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				https://github.com/ggml-org/llama.cpp.git
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	* implementing parallel decoding in server example * crash fixed * save dev progress * refactored sampling function * completion endpoint working * multiple client support * grammar + no stream completion * cached prompt support * chat.mjs support cached prompt + some fixes * server ui now support multiple clients * unused change reverted * fixed timings per slot * add context swap * add changes to README.md * llava multimodal integration * fixed tokens probs * add multimodal input - alfa * refactor code + remove unused comments + improved README.md * fix compilation errors with llvm * notify the user from server ui that multimodality is unavialable * some ci fixes * fix ci make build undefined ref errors * fix long prompt than ctx proposed in #3639 * fixed premature end due stop word * context shift fixed * fix llava implementation * sync README.md changes * readme change * update api like OpenAI * multimodal support enabled by default * fix make bui;d errors * fix multiple clients * fix zig build * new sampling API * latest changes of sampling API * server : coding-style normalization * server : coding-style normalization (part 2) * server : remove beam-search functionality * server : bug fix in ingest_images n_tokens is incremented internally by llama_batch_add * server : use refs + use llama_batch_clear() * server : snake case * server : minor sync * added thread safe pipeline * server : bach has to be allocated for n_parallel sequences * server : no need for atomic int - already using mutex * server : logs + minor code style * server : fix multibyte handle in partial response (#3706) * fix image load + view image in chat * make : silence stb warnings * clip : link to ggml, not to llama * server : fix switch fallthrough * server : fix crash in Debug on macOS (I have no idea why this fixes it!?) * server : refactor ctx_sampling init + n_ctx + names * server : bug fix for prompt caching * Do not save/load image_data to localStorage * editorconfig : new line in index.html * server : completion requests remember slot_id * Update readme to document multimodal in server * server : minor style * Update readme to document multimodal in server * server : hide ctx_sampling->prev behind API (#3696) * server : apply fix from #3722 * server : fix slot reuse * server : add comment about changing slot_state to bool --------- Co-authored-by: FSSRepo <go778sgt@gmail.com> Co-authored-by: Damian Stewart <d@damianstewart.com> Co-authored-by: Steward Garcia <57494570+FSSRepo@users.noreply.github.com> Co-authored-by: Jhen-Jie Hong <iainst0409@gmail.com> Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
		
			
				
	
	
		
			224 lines
		
	
	
		
			9.7 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
			
		
		
	
	
			224 lines
		
	
	
		
			9.7 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
#!/usr/bin/env python3
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import argparse
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from flask import Flask, jsonify, request, Response
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import urllib.parse
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import requests
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import time
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import json
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app = Flask(__name__)
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slot_id = -1
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parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
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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')
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parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
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parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
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parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
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parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
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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')
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parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
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parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
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parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
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args = parser.parse_args()
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def is_present(json, key):
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    try:
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        buf = json[key]
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    except KeyError:
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        return False
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    if json[key] == None:
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        return False
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    return True
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#convert chat to prompt
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def convert_chat(messages):
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    prompt = "" + args.chat_prompt.replace("\\n", "\n")
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    system_n = args.system_name.replace("\\n", "\n")
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    user_n = args.user_name.replace("\\n", "\n")
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    ai_n = args.ai_name.replace("\\n", "\n")
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    stop = args.stop.replace("\\n", "\n")
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    for line in messages:
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        if (line["role"] == "system"):
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            prompt += f"{system_n}{line['content']}"
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        if (line["role"] == "user"):
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            prompt += f"{user_n}{line['content']}"
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        if (line["role"] == "assistant"):
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            prompt += f"{ai_n}{line['content']}{stop}"
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    prompt += ai_n.rstrip()
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    return prompt
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def make_postData(body, chat=False, stream=False):
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    postData = {}
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    if (chat):
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        postData["prompt"] = convert_chat(body["messages"])
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    else:
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        postData["prompt"] = body["prompt"]
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    if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
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    if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
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    if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
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    if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
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    if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
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    if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
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    if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
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    if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
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    if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
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    if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
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    if(is_present(body, "seed")): postData["seed"] = body["seed"]
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    if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
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    if (args.stop != ""):
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        postData["stop"] = [args.stop]
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    else:
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        postData["stop"] = []
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    if(is_present(body, "stop")): postData["stop"] += body["stop"]
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    postData["n_keep"] = -1
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    postData["stream"] = stream
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    postData["cache_prompt"] = True
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    postData["slot_id"] = slot_id
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    return postData
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def make_resData(data, chat=False, promptToken=[]):
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    resData = {
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        "id": "chatcmpl" if (chat) else "cmpl",
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        "object": "chat.completion" if (chat) else "text_completion",
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        "created": int(time.time()),
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        "truncated": data["truncated"],
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        "model": "LLaMA_CPP",
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        "usage": {
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            "prompt_tokens": data["tokens_evaluated"],
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            "completion_tokens": data["tokens_predicted"],
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            "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
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        }
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    }
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    if (len(promptToken) != 0):
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        resData["promptToken"] = promptToken
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    if (chat):
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        #only one choice is supported
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        resData["choices"] = [{
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            "index": 0,
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            "message": {
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                "role": "assistant",
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                "content": data["content"],
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            },
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            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
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        }]
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    else:
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        #only one choice is supported
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        resData["choices"] = [{
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            "text": data["content"],
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            "index": 0,
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            "logprobs": None,
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            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
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        }]
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    return resData
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def make_resData_stream(data, chat=False, time_now = 0, start=False):
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    resData = {
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        "id": "chatcmpl" if (chat) else "cmpl",
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        "object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
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        "created": time_now,
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        "model": "LLaMA_CPP",
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        "choices": [
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            {
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                "finish_reason": None,
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                "index": 0
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            }
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        ]
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    }
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    slot_id = data["slot_id"]
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    if (chat):
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        if (start):
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            resData["choices"][0]["delta"] =  {
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                "role": "assistant"
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            }
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        else:
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            resData["choices"][0]["delta"] =  {
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                "content": data["content"]
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            }
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            if (data["stop"]):
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                resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
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    else:
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        resData["choices"][0]["text"] = data["content"]
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        if (data["stop"]):
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            resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
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    return resData
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@app.route('/chat/completions', methods=['POST'])
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@app.route('/v1/chat/completions', methods=['POST'])
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def chat_completions():
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    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
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        return Response(status=403)
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    body = request.get_json()
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    stream = False
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    tokenize = False
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    if(is_present(body, "stream")): stream = body["stream"]
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    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
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    postData = make_postData(body, chat=True, stream=stream)
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    promptToken = []
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    if (tokenize):
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        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
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        promptToken = tokenData["tokens"]
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    if (not stream):
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        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
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        print(data.json())
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        resData = make_resData(data.json(), chat=True, promptToken=promptToken)
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        return jsonify(resData)
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    else:
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        def generate():
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            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
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            time_now = int(time.time())
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            resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
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            yield 'data: {}\n'.format(json.dumps(resData))
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            for line in data.iter_lines():
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                if line:
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                    decoded_line = line.decode('utf-8')
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                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
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                    yield 'data: {}\n'.format(json.dumps(resData))
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        return Response(generate(), mimetype='text/event-stream')
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@app.route('/completions', methods=['POST'])
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@app.route('/v1/completions', methods=['POST'])
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def completion():
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    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
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        return Response(status=403)
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    body = request.get_json()
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    stream = False
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    tokenize = False
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    if(is_present(body, "stream")): stream = body["stream"]
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    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
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    postData = make_postData(body, chat=False, stream=stream)
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    promptToken = []
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    if (tokenize):
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        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
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        promptToken = tokenData["tokens"]
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    if (not stream):
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        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
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        print(data.json())
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        resData = make_resData(data.json(), chat=False, promptToken=promptToken)
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        return jsonify(resData)
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    else:
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        def generate():
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            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
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            time_now = int(time.time())
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            for line in data.iter_lines():
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                if line:
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                    decoded_line = line.decode('utf-8')
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                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
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                    yield 'data: {}\n'.format(json.dumps(resData))
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        return Response(generate(), mimetype='text/event-stream')
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if __name__ == '__main__':
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    app.run(args.host, port=args.port)
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