调用的百川2 13B在线量化4Bits，没有流式回答我的效果。一次性的把问题返回给我了。请问怎么解决？

[coderwpf]

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")