672d8f0581* expose runtime context length for hub models * runtime context helper review * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
942 lines
34 KiB
Python
942 lines
34 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Inference subprocess entry point.
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Each session runs in a persistent spawn subprocess, giving a clean interpreter
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with no stale module state (solves transformers version-switching). It stays
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alive while a model is loaded, taking commands (generate, load, unload) via
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mp.Queue, and exits on shutdown or unload. Pattern follows core/training/worker.py.
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"""
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from __future__ import annotations
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import base64
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import structlog
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from loggers import get_logger
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import os
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import queue as _queue
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import sys
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import threading
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import time
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import traceback
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from io import BytesIO
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from pathlib import Path
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from typing import Any
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logger = get_logger(__name__)
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from utils.hardware import apply_gpu_ids
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def _activate_transformers_version(model_name: str) -> None:
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"""Activate the correct transformers version BEFORE any ML imports."""
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# Ensure backend is on path for utils imports.
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backend_path = str(Path(__file__).resolve().parent.parent.parent)
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if backend_path not in sys.path:
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sys.path.insert(0, backend_path)
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from utils.transformers_version import activate_transformers_for_subprocess
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activate_transformers_for_subprocess(model_name)
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def _decode_image(image_base64: str):
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"""Decode base64 string to PIL.Image."""
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from PIL import Image
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image_data = base64.b64decode(image_base64)
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return Image.open(BytesIO(image_data))
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def _resize_image(img, max_size: int = 800):
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"""Resize image while maintaining aspect ratio."""
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if img is None:
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return None
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if img.size[0] > max_size or img.size[1] > max_size:
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from PIL import Image
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ratio = min(max_size / img.size[0], max_size / img.size[1])
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new_size = (int(img.size[0] * ratio), int(img.size[1] * ratio))
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return img.resize(new_size, Image.Resampling.LANCZOS)
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return img
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def _send_response(resp_queue: Any, response: dict) -> None:
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"""Send a response to the parent process."""
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try:
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resp_queue.put(response)
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except (OSError, ValueError) as exc:
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logger.error("Failed to send response: %s", exc)
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def _build_model_config(config: dict):
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"""Build a ModelConfig from the config dict."""
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from utils.models import ModelConfig
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model_name = config["model_name"]
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hf_token = config.get("hf_token")
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hf_token = hf_token if hf_token and hf_token.strip() else None
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gguf_variant = config.get("gguf_variant")
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mc = ModelConfig.from_identifier(
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model_id = model_name,
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hf_token = hf_token,
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gguf_variant = gguf_variant,
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)
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if not mc:
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raise ValueError(f"Invalid model identifier: {model_name}")
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return mc
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def _get_hf_download_state(model_names: list[str] | None = None) -> tuple[int, bool] | None:
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"""Return (total_bytes, has_incomplete) for the HF Hub cache, or None on error.
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With *model_names*, only those models' ``blobs/`` dirs are checked (faster);
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accepts multiple names so LoRA loads can watch adapter + base repos at once.
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*has_incomplete* is True when any ``*.incomplete`` files exist (download
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active). None means state could not be determined, so callers skip stall logic.
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"""
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try:
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from huggingface_hub.constants import HF_HUB_CACHE
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cache = Path(HF_HUB_CACHE)
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if not cache.exists():
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return (0, False)
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total = 0
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has_incomplete = False
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blobs_dirs: list[Path] = []
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if model_names:
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from utils.paths import resolve_cached_repo_id_case
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for name in model_names:
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if not name:
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continue
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# Skip local filesystem paths -- HF IDs (org/model) never start
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# with / . ~ or contain backslashes.
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if name.startswith(("/", ".", "~")) or "\\" in name:
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continue
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name = resolve_cached_repo_id_case(name)
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# HF cache dir format: models--org--name (slashes -> --).
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cache_dir_name = "models--" + name.replace("/", "--")
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blobs_dir = cache / cache_dir_name / "blobs"
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if blobs_dir.exists():
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blobs_dirs.append(blobs_dir)
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else:
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blobs_dirs = list(cache.glob("models--*/blobs"))
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for bdir in blobs_dirs:
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for f in bdir.iterdir():
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try:
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if f.is_file():
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total += f.stat().st_size
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if f.name.endswith(".incomplete"):
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has_incomplete = True
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except OSError:
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pass
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return (total, has_incomplete)
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except Exception as e:
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logger.debug("Failed to determine HF download state: %s", e)
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return None
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def _start_heartbeat(
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resp_queue: Any,
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interval: float = 30.0,
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stall_timeout: float = 180.0,
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xet_disabled: bool = False,
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model_names: list[str] | None = None,
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) -> threading.Event:
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"""Start a daemon thread that sends periodic status heartbeats.
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A stall is reported only when ``*.incomplete`` files are present (download
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active) AND cache size hasn't changed for *stall_timeout* seconds. When the
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download finishes the timer resets, so post-download init (quantization, GPU
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weight load) isn't misclassified as a stall. Returns a stop event.
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"""
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stop = threading.Event()
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transport = "https" if xet_disabled else "xet"
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def _beat():
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state = _get_hf_download_state(model_names)
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last_size = state[0] if state is not None else 0
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last_change = time.monotonic()
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while not stop.wait(interval):
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state = _get_hf_download_state(model_names)
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now = time.monotonic()
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# Skip stall logic if we cannot measure the cache.
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if state is None:
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_send_response(
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resp_queue,
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{
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"type": "status",
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"message": f"Loading model ({transport} transport)...",
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"ts": time.time(),
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},
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)
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continue
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current_size, has_incomplete = state
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if current_size != last_size:
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last_size = current_size
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last_change = now
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# Only fire stall while .incomplete files confirm an active download;
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# reset the timer otherwise so model init isn't counted as a stall.
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if not has_incomplete:
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last_change = now
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elif now - last_change >= stall_timeout:
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_send_response(
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resp_queue,
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{
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"type": "stall",
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"message": (
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f"Download appears stalled ({transport} transport) "
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f"-- no progress for {int(now - last_change)}s"
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),
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"ts": time.time(),
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},
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)
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# fire once -- the orchestrator will kill us
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return
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_send_response(
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resp_queue,
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{
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"type": "status",
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"message": f"Loading model ({transport} transport)...",
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"ts": time.time(),
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},
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)
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t = threading.Thread(target = _beat, daemon = True)
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t.start()
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return stop
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def _handle_load(backend, config: dict, resp_queue: Any) -> None:
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"""Handle a load command: load a model into the backend."""
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try:
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mc = _build_model_config(config)
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hf_token = config.get("hf_token")
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hf_token = hf_token if hf_token and hf_token.strip() else None
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# Auto-detect quantization for LoRA adapters.
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load_in_4bit = config.get("load_in_4bit", True)
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if mc.is_lora and mc.path:
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import json
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from pathlib import Path
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adapter_cfg_path = Path(mc.path) / "adapter_config.json"
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if adapter_cfg_path.exists():
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try:
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with open(adapter_cfg_path) as f:
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adapter_cfg = json.load(f)
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training_method = adapter_cfg.get("unsloth_training_method")
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if training_method == "lora" and load_in_4bit:
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logger.info("adapter_config.json says lora — setting load_in_4bit=False")
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load_in_4bit = False
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elif training_method == "qlora" and not load_in_4bit:
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logger.info("adapter_config.json says qlora — setting load_in_4bit=True")
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load_in_4bit = True
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elif not training_method:
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if (
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mc.base_model
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and "-bnb-4bit" not in mc.base_model.lower()
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and load_in_4bit
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):
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logger.info(
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"No training method, base model has no -bnb-4bit — setting load_in_4bit=False"
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)
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load_in_4bit = False
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except Exception as e:
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logger.warning("Could not read adapter_config.json: %s", e)
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# Auto-enable trust_remote_code only for NemotronH/Nano (config parsing
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# bugs require it). Must NOT match Llama-Nemotron (standard Llama arch).
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_NEMOTRON_TRUST_SUBSTRINGS = ("nemotron_h", "nemotron-h", "nemotron-3-nano")
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trust_remote_code = config.get("trust_remote_code", False)
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if not trust_remote_code:
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model_name = config["model_name"]
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_mn_lower = model_name.lower()
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if any(sub in _mn_lower for sub in _NEMOTRON_TRUST_SUBSTRINGS) and (
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_mn_lower.startswith("unsloth/") or _mn_lower.startswith("nvidia/")
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):
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trust_remote_code = True
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logger.info(
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"Auto-enabled trust_remote_code for Nemotron model: %s",
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model_name,
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)
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# Heartbeat every 30s so the orchestrator knows we're alive during slow loads.
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xet_disabled = os.environ.get("HF_HUB_DISABLE_XET") == "1"
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# Watch model + base repos (base download is the LoRA bottleneck).
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watch_repos = [mc.identifier]
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base = getattr(mc, "base_model", None)
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if base and str(base) != mc.identifier:
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watch_repos.append(str(base))
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heartbeat_stop = _start_heartbeat(
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resp_queue,
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interval = 30.0,
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xet_disabled = xet_disabled,
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model_names = watch_repos,
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)
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try:
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success = backend.load_model(
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config = mc,
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max_seq_length = config.get("max_seq_length", 2048),
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load_in_4bit = load_in_4bit,
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hf_token = hf_token,
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trust_remote_code = trust_remote_code,
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gpu_ids = config.get("resolved_gpu_ids"),
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)
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finally:
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heartbeat_stop.set()
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if success:
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# Build model_info for the parent to mirror.
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model_info = {
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"identifier": mc.identifier,
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"display_name": mc.display_name,
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"is_vision": mc.is_vision,
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"is_lora": mc.is_lora,
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"is_gguf": False,
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# MLX backend sets device="mlx"; lets the UI tag MLX models.
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"is_mlx": getattr(backend, "device", None) == "mlx",
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"is_audio": getattr(mc, "is_audio", False),
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"audio_type": getattr(mc, "audio_type", None),
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"has_audio_input": getattr(mc, "has_audio_input", False),
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}
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try:
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_bm = getattr(backend, "models", {}) or {}
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_entry = (
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_bm.get(mc.identifier)
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or _bm.get(getattr(backend, "active_model_name", None))
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or {}
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)
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_context_length = _entry.get("context_length")
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if _context_length is not None:
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model_info["context_length"] = int(_context_length)
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except Exception as _ctx_exc:
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logger.warning("context_length forward failed: %s", _ctx_exc)
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# Forward chat_template_info so the parent can classify capabilities.
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try:
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_bm = getattr(backend, "models", {}) or {}
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_entry = (
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_bm.get(mc.identifier)
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or _bm.get(getattr(backend, "active_model_name", None))
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or {}
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)
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_tpl_info = _entry.get("chat_template_info")
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if isinstance(_tpl_info, dict):
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model_info["chat_template_info"] = {
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"has_template": bool(_tpl_info.get("has_template", False)),
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"template": _tpl_info.get("template"),
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"format_type": _tpl_info.get("format_type", "generic"),
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"template_name": _tpl_info.get("template_name"),
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"special_tokens": _tpl_info.get("special_tokens", {}) or {},
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}
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except Exception as _tpl_exc:
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logger.warning("chat_template_info forward failed: %s", _tpl_exc)
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_send_response(
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resp_queue,
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{
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"type": "loaded",
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"success": True,
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"model_info": model_info,
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"ts": time.time(),
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},
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)
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else:
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_send_response(
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resp_queue,
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{
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"type": "loaded",
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"success": False,
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"error": "Failed to load model",
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"ts": time.time(),
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},
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)
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except Exception as exc:
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_send_response(
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resp_queue,
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{
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"type": "loaded",
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"success": False,
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"error": str(exc),
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"stack": traceback.format_exc(limit = 20),
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"ts": time.time(),
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},
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)
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def _handle_generate(backend, cmd: dict, resp_queue: Any, cancel_event) -> None:
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"""Handle a generate command: stream tokens back via resp_queue.
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cancel_event is an mp.Event the parent can set anytime (user stop, or new
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model load mid-generate); generation stops within 1-2 tokens.
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"""
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request_id = cmd.get("request_id", "")
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try:
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image = None
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image_b64 = cmd.get("image_base64")
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if image_b64:
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image = _decode_image(image_b64)
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image = _resize_image(image)
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gen_kwargs = {
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"messages": cmd["messages"],
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"system_prompt": cmd.get("system_prompt", ""),
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"image": image,
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"temperature": cmd.get("temperature", 0.7),
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"top_p": cmd.get("top_p", 0.9),
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"top_k": cmd.get("top_k", 40),
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"min_p": cmd.get("min_p", 0.0),
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"max_new_tokens": cmd.get("max_new_tokens", 256),
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"repetition_penalty": cmd.get("repetition_penalty", 1.0),
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"cancel_event": cancel_event,
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}
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# Forward only present optional keys so the backend signature can evolve.
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for opt_key in (
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"tools",
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"enable_thinking",
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"reasoning_effort",
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"preserve_thinking",
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):
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if opt_key in cmd:
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gen_kwargs[opt_key] = cmd[opt_key]
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use_adapter = cmd.get("use_adapter")
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if use_adapter is not None:
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generator = backend.generate_with_adapter_control(
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use_adapter = use_adapter,
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**gen_kwargs,
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)
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else:
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generator = backend.generate_chat_response(**gen_kwargs)
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logger.info("Starting text generation for request_id=%s", request_id)
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for cumulative_text in generator:
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# cancel_event is an mp.Event — checked instantly, no queue polling.
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if cancel_event.is_set():
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logger.info("Generation cancelled for request %s", request_id)
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break
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|
_send_response(
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resp_queue,
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{
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"type": "token",
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"request_id": request_id,
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"text": cumulative_text,
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"ts": time.time(),
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},
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)
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|
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|
_send_response(
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resp_queue,
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{
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"type": "gen_done",
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"request_id": request_id,
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# usage/timings from the MLX backend (None elsewhere).
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"stats": getattr(backend, "last_generation_stats", None),
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"ts": time.time(),
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},
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)
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logger.info("Finished text generation for request_id=%s", request_id)
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except Exception as exc:
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logger.error("Generation error: %s", exc, exc_info = True)
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_send_response(
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resp_queue,
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{
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"type": "gen_error",
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"request_id": request_id,
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"error": str(exc),
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"stack": traceback.format_exc(limit = 20),
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"ts": time.time(),
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},
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)
|
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|
|
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def _handle_generate_audio(backend, cmd: dict, resp_queue: Any) -> None:
|
|
"""Handle TTS audio generation — returns WAV bytes + sample_rate."""
|
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request_id = cmd.get("request_id", "")
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try:
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logger.info("Starting audio generation for request_id=%s", request_id)
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wav_bytes, sample_rate = backend.generate_audio_response(
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text = cmd["text"],
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temperature = cmd.get("temperature", 0.6),
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top_p = cmd.get("top_p", 0.95),
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top_k = cmd.get("top_k", 50),
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min_p = cmd.get("min_p", 0.0),
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max_new_tokens = cmd.get("max_new_tokens", 2048),
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repetition_penalty = cmd.get("repetition_penalty", 1.0),
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use_adapter = cmd.get("use_adapter"),
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)
|
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|
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# Send WAV bytes as base64 (bytes can't go through mp.Queue directly).
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|
_send_response(
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resp_queue,
|
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{
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"type": "audio_done",
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|
"request_id": request_id,
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|
"wav_base64": base64.b64encode(wav_bytes).decode("ascii"),
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"sample_rate": sample_rate,
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"ts": time.time(),
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},
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)
|
|
logger.info("Finished audio generation for request_id=%s", request_id)
|
|
|
|
except Exception as exc:
|
|
logger.error("Audio generation error: %s", exc, exc_info = True)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "audio_error",
|
|
"request_id": request_id,
|
|
"error": str(exc),
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
|
|
def _handle_generate_audio_input(backend, cmd: dict, resp_queue: Any, cancel_event) -> None:
|
|
"""Handle audio input generation (ASR/Whisper) — streams text tokens back."""
|
|
request_id = cmd.get("request_id", "")
|
|
|
|
try:
|
|
import numpy as np
|
|
|
|
# numpy arrays can't go through mp.Queue, so decode from list.
|
|
audio_array = np.array(cmd["audio_data"], dtype = np.float32)
|
|
|
|
audio_type = cmd.get("audio_type")
|
|
|
|
if audio_type == "whisper":
|
|
generator = backend.generate_whisper_response(
|
|
audio_array = audio_array,
|
|
cancel_event = cancel_event,
|
|
)
|
|
else:
|
|
generator = backend.generate_audio_input_response(
|
|
messages = cmd.get("messages", []),
|
|
system_prompt = cmd.get("system_prompt", ""),
|
|
audio_array = audio_array,
|
|
temperature = cmd.get("temperature", 0.7),
|
|
top_p = cmd.get("top_p", 0.9),
|
|
top_k = cmd.get("top_k", 40),
|
|
min_p = cmd.get("min_p", 0.0),
|
|
max_new_tokens = cmd.get("max_new_tokens", 512),
|
|
repetition_penalty = cmd.get("repetition_penalty", 1.0),
|
|
cancel_event = cancel_event,
|
|
)
|
|
|
|
logger.info("Starting audio input generation for request_id=%s", request_id)
|
|
|
|
for text_chunk in generator:
|
|
if cancel_event.is_set():
|
|
logger.info("Audio input generation cancelled for request %s", request_id)
|
|
break
|
|
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "token",
|
|
"request_id": request_id,
|
|
"text": text_chunk,
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "gen_done",
|
|
"request_id": request_id,
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
logger.info("Finished audio input generation for request_id=%s", request_id)
|
|
|
|
except Exception as exc:
|
|
logger.error("Audio input generation error: %s", exc, exc_info = True)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "gen_error",
|
|
"request_id": request_id,
|
|
"error": str(exc),
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
|
|
def _handle_unload(backend, cmd: dict, resp_queue: Any) -> None:
|
|
"""Handle an unload command."""
|
|
model_name = cmd.get("model_name", "")
|
|
try:
|
|
if model_name and model_name in backend.models:
|
|
backend.unload_model(model_name)
|
|
elif backend.active_model_name:
|
|
backend.unload_model(backend.active_model_name)
|
|
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "unloaded",
|
|
"model_name": model_name,
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
except Exception as exc:
|
|
logger.error("Unload error: %s", exc)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "unloaded",
|
|
"model_name": model_name,
|
|
"error": str(exc),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
|
|
def run_inference_process(*, cmd_queue: Any, resp_queue: Any, cancel_event, config: dict) -> None:
|
|
"""Subprocess entrypoint. Persistent — runs the command loop until shutdown.
|
|
|
|
Args:
|
|
cmd_queue: mp.Queue for receiving commands from parent.
|
|
resp_queue: mp.Queue for sending responses to parent.
|
|
cancel_event: mp.Event the parent sets to cancel generation.
|
|
config: Initial configuration dict with model info.
|
|
"""
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
|
os.environ["PYTHONWARNINGS"] = "ignore" # Suppress warnings at C-level before imports
|
|
|
|
if config.get("disable_xet"):
|
|
os.environ["HF_HUB_DISABLE_XET"] = "1"
|
|
logger.info("Xet transport disabled (HF_HUB_DISABLE_XET=1)")
|
|
|
|
import warnings
|
|
from loggers.config import LogConfig
|
|
|
|
if os.getenv("ENVIRONMENT_TYPE", "production") == "production":
|
|
warnings.filterwarnings("ignore")
|
|
|
|
LogConfig.setup_logging(
|
|
service_name = "unsloth-studio-inference-worker",
|
|
env = os.getenv("ENVIRONMENT_TYPE", "production"),
|
|
)
|
|
|
|
apply_gpu_ids(config.get("resolved_gpu_ids"))
|
|
|
|
model_name = config["model_name"]
|
|
|
|
# ── 0. MLX fast-path — skip torch/transformers ──
|
|
backend_path = str(Path(__file__).resolve().parent.parent.parent)
|
|
if backend_path not in sys.path:
|
|
sys.path.insert(0, backend_path)
|
|
|
|
from utils.hardware import hardware as _hw
|
|
|
|
_hw.detect_hardware()
|
|
if _hw.DEVICE == _hw.DeviceType.MLX:
|
|
try:
|
|
_activate_transformers_version(model_name)
|
|
except Exception:
|
|
pass
|
|
try:
|
|
from core.inference.mlx_inference import MLXInferenceBackend
|
|
|
|
backend = MLXInferenceBackend()
|
|
_send_response(
|
|
resp_queue,
|
|
{"type": "status", "message": "Loading model...", "ts": time.time()},
|
|
)
|
|
_handle_load(backend, config, resp_queue)
|
|
except Exception as exc:
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "error",
|
|
"error": f"MLX inference init failed: {exc}",
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
return
|
|
|
|
# Enter the same command loop as the GPU path.
|
|
logger.info("MLX inference subprocess ready, entering command loop")
|
|
while True:
|
|
try:
|
|
cmd = cmd_queue.get(timeout = 1.0)
|
|
except _queue.Empty:
|
|
continue
|
|
except (EOFError, OSError):
|
|
return
|
|
if cmd is None:
|
|
continue
|
|
cmd_type = cmd.get("type", "")
|
|
try:
|
|
if cmd_type == "generate":
|
|
cancel_event.clear()
|
|
_handle_generate(backend, cmd, resp_queue, cancel_event)
|
|
elif cmd_type == "load":
|
|
if backend.active_model_name:
|
|
backend.unload_model(backend.active_model_name)
|
|
_handle_load(backend, cmd, resp_queue)
|
|
elif cmd_type == "unload":
|
|
_handle_unload(backend, cmd, resp_queue)
|
|
elif cmd_type == "cancel":
|
|
cancel_event.set()
|
|
elif cmd_type == "reset":
|
|
cancel_event.set()
|
|
backend.reset_generation_state()
|
|
_send_response(resp_queue, {"type": "reset_ack", "ts": time.time()})
|
|
elif cmd_type == "status":
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "status_response",
|
|
"active_model": backend.active_model_name,
|
|
"models": {
|
|
k: {kk: vv for kk, vv in v.items() if kk != "model"}
|
|
for k, v in backend.models.items()
|
|
},
|
|
"loading": list(backend.loading_models),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
elif cmd_type == "shutdown":
|
|
return
|
|
except Exception as exc:
|
|
logger.error("MLX command error (%s): %s", cmd_type, exc)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "gen_error" if cmd_type == "generate" else "error",
|
|
"request_id": cmd.get("request_id"),
|
|
"error": str(exc),
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
return
|
|
|
|
# ── 1. Activate transformers version BEFORE any ML imports ──
|
|
try:
|
|
_activate_transformers_version(model_name)
|
|
except Exception as exc:
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "error",
|
|
"error": f"Failed to activate transformers version: {exc}",
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
return
|
|
|
|
# ── 1b. Windows: check Triton availability (must precede import torch) ──
|
|
if sys.platform == "win32":
|
|
try:
|
|
import triton # noqa: F401
|
|
logger.info("Triton available — torch.compile enabled")
|
|
except ImportError:
|
|
os.environ["TORCHDYNAMO_DISABLE"] = "1"
|
|
logger.warning(
|
|
"Triton not found on Windows — torch.compile disabled. "
|
|
'Install for better performance: pip install "triton-windows<3.7"'
|
|
)
|
|
|
|
# ── 2. Import ML libraries (fresh in this clean process) ──
|
|
try:
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "status",
|
|
"message": "Importing Unsloth...",
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
backend_path = str(Path(__file__).resolve().parent.parent.parent)
|
|
if backend_path not in sys.path:
|
|
sys.path.insert(0, backend_path)
|
|
|
|
from core.inference.inference import InferenceBackend
|
|
|
|
import transformers
|
|
|
|
logger.info("Subprocess loaded transformers %s", transformers.__version__)
|
|
|
|
except Exception as exc:
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "error",
|
|
"error": f"Failed to import ML libraries: {exc}",
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
return
|
|
|
|
# ── 3. Create inference backend and load initial model ──
|
|
try:
|
|
backend = InferenceBackend()
|
|
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "status",
|
|
"message": "Loading model...",
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
_handle_load(backend, config, resp_queue)
|
|
|
|
except Exception as exc:
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "error",
|
|
"error": f"Failed to initialize inference backend: {exc}",
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
return
|
|
|
|
# ── 4. Command loop — process commands until shutdown ──
|
|
# cancel_event is an mp.Event the parent can set anytime to cancel
|
|
# generation instantly (no queue polling needed).
|
|
logger.info("Inference subprocess ready, entering command loop")
|
|
|
|
while True:
|
|
try:
|
|
cmd = cmd_queue.get(timeout = 1.0)
|
|
except _queue.Empty:
|
|
continue
|
|
except (EOFError, OSError):
|
|
logger.info("Command queue closed, shutting down")
|
|
return
|
|
|
|
if cmd is None:
|
|
continue
|
|
|
|
cmd_type = cmd.get("type", "")
|
|
logger.info("Received command: %s", cmd_type)
|
|
|
|
try:
|
|
if cmd_type == "generate":
|
|
cancel_event.clear()
|
|
_handle_generate(backend, cmd, resp_queue, cancel_event)
|
|
|
|
elif cmd_type == "load":
|
|
# Unload the current model before loading the new one.
|
|
if backend.active_model_name:
|
|
backend.unload_model(backend.active_model_name)
|
|
_handle_load(backend, cmd, resp_queue)
|
|
|
|
elif cmd_type == "generate_audio":
|
|
cancel_event.clear()
|
|
_handle_generate_audio(backend, cmd, resp_queue)
|
|
|
|
elif cmd_type == "generate_audio_input":
|
|
cancel_event.clear()
|
|
_handle_generate_audio_input(backend, cmd, resp_queue, cancel_event)
|
|
|
|
elif cmd_type == "unload":
|
|
_handle_unload(backend, cmd, resp_queue)
|
|
|
|
elif cmd_type == "cancel":
|
|
# Redundant with mp.Event but handle gracefully.
|
|
cancel_event.set()
|
|
logger.info("Cancel command received")
|
|
|
|
elif cmd_type == "reset":
|
|
cancel_event.set()
|
|
backend.reset_generation_state()
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "reset_ack",
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
elif cmd_type == "status":
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "status_response",
|
|
"active_model": backend.active_model_name,
|
|
"models": {
|
|
name: {
|
|
"is_vision": info.get("is_vision", False),
|
|
"is_lora": info.get("is_lora", False),
|
|
"context_length": info.get("context_length"),
|
|
}
|
|
for name, info in backend.models.items()
|
|
},
|
|
"loading": list(backend.loading_models),
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
elif cmd_type == "shutdown":
|
|
logger.info("Shutdown command received, exiting")
|
|
for model_name in list(backend.models.keys()):
|
|
try:
|
|
backend.unload_model(model_name)
|
|
except Exception:
|
|
pass
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "shutdown_ack",
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
return
|
|
|
|
else:
|
|
logger.warning("Unknown command type: %s", cmd_type)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "error",
|
|
"error": f"Unknown command type: {cmd_type}",
|
|
"ts": time.time(),
|
|
},
|
|
)
|
|
|
|
except Exception as exc:
|
|
logger.error("Error handling command '%s': %s", cmd_type, exc, exc_info = True)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "error",
|
|
"error": f"Command '{cmd_type}' failed: {exc}",
|
|
"stack": traceback.format_exc(limit = 20),
|
|
"ts": time.time(),
|
|
},
|
|
)
|