mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-03 20:22:49 +08:00
212 lines
7.9 KiB
Python
212 lines
7.9 KiB
Python
import json
|
|
import os
|
|
import uuid
|
|
from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
|
|
|
|
from ..data import Role as DataRole
|
|
from ..extras.logging import get_logger
|
|
from ..extras.packages import is_fastapi_available, is_pillow_available
|
|
from .common import dictify, jsonify
|
|
from .protocol import (
|
|
ChatCompletionMessage,
|
|
ChatCompletionResponse,
|
|
ChatCompletionResponseChoice,
|
|
ChatCompletionResponseUsage,
|
|
ChatCompletionStreamResponse,
|
|
ChatCompletionStreamResponseChoice,
|
|
Finish,
|
|
Function,
|
|
FunctionCall,
|
|
Role,
|
|
ScoreEvaluationResponse,
|
|
)
|
|
|
|
|
|
if is_fastapi_available():
|
|
from fastapi import HTTPException, status
|
|
|
|
|
|
if is_pillow_available():
|
|
import requests
|
|
from PIL import Image
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from numpy.typing import NDArray
|
|
|
|
from ..chat import ChatModel
|
|
from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
|
|
|
|
|
|
logger = get_logger(__name__)
|
|
ROLE_MAPPING = {
|
|
Role.USER: DataRole.USER.value,
|
|
Role.ASSISTANT: DataRole.ASSISTANT.value,
|
|
Role.SYSTEM: DataRole.SYSTEM.value,
|
|
Role.FUNCTION: DataRole.FUNCTION.value,
|
|
Role.TOOL: DataRole.OBSERVATION.value,
|
|
}
|
|
|
|
|
|
def _process_request(
|
|
request: "ChatCompletionRequest",
|
|
) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional["NDArray"]]:
|
|
logger.info("==== request ====\n{}".format(json.dumps(dictify(request), indent=2, ensure_ascii=False)))
|
|
|
|
if len(request.messages) == 0:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
|
|
|
|
if request.messages[0].role == Role.SYSTEM:
|
|
system = request.messages.pop(0).content
|
|
else:
|
|
system = None
|
|
|
|
if len(request.messages) % 2 == 0:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
|
|
|
|
input_messages = []
|
|
image = None
|
|
for i, message in enumerate(request.messages):
|
|
if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
|
|
elif i % 2 == 1 and message.role not in [Role.ASSISTANT, Role.FUNCTION]:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
|
|
|
|
if message.role == Role.ASSISTANT and isinstance(message.tool_calls, list) and len(message.tool_calls):
|
|
name = message.tool_calls[0].function.name
|
|
arguments = message.tool_calls[0].function.arguments
|
|
content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
|
|
input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
|
|
elif isinstance(message.content, list):
|
|
for input_item in message.content:
|
|
if input_item.type == "text":
|
|
input_messages.append({"role": ROLE_MAPPING[message.role], "content": input_item.text})
|
|
else:
|
|
image_url = input_item.image_url.url
|
|
if os.path.isfile(image_url):
|
|
image_path = open(image_url, "rb")
|
|
else:
|
|
image_path = requests.get(image_url, stream=True).raw
|
|
|
|
image = Image.open(image_path).convert("RGB")
|
|
else:
|
|
input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
|
|
|
|
tool_list = request.tools
|
|
if isinstance(tool_list, list) and len(tool_list):
|
|
try:
|
|
tools = json.dumps([dictify(tool.function) for tool in tool_list], ensure_ascii=False)
|
|
except Exception:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
|
|
else:
|
|
tools = None
|
|
|
|
return input_messages, system, tools, image
|
|
|
|
|
|
def _create_stream_chat_completion_chunk(
|
|
completion_id: str,
|
|
model: str,
|
|
delta: "ChatCompletionMessage",
|
|
index: Optional[int] = 0,
|
|
finish_reason: Optional["Finish"] = None,
|
|
) -> str:
|
|
choice_data = ChatCompletionStreamResponseChoice(index=index, delta=delta, finish_reason=finish_reason)
|
|
chunk = ChatCompletionStreamResponse(id=completion_id, model=model, choices=[choice_data])
|
|
return jsonify(chunk)
|
|
|
|
|
|
async def create_chat_completion_response(
|
|
request: "ChatCompletionRequest", chat_model: "ChatModel"
|
|
) -> "ChatCompletionResponse":
|
|
completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
|
|
input_messages, system, tools, image = _process_request(request)
|
|
responses = await chat_model.achat(
|
|
input_messages,
|
|
system,
|
|
tools,
|
|
image,
|
|
do_sample=request.do_sample,
|
|
temperature=request.temperature,
|
|
top_p=request.top_p,
|
|
max_new_tokens=request.max_tokens,
|
|
num_return_sequences=request.n,
|
|
stop=request.stop,
|
|
)
|
|
|
|
prompt_length, response_length = 0, 0
|
|
choices = []
|
|
for i, response in enumerate(responses):
|
|
if tools:
|
|
result = chat_model.engine.template.format_tools.extract(response.response_text)
|
|
else:
|
|
result = response.response_text
|
|
|
|
if isinstance(result, tuple):
|
|
name, arguments = result
|
|
function = Function(name=name, arguments=arguments)
|
|
tool_call = FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function)
|
|
response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=[tool_call])
|
|
finish_reason = Finish.TOOL
|
|
else:
|
|
response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
|
|
finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
|
|
|
|
choices.append(ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason))
|
|
prompt_length = response.prompt_length
|
|
response_length += response.response_length
|
|
|
|
usage = ChatCompletionResponseUsage(
|
|
prompt_tokens=prompt_length,
|
|
completion_tokens=response_length,
|
|
total_tokens=prompt_length + response_length,
|
|
)
|
|
|
|
return ChatCompletionResponse(id=completion_id, model=request.model, choices=choices, usage=usage)
|
|
|
|
|
|
async def create_stream_chat_completion_response(
|
|
request: "ChatCompletionRequest", chat_model: "ChatModel"
|
|
) -> AsyncGenerator[str, None]:
|
|
completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
|
|
input_messages, system, tools, image = _process_request(request)
|
|
if tools:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
|
|
|
|
if request.n > 1:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream multiple responses.")
|
|
|
|
yield _create_stream_chat_completion_chunk(
|
|
completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(role=Role.ASSISTANT, content="")
|
|
)
|
|
async for new_token in chat_model.astream_chat(
|
|
input_messages,
|
|
system,
|
|
tools,
|
|
image,
|
|
do_sample=request.do_sample,
|
|
temperature=request.temperature,
|
|
top_p=request.top_p,
|
|
max_new_tokens=request.max_tokens,
|
|
stop=request.stop,
|
|
):
|
|
if len(new_token) != 0:
|
|
yield _create_stream_chat_completion_chunk(
|
|
completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(content=new_token)
|
|
)
|
|
|
|
yield _create_stream_chat_completion_chunk(
|
|
completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
|
|
)
|
|
yield "[DONE]"
|
|
|
|
|
|
async def create_score_evaluation_response(
|
|
request: "ScoreEvaluationRequest", chat_model: "ChatModel"
|
|
) -> "ScoreEvaluationResponse":
|
|
if len(request.messages) == 0:
|
|
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
|
|
|
|
scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
|
|
return ScoreEvaluationResponse(model=request.model, scores=scores)
|