6f77c63229
- Workers now compute backend_path and venv_t5 locally via Path(__file__) - Moved .venv_t5 to ~/.unsloth/studio/.venv_t5 - Added ensure_studio_directories() call on server startup - Expanded CLI studio command into sub-app with setup subcommand
617 lines
22 KiB
Python
617 lines
22 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only - See /studio/LICENSE.AGPL-3.0
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# Copyright © 2025 Unsloth AI
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"""
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Inference subprocess entry point.
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Each inference session runs in a persistent subprocess (mp.get_context("spawn")).
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This gives us a clean Python interpreter with no stale module state —
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solving the transformers version-switching problem completely.
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The subprocess stays alive while a model is loaded, accepting commands
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(generate, load, unload) via mp.Queue. It exits on shutdown or unload.
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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 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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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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If the model needs transformers 5.x, prepend the pre-installed .venv_t5/
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directory to sys.path. Otherwise do nothing (default 4.57.x in .venv/).
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"""
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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 needs_transformers_5, _resolve_base_model
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resolved = _resolve_base_model(model_name)
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if needs_transformers_5(resolved):
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venv_t5 = os.path.join(os.path.expanduser("~"), ".unsloth", "studio", ".venv_t5")
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if os.path.isdir(venv_t5):
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sys.path.insert(0, venv_t5)
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logger.info("Activated transformers 5.x from %s", venv_t5)
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else:
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# Fallback: pip install at runtime (slower, ~10-15s)
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logger.warning(".venv_t5 not found at %s — installing at runtime", venv_t5)
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import subprocess as sp
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os.makedirs(venv_t5, exist_ok=True)
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r1 = sp.run(
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[sys.executable, "-m", "pip", "install", "--target", venv_t5,
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"--no-deps", "transformers==5.2.0"],
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stdout=sp.PIPE, stderr=sp.STDOUT,
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)
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r2 = sp.run(
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[sys.executable, "-m", "pip", "install", "--target", venv_t5,
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"--no-deps", "huggingface_hub==1.3.0"],
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stdout=sp.PIPE, stderr=sp.STDOUT,
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)
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if r1.returncode != 0 or r2.returncode != 0:
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raise RuntimeError(
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f"Failed to install transformers 5.x into {venv_t5}. "
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f"pip returncode: transformers={r1.returncode}, huggingface_hub={r2.returncode}"
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)
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sys.path.insert(0, venv_t5)
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# Propagate to child subprocesses (e.g. GGUF converter)
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_pp = os.environ.get("PYTHONPATH", "")
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os.environ["PYTHONPATH"] = venv_t5 + (os.pathsep + _pp if _pp else "")
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else:
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logger.info("Using default transformers (4.57.x) for %s", 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 _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 mc.base_model and "-bnb-4bit" not in mc.base_model.lower() and load_in_4bit:
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logger.info("No training method, base model has no -bnb-4bit — setting load_in_4bit=False")
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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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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=config.get("trust_remote_code", False),
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)
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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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"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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_send_response(resp_queue, {
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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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else:
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_send_response(resp_queue, {
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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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except Exception as exc:
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_send_response(resp_queue, {
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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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def _handle_generate(
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backend,
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cmd: dict,
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resp_queue: Any,
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cancel_event,
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) -> 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 shared with the parent process.
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The parent can set it at any time (e.g. user stops generation,
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or user loads a new model while generating) and generation
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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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# Decode image if provided
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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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# Build generation kwargs
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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", 1.1),
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"cancel_event": cancel_event,
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}
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# Choose generation path
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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(resp_queue, {
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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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_send_response(resp_queue, {
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"type": "gen_done",
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"request_id": request_id,
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"ts": time.time(),
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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(resp_queue, {
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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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def _handle_generate_audio(
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backend,
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cmd: dict,
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resp_queue: Any,
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) -> None:
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"""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", 1.1),
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use_adapter=cmd.get("use_adapter"),
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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(resp_queue, {
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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)
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except Exception as exc:
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logger.error("Audio generation error: %s", exc, exc_info=True)
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_send_response(resp_queue, {
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"type": "audio_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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def _handle_generate_audio_input(
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backend,
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cmd: dict,
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resp_queue: Any,
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cancel_event,
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) -> None:
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"""Handle audio input generation (ASR/Whisper) — streams text tokens back."""
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request_id = cmd.get("request_id", "")
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try:
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import numpy as np
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# Decode audio array from list (numpy arrays can't go through mp.Queue)
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audio_array = np.array(cmd["audio_data"], dtype=np.float32)
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audio_type = cmd.get("audio_type")
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if audio_type == "whisper":
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generator = backend.generate_whisper_response(
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audio_array=audio_array,
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cancel_event=cancel_event,
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)
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else:
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generator = backend.generate_audio_input_response(
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messages=cmd.get("messages", []),
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system_prompt=cmd.get("system_prompt", ""),
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audio_array=audio_array,
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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", 512),
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repetition_penalty=cmd.get("repetition_penalty", 1.1),
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cancel_event=cancel_event,
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)
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logger.info("Starting audio input generation for request_id=%s", request_id)
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for text_chunk in generator:
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if cancel_event.is_set():
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logger.info("Audio input generation cancelled for request %s", request_id)
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break
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_send_response(resp_queue, {
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"type": "token",
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"request_id": request_id,
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"text": text_chunk,
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"ts": time.time(),
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})
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_send_response(resp_queue, {
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"type": "gen_done",
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"request_id": request_id,
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"ts": time.time(),
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})
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logger.info("Finished audio input generation for request_id=%s", request_id)
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except Exception as exc:
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logger.error("Audio input generation error: %s", exc, exc_info=True)
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_send_response(resp_queue, {
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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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def _handle_unload(backend, cmd: dict, resp_queue: Any) -> None:
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"""Handle an unload command."""
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model_name = cmd.get("model_name", "")
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try:
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if model_name and model_name in backend.models:
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backend.unload_model(model_name)
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elif backend.active_model_name:
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backend.unload_model(backend.active_model_name)
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_send_response(resp_queue, {
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"type": "unloaded",
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"model_name": model_name,
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"ts": time.time(),
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})
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except Exception as exc:
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logger.error("Unload error: %s", exc)
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_send_response(resp_queue, {
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"type": "unloaded",
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"model_name": model_name,
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"error": str(exc),
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"ts": time.time(),
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})
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def run_inference_process(
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*,
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cmd_queue: Any,
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resp_queue: Any,
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cancel_event,
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config: dict,
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) -> None:
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"""Subprocess entrypoint. Persistent — runs command loop until shutdown.
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Args:
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cmd_queue: mp.Queue for receiving commands from parent.
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resp_queue: mp.Queue for sending responses to parent.
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cancel_event: mp.Event shared with parent — set by parent to cancel generation.
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config: Initial configuration dict with model info.
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"""
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["PYTHONWARNINGS"] = "ignore" # Suppress warnings at C-level before imports
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import warnings
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from loggers.config import LogConfig
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if os.getenv("ENVIRONMENT_TYPE", "production") == "production":
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warnings.filterwarnings("ignore")
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LogConfig.setup_logging(
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service_name="unsloth-studio-inference-worker",
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env=os.getenv("ENVIRONMENT_TYPE", "production"),
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)
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model_name = config["model_name"]
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# ── 1. Activate correct transformers version BEFORE any ML imports ──
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try:
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_activate_transformers_version(model_name)
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except Exception as exc:
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_send_response(resp_queue, {
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"type": "error",
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"error": f"Failed to activate transformers version: {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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return
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# ── 1b. On Windows, check Triton availability (must be before import torch) ──
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if sys.platform == "win32":
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try:
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import triton # noqa: F401
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logger.info("Triton available — torch.compile enabled")
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except ImportError:
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os.environ["TORCHDYNAMO_DISABLE"] = "1"
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logger.warning(
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"Triton not found on Windows — torch.compile disabled. "
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'Install for better performance: pip install "triton-windows<3.7"'
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)
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# ── 2. Import ML libraries (fresh in this clean process) ──
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try:
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_send_response(resp_queue, {
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"type": "status",
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"message": "Importing ML libraries...",
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"ts": time.time(),
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})
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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 core.inference.inference import InferenceBackend
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import transformers
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logger.info("Subprocess loaded transformers %s", transformers.__version__)
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except Exception as exc:
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_send_response(resp_queue, {
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"type": "error",
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"error": f"Failed to import ML libraries: {exc}",
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"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 shared with parent — parent can set it
|
|
# at any time 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":
|
|
# Load a new model (reusing this subprocess)
|
|
# First unload current model
|
|
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":
|
|
# Return current 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),
|
|
}
|
|
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")
|
|
# Unload all models
|
|
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(),
|
|
})
|