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import json
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
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 TYPE_CHECKING:
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]], str, str]:
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 = ""
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 = []
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})
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 = ""
return input_messages, system, tools
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 = _process_request(request)
responses = await chat_model.achat(
input_messages,
system,
tools,
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 = _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,
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)
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