589 lines
19 KiB
Python
589 lines
19 KiB
Python
import os
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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 torch
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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, Tuple
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from pydantic import BaseModel, Field
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from transformers import AutoModelForCausalLM, AutoTokenizer, LogitsProcessor
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from sse_starlette.sse import EventSourceResponse
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EventSourceResponse.DEFAULT_PING_INTERVAL = 10000
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MODEL_PATH = os.environ.get('MODEL_PATH', 'THU/glm-4-9b-chat')
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TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
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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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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] = []
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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 ChatMessage(BaseModel):
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role: Literal["user", "assistant", "system", "tool"]
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content: str = None
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name: Optional[str] = None
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function_call: Optional[FunctionCallResponse] = None
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class DeltaMessage(BaseModel):
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role: Optional[Literal["user", "assistant", "system"]] = None
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content: Optional[str] = None
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function_call: Optional[FunctionCallResponse] = None
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class EmbeddingRequest(BaseModel):
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input: Union[List[str], str]
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model: str
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class CompletionUsage(BaseModel):
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prompt_tokens: int
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completion_tokens: int
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total_tokens: int
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class EmbeddingResponse(BaseModel):
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data: list
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model: str
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object: str
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usage: CompletionUsage
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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 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 ChatCompletionResponseChoice(BaseModel):
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index: int
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message: ChatMessage
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finish_reason: Literal["stop", "length", "function_call"]
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class ChatCompletionResponseStreamChoice(BaseModel):
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delta: DeltaMessage
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finish_reason: Optional[Literal["stop", "length", "function_call"]]
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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
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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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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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def process_response(output: str, use_tool: bool = False) -> Union[str, dict]:
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content = ""
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for response in output.split(""):
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metadata, content = response.split("\n", maxsplit=1)
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if not metadata.strip():
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content = content.strip()
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else:
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if use_tool:
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content = "\n".join(content.split("\n")[1:-1])
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parameters = eval(content)
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content = {
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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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else:
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content = {
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"name": metadata.strip(),
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"content": content
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}
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return content
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@torch.inference_mode()
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def generate_stream_glm4(params: dict):
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global engine, tokenizer
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echo = params.get("echo", True)
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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=True,
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return_tensors="pt",return_dict=True)
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inputs = inputs.to(engine.device)
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input_echo_len = len(inputs["input_ids"][0])
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if input_echo_len >= engine.config.seq_length:
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print(f"Input length larger than {model.config.seq_length}")
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eos_token_id = [tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|user|>"),
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tokenizer.convert_tokens_to_ids("<|observation|>")]
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gen_kwargs = {
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"max_new_tokens": max_new_tokens,
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"do_sample": True if temperature > 1e-5 else False,
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"top_p": top_p,
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"repetition_penalty": repetition_penalty,
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"logits_processor": [InvalidScoreLogitsProcessor()],
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}
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if temperature > 1e-5:
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gen_kwargs["temperature"] = temperature
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total_len = 0
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for total_ids in engine.stream_generate(**inputs, eos_token_id=eos_token_id, **gen_kwargs):
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total_ids = total_ids.tolist()[0]
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total_len = len(total_ids)
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if echo:
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output_ids = total_ids[:-1]
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else:
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output_ids = total_ids[input_echo_len:-1]
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response = tokenizer.decode(output_ids)
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if response and response[-1] != "<EFBFBD>":
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response, stop_found = apply_stopping_strings(response, ["<|observation|>"])
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yield {
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"text": response,
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"usage": {
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"prompt_tokens": input_echo_len,
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"completion_tokens": total_len - input_echo_len,
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"total_tokens": total_len,
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},
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"finish_reason": "function_call" if stop_found else None,
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}
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if stop_found:
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break
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# Only last stream result contains finish_reason, we set finish_reason as stop
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ret = {
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"text": response,
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"usage": {
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"prompt_tokens": input_echo_len,
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"completion_tokens": total_len - input_echo_len,
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"total_tokens": total_len,
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},
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"finish_reason": "stop",
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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 apply_stopping_strings(reply, stop_strings) -> Tuple[str, bool]:
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stop_found = False
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for string in stop_strings:
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idx = reply.find(string)
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if idx != -1:
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reply = reply[:idx]
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stop_found = True
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break
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if not stop_found:
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# If something like "\nYo" is generated just before "\nYou: is completed, trim it
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for string in stop_strings:
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for j in range(len(string) - 1, 0, -1):
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if reply[-j:] == string[:j]:
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reply = reply[:-j]
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break
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else:
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continue
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break
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return reply, stop_found
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def process_messages(messages, tools=None, tool_choice="none"):
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_messages = messages
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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 != "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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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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# add to metadata
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if isinstance(tool_choice, dict) and tools:
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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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if role == "function":
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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 == "assistant" and func_call is not None:
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for response in content.split(""):
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metadata, sub_content = response.split("\n", maxsplit=1)
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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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messages.append({"role": role, "content": content})
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return 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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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{gen_params}")
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if request.stream:
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predict_stream_generator = predict_stream(request.model, gen_params)
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output = await anext(predict_stream_generator)
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if not output and 'get_' in output:
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return EventSourceResponse(predict_stream_generator, media_type="text/event-stream")
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logger.debug(f"First result output：\n{output}")
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function_call = None
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if output and request.tools:
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try:
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function_call = process_response(output, use_tool=True)
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except:
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logger.warning("Failed to parse tool call")
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# CallFunction
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if isinstance(function_call, dict):
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function_call = FunctionCallResponse(**function_call)
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tool_response = ""
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if not gen_params.get("messages"):
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gen_params["messages"] = []
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gen_params["messages"].append(ChatMessage(role="assistant", content=output))
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gen_params["messages"].append(ChatMessage(role="tool", name=function_call.name, content=tool_response))
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generate = predict(request.model, gen_params)
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return EventSourceResponse(generate, media_type="text/event-stream")
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else:
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generate = parse_output_text(request.model, output)
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return EventSourceResponse(generate, media_type="text/event-stream")
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response = ""
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for response in generate_stream_glm4(gen_params):
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pass
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if response["text"].startswith("\n"):
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response["text"] = response["text"][1:]
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response["text"] = response["text"].strip()
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usage = UsageInfo()
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function_call, finish_reason = None, "stop"
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if request.tools:
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try:
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function_call = process_response(response["text"], use_tool=True)
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except:
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logger.warning(
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"Failed to parse tool call, maybe the response is not a function call(such as cogview drawing) or have been answered.")
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if isinstance(function_call, dict):
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finish_reason = "function_call"
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function_call = FunctionCallResponse(**function_call)
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message = ChatMessage(
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role="assistant",
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content=response["text"],
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function_call=function_call if isinstance(function_call, FunctionCallResponse) else None,
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)
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logger.debug(f"==== message ====\n{message}")
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choice_data = ChatCompletionResponseChoice(
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index=0,
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message=message,
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finish_reason=finish_reason,
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)
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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(
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model=request.model,
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id="", # for open_source model, id is empty
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choices=[choice_data],
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object="chat.completion",
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usage=usage
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)
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async def predict(model_id: str, params: dict):
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(role="assistant"),
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finish_reason=None
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)
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chunk = ChatCompletionResponse(model=model_id, id="", choices=[choice_data], object="chat.completion.chunk")
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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previous_text = ""
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for new_response in generate_stream_glm4(params):
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decoded_unicode = new_response["text"]
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delta_text = decoded_unicode[len(previous_text):]
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previous_text = decoded_unicode
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finish_reason = new_response["finish_reason"]
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if len(delta_text) == 0 and finish_reason != "function_call":
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continue
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function_call = None
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if finish_reason == "function_call":
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try:
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function_call = process_response(decoded_unicode, use_tool=True)
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except:
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logger.warning(
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"Failed to parse tool call, maybe the response is not a tool call or have been answered.")
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if isinstance(function_call, dict):
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function_call = FunctionCallResponse(**function_call)
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delta = DeltaMessage(
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content=delta_text,
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role="assistant",
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function_call=function_call if isinstance(function_call, FunctionCallResponse) else None,
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=delta,
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finish_reason=finish_reason
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)
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chunk = ChatCompletionResponse(
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model=model_id,
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id="",
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choices=[choice_data],
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object="chat.completion.chunk"
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)
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(),
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finish_reason="stop"
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)
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chunk = ChatCompletionResponse(
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model=model_id,
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id="",
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choices=[choice_data],
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object="chat.completion.chunk"
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)
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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yield '[DONE]'
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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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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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if not is_function_call and len(output) > 7:
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is_function_call = output and 'get_' in output
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if is_function_call:
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continue
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finish_reason = new_response["finish_reason"]
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if not has_send_first_chunk:
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message = DeltaMessage(
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content="",
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role="assistant",
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function_call=None,
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=message,
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finish_reason=finish_reason
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)
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chunk = ChatCompletionResponse(
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model=model_id,
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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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)
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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send_msg = delta_text if has_send_first_chunk else output
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has_send_first_chunk = True
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message = DeltaMessage(
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content=send_msg,
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role="assistant",
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function_call=None,
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=message,
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finish_reason=finish_reason
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)
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chunk = ChatCompletionResponse(
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model=model_id,
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id="",
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choices=[choice_data],
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created=int(time.time()),
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object="chat.completion.chunk"
|
||
)
|
||
yield "{}".format(chunk.model_dump_json(exclude_unset=True))
|
||
|
||
if is_function_call:
|
||
yield output
|
||
else:
|
||
yield '[DONE]'
|
||
|
||
|
||
async def parse_output_text(model_id: str, value: str):
|
||
choice_data = ChatCompletionResponseStreamChoice(
|
||
index=0,
|
||
delta=DeltaMessage(role="assistant", content=value),
|
||
finish_reason=None
|
||
)
|
||
chunk = ChatCompletionResponse(model=model_id, id="", choices=[choice_data], object="chat.completion.chunk")
|
||
yield "{}".format(chunk.model_dump_json(exclude_unset=True))
|
||
choice_data = ChatCompletionResponseStreamChoice(
|
||
index=0,
|
||
delta=DeltaMessage(),
|
||
finish_reason="stop"
|
||
)
|
||
chunk = ChatCompletionResponse(model=model_id, id="", choices=[choice_data], object="chat.completion.chunk")
|
||
yield "{}".format(chunk.model_dump_json(exclude_unset=True))
|
||
yield '[DONE]'
|
||
|
||
|
||
if __name__ == "__main__":
|
||
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
|
||
|
||
engine = AutoModelForCausalLM.from_pretrained(MODEL_PATH,trust_remote_code=True,load_in_4bit=True,torch_dtype=torch.bfloat16,device_map="cuda",).eval() # 4bit 量化
|
||
#engine = AutoModelForCausalLM.from_pretrained(MODEL_PATH,trust_remote_code=True,torch_dtype=torch.bfloat16,device_map="cuda",).eval() #无量化
|
||
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1) #请求时,必须 stream=False， 否则返回空内容。
|