hf_backend.py

# hf_backend.py
import time, logging, json
from contextlib import nullcontext
from typing import Any, Dict, AsyncIterable, Tuple

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
from backends_base import ChatBackend, ImagesBackend
from config import settings

logger = logging.getLogger(__name__)

def _snippet(txt: str, n: int = 800) -> str:
    if not isinstance(txt, str):
        return f"<non-str:{type(txt)}>"
    return txt if len(txt) <= n else txt[:n] + f"... <+{len(txt)-n} chars>"

try:
    import spaces
    from spaces.zero import client as zero_client
except ImportError:
    spaces, zero_client = None, None

MODEL_ID = settings.LlmHFModelID or "Qwen/Qwen2.5-1.5B-Instruct"
logger.info(f"[init] MODEL_ID={MODEL_ID}")

tokenizer, load_error = None, None
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True, use_fast=False)
    has_template = hasattr(tokenizer, "apply_chat_template") and getattr(tokenizer, "chat_template", None)
    logger.info(f"[init] tokenizer loaded. chat_template={'yes' if has_template else 'no'}")
except Exception as e:
    load_error = f"Failed to load tokenizer: {e}"
    logger.exception(load_error)

def _pick_cpu_dtype() -> torch.dtype:
    try:
        if hasattr(torch, "cpu") and hasattr(torch.cpu, "is_bf16_supported") and torch.cpu.is_bf16_supported():
            logger.info("[dtype] CPU BF16 supported -> torch.bfloat16")
            return torch.bfloat16
    except Exception as e:
        logger.warning(f"[dtype] BF16 probe failed: {e}")
    logger.info("[dtype] fallback -> torch.float32")
    return torch.float32

_MODEL_CACHE: Dict[tuple[str, torch.dtype], AutoModelForCausalLM] = {}

def _get_model(device: str, dtype: torch.dtype) -> Tuple[AutoModelForCausalLM, torch.dtype]:
    key = (device, dtype)
    if key in _MODEL_CACHE:
        logger.info(f"[cache] hit model for device={device} dtype={dtype}")
        return _MODEL_CACHE[key], dtype

    logger.info(f"[load] begin from_pretrained device={device} dtype={dtype}")
    cfg = AutoConfig.from_pretrained(MODEL_ID, trust_remote_code=True)
    if hasattr(cfg, "quantization_config"):
        logger.warning("[load] removing quantization_config from config to avoid FP8 path")
        delattr(cfg, "quantization_config")

    eff_dtype = dtype
    try:
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_ID,
            config=cfg,
            torch_dtype=dtype,
            trust_remote_code=True,
            device_map="auto" if device != "cpu" else {"": "cpu"},
            low_cpu_mem_usage=False,
        )
    except Exception as e:
        if device == "cpu" and dtype == torch.bfloat16:
            logger.warning(f"[load] BF16 load failed on CPU ({e}). retry FP32.")
            eff_dtype = torch.float32
            model = AutoModelForCausalLM.from_pretrained(
                MODEL_ID,
                config=cfg,
                torch_dtype=eff_dtype,
                trust_remote_code=True,
                device_map={"": "cpu"},
                low_cpu_mem_usage=False,
            )
        else:
            logger.exception("[load] from_pretrained failed")
            raise

    if device == "cpu":
        logger.info(f"[load] casting all weights to CPU dtype={eff_dtype}")
        model = model.to(device=device, dtype=eff_dtype)
    else:
        logger.info(f"[load] moving model to device={device} (no recast)")
        model = model.to(device=device)

    model.eval()
    try:
        first_dtype = next(model.parameters()).dtype
        logger.info(f"[load] ready. effective_dtype={eff_dtype} first_param_dtype={first_dtype}")
    except Exception:
        logger.info(f"[load] ready. effective_dtype={eff_dtype} (param dtype probe failed)")

    _MODEL_CACHE[(device, eff_dtype)] = model
    return model, eff_dtype

def _max_context(model, tokenizer) -> int:
    # Prefer model config; fallback to tokenizer hint
    mc = getattr(getattr(model, "config", None), "max_position_embeddings", None)
    if isinstance(mc, int) and mc > 0:
        return mc
    tk = getattr(tokenizer, "model_max_length", None)
    if isinstance(tk, int) and tk > 0 and tk < 10**12:
        return tk
    return 32768  # safe default for Qwen3

def _build_inputs_with_truncation(prompt: str, device: str, max_new_tokens: int, model, tokenizer):
    toks = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
    input_ids = toks["input_ids"]
    attn = toks.get("attention_mask", None)

    ctx = _max_context(model, tokenizer)
    limit = max(8, ctx - max_new_tokens)
    in_len = input_ids.shape[-1]
    if in_len > limit:
        # left-truncate to fit context
        cut = in_len - limit
        input_ids = input_ids[:, -limit:]
        if attn is not None:
            attn = attn[:, -limit:]
        logger.warning(f"[truncate] prompt_tokens={in_len} > limit={limit}. truncated_left_by={cut} to fit ctx={ctx}, new_input={input_ids.shape[-1]}, max_new={max_new_tokens}")

    inputs = {"input_ids": input_ids}
    if attn is not None:
        inputs["attention_mask"] = attn

    # move to device
    inputs = {k: v.to(device) if hasattr(v, "to") else v for k, v in inputs.items()}
    return inputs, in_len, ctx, limit

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", [])
        tools = request.get("tools")
        temperature = float(request.get("temperature", settings.LlmTemp or 0.7))
        req_max_tokens = int(request.get("max_tokens", settings.LlmOpenAICtxSize or 512))

        rid = f"chatcmpl-hf-{int(time.time())}"
        now = int(time.time())

        logger.info(f"[req] rid={rid} temp={temperature} req_max_tokens={req_max_tokens} "
                    f"msgs={len(messages)} tools={'yes' if tools else 'no'} "
                    f"spaces={'yes' if spaces else 'no'} cuda={'yes' if torch.cuda.is_available() else 'no'}")

        x_ip_token = request.get("x_ip_token")
        if x_ip_token and zero_client:
            zero_client.HEADERS["X-IP-Token"] = x_ip_token
            logger.info("[req] injected X-IP-Token into ZeroGPU headers")

        # Build prompt
        if hasattr(tokenizer, "apply_chat_template") and getattr(tokenizer, "chat_template", None):
            try:
                prompt = tokenizer.apply_chat_template(
                    messages,
                    #tools=tools,
                    tokenize=False,
                    add_generation_prompt=True,
                )
                logger.info(f"[prompt] built via chat_template. len={len(prompt)}\n{_snippet(prompt, 800)}")
            except Exception as e:
                logger.warning(f"[prompt] chat_template failed -> fallback. err={e}")
                prompt = messages[-1]["content"] if messages else "(empty)"
                logger.info(f"[prompt] fallback content len={len(prompt)}\n{_snippet(prompt, 800)}")
        else:
            prompt = messages[-1]["content"] if messages else "(empty)"
            logger.info(f"[prompt] no template. using last user text len={len(prompt)}\n{_snippet(prompt, 800)}")

        def _run_once(prompt: str, device: str, req_dtype: torch.dtype) -> str:
            model, eff_dtype = _get_model(device, req_dtype)

            # Clamp max_new_tokens for CPU to prevent stalls
            if device == "cpu":
                max_new_tokens = min(req_max_tokens, 512)
            else:
                max_new_tokens = req_max_tokens

            # Build inputs with context-aware truncation
            inputs, orig_in_len, ctx, limit = _build_inputs_with_truncation(prompt, device, max_new_tokens, model, tokenizer)

            logger.info(f"[gen] device={device} dtype={eff_dtype} input_tokens={inputs['input_ids'].shape[-1]} "
                        f"(orig={orig_in_len}) max_ctx={ctx} limit_for_input={limit} max_new_tokens={max_new_tokens}")

            # Sampling settings
            do_sample = temperature > 1e-6
            temp = max(1e-5, temperature) if do_sample else 0.0

            # ids
            eos_id = tokenizer.eos_token_id
            pad_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else eos_id

            with torch.inference_mode():
                if device != "cpu":
                    autocast_ctx = torch.autocast(device_type=device, dtype=eff_dtype)
                else:
                    autocast_ctx = torch.cpu.amp.autocast(dtype=torch.bfloat16) if eff_dtype == torch.bfloat16 else nullcontext()

                gen_kwargs = dict(
                    max_new_tokens=max_new_tokens,
                    temperature=temp,
                    do_sample=do_sample,
                    use_cache=True,
                    eos_token_id=eos_id,
                    pad_token_id=pad_id,
                )
                logger.info(f"[gen] kwargs={gen_kwargs}")

                with autocast_ctx:
                    outputs = model.generate(**inputs, **gen_kwargs)

            # Slice generated continuation only
            input_len = inputs["input_ids"].shape[-1]
            generated_ids = outputs[0][input_len:]
            logger.info(f"[gen] new_tokens={generated_ids.shape[-1]}")
            text = tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
            logger.info(f"[gen] text len={len(text)}\n{_snippet(text, 1200)}")
            return text

        if spaces:
            @spaces.GPU(duration=120)
            def run_once(prompt: str) -> str:
                if torch.cuda.is_available():
                    logger.info("[path] ZeroGPU + CUDA")
                    return _run_once(prompt, device="cuda", req_dtype=torch.float16)
                logger.info("[path] ZeroGPU but no CUDA -> CPU fallback")
                return _run_once(prompt, device="cpu", req_dtype=_pick_cpu_dtype())
            text = run_once(prompt)
        else:
            logger.info("[path] CPU-only runtime")
            text = _run_once(prompt, device="cpu", req_dtype=_pick_cpu_dtype())

        chunk = {
            "id": rid,
            "object": "chat.completion.chunk",
            "created": now,
            "model": MODEL_ID,
            "choices": [
                {"index": 0, "delta": {"role": "assistant", "content": text}, "finish_reason": "stop"}
            ],
        }
        logger.info(f"[out] chunk summary -> id={rid} content_len={len(text)}")
        yield chunk


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="