Request & req, Response & res) {
auto lock = llama.lock();
const json body = json::parse(req.body); const json body = json::parse(req.body);
llama.rewind(); llama.rewind();
@@ -950,18 +1090,27 @@ int main(int argc, char ** argv) {
res.status = 500; res.status = 500;
}); });
svr.set_error_handler([](const Request &, Response & res) {
res.set_content("File Not Found", "text/plain");
res.status = 404;
});
// set timeouts and change hostname and port // 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); svr.set_write_timeout(sparams.write_timeout);
if (!svr.bind_to_port(sparams.hostname, sparams.port)) { if (!svr.bind_to_port(sparams.hostname, sparams.port)) {
LOG_ERROR("couldn't bind to server socket", { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
{ "hostname", sparams.hostname },
{ "port", sparams.port },
});
return 1; return 1;
} }
// Set the base directory for serving static files
svr.set_base_dir(sparams.public_path);
// to make it ctrl+clickable:
fprintf(stdout, "\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
LOG_INFO("HTTP server listening", { LOG_INFO("HTTP server listening", {
{ "hostname", sparams.hostname }, { "hostname", sparams.hostname },
{ "port", sparams.port }, { "port", sparams.port },

2
ggml-cuda.cu
View File

@@ -2835,7 +2835,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
} }
void ggml_cuda_free_data(struct ggml_tensor * tensor) { void ggml_cuda_free_data(struct ggml_tensor * tensor) {
if (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) { if (!tensor || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
return; return;
} }

2
ggml-opencl.cpp
View File

@@ -1376,7 +1376,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
const int64_t ne00 = src0->ne[0]; const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1]; const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2]; const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[2]; const int64_t ne03 = src0->ne[3];
const int64_t ne0 = ne00 * ne01 * ne02 * ne03; const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
const int64_t ne10 = src1->ne[0]; const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1]; const int64_t ne11 = src1->ne[1];

1448
ggml.c
View File

File diff suppressed because it is too large Load Diff

118
ggml.h
View File

@@ -201,6 +201,8 @@
#define GGML_MAX_NAME 48 #define GGML_MAX_NAME 48
#define GGML_DEFAULT_N_THREADS 4 #define GGML_DEFAULT_N_THREADS 4
#define GGML_UNUSED(x) (void)(x)
#define GGML_ASSERT(x) \ #define GGML_ASSERT(x) \
do { \ do { \
if (!(x)) { \ if (!(x)) { \
@@ -209,6 +211,30 @@
} \ } \
} while (0) } while (0)
// used to copy the number of elements and stride in bytes of tensors into local variables.
// main purpose is to reduce code duplication and improve readability.
//
// example:
//
// GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
// GGML_TENSOR_LOCALS(size_t, nb1, src1, nb);
//
#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
const type prefix##0 = (pointer)->array[0]; \
GGML_UNUSED(prefix##0);
#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
GGML_TENSOR_LOCALS_1 (type, prefix, pointer, array) \
const type prefix##1 = (pointer)->array[1]; \
GGML_UNUSED(prefix##1);
#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
GGML_TENSOR_LOCALS_2 (type, prefix, pointer, array) \
const type prefix##2 = (pointer)->array[2]; \
GGML_UNUSED(prefix##2);
#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
GGML_TENSOR_LOCALS_3 (type, prefix, pointer, array) \
const type prefix##3 = (pointer)->array[3]; \
GGML_UNUSED(prefix##3);
#ifdef __cplusplus #ifdef __cplusplus
extern "C" { extern "C" {
#endif #endif
@@ -295,12 +321,15 @@ extern "C" {
GGML_OP_SUM, GGML_OP_SUM,
GGML_OP_SUM_ROWS, GGML_OP_SUM_ROWS,
GGML_OP_MEAN, GGML_OP_MEAN,
GGML_OP_ARGMAX,
GGML_OP_REPEAT, GGML_OP_REPEAT,
GGML_OP_REPEAT_BACK, GGML_OP_REPEAT_BACK,
GGML_OP_ABS, GGML_OP_ABS,
GGML_OP_SGN, GGML_OP_SGN,
GGML_OP_NEG, GGML_OP_NEG,
GGML_OP_STEP, GGML_OP_STEP,
GGML_OP_TANH,
GGML_OP_ELU,
GGML_OP_RELU, GGML_OP_RELU,
GGML_OP_GELU, GGML_OP_GELU,
GGML_OP_GELU_QUICK, GGML_OP_GELU_QUICK,
@@ -332,9 +361,8 @@ extern "C" {
GGML_OP_ROPE_BACK, GGML_OP_ROPE_BACK,
GGML_OP_ALIBI, GGML_OP_ALIBI,
GGML_OP_CLAMP, GGML_OP_CLAMP,
GGML_OP_CONV_1D_S1_PH, GGML_OP_CONV_1D,
GGML_OP_CONV_1D_S2_PH, GGML_OP_CONV_2D,
GGML_OP_CONV_2D_SK_P0,
GGML_OP_FLASH_ATTN, GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF, GGML_OP_FLASH_FF,
@@ -690,6 +718,11 @@ extern "C" {
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a); struct ggml_tensor * a);
// argmax along rows
GGML_API struct ggml_tensor * ggml_argmax(
struct ggml_context * ctx,
struct ggml_tensor * a);
// if a is the same shape as b, and a is not parameter, return a // if a is the same shape as b, and a is not parameter, return a
// otherwise, return a new tensor: repeat(a) to fit in b // otherwise, return a new tensor: repeat(a) to fit in b
GGML_API struct ggml_tensor * ggml_repeat( GGML_API struct ggml_tensor * ggml_repeat(
@@ -734,6 +767,22 @@ extern "C" {
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a); struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_tanh(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_tanh_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_elu(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_elu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_relu( GGML_API struct ggml_tensor * ggml_relu(
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a); struct ggml_tensor * a);
@@ -1084,58 +1133,33 @@ extern "C" {
float min, float min,
float max); float max);
// TODO: implement general-purpose convolutions GGML_API struct ggml_tensor * ggml_conv_1d(
// GGML_API struct ggml_tensor * ggml_conv_1d(
// struct ggml_context * ctx,
// struct ggml_tensor * a,
// struct ggml_tensor * b,
// int s0
// int p0,
// int d0);
//
// GGML_API struct ggml_tensor * ggml_conv_2d(
// struct ggml_context * ctx,
// struct ggml_tensor * a,
// struct ggml_tensor * b,
// int s0,
// int s1,
// int p0,
// int p1,
// int d0,
// int d1);
// padding = half
// TODO: we don't support extra parameters for now
// that's why we are hard-coding the stride, padding, and dilation
// not great ..
// example:
// a: 3 80 768 1
// b: 3000 80 1 1
// res: 3000 768 1 1
// used in whisper
GGML_API struct ggml_tensor * ggml_conv_1d_s1_ph(
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a, struct ggml_tensor * a,
struct ggml_tensor * b); struct ggml_tensor * b,
int s0, // stride
int p0, // padding
int d0); // dilation
// used in whisper GGML_API struct ggml_tensor * ggml_conv_2d(
GGML_API struct ggml_tensor * ggml_conv_1d_s2_ph(
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a, struct ggml_tensor * a,
struct ggml_tensor * b); struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1);
// kernel size is a->ne[0] x a->ne[1] // conv_1d with padding = half
// stride is equal to kernel size // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
// padding is zero GGML_API struct ggml_tensor* ggml_conv_1d_ph(
// example:
// a: 16 16 3 768
// b: 1024 1024 3 1
// res: 64 64 768 1
// used in sam
GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a, struct ggml_tensor * a,
struct ggml_tensor * b); struct ggml_tensor * b,
int s,
int d);
GGML_API struct ggml_tensor * ggml_flash_attn( GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx, struct ggml_context * ctx,

8
llama.cpp
View File

@@ -194,8 +194,8 @@ struct llama_layer {
}; };
struct llama_kv_cache { struct llama_kv_cache {
struct ggml_tensor * k; struct ggml_tensor * k = NULL;
struct ggml_tensor * v; struct ggml_tensor * v = NULL;
struct ggml_context * ctx = NULL; struct ggml_context * ctx = NULL;
@@ -481,9 +481,7 @@ struct llama_file_loader {
std::string word = file.read_string(len); std::string word = file.read_string(len);
float score = 0.0f; float score = 0.0f;
if (file_version >= LLAMA_FILE_VERSION_GGMF_V1) { file.read_raw(&score, sizeof(score));
file.read_raw(&score, sizeof(score));
}
vocab.token_to_id[word] = i; vocab.token_to_id[word] = i;

11
scripts/sync-ggml.sh
View File

@@ -1,6 +1,11 @@
#!/bin/bash #!/bin/bash
cp -rpv ../ggml/src/ggml.c ./ggml.c cp -rpv ../ggml/src/ggml.c ./ggml.c
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h
cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h
cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp
cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h
cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m
cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h