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# hf_backend.py (patched)
import time, logging, os
from typing import Any, Dict, AsyncIterable
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from backends_base import ChatBackend, ImagesBackend
from config import settings
try:
import spaces
from spaces.zero.client import SpaceZeroClient
except ImportError:
spaces, SpaceZeroClient = None, None
logger = logging.getLogger(__name__)
MODEL_ID = settings.LlmHFModelID or "Qwen/Qwen2.5-1.5B-Instruct"
logger.info(f"Loading {MODEL_ID} on CPU at startup (ZeroGPU safe)...")
tokenizer, model, load_error = None, None, None
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float32,
trust_remote_code=True,
)
model.eval()
except Exception as e:
load_error = f"Failed to load model/tokenizer: {e}"
logger.exception(load_error)
def pick_device() -> str:
if torch.cuda.is_available():
return "cuda"
if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
return "mps"
return "cpu"
def pick_dtype(device: str) -> torch.dtype:
if device == "cuda":
major, _ = torch.cuda.get_device_capability()
return torch.bfloat16 if major >= 8 else torch.float16
if device == "mps":
return torch.float16
return torch.float32
class HFChatBackend(ChatBackend):
async def stream(self, request: Dict[str, Any]) -> AsyncIterable[Dict[str, Any]]:
if load_error:
raise RuntimeError(load_error)
messages = request.get("messages", [])
prompt = messages[-1]["content"] if messages else "(empty)"
temperature = float(request.get("temperature", settings.LlmTemp or 0.7))
max_tokens = int(request.get("max_tokens", settings.LlmOpenAICtxSize or 512))
rid = f"chatcmpl-hf-{int(time.time())}"
now = int(time.time())
# --- ✅ Extract X-IP-Token from RabbitMQ message
x_ip_token = request.get("x_ip_token")
headers = {}
if x_ip_token:
headers["X-IP-Token"] = x_ip_token
logger.info("Using X-IP-Token from request for ZeroGPU attribution")
def _gpu_inference_fn(prompt: str) -> str:
device = pick_device()
dtype = pick_dtype(device)
model.to(device=device, dtype=dtype).eval()
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.inference_mode(), torch.autocast(device_type=device, dtype=dtype):
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
if spaces and SpaceZeroClient:
# Use a custom SpaceZeroClient with headers
client = SpaceZeroClient(headers=headers or None)
try:
text = await client.run(_gpu_inference_fn, args=[prompt], duration=120)
except Exception:
logger.exception("HF inference (ZeroGPU) failed")
raise
else:
# CPU fallback
inputs = tokenizer(prompt, return_tensors="pt")
with torch.inference_mode():
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
yield {
"id": rid,
"object": "chat.completion.chunk",
"created": now,
"model": MODEL_ID,
"choices": [
{"index": 0, "delta": {"content": text}, "finish_reason": "stop"}
],
}
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