463 lines
16 KiB
Python
463 lines
16 KiB
Python
import time
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from asyncio.log import logger
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import uvicorn
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import gc
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import json
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import random
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import string
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import logging
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import torch
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from vllm import SamplingParams, AsyncEngineArgs, AsyncLLMEngine
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from fastapi import FastAPI, HTTPException, Response
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from fastapi.middleware.cors import CORSMiddleware
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from contextlib import asynccontextmanager
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from typing import List, Literal, Optional, Union
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from pydantic import BaseModel, Field
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from transformers import AutoTokenizer, LogitsProcessor
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from sse_starlette.sse import EventSourceResponse
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EventSourceResponse.DEFAULT_PING_INTERVAL = 1000
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MODEL_PATH = 'THUDM/glm-4-9b-chat'
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# max model length 128k
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MAX_MODEL_LENGTH = 8192
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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yield
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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app = FastAPI(lifespan=lifespan)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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def generate_id(prefix: str) -> str:
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suffix = ''.join(random.choices(string.ascii_letters + string.digits, k=24))
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return f"{prefix}-{suffix}"
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class ModelCard(BaseModel):
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id: str
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object: str = "model"
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created: int = Field(default_factory=lambda: int(time.time()))
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owned_by: str = "owner"
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root: Optional[str] = None
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parent: Optional[str] = None
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permission: Optional[list] = None
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class ModelList(BaseModel):
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object: str = "list"
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data: List[ModelCard] = ["glm-4"]
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class FunctionCall(BaseModel):
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name: str
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arguments: str
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class FunctionCallResponse(BaseModel):
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name: Optional[str] = None
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arguments: Optional[str] = None
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class UsageInfo(BaseModel):
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prompt_tokens: int = 0
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total_tokens: int = 0
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completion_tokens: Optional[int] = 0
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class ChatCompletionMessageToolCall(BaseModel):
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id: Optional[str] = Field(default_factory=lambda: generate_id('call'))
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function: FunctionCall
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type: Optional[Literal["function"]] = 'function'
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class ChatMessage(BaseModel):
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role: Literal["user", "assistant", "system", "function", "tool"]
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content: Optional[str] = None
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function_call: Optional[FunctionCall] = None
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tool_calls: Optional[List[ChatCompletionMessageToolCall]] = None
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class DeltaMessage(BaseModel):
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role: Optional[Literal["user", "assistant", "function", "system"]] = None
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content: Optional[str] = None
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function_call: Optional[FunctionCall] = None
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tool_calls: Optional[List[ChatCompletionMessageToolCall]] = None
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class ChatCompletionResponseChoice(BaseModel):
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index: int
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message: ChatMessage
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finish_reason: Literal["stop", "length", "tool_calls"]
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class ChatCompletionResponseStreamChoice(BaseModel):
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delta: DeltaMessage
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finish_reason: Optional[Literal["stop", "length", "tool_calls"]]
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index: int
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class ChatCompletionResponse(BaseModel):
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model: str
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id: str = Field(default_factory=lambda: generate_id('chatcmpl'))
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object: Literal["chat.completion", "chat.completion.chunk"]
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choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
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created: Optional[int] = Field(default_factory=lambda: int(time.time()))
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usage: Optional[UsageInfo] = None
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@staticmethod
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def _convert_to_tool_calls_from_content(content: str) -> Union[List[ChatCompletionMessageToolCall], str]:
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tool_calls = []
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content = content.strip()
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for response in content.split("<|assistant|>"):
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if "\n" in response:
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metadata, content = response.split("\n", maxsplit=1)
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else:
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metadata, content = "", response
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if metadata.strip():
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parameters = eval(content.strip())
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function_call = FunctionCall(
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name=metadata.strip(),
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arguments=json.dumps(parameters, ensure_ascii=False)
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)
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tool_calls.append(ChatCompletionMessageToolCall(function=function_call))
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return tool_calls if len(tool_calls) > 0 else content
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@staticmethod
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def stream_reply(model_id: str, content: str, finish_reason: str, use_tool: bool = False) -> str:
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if content.startswith("\n"):
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content = content[1:]
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tool_calls = None
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if use_tool:
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parsed_tool_calls = ChatCompletionResponse._convert_to_tool_calls_from_content(content)
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if isinstance(parsed_tool_calls, list):
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tool_calls = parsed_tool_calls
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finish_reason = "tool_calls"
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content = None
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(role="assistant", content=content, tool_calls=tool_calls),
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finish_reason=finish_reason
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)
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return ChatCompletionResponse(
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model=model_id,
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choices=[choice_data],
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created=int(time.time()),
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object="chat.completion.chunk"
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).model_dump_json(exclude_none=True)
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@staticmethod
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def reply(model_id: str, content: str, finish_reason: str, use_tool: bool = False, usage: UsageInfo = None) \
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-> 'ChatCompletionResponse':
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if content.startswith("\n"):
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content = content[1:]
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tool_calls = None
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if use_tool:
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parsed_tool_calls = ChatCompletionResponse._convert_to_tool_calls_from_content(content)
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if isinstance(parsed_tool_calls, list):
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tool_calls = parsed_tool_calls
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finish_reason = "tool_calls"
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content = None
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choice_data = ChatCompletionResponseChoice(
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index=0,
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message=ChatMessage(role="assistant", content=content, tool_calls=tool_calls),
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finish_reason=finish_reason
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)
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return ChatCompletionResponse(
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model=model_id,
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choices=[choice_data],
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created=int(time.time()),
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object="chat.completion",
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usage=usage
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)
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class ChatCompletionRequest(BaseModel):
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model: str
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messages: List[ChatMessage]
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temperature: Optional[float] = 0.8
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top_p: Optional[float] = 0.8
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max_tokens: Optional[int] = None
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stream: Optional[bool] = False
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tools: Optional[Union[dict, List[dict]]] = None
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tool_choice: Optional[Union[str, dict]] = None
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repetition_penalty: Optional[float] = 1.1
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class InvalidScoreLogitsProcessor(LogitsProcessor):
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def __call__(
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self, input_ids: torch.LongTensor, scores: torch.FloatTensor
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) -> torch.FloatTensor:
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if torch.isnan(scores).any() or torch.isinf(scores).any():
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scores.zero_()
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scores[..., 5] = 5e4
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return scores
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@torch.inference_mode()
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async def generate_stream_glm4(params):
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messages = params["messages"]
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tools = params["tools"]
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tool_choice = params["tool_choice"]
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temperature = float(params.get("temperature", 1.0))
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repetition_penalty = float(params.get("repetition_penalty", 1.0))
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top_p = float(params.get("top_p", 1.0))
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max_new_tokens = int(params.get("max_tokens", 8192))
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messages = process_messages(messages, tools=tools, tool_choice=tool_choice)
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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params_dict = {
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"n": 1,
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"best_of": 1,
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"presence_penalty": 1.0,
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"frequency_penalty": 0.0,
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"temperature": temperature,
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"top_p": top_p,
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"top_k": -1,
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"repetition_penalty": repetition_penalty,
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"use_beam_search": False,
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"length_penalty": 1,
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"early_stopping": False,
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"stop_token_ids": [151329, 151336, 151338],
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"ignore_eos": False,
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"max_tokens": max_new_tokens,
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"logprobs": None,
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"prompt_logprobs": None,
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"skip_special_tokens": True,
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}
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sampling_params = SamplingParams(**params_dict)
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async for output in engine.generate(inputs=inputs, sampling_params=sampling_params, request_id=f"{time.time()}"):
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output_len = len(output.outputs[0].token_ids)
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input_len = len(output.prompt_token_ids)
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ret = {
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"text": output.outputs[0].text,
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"usage": {
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"prompt_tokens": input_len,
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"completion_tokens": output_len,
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"total_tokens": output_len + input_len
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},
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"finish_reason": output.outputs[0].finish_reason,
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}
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yield ret
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gc.collect()
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torch.cuda.empty_cache()
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def process_messages(messages, tools=None, tool_choice=None):
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_messages = messages
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processed_messages = []
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msg_has_sys = False
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def filter_tools(tool_choice, tools):
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function_name = tool_choice.get('function', {}).get('name', None)
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if not function_name:
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return []
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filtered_tools = [
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tool for tool in tools
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if tool.get('function', {}).get('name') == function_name
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]
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return filtered_tools
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if tool_choice and tool_choice != "none":
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if isinstance(tool_choice, dict):
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tools = filter_tools(tool_choice, tools)
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if tools:
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processed_messages.append(
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{
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"role": "system",
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"content": None,
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"tools": tools
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}
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)
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msg_has_sys = True
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if isinstance(tool_choice, dict) and tools:
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processed_messages.append(
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{
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"role": "assistant",
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"metadata": tool_choice["function"]["name"],
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"content": ""
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}
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)
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for m in _messages:
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role, content, func_call = m.role, m.content, m.function_call
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tool_calls = getattr(m, 'tool_calls', None)
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if role == "function":
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processed_messages.append(
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{
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"role": "observation",
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"content": content
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}
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)
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elif role == "tool":
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processed_messages.append(
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{
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"role": "observation",
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"content": content,
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"function_call": True
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}
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)
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elif role == "assistant":
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if tool_calls:
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for tool_call in tool_calls:
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processed_messages.append(
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{
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"role": "assistant",
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"metadata": tool_call.function.name,
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"content": tool_call.function.arguments
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}
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)
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else:
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for response in content.split("\n"):
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if "\n" in response:
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metadata, sub_content = response.split("\n", maxsplit=1)
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else:
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metadata, sub_content = "", response
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processed_messages.append(
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{
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"role": role,
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"metadata": metadata,
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"content": sub_content.strip()
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}
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)
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else:
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if role == "system" and msg_has_sys:
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msg_has_sys = False
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continue
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processed_messages.append({"role": role, "content": content})
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if not tools or tool_choice == "none":
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for m in _messages:
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if m.role == 'system':
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processed_messages.insert(0, {"role": m.role, "content": m.content})
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break
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return processed_messages
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@app.get("/health")
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async def health() -> Response:
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"""Health check."""
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return Response(status_code=200)
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@app.get("/v1/models", response_model=ModelList)
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async def list_models():
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model_card = ModelCard(id="glm-4")
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return ModelList(data=[model_card])
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@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
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async def create_chat_completion(request: ChatCompletionRequest):
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if len(request.messages) < 1 or request.messages[-1].role == "assistant":
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raise HTTPException(status_code=400, detail="Invalid request")
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if request.tool_choice is None:
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request.tool_choice = "auto" if request.tools else "none"
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gen_params = dict(
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messages=request.messages,
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temperature=request.temperature,
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top_p=request.top_p,
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max_tokens=request.max_tokens or 1024,
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echo=False,
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stream=request.stream,
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repetition_penalty=request.repetition_penalty,
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tools=request.tools,
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tool_choice=request.tool_choice,
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)
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logger.debug(f"==== request ====\n{request.model_dump_json()}")
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if request.stream:
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predict_stream_generator = predict_stream(request.model, gen_params)
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return EventSourceResponse(predict_stream_generator, media_type="text/event-stream", sep="\n")
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response = ""
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async for response in generate_stream_glm4(gen_params):
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pass
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is_tool_call = is_return_tool_call(response["text"], request.tools)
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usage = UsageInfo()
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task_usage = UsageInfo.model_validate(response["usage"])
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for usage_key, usage_value in task_usage.model_dump().items():
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setattr(usage, usage_key, getattr(usage, usage_key) + usage_value)
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return ChatCompletionResponse.reply(request.model, response["text"], response["finish_reason"], is_tool_call, usage)
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def calc_max_tool_name_len(tools: Optional[List[dict]]) -> int:
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max_tool_name_len = 0
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if not tools:
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return max_tool_name_len
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tool_names = [tool['function']['name'] for tool in tools if 'function' in tool and 'name' in tool['function']]
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max_tool_name_len = max(len(tool_name) for tool_name in tool_names)
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return max_tool_name_len
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def is_return_tool_call(output: str, tools: Optional[List[dict]]) -> bool:
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if not tools:
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return False
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output = output.strip()
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tool_names = [tool['function']['name'] for tool in tools if 'function' in tool and 'name' in tool['function']]
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return any(output.startswith(name) for name in tool_names)
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async def predict_stream(model_id, gen_params):
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output = ""
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is_function_call = False
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has_send_first_chunk = False
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tools = gen_params.get("tools")
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max_tool_name_len = calc_max_tool_name_len(tools)
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finish_reason = "stop"
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async for new_response in generate_stream_glm4(gen_params):
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decoded_unicode = new_response["text"]
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delta_text = decoded_unicode[len(output):]
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output = decoded_unicode
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# read an extra char because the first generate char may be \n
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if len(output) <= max_tool_name_len:
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continue
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if not is_function_call:
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is_function_call = is_return_tool_call(output, tools)
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if is_function_call:
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continue
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else:
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finish_reason = new_response["finish_reason"]
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send_msg = delta_text if has_send_first_chunk else output[1:] if output.startswith("\n") else output
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has_send_first_chunk = True
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yield ChatCompletionResponse.stream_reply(model_id, send_msg, finish_reason)
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# if the total output length less than the max tool name length, has_send_first_chunk = False
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if is_function_call or not has_send_first_chunk:
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yield ChatCompletionResponse.stream_reply(model_id, output, finish_reason, is_function_call)
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yield '[DONE]'
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if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
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engine_args = AsyncEngineArgs(
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model=MODEL_PATH,
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tokenizer=MODEL_PATH,
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tensor_parallel_size=1,
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dtype="bfloat16",
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trust_remote_code=True,
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# 占用显存的比例，请根据你的显卡显存大小设置合适的值，例如，如果你的显卡有80G，您只想使用24G，请按照24/80=0.3设置
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gpu_memory_utilization=0.9,
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enforce_eager=True,
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worker_use_ray=False,
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engine_use_ray=False,
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disable_log_requests=True,
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max_model_len=MAX_MODEL_LENGTH,
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)
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engine = AsyncLLMEngine.from_engine_args(engine_args)
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uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
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