完整代码如下：# encoding: utf-8
import json
import time
import uuid
from threading import Thread

import torch
from flask import Flask, current_app, request, Blueprint, stream_with_context
from flask_cors import CORS
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.generation.utils import GenerationConfig
from transformers.generation.streamers import TextIteratorStreamer
from marshmallow import validate
from flasgger import APISpec, Schema, Swagger, fields
from apispec.ext.marshmallow import MarshmallowPlugin
from apispec_webframeworks.flask import FlaskPlugin

class Transformers():
def init(self, app=None, tokenizer=None, model=None):
self.chat = None
if app is not None:
self.init_app(app, tokenizer, model)

def init_app(self, app, tokenizer=None, model=None, chat=None):
    self.tokenizer = tokenizer
    self.model = model
    if chat is None:
        self.chat = model.chat

tfs = Transformers()
base_tfs = Transformers()

models_bp = Blueprint('Models', name, url_prefix='/v1/models')
chat_bp = Blueprint('Chat', name, url_prefix='/v1/chat')
completions_bp = Blueprint('Completions', name, url_prefix='/v1/completions')

def sse(line, field="data"):
return "{}: {}\n\n".format(
field, json.dumps(line, ensure_ascii=False) if isinstance(line, dict) else line)

def empty_cache():
if torch.backends.mps.is_available():
torch.mps.empty_cache()

def create_app():
app = Flask(name)
CORS(app)
app.register_blueprint(models_bp)
app.register_blueprint(chat_bp)
app.register_blueprint(completions_bp)

@app.after_request
def after_request(resp):
    empty_cache()
    return resp

# Init Swagger
spec = APISpec(
    title='My OpenAI api',
    version='0.0.1',
    openapi_version='3.0.2',
    plugins=[
        FlaskPlugin(),
        MarshmallowPlugin(),
    ],
)

bearer_scheme = {"type": "http", "scheme": "bearer"}
spec.components.security_scheme("bearer", bearer_scheme)
template = spec.to_flasgger(
    app,
    paths=[list_models, create_chat_completion, create_completion]
)

app.config['SWAGGER'] = {"openapi": "3.0.2"}
Swagger(app, template=template)

# Init transformers
#model_name = "./Baichuan2-13B-Chat-4bits"
#tokenizer = AutoTokenizer.from_pretrained(
#        model_name, use_fast=False, trust_remote_code=True)
#model = AutoModelForCausalLM.from_pretrained(
#        model_name, device_map="auto", trust_remote_code=True)
#model.generation_config = GenerationConfig.from_pretrained(model_name)
print("init model ...")
model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-13B-Chat", torch_dtype=torch.float16, trust_remote_code=True)
model = model.quantize(4).cuda()
model.generation_config = GenerationConfig.from_pretrained(
    "baichuan-inc/Baichuan2-13B-Chat"
)
tokenizer = AutoTokenizer.from_pretrained(
    "baichuan-inc/Baichuan2-13B-Chat",
    use_fast=False,
    trust_remote_code=True
)

tfs.init_app(app, tokenizer, model)
base_tfs.init_app(app, tokenizer, model)

return app

class ModelSchema(Schema):
id = fields.Str()
object = fields.Str(dump_default="model", metadata={"example": "model"})
created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
owned_by = fields.Str(dump_default="owner", metadata={"example": "owner"})

class ModelListSchema(Schema):
object = fields.Str(dump_default="list", metadata={"example": "list"})
data = fields.List(fields.Nested(ModelSchema), dump_default=[])

class ChatMessageSchema(Schema):
role = fields.Str(required=True, metadata={"example": "system"})
content = fields.Str(required=True, metadata={"example": "You are a helpful assistant."})

class CreateChatCompletionSchema(Schema):
model = fields.Str(required=True, metadata={"example": "gpt-3.5-turbo"})
messages = fields.List(
fields.Nested(ChatMessageSchema), required=True,
metadata={"example": [
ChatMessageSchema().dump({"role": "system", "content": "You are a helpful assistant."}),
ChatMessageSchema().dump({"role": "user", "content": "Hello!"})
]}
)
temperature = fields.Float(load_default=1.0, metadata={"example": 1.0})
top_p = fields.Float(load_default=1.0, metadata={"example": 1.0})
n = fields.Int(load_default=1, metadata={"example": 1})
max_tokens = fields.Int(load_default=None, metadata={"example": None})
stream = fields.Bool(load_default=False, example=False)
presence_penalty = fields.Float(load_default=0.0, example=0.0)
frequency_penalty = fields.Float(load_default=0.0, example=0.0)

class ChatCompletionChoiceSchema(Schema):
index = fields.Int(metadata={"example": 0})
message = fields.Nested(ChatMessageSchema, metadata={
"example": ChatMessageSchema().dump(
{"role": "assistant", "content": "\n\nHello there, how may I assist you today?"}
)})
finish_reason = fields.Str(
validate=validate.OneOf(["stop", "length", "content_filter", "function_call"]),
metadata={"example": "stop"})

class ChatCompletionSchema(Schema):
id = fields.Str(
dump_default=lambda: uuid.uuid4().hex,
metadata={"example": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7"})
object = fields.Constant("chat.completion")
created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
model = fields.Str(metadata={"example": "gpt-3.5-turbo"})
choices = fields.List(fields.Nested(ChatCompletionChoiceSchema))

class ChatDeltaSchema(Schema):
role = fields.Str(metadata={"example": "assistant"})
content = fields.Str(required=True, metadata={"example": "Hello"})

class ChatCompletionChunkChoiceSchema(Schema):
index = fields.Int(metadata={"example": 0})
delta = fields.Nested(ChatDeltaSchema, metadata={"example": ChatDeltaSchema().dump(
{"role": "assistant", "example": "Hello"})})
finish_reason = fields.Str(
validate=validate.OneOf(["stop", "length", "content_filter", "function_call"]),
metadata={"example": "stop"})

class ChatCompletionChunkShema(Schema):
id = fields.Str(
dump_default=lambda: uuid.uuid4().hex,
metadata={"example": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7"})
object = fields.Constant("chat.completion.chunk")
created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
model = fields.Str(metadata={"example": "gpt-3.5-turbo"})
choices = fields.List(fields.Nested(ChatCompletionChunkChoiceSchema))

class CreateCompletionSchema(Schema):
model = fields.Str(required=True, metadata={"example": "gpt-3.5-turbo"})
prompt = fields.Raw(metadata={"example": "Say this is a test"})
max_tokens = fields.Int(load_default=16, metadata={"example": 256})
temperature = fields.Float(load_default=1.0, metadata={"example": 1.0})
top_p = fields.Float(load_default=1.0, metadata={"example": 1.0})
n = fields.Int(load_default=1, metadata={"example": 1})
stream = fields.Bool(load_default=False, example=False)
logit_bias = fields.Dict(load_default=None, example={})
presence_penalty = fields.Float(load_default=0.0, example=0.0)
frequency_penalty = fields.Float(load_default=0.0, example=0.0)

class CompletionChoiceSchema(Schema):
index = fields.Int(load_default=0, metadata={"example": 0})
text = fields.Str(required=True, metadata={"example": "登鹳雀楼->王之涣\n夜雨寄北->"})
logprobs = fields.Dict(load_default=None, metadata={"example": {}})
finish_reason = fields.Str(
validate=validate.OneOf(["stop", "length", "content_filter", "function_call"]),
metadata={"example": "stop"})

class CompletionUsageSchema(Schema):
prompt_tokens = fields.Int(metadata={"example": 5})
completion_tokens = fields.Int(metadata={"example": 7})
total_tokens = fields.Int(metadata={"example": 12})

class CompletionSchema(Schema):
id = fields.Str(
dump_default=lambda: uuid.uuid4().hex,
metadata={"example": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7"})
object = fields.Constant("text_completion")
created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
model = fields.Str(metadata={"example": "gpt-3.5-turbo"})
choices = fields.List(fields.Nested(CompletionChoiceSchema))
usage = fields.Nested(CompletionUsageSchema)

@models_bp.route("")
def list_models():
"""
List models
---
get:
tags:
- Models
description: Lists the currently available models,
and provides basic information about each one such as the owner and availability.
security:
- bearer: []
responses:
200:
description: Models returned
content:
application/json:
schema: ModelListSchema
"""

model = ModelSchema().dump({"id": "gpt-3.5-turbo"})
return ModelListSchema().dump({"data": [model]})

@stream_with_context
def stream_chat_generate(messages):
delta = ChatDeltaSchema().dump(
{"role": "assistant"})
choice = ChatCompletionChunkChoiceSchema().dump(
{"index": 0, "delta": delta, "finish_reason": None})

yield sse(
    ChatCompletionChunkShema().dump({
        "model": "gpt-3.5-turbo",
        "choices": [choice]})
)

position = 0
for response in tfs.chat(
        tfs.tokenizer,
        messages,
        stream=True):
    content = response[position:]
    if not content:
        continue
    empty_cache()
    delta = ChatDeltaSchema().dump(
            {"content": content})
    choice = ChatCompletionChunkChoiceSchema().dump(
            {"index": 0, "delta": delta, "finish_reason": None})

    yield sse(
        ChatCompletionChunkShema().dump({
            "model": "gpt-3.5-turbo",
            "choices": [choice]})
    )
    position = len(response)

choice = ChatCompletionChunkChoiceSchema().dump(
        {"index": 0, "delta": {}, "finish_reason": "stop"})

yield sse(
    ChatCompletionChunkShema().dump({
        "model": "gpt-3.5-turbo",
        "choices": [choice]})
)

yield sse('[DONE]')

@chat_bp.route("/completions", methods=['POST'])
def create_chat_completion():
"""Create chat completion
---
post:
tags:
- Chat
description: Creates a model response for the given chat conversation.
requestBody:
request: True
content:
application/json:
schema: CreateChatCompletionSchema
security:
- bearer: []
responses:
200:
description: ChatCompletion return
content:
application/json:
schema:
oneOf:
- ChatCompletionSchema
- ChatCompletionChunkShema
"""

create_chat_completion = CreateChatCompletionSchema().load(request.json)

if create_chat_completion["stream"]:
    return current_app.response_class(
        stream_chat_generate(create_chat_completion["messages"]),
        mimetype="text/event-stream"
    )
else:
    response = tfs.chat(tfs.tokenizer, create_chat_completion["messages"])

    message = ChatMessageSchema().dump(
            {"role": "assistant", "content": response})
    choice = ChatCompletionChoiceSchema().dump(
            {"index": 0, "message": message, "finish_reason": "stop"})
    return ChatCompletionSchema().dump({
        "model": "gpt-3.5-turbo",
        "choices": [choice]})

@stream_with_context
def stream_generate(prompts, **generate_kwargs):
finish_choices = []
for index, prompt in enumerate(prompts):
choice = CompletionChoiceSchema().dump(
{"index": index, "text": "\n\n", "logprobs": None, "finish_reason": None})

    yield sse(
        CompletionSchema().dump(
            {"model": "gpt-3.5-turbo-instruct", "choices": [choice]})
    )

    inputs = base_tfs.tokenizer(prompt, padding=True, return_tensors='pt')
    inputs = inputs.to(base_tfs.model.device)
    streamer = TextIteratorStreamer(
        base_tfs.tokenizer,
        decode_kwargs={"skip_special_tokens": True})
    Thread(
        target=base_tfs.model.generate, kwargs=dict(
            inputs, streamer=streamer,
            repetition_penalty=1.1, **generate_kwargs)
    ).start()

    finish_reason = None
    for text in streamer:
        if not text:
            continue
        empty_cache()
        if text.endswith(base_tfs.tokenizer.eos_token):
            finish_reason = "stop"
            break

        choice = CompletionChoiceSchema().dump(
            {"index": index, "text": text, "logprobs": None, "finish_reason": None})

        yield sse(
            CompletionSchema().dump(
                {"model": "gpt-3.5-turbo-instruct", "choices": [choice]})
        )
    else:
        finish_reason = "length"
        choice = CompletionChoiceSchema().dump(
            {"index": index, "text": text, "logprobs": None, "finish_reason": finish_reason})
        yield sse(
            CompletionSchema().dump(
                {"model": "gpt-3.5-turbo-instruct", "choices": [choice]})
        )

    choice = CompletionChoiceSchema().dump(
        {"index": index, "text": "", "logprobs": None, "finish_reason": finish_reason})
    finish_choices.append(choice)

yield sse(
    CompletionSchema().dump(
        {"model": "gpt-3.5-turbo-instruct", "choices": finish_choices})
)

yield sse('[DONE]')

@completions_bp.route("", methods=["POST"])
def create_completion():
"""Create completion
---
post:
tags:
- Completions
description: Creates a completion for the provided prompt and parameters.
requestBody:
request: True
content:
application/json:
schema: CreateCompletionSchema
security:
- bearer: []
responses:
200:
description: Completion return
content:
application/json:
schema:
CompletionSchema
"""
create_completion = CreateCompletionSchema().load(request.json)

prompt = create_completion["prompt"]
prompts = prompt if isinstance(prompt, list) else [prompt]

if create_completion["stream"]:
    return current_app.response_class(
        stream_generate(prompts, max_new_tokens=create_completion["max_tokens"]),
        mimetype="text/event-stream"
    )
else:
    choices = []
    prompt_tokens = 0
    completion_tokens = 0
    for index, prompt in enumerate(prompts):
        inputs = base_tfs.tokenizer(prompt, return_tensors='pt')
        inputs = inputs.to(base_tfs.model.device)
        prompt_tokens += len(inputs["input_ids"][0])
        pred = base_tfs.model.generate(
            **inputs, max_new_tokens=create_completion["max_tokens"], repetition_penalty=1.1)

        completion_tokens += len(pred.cpu()[0])
        resp = base_tfs.tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)

        finish_reason = None
        if resp.endswith(base_tfs.tokenizer.eos_token):
            finish_reason = "stop"
            resp = resp[:-len(base_tfs.tokenizer.eos_token)]
        else:
            finish_reason = "length"

        choices.append(
            CompletionChoiceSchema().dump(
                {"index": index, "text": resp, "logprobs": {}, "finish_reason": finish_reason})
        )
    usage = CompletionUsageSchema().dump({
        "prompt_tokens": prompt_tokens,
        "completion_tokens": completion_tokens,
        "total_tokens": prompt_tokens+completion_tokens})

    return CompletionSchema().dump(
            {"model": "gpt-3.5-turbo-instruct", "choices": choices, "usage": usage})

app = create_app()

if name == 'main':
app.run(debug=False, host="0.0.0.0")

如题

你好，我使用了你这个项目想要对使用Baichuan2微调后的模型进行api的部署，代码中修改了model_name为自己微调后模型的路径，使用postman进行api测试的时候，请问其中的Body部分的字段应该怎么填写？

`template = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of Args
Question: {input}
{agent_scratchpad}"""

prompt = CustomPromptTemplate(
template=template,
tools_getter=get_tools,
# This omits the agent_scratchpad, tools, and tool_names variables because those are generated dynamically
# This includes the intermediate_steps variable because that is needed
input_variables=["input", "intermediate_steps"]
)

print(f""" prompt --------> {prompt.}""")

LLM chain consisting of the LLM and a prompt

llm_chain = LLMChain(llm=llm, prompt=prompt)

output_parser = CustomOutputParser()

tools = get_tools("whats the weather?")
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=[" Observation:"],
allowed_tools=tool_names
)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("please speak loud about laugh.")`

/v1/competations
在apidoc中测试，如果不加‘stop’参数可以请求成功，但由于需要多轮对话需要stop参数，请问怎么解决？

请问可以返回usage中的token信息吗

启动后，调用chat/completions接口，服务端报错：