mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-01 00:39:00 +01:00
Merge branch 'master' into HEAD
This commit is contained in:
Georgi Gerganov
commit
8e9af803ba
2
.github/workflows/build.yml
vendored
2
.github/workflows/build.yml
vendored
@ -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
|
||||
|
||||
|
||||
@ -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" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_SERVER "llama: build server example" OFF)
|
||||
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
|
||||
|
||||
#
|
||||
# Build info header
|
||||
|
||||
14
Makefile
14
Makefile
@ -1,11 +1,5 @@
|
||||
# Define the default target now so that it is always the first target
|
||||
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test
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||||
|
||||
ifdef LLAMA_BUILD_SERVER
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||||
BUILD_TARGETS += server
|
||||
LLAMA_SERVER_VERBOSE ?= 1
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server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
|
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endif
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||||
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test
|
||||
|
||||
default: $(BUILD_TARGETS)
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||||
|
||||
@ -61,6 +55,10 @@ else
|
||||
CXXFLAGS += -DNDEBUG
|
||||
endif
|
||||
|
||||
ifdef LLAMA_SERVER_VERBOSE
|
||||
CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
|
||||
endif
|
||||
|
||||
# warnings
|
||||
CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
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||||
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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
219
examples/server/api_like_OAI.py
Executable 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)
|
||||
193
examples/server/completion.js.hpp
Normal file
193
examples/server/completion.js.hpp
Normal file
@ -0,0 +1,193 @@
|
||||
unsigned char completion_js[] = {
|
||||
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|
||||
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|
||||
0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72,
|
||||
0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64,
|
||||
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|
||||
0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a,
|
||||
0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70,
|
||||
0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x5d, 0x0a, 0x7d,
|
||||
0x3b, 0x0a, 0x0a, 0x2f, 0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x54, 0x68,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x20,
|
||||
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|
||||
0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x2d, 0x20, 0x61, 0x6e,
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|
||||
};
|
||||
unsigned int completion_js_len = 2275;
|
||||
22
examples/server/deps.sh
Executable file
22
examples/server/deps.sh
Executable 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
846
examples/server/index.html.hpp
Normal file
@ -0,0 +1,846 @@
|
||||
unsigned char index_html[] = {
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|
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0x6d, 0x6c, 0x3e, 0x0a
|
||||
};
|
||||
unsigned int index_html_len = 10108;
|
||||
1851
examples/server/index.js.hpp
Normal file
1851
examples/server/index.js.hpp
Normal file
File diff suppressed because it is too large
Load Diff
81
examples/server/public/completion.js
Normal file
81
examples/server/public/completion.js
Normal file
@ -0,0 +1,81 @@
|
||||
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;
|
||||
}
|
||||
359
examples/server/public/index.html
Normal file
359
examples/server/public/index.html
Normal file
@ -0,0 +1,359 @@
|
||||
<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
1
examples/server/public/index.js
Normal file
File diff suppressed because one or more lines are too long
@ -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 },
|
||||
|
||||
@ -2835,7 +2835,7 @@ void ggml_cuda_transform_tensor(void * 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;
|
||||
}
|
||||
|
||||
|
||||
@ -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];
|
||||
|
||||
118
ggml.h
118
ggml.h
@ -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,
|
||||
|
||||
@ -194,8 +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;
|
||||
|
||||
@ -481,9 +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;
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user