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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
import io
import json
import os
import re
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, is_requests_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():
from PIL import Image
if is_requests_available():
import requests
if TYPE_CHECKING:
from ..chat import ChatModel
from ..data.mm_plugin import ImageInput
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["ImageInput"]]:
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):
tool_calls = [
{"name": tool_call.function.name, "arguments": tool_call.function.arguments}
for tool_call in message.tool_calls
]
content = json.dumps(tool_calls, 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 re.match(r"^data:image\/(png|jpg|jpeg|gif|bmp);base64,(.+)$", image_url): # base64 image
image_stream = io.BytesIO(base64.b64decode(image_url.split(",", maxsplit=1)[1]))
elif os.path.isfile(image_url): # local file
image_stream = open(image_url, "rb")
else: # web uri
image_stream = requests.get(image_url, stream=True).raw
image = Image.open(image_stream).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 json.JSONDecodeError:
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.extract_tool(response.response_text)
else:
result = response.response_text
if isinstance(result, list):
tool_calls = []
for tool in result:
function = Function(name=tool[0], arguments=tool[1])
tool_calls.append(FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function))
response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=tool_calls)
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":
score_id = "scoreval-{}".format(uuid.uuid4().hex)
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(id=score_id, model=request.model, scores=scores)
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