869 lines
31 KiB
Python
869 lines
31 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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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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from utils.hardware import apply_gpu_ids
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# studio/backend root, prepended to sys.path so the spawned subprocess can
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# import the utils/core packages.
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_BACKEND_PATH = str(Path(__file__).resolve().parent.parent.parent)
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def _ensure_backend_on_path() -> None:
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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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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_on_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; stamps ``ts`` if absent."""
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response.setdefault("ts", time.time())
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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 _clean_token(value: str | None) -> str | None:
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"""Normalize an HF token: blank or whitespace-only becomes None."""
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return value if value and value.strip() else None
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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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mc = ModelConfig.from_identifier(
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model_id = model_name,
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hf_token = _clean_token(config.get("hf_token")),
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gguf_variant = config.get("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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_NEMOTRON_TRUST_SUBSTRINGS = ("nemotron_h", "nemotron-h", "nemotron-3-nano")
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def _needs_nemotron_trust(model_name: str, hf_token: str | None = None) -> bool:
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"""Whether *model_name* is a NemotronH/Nano model that needs trust_remote_code.
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NemotronH/Nano have config-parsing bugs that require it. Must NOT match
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Llama-Nemotron (standard Llama arch), so also require the unsloth/ or nvidia/
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namespace, and a genuine first-party Hub repo (not a local path or a spoof
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name starting with "unsloth/"). The repo check is authenticated so private
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first-party repos still resolve, and runs only after the cheap checks pass.
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"""
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mn = model_name.lower()
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if not (
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any(sub in mn for sub in _NEMOTRON_TRUST_SUBSTRINGS)
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and (mn.startswith("unsloth/") or mn.startswith("nvidia/"))
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):
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return False
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from utils.security.trusted_org import is_trusted_org_repo
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return is_trusted_org_repo(model_name, hf_token = hf_token)
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def _resolve_lora_4bit(mc, load_in_4bit: bool) -> bool:
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"""Reconcile load_in_4bit with a LoRA adapter's recorded training method.
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lora -> base is full precision (4bit off); qlora -> base is quantized (4bit
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on); unknown method -> force off only when the base is not a -bnb-4bit repo.
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A missing or unreadable adapter_config.json leaves the value unchanged.
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"""
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if not (mc.is_lora and mc.path):
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return load_in_4bit
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adapter_cfg_path = Path(mc.path) / "adapter_config.json"
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if not adapter_cfg_path.exists():
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return load_in_4bit
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import json
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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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return False
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if 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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return True
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if (
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not training_method
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and 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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return 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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return load_in_4bit
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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 = _clean_token(config.get("hf_token"))
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load_in_4bit = _resolve_lora_4bit(mc, config.get("load_in_4bit", True))
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trust_remote_code = config.get("trust_remote_code", False)
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if not trust_remote_code and _needs_nemotron_trust(config["model_name"], hf_token = hf_token):
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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", config["model_name"]
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)
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# Malware gate: a poisoned pickle deserializes during from_pretrained even
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# with trust_remote_code False, so check HF's security scan (metadata-only)
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# every load. For a LoRA, gate the base whose weights deserialize.
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from utils.security import evaluate_file_security, security_load_subdirs
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malware_targets = [config["model_name"]]
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if mc.is_lora and getattr(mc, "base_model", None):
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malware_targets.append(str(mc.base_model))
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for target in dict.fromkeys(malware_targets):
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_fs = evaluate_file_security(
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target, hf_token = hf_token, load_subdirs = security_load_subdirs(target, hf_token)
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)
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if _fs.blocked:
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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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"message": _fs.reason,
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"error_kind": "malware_blocked",
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"security": _fs.response_payload(),
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},
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)
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return
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# Consent gate: scan auto_map code before it runs; block CRITICAL/HIGH
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# unless pinned-approved. For a LoRA, gate the base whose code runs.
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if trust_remote_code:
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from utils.security import evaluate_remote_code_consent_for_targets
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consent_targets = [config["model_name"]]
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if mc.is_lora and getattr(mc, "base_model", None):
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consent_targets.append(str(mc.base_model))
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# Scan adapter + base as one unit, pinned by a single fingerprint.
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_rc = evaluate_remote_code_consent_for_targets(
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consent_targets,
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hf_token = hf_token,
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trust_remote_code = True,
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approved_fingerprint = config.get("approved_remote_code_fingerprint"),
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)
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if _rc.blocked:
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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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"message": (
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f"Model '{_rc.model_name}' ships custom code flagged as "
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f"{_rc.max_severity} by the security scan. Review "
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f"and approve it to proceed."
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),
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"error_kind": "remote_code_blocked",
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"remote_code": _rc.response_payload(),
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},
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)
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return
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# Heartbeat keeps the orchestrator's inactivity deadline alive during slow
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# loads; a no-progress Xet download is reported as a stall so the parent
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# can respawn over HTTP. Watch model + base repos (base is the LoRA
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# download bottleneck).
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from utils.hf_xet_fallback import start_watchdog
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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_watchdog(
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repo_ids = watch_repos,
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on_stall = lambda msg: _send_response(resp_queue, {"type": "stall", "message": msg}),
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on_heartbeat = lambda msg: _send_response(resp_queue, {"type": "status", "message": msg}),
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xet_disabled = os.environ.get("HF_HUB_DISABLE_XET") == "1",
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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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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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_bm = getattr(backend, "models", {}) or {}
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_entry = (
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_bm.get(mc.identifier) or _bm.get(getattr(backend, "active_model_name", None)) or {}
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)
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try:
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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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_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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},
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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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},
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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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},
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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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},
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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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},
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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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},
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)
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def _handle_generate_audio(backend, cmd: dict, resp_queue: Any) -> 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", 1.0),
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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(
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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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},
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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(
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resp_queue,
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{
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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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},
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)
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def _handle_generate_audio_input(backend, cmd: dict, resp_queue: Any, cancel_event) -> 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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# numpy arrays can't go through mp.Queue, so decode from list.
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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", 1.0),
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cancel_event = cancel_event,
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)
|
|
|
|
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,
|
|
},
|
|
)
|
|
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "gen_done",
|
|
"request_id": request_id,
|
|
},
|
|
)
|
|
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),
|
|
},
|
|
)
|
|
|
|
|
|
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,
|
|
},
|
|
)
|
|
except Exception as exc:
|
|
logger.error("Unload error: %s", exc)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "unloaded",
|
|
"model_name": model_name,
|
|
"error": str(exc),
|
|
},
|
|
)
|
|
|
|
|
|
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 ──
|
|
_ensure_backend_on_path()
|
|
|
|
from utils.hardware import hardware as _hw
|
|
|
|
_hw.detect_hardware()
|
|
if _hw.DEVICE == _hw.DeviceType.MLX:
|
|
# Non-fatal: fall through with the installed version, but log the cause
|
|
# instead of swallowing it (issue #6103).
|
|
try:
|
|
_activate_transformers_version(model_name)
|
|
except Exception as exc:
|
|
logger.warning(
|
|
"Failed to activate transformers version for '%s' (MLX inference); "
|
|
"inference may fail if this model requires a specific version. Error: %s",
|
|
model_name,
|
|
exc,
|
|
)
|
|
try:
|
|
from core.inference.mlx_inference import MLXInferenceBackend
|
|
|
|
backend = MLXInferenceBackend()
|
|
_send_response(
|
|
resp_queue,
|
|
{"type": "status", "message": "Loading model..."},
|
|
)
|
|
_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),
|
|
},
|
|
)
|
|
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"})
|
|
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),
|
|
},
|
|
)
|
|
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),
|
|
},
|
|
)
|
|
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),
|
|
},
|
|
)
|
|
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...",
|
|
},
|
|
)
|
|
|
|
_ensure_backend_on_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),
|
|
},
|
|
)
|
|
return
|
|
|
|
# ── 3. Create inference backend and load initial model ──
|
|
try:
|
|
backend = InferenceBackend()
|
|
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "status",
|
|
"message": "Loading model...",
|
|
},
|
|
)
|
|
|
|
_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),
|
|
},
|
|
)
|
|
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":
|
|
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",
|
|
},
|
|
)
|
|
|
|
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),
|
|
},
|
|
)
|
|
|
|
elif cmd_type == "shutdown":
|
|
logger.info("Shutdown command received, exiting")
|
|
for name in list(backend.models.keys()):
|
|
try:
|
|
backend.unload_model(name)
|
|
except Exception:
|
|
pass
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "shutdown_ack",
|
|
},
|
|
)
|
|
return
|
|
|
|
else:
|
|
logger.warning("Unknown command type: %s", cmd_type)
|
|
_send_response(
|
|
resp_queue,
|
|
{
|
|
"type": "error",
|
|
"error": f"Unknown command type: {cmd_type}",
|
|
},
|
|
)
|
|
|
|
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),
|
|
},
|
|
)
|