File size: 4,261 Bytes
2dcb7ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
# hf_backend.py
import time, logging, os, contextlib
from typing import Any, Dict, AsyncIterable, List
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from backends_base import ChatBackend, ImagesBackend
from config import settings
try:
import spaces
except ImportError:
spaces = None
logger = logging.getLogger(__name__)
# --- Load model/tokenizer on CPU at import time (ZeroGPU safe) ---
MODEL_ID = settings.LlmHFModelID or "Qwen/Qwen2.5-1.5B-Instruct"
logger.info(f"Loading {MODEL_ID} on CPU at startup (ZeroGPU safe)...")
tokenizer = None
model = None
load_error = 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, # CPU-safe default
trust_remote_code=True,
)
model.eval()
except Exception as e:
load_error = f"Failed to load model/tokenizer: {e}"
logger.exception(load_error)
# --- Device helpers ---
def pick_device() -> str:
forced = os.getenv("FORCE_DEVICE", "").lower().strip()
if forced in {"cpu", "cuda", "mps"}:
return forced
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
# --- Backend class ---
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())
if spaces:
@spaces.GPU(duration=120) # allow longer run
def run_once(prompt: str) -> str:
device = pick_device()
dtype = pick_dtype(device)
# Move model to GPU if needed
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)
else:
def run_once(prompt: str) -> str:
inputs = tokenizer(prompt, return_tensors="pt")
with torch.inference_mode():
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
try:
text = run_once(prompt)
yield {
"id": rid,
"object": "chat.completion.chunk",
"created": now,
"model": MODEL_ID,
"choices": [
{"index": 0, "delta": {"content": text}, "finish_reason": "stop"}
],
}
except Exception:
logger.exception("HF inference failed")
raise
class StubImagesBackend(ImagesBackend):
async def generate_b64(self, request: Dict[str, Any]) -> str:
logger.warning("Image generation not supported in HF backend.")
return (
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR4nGP4BwQACfsD/etCJH0AAAAASUVORK5CYII="
)
|