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19 Commits

Author SHA1 Message Date
Howard Su
051c70dcd5 llama: Don't double count the sampling time (#2107) 2023-07-05 18:31:23 +08:00
Johannes Gäßler
9e4475f5cf Fixed OpenCL offloading prints (#2082) 2023-07-05 08:58:05 +02:00
Nigel Bosch
7f0e9a775e embd-input: Fix input embedding example unsigned int seed (#2105) 2023-07-05 07:33:33 +08:00
Georgi Gerganov
b472f3fca5 readme : add link web chat PR 2023-07-04 22:25:22 +03:00
Georgi Gerganov
ed9a54e512 ggml : sync latest (new ops, macros, refactoring) (#2106)
- add ggml_argmax()
- add ggml_tanh()
- add ggml_elu()
- refactor ggml_conv_1d() and variants
- refactor ggml_conv_2d() and variants
- add helper macros to reduce code duplication in ggml.c
2023-07-04 21:54:11 +03:00
jwj7140
f257fd2550 Add an API example using server.cpp similar to OAI. (#2009)
* add api_like_OAI.py
* add evaluated token count to server
* add /v1/ endpoints binding
2023-07-04 21:06:12 +03:00
Tobias Lütke
7ee76e45af Simple webchat for server (#1998)
* expose simple web interface on root domain

* embed index and add --path for choosing static dir

* allow server to multithread

because web browsers send a lot of garbage requests we want the server
to multithread when serving 404s for favicon's etc. To avoid blowing up
llama we just take a mutex when it's invoked.


* let's try this with the xxd tool instead and see if msvc is happier with that

* enable server in Makefiles

* add /completion.js file to make it easy to use the server from js

* slightly nicer css

* rework state management into session, expose historyTemplate to settings

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-04 16:05:27 +02:00
Henri Vasserman
acc111caf9 Allow old Make to build server. (#2098)
Also make server build by default.

Tested with Make 3.82
2023-07-04 15:38:04 +03:00
ZhouYuChen
23c7c6fc91 Update Makefile: clean simple (#2097) 2023-07-04 14:15:16 +02:00
Erik Scholz
698efad5fb CI: make the brew update temporarily optional. (#2092)
until they decide to fix the brew installation in the macos runners.
see the open issues. eg https://github.com/actions/runner-images/pull/7710
2023-07-04 01:50:12 +02:00
Govlzkoy
14a2cc71f6 [ggml] fix index for ne03 value in ggml_cl_mul_f32 (#2088) 2023-07-04 07:50:00 +08:00
Henri Vasserman
1cf14ccef1 fix server crashes (#2076) 2023-07-04 00:05:23 +03:00
Howard Su
cc45a7feb8 Fix crash of test-tokenizer-0 under Debug build (#2064)
* Fix crash of test-tokenizer-0 under Debug build

* Change per comment
2023-07-03 20:43:55 +02:00
Howard Su
55dbb915cc [llama] No need to check file version when loading vocab score (#2079) 2023-07-03 19:58:58 +08:00
WangHaoranRobin
d7d2e6a0f0 server: add option to output probabilities for completion (#1962)
* server: add option to output probabilities for completion
* server: fix issue when handling probability output for incomplete tokens for multibyte character generation
* server: fix llama_sample_top_k order
* examples/common.h: put all bool variables in gpt_params together
2023-07-03 00:38:44 +03:00
Georgi Gerganov
46088f7231 ggml : fix build with OpenBLAS (close #2066) 2023-07-02 09:46:46 +03:00
Johannes Gäßler
0bc2cdfc87 Better CUDA synchronization logic (#2057) 2023-07-01 21:49:44 +02:00
Johannes Gäßler
befb3a3562 Test-based VRAM scratch size + context adjustment (#2056) 2023-07-01 21:47:26 +02:00
Daniel Drake
b213227067 cmake : don't force -mcpu=native on aarch64 (#2063)
It's currently not possible to cross-compile llama.cpp for aarch64
because CMakeLists.txt forces -mcpu=native for that target.

-mcpu=native doesn't make sense if your build host is not the
target architecture, and clang rejects it for that reason, aborting the
build. This can be easily reproduced using the current Android NDK to build
for aarch64 on an x86_64 host.

If there is not a specific CPU-tuning target for aarch64 then -mcpu
should be omitted completely. I think that makes sense, there is not
enough variance in the aarch64 instruction set to warrant a fixed -mcpu
optimization at this point. And if someone is building natively and wishes
to enable any possible optimizations for the host device, then there is
already the LLAMA_NATIVE option available.

Fixes #495.
2023-07-01 21:31:44 +03:00
23 changed files with 4501 additions and 1167 deletions

View File

@@ -111,6 +111,7 @@ jobs:
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update
@@ -129,6 +130,7 @@ jobs:
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update

View File

@@ -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_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" OFF)
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
#
# Build info header
@@ -386,11 +386,6 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES
if (MSVC)
# TODO: arm msvc?
else()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
add_compile_options(-mcpu=native)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
# Raspberry Pi 1, Zero
add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access)

View File

@@ -1,11 +1,5 @@
# 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
ifdef LLAMA_BUILD_SERVER
BUILD_TARGETS += server
LLAMA_SERVER_VERBOSE ?= 1
server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
endif
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test
default: $(BUILD_TARGETS)
@@ -61,6 +55,10 @@ else
CXXFLAGS += -DNDEBUG
endif
ifdef LLAMA_SERVER_VERBOSE
CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
endif
# warnings
CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
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)
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

View File

@@ -11,6 +11,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
**Hot topics:**
- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998
- k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001
- New roadmap: https://github.com/users/ggerganov/projects/7
- Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985

View File

@@ -31,7 +31,7 @@ struct gpt_params {
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
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
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_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 random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs

View File

@@ -29,7 +29,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);

View File

@@ -190,3 +190,19 @@ Run with bash:
```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
View File

@@ -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)

View File

@@ -0,0 +1,193 @@
unsigned char completion_js[] = {
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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

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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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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<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>

File diff suppressed because one or more lines are too long

View File

@@ -2,8 +2,6 @@
#include "llama.h"
#include "build-info.h"
// single thread
#define CPPHTTPLIB_THREAD_POOL_COUNT 1
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
@@ -12,6 +10,11 @@
#include "httplib.h"
#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
#define SERVER_VERBOSE 1
#endif
@@ -21,11 +24,23 @@ using json = nlohmann::json;
struct server_params {
std::string hostname = "127.0.0.1";
std::string public_path = "examples/server/public";
int32_t port = 8080;
int32_t read_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) {
size_t 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);
}
// 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;
#if SERVER_VERBOSE != 1
@@ -107,7 +156,9 @@ struct llama_server_context {
bool stream = false;
bool has_next_token = false;
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 n_past = 0;
size_t n_remain = 0;
@@ -126,6 +177,12 @@ struct llama_server_context {
std::string stopping_word;
int32_t multibyte_pending = 0;
std::mutex mutex;
std::unique_lock<std::mutex> lock() {
return std::unique_lock<std::mutex>(mutex);
}
~llama_server_context() {
if (ctx) {
llama_free(ctx);
@@ -139,9 +196,11 @@ struct llama_server_context {
void rewind() {
params.antiprompt.clear();
num_prompt_tokens = 0;
num_tokens_predicted = 0;
generated_text = "";
generated_text.reserve(params.n_ctx);
generated_token_probs.clear();
truncated = false;
stopped_eos = false;
stopped_word = false;
@@ -169,17 +228,18 @@ struct llama_server_context {
void loadPrompt() {
params.prompt.insert(0, 1, ' '); // always add a first space
std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
num_prompt_tokens = prompt_tokens.size();
if (params.n_keep < 0) {
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);
// 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;
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());
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;
prompt_tokens = new_tokens;
} 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::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
n_past = common_part(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.
n_past--;
}
@@ -221,8 +281,9 @@ struct llama_server_context {
llama_set_rng_seed(ctx, params.seed);
}
llama_token nextToken() {
llama_token result = -1;
completion_token_output nextToken() {
completion_token_output result;
result.tok = -1;
if (embd.size() >= (size_t)params.n_ctx) {
// Reset context
@@ -261,7 +322,8 @@ struct llama_server_context {
if (params.n_predict == 0) {
has_next_token = false;
return llama_token_eos();
result.tok = llama_token_eos();
return result;
}
// 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_eta = params.mirostat_eta;
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);
@@ -312,35 +374,42 @@ struct llama_server_context {
if (temp <= 0) {
// 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 {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
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) {
static float mirostat_mu = 2.0f * mirostat_tau;
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 {
// Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
size_t min_keep = std::max(1, n_probs);
llama_sample_top_k(ctx, &candidates_p, top_k, min_keep);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep);
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);
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.push_back(id);
last_n_tokens.push_back(result.tok);
num_tokens_predicted++;
}
// add it to the context
embd.push_back(id);
result = id;
embd.push_back(result.tok);
// decrement remaining sampling budget
--n_remain;
@@ -382,12 +451,16 @@ struct llama_server_context {
return stop_pos;
}
std::string doCompletion() {
const llama_token token = nextToken();
completion_token_output doCompletion() {
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;
if (params.n_probs > 0) {
generated_token_probs.push_back(token_with_probs);
}
if (multibyte_pending > 0) {
multibyte_pending -= token_text.size();
} else if (token_text.size() == 1) {
@@ -416,8 +489,8 @@ struct llama_server_context {
}
LOG_VERBOSE("next token", {
{ "token", token },
{ "token_text", llama_token_to_str(ctx, 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 },
@@ -427,7 +500,7 @@ struct llama_server_context {
{ "stopping_word", stopping_word },
});
return token_text;
return token_with_probs;
}
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, " --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, " --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, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
fprintf(stderr, "\n");
@@ -505,6 +579,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
break;
}
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") {
if (++i >= argc) {
invalid_param = true;
@@ -669,6 +749,7 @@ static json format_generation_settings(llama_server_context & llama) {
{ "ignore_eos", ignore_eos },
{ "stream", llama.stream },
{ "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) {
return json {
static json format_final_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
json res = json {
{ "content", content },
{ "stop", true },
{ "model", llama.params.model_alias },
{ "tokens_predicted", llama.num_tokens_predicted },
{ "tokens_evaluated", llama.num_prompt_tokens },
{ "generation_settings", format_generation_settings(llama) },
{ "prompt", llama.params.prompt },
{ "truncated", llama.truncated },
@@ -692,13 +775,25 @@ static json format_final_response(llama_server_context & llama, const std::strin
{ "stopped_limit", llama.stopped_limit },
{ "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) {
return json {
static json format_partial_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
json res = json {
{ "content", content },
{ "stop", false },
};
if (llama.params.n_probs > 0) {
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
}
return res;
}
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.seed = body.value("seed", default_params.seed);
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();
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));
}
static void log_server_request(const Request & req, const Response & res) {
LOG_INFO("request", {
{ "remote_addr", req.remote_addr },
{ "remote_port", req.remote_port },
{ "status", res.status },
{ "method", req.method },
{ "path", req.path },
{ "params", req.params },
});
LOG_VERBOSE("request", {
{ "request", req.body },
{ "response", res.body },
});
}
int main(int argc, char ** argv) {
// own arguments required by this example
gpt_params params;
@@ -809,16 +912,34 @@ int main(int argc, char ** argv) {
Server svr;
svr.set_default_headers({
{ "Server", "llama.cpp" },
{ "Access-Control-Allow-Origin", "*" },
{ "Access-Control-Allow-Headers", "content-type" }
});
// this is only called if no index.js is found in the public --path
svr.Get("/index.js", [](const Request &, Response & res) {
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
return false;
});
// this is only called if no index.html is found in the public --path
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) {
auto lock = llama.lock();
llama.rewind();
llama_reset_timings(llama.ctx);
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;
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,
token_text.size(), STOP_FULL);
@@ -844,7 +966,7 @@ int main(int argc, char ** argv) {
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);
@@ -853,9 +975,11 @@ int main(int argc, char ** argv) {
} else {
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 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) {
continue;
}
@@ -878,10 +1002,22 @@ int main(int argc, char ** argv) {
const std::string to_send = llama.generated_text.substr(pos, stop_pos);
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
? format_partial_response(to_send)
? format_partial_response(llama, to_send, probs_output)
// 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 =
"data: " +
@@ -912,6 +1048,8 @@ int main(int argc, char ** argv) {
});
svr.Post("/tokenize", [&llama](const Request & req, Response & res) {
auto lock = llama.lock();
const json body = json::parse(req.body);
const std::string content = body.value("content", "");
const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
@@ -920,6 +1058,8 @@ int main(int argc, char ** argv) {
});
svr.Post("/embedding", [&llama](const Request & req, Response & res) {
auto lock = llama.lock();
const json body = json::parse(req.body);
llama.rewind();
@@ -950,18 +1090,27 @@ int main(int argc, char ** argv) {
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
svr.set_read_timeout(sparams.read_timeout);
svr.set_write_timeout(sparams.write_timeout);
if (!svr.bind_to_port(sparams.hostname, sparams.port)) {
LOG_ERROR("couldn't bind to server socket", {
{ "hostname", sparams.hostname },
{ "port", sparams.port },
});
fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
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", {
{ "hostname", sparams.hostname },
{ "port", sparams.port },

View File

@@ -214,6 +214,11 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
#endif
struct ggml_tensor_extra_gpu {
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
cudaEvent_t events[GGML_CUDA_MAX_DEVICES]; // events for synchronizing multiple GPUs
};
static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
@@ -1970,7 +1975,6 @@ inline void ggml_cuda_op_add(
} else {
GGML_ASSERT(false);
}
CUDA_CHECK(cudaGetLastError());
(void) src1;
(void) dst;
@@ -2002,7 +2006,6 @@ inline void ggml_cuda_op_mul(
// compute
mul_f32_cuda(src0_ddf_i01, src1_ddf_i01, dst_ddf_i01, ne00, ne10, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
}
(void) dst;
@@ -2023,7 +2026,6 @@ inline void ggml_cuda_op_silu(
// compute
silu_f32_cuda(src0_ddf_i, dst_ddf_i, ne00*i01_diff, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) src1;
(void) dst;
@@ -2046,7 +2048,6 @@ inline void ggml_cuda_op_rms_norm(
// compute
rms_norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) src1;
(void) dst;
@@ -2125,7 +2126,6 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec(
GGML_ASSERT(false);
break;
}
CUDA_CHECK(cudaGetLastError());
#ifdef GGML_CUDA_DMMV_F16
if (src1_convert_f16) {
@@ -2202,7 +2202,6 @@ inline void ggml_cuda_op_rope(
// compute
rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) dst;
(void) src0_ddq_i;
@@ -2226,7 +2225,6 @@ inline void ggml_cuda_op_diag_mask_inf(
// compute
diag_mask_inf_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_past, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) dst;
(void) src0_ddq_i;
@@ -2248,7 +2246,6 @@ inline void ggml_cuda_op_soft_max(
// compute
soft_max_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) src1;
(void) dst;
@@ -2344,10 +2341,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0};
size_t dst_asf[GGML_CUDA_MAX_DEVICES] = {0};
// if multiple GPUs are used they need to wait for the main GPU to finish
// if multiple devices are used they need to wait for the main device
// here an event is recorded that signifies that the main device has finished calculating the input data
if (split && g_device_count > 1) {
CUDA_CHECK(cudaSetDevice(g_main_device));
CUDA_CHECK(cudaDeviceSynchronize());
CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device], g_cudaStreams_main[g_main_device]));
}
for (int id = 0; id < g_device_count; ++id) {
@@ -2373,6 +2371,12 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
int64_t row_diff = row_high - row_low;
cudaSetDevice(id);
cudaStream_t cudaStream_main = g_cudaStreams_main[id];
// wait for main GPU data if necessary
if (split && id != g_main_device) {
CUDA_CHECK(cudaStreamWaitEvent(cudaStream_main, src0_extra->events[g_main_device]));
}
if (src0_on_device && src0_is_contiguous) {
if (src0_is_f32) {
@@ -2448,8 +2452,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
}
const int64_t i11 = i13*ne12 + i12;
cudaStream_t cudaStream_main = g_cudaStreams_main[id];
// for split tensors the data begins at i0 == i0_offset_low
char * src0_ddq_i = src0_ddq[id] + (i0 - i0_offset_low)*src0_stride*src0_ts/src0_bs;
float * src0_ddf_i = src0_ddf[id] + (i0 - i0_offset_low)*src0_stride;
@@ -2509,6 +2511,7 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
// do the computation
op(src0, src1, dst, src0_ddq_i, src0_ddf_i, src1_ddf_i, dst_ddf_i, i02, i01_low, i01_high, i11, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
// copy dst to host or other device if necessary
if (!dst_on_device) {
@@ -2538,6 +2541,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i, dst_stride*sizeof(float), kind, cudaStream_main));
}
}
// signify to main device that other device is done
if (split && g_device_count > 1 && id != g_main_device) {
CUDA_CHECK(cudaEventRecord(src0_extra->events[id], cudaStream_main));
}
}
}
}
@@ -2549,7 +2557,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
}
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaDeviceSynchronize());
if (src0_asq[id] > 0) {
ggml_cuda_pool_free(src0_ddq[id], src0_asq[id]);
@@ -2564,6 +2571,21 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
ggml_cuda_pool_free(dst_ddf[id], dst_asf[id]);
}
}
// main device waits for all other devices to be finished
if (split && g_device_count > 1) {
CUDA_CHECK(cudaSetDevice(g_main_device));
for (int id = 0; id < g_device_count; ++id) {
if (id != g_main_device) {
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams_main[g_main_device], src0_extra->events[id]));
}
}
}
if (dst->backend == GGML_BACKEND_CPU) {
CUDA_CHECK(cudaSetDevice(g_main_device));
CUDA_CHECK(cudaDeviceSynchronize());
}
}
void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -2803,25 +2825,32 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
extra->data_device[id] = buf;
if (backend == GGML_BACKEND_GPU_SPLIT) {
CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id], cudaEventDisableTiming));
}
}
tensor->extra = extra;
}
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;
}
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
for (int id = 0; id < g_device_count; ++id) {
if (extra->data_device[id] == nullptr) {
continue;
if (extra->data_device[id] != nullptr) {
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaFree(extra->data_device[id]));
}
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaFree(extra->data_device[id]));
if (extra->events[id] != nullptr) {
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaEventDestroy(extra->events[id]));
}
}
delete extra;

View File

@@ -8,10 +8,6 @@ extern "C" {
#define GGML_CUDA_MAX_DEVICES 16
struct ggml_tensor_extra_gpu {
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
};
void ggml_init_cublas(void);
void ggml_cuda_set_tensor_split(const float * tensor_split);

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 ne01 = src0->ne[1];
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 ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];

1537
ggml.c

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_DEFAULT_N_THREADS 4
#define GGML_UNUSED(x) (void)(x)
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
@@ -209,6 +211,30 @@
} \
} 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
extern "C" {
#endif
@@ -295,12 +321,15 @@ extern "C" {
GGML_OP_SUM,
GGML_OP_SUM_ROWS,
GGML_OP_MEAN,
GGML_OP_ARGMAX,
GGML_OP_REPEAT,
GGML_OP_REPEAT_BACK,
GGML_OP_ABS,
GGML_OP_SGN,
GGML_OP_NEG,
GGML_OP_STEP,
GGML_OP_TANH,
GGML_OP_ELU,
GGML_OP_RELU,
GGML_OP_GELU,
GGML_OP_GELU_QUICK,
@@ -332,9 +361,8 @@ extern "C" {
GGML_OP_ROPE_BACK,
GGML_OP_ALIBI,
GGML_OP_CLAMP,
GGML_OP_CONV_1D_S1_PH,
GGML_OP_CONV_1D_S2_PH,
GGML_OP_CONV_2D_SK_P0,
GGML_OP_CONV_1D,
GGML_OP_CONV_2D,
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
@@ -690,6 +718,11 @@ extern "C" {
struct ggml_context * ctx,
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
// otherwise, return a new tensor: repeat(a) to fit in b
GGML_API struct ggml_tensor * ggml_repeat(
@@ -734,6 +767,22 @@ extern "C" {
struct ggml_context * ctx,
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(
struct ggml_context * ctx,
struct ggml_tensor * a);
@@ -1084,58 +1133,33 @@ extern "C" {
float min,
float max);
// TODO: implement general-purpose convolutions
// 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(
GGML_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
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_1d_s2_ph(
GGML_API struct ggml_tensor * ggml_conv_2d(
struct ggml_context * ctx,
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]
// stride is equal to kernel size
// padding is zero
// 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(
// conv_1d with padding = half
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * b,
int s,
int d);
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,

View File

@@ -66,6 +66,7 @@ enum e_model {
MODEL_65B,
};
static const size_t kB = 1024;
static const size_t MB = 1024*1024;
// computed for n_ctx == 2048
@@ -129,6 +130,34 @@ static const std::map<e_model, size_t> & MEM_REQ_EVAL()
return k_sizes;
}
// amount of VRAM needed per batch size to hold temporary results
// the values for 3b and 65b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 512ull * kB },
{ MODEL_7B, 512ull * kB },
{ MODEL_13B, 640ull * kB },
{ MODEL_30B, 768ull * kB },
{ MODEL_65B, 1536ull * kB },
};
return k_sizes;
}
// amount of VRAM needed per batch size and context to hold temporary results
// the values for 3b and 65b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 128ull },
{ MODEL_7B, 128ull },
{ MODEL_13B, 160ull },
{ MODEL_30B, 208ull },
{ MODEL_65B, 416ull },
};
return k_sizes;
}
// default hparams (LLaMA 7B)
struct llama_hparams {
uint32_t n_vocab = 32000;
@@ -165,8 +194,8 @@ struct llama_layer {
};
struct llama_kv_cache {
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_tensor * k = NULL;
struct ggml_tensor * v = NULL;
struct ggml_context * ctx = NULL;
@@ -452,9 +481,7 @@ struct llama_file_loader {
std::string word = file.read_string(len);
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;
@@ -1118,14 +1145,18 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
ggml_cuda_set_scratch_size(0); // disable scratch
} else {
vram_scratch = n_batch * MB;
const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type);
const size_t vram_scratch_per_context = VRAM_REQ_SCRATCH_PER_CONTEXT().at(model.type);
vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context);
ggml_cuda_set_scratch_size(vram_scratch);
if (n_gpu_layers > 0) {
fprintf(stderr, "%s: allocating batch_size x 1 MB = %zd MB VRAM for the scratch buffer\n",
__func__, vram_scratch / MB);
fprintf(stderr, "%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n",
__func__, vram_scratch_base / kB, vram_scratch_per_context,
(vram_scratch + MB - 1) / MB); // round up
}
}
#endif // GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
@@ -1134,6 +1165,10 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__);
}
size_t vram_kv_cache = 0;
#ifdef GGML_USE_CUBLAS
const int max_backend_supported_layers = hparams.n_layer + 3;
const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
if (n_gpu_layers > (int) hparams.n_layer + 1) {
if (low_vram) {
fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
@@ -1150,14 +1185,18 @@ static void llama_model_load_internal(
vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2;
}
}
const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
#elif defined(GGML_USE_CLBLAST)
const int max_backend_supported_layers = hparams.n_layer + 1;
const int max_offloadable_layers = hparams.n_layer + 1;
#endif // GGML_USE_CUBLAS
fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n",
__func__, std::min(n_gpu_layers, max_offloadable_layers), hparams.n_layer + 3);
__func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
fprintf(stderr, "%s: total VRAM used: %zu MB\n",
__func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
#else
(void) n_gpu_layers;
#endif
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
}
// populate `tensors_by_name`
@@ -1866,10 +1905,10 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can
return;
}
const int64_t t_start_sample_us = ggml_time_us();
llama_sample_softmax(ctx, candidates);
const int64_t t_start_sample_us = ggml_time_us();
// Compute the cumulative probabilities
float cum_sum = 0.0f;
size_t last_idx = candidates->size;
@@ -1898,9 +1937,8 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
return;
}
const int64_t t_start_sample_us = ggml_time_us();
llama_sample_softmax(nullptr, candidates);
const int64_t t_start_sample_us = ggml_time_us();
// Compute the first and second derivatives
std::vector<float> first_derivatives(candidates->size - 1);
@@ -1952,11 +1990,11 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
return;
}
const int64_t t_start_sample_us = ggml_time_us();
// Compute the softmax of logits and calculate entropy
llama_sample_softmax(nullptr, candidates);
const int64_t t_start_sample_us = ggml_time_us();
float entropy = 0.0f;
for (size_t i = 0; i < candidates->size; ++i) {
entropy += -candidates->data[i].p * logf(candidates->data[i].p);
@@ -2125,13 +2163,11 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
ctx->n_sample++;
}
return X;
}
llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
assert(ctx);
int64_t t_start_sample_us;
t_start_sample_us = ggml_time_us();
@@ -2146,13 +2182,14 @@ llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_tok
candidates->size = 1;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
// Normalize the probabilities of the remaining words
llama_sample_softmax(ctx, candidates);
// Sample the next word X from the remaining words
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
llama_token X = llama_sample_token(ctx, candidates);
t_start_sample_us = ggml_time_us();

View File

@@ -1,6 +1,11 @@
#!/bin/bash
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.c ./ggml.c
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