1295 lines
49 KiB
Python
1295 lines
49 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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llama-server inference backend for GGUF models.
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Manages a llama-server subprocess and proxies chat completions
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through its OpenAI-compatible /v1/chat/completions endpoint.
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"""
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import atexit
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import json
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import struct
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import structlog
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from loggers import get_logger
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import shutil
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import signal
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import socket
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import subprocess
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import threading
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import time
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from pathlib import Path
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from typing import Generator, Optional
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import httpx
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logger = get_logger(__name__)
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class LlamaCppBackend:
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"""
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Manages a llama-server subprocess for GGUF model inference.
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Lifecycle:
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1. load_model() — starts llama-server with the GGUF file
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2. generate_chat_completion() — proxies to /v1/chat/completions, streams back
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3. unload_model() — terminates llama-server subprocess
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"""
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def __init__(self):
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self._process: Optional[subprocess.Popen] = None
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self._port: Optional[int] = None
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self._model_identifier: Optional[str] = None
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self._gguf_path: Optional[str] = None
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self._hf_repo: Optional[str] = None
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self._hf_variant: Optional[str] = None
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self._is_vision: bool = False
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self._healthy = False
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self._context_length: Optional[int] = None
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self._chat_template: Optional[str] = None
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self._lock = threading.Lock()
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self._stdout_lines: list[str] = []
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self._stdout_thread: Optional[threading.Thread] = None
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self._cancel_event = threading.Event()
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self._kill_orphaned_servers()
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atexit.register(self._cleanup)
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# ── Properties ────────────────────────────────────────────────
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@property
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def is_loaded(self) -> bool:
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return self._process is not None and self._healthy
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@property
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def is_active(self) -> bool:
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"""True if a llama-server process exists (loading or loaded)."""
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return self._process is not None
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@property
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def base_url(self) -> str:
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return f"http://127.0.0.1:{self._port}"
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@property
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def model_identifier(self) -> Optional[str]:
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return self._model_identifier
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@property
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def is_vision(self) -> bool:
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return self._is_vision
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@property
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def hf_variant(self) -> Optional[str]:
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return self._hf_variant
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@property
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def context_length(self) -> Optional[int]:
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return self._context_length
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@property
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def chat_template(self) -> Optional[str]:
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return self._chat_template
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# ── Binary discovery ──────────────────────────────────────────
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@staticmethod
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def _find_llama_server_binary() -> Optional[str]:
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"""
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Locate the llama-server binary.
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Search order:
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1. LLAMA_SERVER_PATH environment variable (direct path to binary)
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1b. UNSLOTH_LLAMA_CPP_PATH env var (custom llama.cpp install dir)
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2. ~/.unsloth/llama.cpp/llama-server (make build, root dir)
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3. ~/.unsloth/llama.cpp/build/bin/llama-server (cmake build, Linux)
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4. ~/.unsloth/llama.cpp/build/bin/Release/llama-server.exe (cmake build, Windows)
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5. ./llama.cpp/llama-server (legacy: make build, root dir)
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6. ./llama.cpp/build/bin/llama-server (legacy: cmake in-tree build)
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7. llama-server on PATH (system install)
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8. ./bin/llama-server (legacy: extracted binary)
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"""
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import os
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import sys
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binary_name = "llama-server.exe" if sys.platform == "win32" else "llama-server"
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# 1. Env var — direct path to binary
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env_path = os.environ.get("LLAMA_SERVER_PATH")
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if env_path and Path(env_path).is_file():
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return env_path
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# 1b. UNSLOTH_LLAMA_CPP_PATH — custom llama.cpp install directory
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custom_llama_cpp = os.environ.get("UNSLOTH_LLAMA_CPP_PATH")
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if custom_llama_cpp:
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custom_dir = Path(custom_llama_cpp)
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# Root dir (make builds)
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root_bin = custom_dir / binary_name
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if root_bin.is_file():
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return str(root_bin)
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# build/bin/ (cmake builds on Linux)
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cmake_bin = custom_dir / "build" / "bin" / binary_name
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if cmake_bin.is_file():
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return str(cmake_bin)
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# build/bin/Release/ (cmake builds on Windows)
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if sys.platform == "win32":
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win_bin = custom_dir / "build" / "bin" / "Release" / binary_name
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if win_bin.is_file():
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return str(win_bin)
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# 2–4. ~/.unsloth/llama.cpp (primary — setup.sh / setup.ps1 build here)
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unsloth_home = Path.home() / ".unsloth" / "llama.cpp"
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# Root dir (make builds copy binaries here)
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home_root = unsloth_home / binary_name
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if home_root.is_file():
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return str(home_root)
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# build/bin/ (cmake builds on Linux)
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home_linux = unsloth_home / "build" / "bin" / binary_name
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if home_linux.is_file():
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return str(home_linux)
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# 3. Windows MSVC build has Release subdir
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if sys.platform == "win32":
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home_win = unsloth_home / "build" / "bin" / "Release" / binary_name
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if home_win.is_file():
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return str(home_win)
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# 5–6. Legacy: in-tree build (older setup.sh / setup.ps1 versions)
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project_root = Path(__file__).resolve().parents[4]
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# Root dir (make builds)
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root_path = project_root / "llama.cpp" / binary_name
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if root_path.is_file():
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return str(root_path)
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# build/bin/ (cmake builds)
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build_path = project_root / "llama.cpp" / "build" / "bin" / binary_name
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if build_path.is_file():
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return str(build_path)
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if sys.platform == "win32":
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win_path = (
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project_root / "llama.cpp" / "build" / "bin" / "Release" / binary_name
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)
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if win_path.is_file():
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return str(win_path)
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# 7. System PATH
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system_path = shutil.which("llama-server")
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if system_path:
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return system_path
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# 8. Legacy: extracted to bin/
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bin_path = project_root / "bin" / binary_name
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if bin_path.is_file():
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return str(bin_path)
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return None
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# ── GPU allocation ────────────────────────────────────────────
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@staticmethod
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def _get_gguf_size_bytes(model_path: str) -> int:
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"""Get total GGUF size in bytes, including split shards."""
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import re
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main = Path(model_path)
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total = main.stat().st_size
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# Check for split shards (e.g., model-00001-of-00003.gguf)
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shard_pat = re.compile(r"^(.*)-(\d{5})-of-(\d{5})\.gguf$")
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m = shard_pat.match(main.name)
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if m:
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prefix, _, num_total = m.group(1), m.group(2), m.group(3)
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sibling_pat = re.compile(
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r"^"
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+ re.escape(prefix)
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+ r"-\d{5}-of-"
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+ re.escape(num_total)
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+ r"\.gguf$"
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)
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for sibling in main.parent.iterdir():
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if sibling != main and sibling_pat.match(sibling.name):
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total += sibling.stat().st_size
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return total
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@staticmethod
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def _get_gpu_free_memory() -> list[tuple[int, int]]:
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"""Query free memory per GPU via nvidia-smi.
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Returns list of (gpu_index, free_mib) sorted by index.
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Respects CUDA_VISIBLE_DEVICES if set.
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Returns empty list if nvidia-smi is not available.
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"""
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import os
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try:
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result = subprocess.run(
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[
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"nvidia-smi",
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"--query-gpu=index,memory.free",
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"--format=csv,noheader,nounits",
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],
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capture_output = True,
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text = True,
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timeout = 10,
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)
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if result.returncode != 0:
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return []
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# Parse which GPUs are allowed by existing CUDA_VISIBLE_DEVICES
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allowed = None
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cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
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if cvd is not None and cvd.strip():
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try:
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allowed = set(int(x.strip()) for x in cvd.split(","))
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except ValueError:
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pass # Non-numeric (e.g., "GPU-uuid"), ignore filter
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gpus = []
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for line in result.stdout.strip().splitlines():
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parts = line.split(",")
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if len(parts) == 2:
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idx = int(parts[0].strip())
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free_mib = int(parts[1].strip())
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if allowed is not None and idx not in allowed:
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continue
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gpus.append((idx, free_mib))
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return gpus
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except Exception:
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return []
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@staticmethod
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def _select_gpus(
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model_size_bytes: int,
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gpus: list[tuple[int, int]],
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) -> tuple[Optional[list[int]], bool]:
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"""Pick GPU(s) for a model based on file size and free memory.
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Uses GGUF file size as a rough proxy for VRAM usage (actual usage
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is higher due to KV cache and compute buffers, but 70% threshold
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accounts for that).
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Returns (gpu_indices, use_fit):
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- ([1], False) model fits on 1 GPU at 70% of free
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- ([1, 2], False) model needs 2 GPUs
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- (None, True) model too large, let --fit handle it
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"""
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if not gpus:
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return None, True
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model_size_mib = model_size_bytes / (1024 * 1024)
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# Sort GPUs by free memory descending
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ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
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# Try fitting on 1 GPU (70% of free memory threshold)
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if ranked[0][1] * 0.70 >= model_size_mib:
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return [ranked[0][0]], False
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# Try fitting on N GPUs (accumulate free memory from most-free)
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cumulative = 0
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selected = []
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for idx, free_mib in ranked:
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selected.append(idx)
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cumulative += free_mib * 0.70
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if cumulative >= model_size_mib:
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return sorted(selected), False
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# Model is too large even for all GPUs, let --fit handle it
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return None, True
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# ── Variant fallback ────────────────────────────────────────────
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@staticmethod
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def _find_smallest_fitting_variant(
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hf_repo: str,
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free_bytes: int,
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hf_token: Optional[str] = None,
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) -> Optional[tuple[str, int]]:
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"""Find the smallest GGUF variant (including all shards) that fits.
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Groups split shards by variant prefix and sums their sizes.
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For example, UD-Q4_K_XL with 9 shards of 50 GB each = 450 GB total.
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Returns (first_shard_filename, total_size_bytes) or None if nothing fits.
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"""
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import re
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try:
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from huggingface_hub import get_paths_info, list_repo_files
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files = list_repo_files(hf_repo, token = hf_token)
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gguf_files = [
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f for f in files if f.endswith(".gguf") and "mmproj" not in f.lower()
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]
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if not gguf_files:
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return None
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# Get sizes for all GGUF files
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path_infos = list(get_paths_info(hf_repo, gguf_files, token = hf_token))
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size_map = {p.path: (p.size or 0) for p in path_infos}
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# Group files by variant: shards share a prefix before -NNNNN-of-NNNNN
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shard_pat = re.compile(r"^(.*)-\d{5}-of-\d{5}\.gguf$")
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variants: dict[str, list[str]] = {}
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for f in gguf_files:
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m = shard_pat.match(f)
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key = m.group(1) if m else f
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variants.setdefault(key, []).append(f)
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# Sum shard sizes per variant, track the first shard (for download)
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variant_sizes: list[tuple[str, int, list[str]]] = []
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for key, shard_files in variants.items():
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total = sum(size_map.get(f, 0) for f in shard_files)
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first = sorted(shard_files)[0]
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variant_sizes.append((first, total, shard_files))
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# Sort by total size ascending and pick the smallest that fits
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variant_sizes.sort(key = lambda x: x[1])
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for first_file, total_size, _ in variant_sizes:
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if total_size > 0 and total_size <= free_bytes:
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return first_file, total_size
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return None
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except Exception:
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return None
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# ── Port allocation ───────────────────────────────────────────
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@staticmethod
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def _find_free_port() -> int:
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"""Find an available TCP port."""
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.bind(("127.0.0.1", 0))
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return s.getsockname()[1]
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# ── Stdout drain (prevents pipe deadlock on Windows) ─────────
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def _drain_stdout(self):
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"""
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Read lines from the subprocess stdout in a background thread.
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This prevents a pipe-buffer deadlock on Windows where the default
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pipe buffer is only ~4 KB. Without draining, llama-server blocks
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on writes and never becomes healthy.
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"""
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try:
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for line in self._process.stdout:
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line = line.rstrip()
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if line:
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self._stdout_lines.append(line)
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logger.info(f"[llama-server] {line}")
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except (ValueError, OSError):
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# Pipe closed — process is terminating
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pass
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# GGUF KV type sizes for fast skipping
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_GGUF_TYPE_SIZE = {
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0: 1,
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1: 1,
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2: 2,
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3: 2,
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4: 4,
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5: 4,
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6: 4,
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7: 1,
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10: 8,
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11: 8,
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12: 8,
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}
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@staticmethod
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def _gguf_skip_value(f, vtype: int) -> None:
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"""Skip a GGUF KV value without reading it."""
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sz = LlamaCppBackend._GGUF_TYPE_SIZE.get(vtype)
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if sz is not None:
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f.seek(sz, 1)
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elif vtype == 8: # STRING
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slen = struct.unpack("<Q", f.read(8))[0]
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f.seek(slen, 1)
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elif vtype == 9: # ARRAY
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atype = struct.unpack("<I", f.read(4))[0]
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alen = struct.unpack("<Q", f.read(8))[0]
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elem_sz = LlamaCppBackend._GGUF_TYPE_SIZE.get(atype)
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if elem_sz is not None:
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f.seek(elem_sz * alen, 1)
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elif atype == 8:
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for _ in range(alen):
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slen = struct.unpack("<Q", f.read(8))[0]
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f.seek(slen, 1)
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else:
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for _ in range(alen):
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LlamaCppBackend._gguf_skip_value(f, atype)
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def _read_gguf_metadata(self, gguf_path: str) -> None:
|
||
"""Read context_length and chat_template from a GGUF file's KV header.
|
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||
Parses only the KV pairs we need (~30ms even for multi-GB files).
|
||
For split GGUFs, metadata is always in shard 1.
|
||
"""
|
||
try:
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||
WANTED = {"general.architecture", "tokenizer.chat_template"}
|
||
arch = None
|
||
ctx_key = None
|
||
|
||
with open(gguf_path, "rb") as f:
|
||
magic = struct.unpack("<I", f.read(4))[0]
|
||
if magic != 0x46554747: # b"GGUF" as little-endian u32
|
||
return
|
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_version = struct.unpack("<I", f.read(4))[0]
|
||
_tensor_count, kv_count = struct.unpack("<QQ", f.read(16))
|
||
|
||
for _ in range(kv_count):
|
||
key_len = struct.unpack("<Q", f.read(8))[0]
|
||
key = f.read(key_len).decode("utf-8")
|
||
vtype = struct.unpack("<I", f.read(4))[0]
|
||
|
||
if key in WANTED or (ctx_key and key == ctx_key):
|
||
# Read this value
|
||
if vtype == 8: # STRING
|
||
slen = struct.unpack("<Q", f.read(8))[0]
|
||
val_s = f.read(slen).decode("utf-8")
|
||
if key == "general.architecture":
|
||
arch = val_s
|
||
ctx_key = f"{arch}.context_length"
|
||
elif key == "tokenizer.chat_template":
|
||
self._chat_template = val_s
|
||
elif vtype == 4: # UINT32
|
||
val_i = struct.unpack("<I", f.read(4))[0]
|
||
if ctx_key and key == ctx_key:
|
||
self._context_length = val_i
|
||
elif vtype == 10: # UINT64
|
||
val_i = struct.unpack("<Q", f.read(8))[0]
|
||
if ctx_key and key == ctx_key:
|
||
self._context_length = val_i
|
||
else:
|
||
self._gguf_skip_value(f, vtype)
|
||
else:
|
||
self._gguf_skip_value(f, vtype)
|
||
|
||
if self._context_length:
|
||
logger.info(f"GGUF metadata: context_length={self._context_length}")
|
||
if self._chat_template:
|
||
logger.info(
|
||
f"GGUF metadata: chat_template={len(self._chat_template)} chars"
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"Failed to read GGUF metadata: {e}")
|
||
|
||
# ── HF download (no lock held) ───────────────────────────────
|
||
|
||
def _download_gguf(
|
||
self,
|
||
*,
|
||
hf_repo: str,
|
||
hf_variant: Optional[str] = None,
|
||
hf_token: Optional[str] = None,
|
||
) -> str:
|
||
"""Download GGUF file(s) from HuggingFace. Returns local path.
|
||
|
||
Runs WITHOUT self._lock so that unload_model() can set
|
||
_cancel_event at any time. Checks _cancel_event between
|
||
each shard download.
|
||
"""
|
||
try:
|
||
from huggingface_hub import hf_hub_download
|
||
except ImportError:
|
||
raise RuntimeError(
|
||
"huggingface_hub is required for HF model loading. "
|
||
"Install it with: pip install huggingface_hub"
|
||
)
|
||
|
||
# Determine the filename from the variant
|
||
gguf_filename = None
|
||
gguf_extra_shards: list[str] = []
|
||
if hf_variant:
|
||
try:
|
||
import re
|
||
from huggingface_hub import list_repo_files
|
||
|
||
files = list_repo_files(hf_repo, token = hf_token)
|
||
variant_lower = hf_variant.lower()
|
||
boundary = re.compile(
|
||
r"(?<![a-zA-Z0-9])" + re.escape(variant_lower) + r"(?![a-zA-Z0-9])"
|
||
)
|
||
gguf_files = sorted(
|
||
f
|
||
for f in files
|
||
if f.endswith(".gguf") and boundary.search(f.lower())
|
||
)
|
||
if gguf_files:
|
||
gguf_filename = gguf_files[0]
|
||
shard_pat = re.compile(r"^(.*)-\d{5}-of-(\d{5})\.gguf$")
|
||
m = shard_pat.match(gguf_filename)
|
||
if m:
|
||
prefix = m.group(1)
|
||
total = m.group(2)
|
||
sibling_pat = re.compile(
|
||
r"^"
|
||
+ re.escape(prefix)
|
||
+ r"-\d{5}-of-"
|
||
+ re.escape(total)
|
||
+ r"\.gguf$"
|
||
)
|
||
gguf_extra_shards = [
|
||
f for f in gguf_files[1:] if sibling_pat.match(f)
|
||
]
|
||
except Exception as e:
|
||
logger.warning(f"Could not list repo files: {e}")
|
||
|
||
if not gguf_filename:
|
||
repo_name = hf_repo.split("/")[-1].replace("-GGUF", "")
|
||
gguf_filename = f"{repo_name}-{hf_variant}.gguf"
|
||
|
||
# Check disk space and fall back to a smaller variant if needed
|
||
all_gguf_files = [gguf_filename] + gguf_extra_shards
|
||
try:
|
||
import os
|
||
|
||
from huggingface_hub import get_paths_info
|
||
|
||
path_infos = list(get_paths_info(hf_repo, all_gguf_files, token = hf_token))
|
||
total_download_bytes = sum((p.size or 0) for p in path_infos)
|
||
|
||
if total_download_bytes > 0:
|
||
cache_dir = os.environ.get(
|
||
"HF_HUB_CACHE",
|
||
str(Path.home() / ".cache" / "huggingface" / "hub"),
|
||
)
|
||
Path(cache_dir).mkdir(parents = True, exist_ok = True)
|
||
free_bytes = shutil.disk_usage(cache_dir).free
|
||
|
||
total_gb = total_download_bytes / (1024**3)
|
||
free_gb = free_bytes / (1024**3)
|
||
|
||
logger.info(
|
||
f"GGUF download: {total_gb:.1f} GB needed, "
|
||
f"{free_gb:.1f} GB free on disk"
|
||
)
|
||
|
||
if total_download_bytes > free_bytes:
|
||
smaller = self._find_smallest_fitting_variant(
|
||
hf_repo,
|
||
free_bytes,
|
||
hf_token,
|
||
)
|
||
if smaller:
|
||
fallback_file, fallback_size = smaller
|
||
logger.info(
|
||
f"Selected variant too large ({total_gb:.1f} GB), "
|
||
f"falling back to {fallback_file} ({fallback_size / (1024**3):.1f} GB)"
|
||
)
|
||
gguf_filename = fallback_file
|
||
import re as _re
|
||
|
||
_shard_pat = _re.compile(r"^(.*)-\d{5}-of-\d{5}\.gguf$")
|
||
_m = _shard_pat.match(gguf_filename)
|
||
_prefix = _m.group(1) if _m else None
|
||
if _prefix:
|
||
gguf_extra_shards = sorted(
|
||
f
|
||
for f in all_gguf_files
|
||
if f.startswith(_prefix)
|
||
and f != gguf_filename
|
||
and "mmproj" not in f.lower()
|
||
)
|
||
else:
|
||
gguf_extra_shards = []
|
||
else:
|
||
raise RuntimeError(
|
||
f"Not enough disk space to download any variant. "
|
||
f"Only {free_gb:.1f} GB free in {cache_dir}"
|
||
)
|
||
except RuntimeError:
|
||
raise
|
||
except Exception as e:
|
||
logger.warning(f"Could not check disk space: {e}")
|
||
|
||
logger.info(
|
||
f"Downloading GGUF: {hf_repo}/{gguf_filename}"
|
||
+ (f" (+{len(gguf_extra_shards)} shards)" if gguf_extra_shards else "")
|
||
)
|
||
try:
|
||
if self._cancel_event.is_set():
|
||
raise RuntimeError("Cancelled")
|
||
local_path = hf_hub_download(
|
||
repo_id = hf_repo,
|
||
filename = gguf_filename,
|
||
token = hf_token,
|
||
)
|
||
for shard in gguf_extra_shards:
|
||
if self._cancel_event.is_set():
|
||
raise RuntimeError("Cancelled")
|
||
logger.info(f"Downloading GGUF shard: {shard}")
|
||
hf_hub_download(
|
||
repo_id = hf_repo,
|
||
filename = shard,
|
||
token = hf_token,
|
||
)
|
||
except RuntimeError as e:
|
||
if "Cancelled" in str(e):
|
||
raise
|
||
raise RuntimeError(
|
||
f"Failed to download GGUF file '{gguf_filename}' from {hf_repo}: {e}"
|
||
)
|
||
except Exception as e:
|
||
raise RuntimeError(
|
||
f"Failed to download GGUF file '{gguf_filename}' from {hf_repo}: {e}"
|
||
)
|
||
|
||
logger.info(f"GGUF downloaded to: {local_path}")
|
||
return local_path
|
||
|
||
def _download_mmproj(
|
||
self,
|
||
*,
|
||
hf_repo: str,
|
||
hf_token: Optional[str] = None,
|
||
) -> Optional[str]:
|
||
"""Download the mmproj (vision projection) file from a GGUF repo.
|
||
|
||
Prefers mmproj-F16.gguf, falls back to any mmproj*.gguf file.
|
||
Returns the local path, or None if no mmproj file exists.
|
||
"""
|
||
try:
|
||
from huggingface_hub import hf_hub_download, list_repo_files
|
||
|
||
files = list_repo_files(hf_repo, token = hf_token)
|
||
mmproj_files = sorted(
|
||
f for f in files if f.endswith(".gguf") and "mmproj" in f.lower()
|
||
)
|
||
if not mmproj_files:
|
||
return None
|
||
|
||
# Prefer F16 variant
|
||
target = None
|
||
for f in mmproj_files:
|
||
if "f16" in f.lower():
|
||
target = f
|
||
break
|
||
if target is None:
|
||
target = mmproj_files[0]
|
||
|
||
logger.info(f"Downloading mmproj: {hf_repo}/{target}")
|
||
local_path = hf_hub_download(
|
||
repo_id = hf_repo,
|
||
filename = target,
|
||
token = hf_token,
|
||
)
|
||
return local_path
|
||
except Exception as e:
|
||
logger.warning(f"Could not download mmproj: {e}")
|
||
return None
|
||
|
||
# ── Lifecycle ─────────────────────────────────────────────────
|
||
|
||
def load_model(
|
||
self,
|
||
*,
|
||
# Local mode: pass a path to a .gguf file
|
||
gguf_path: Optional[str] = None,
|
||
# Vision projection (mmproj) for local vision models
|
||
mmproj_path: Optional[str] = None,
|
||
# HF mode: let llama-server download via -hf "repo:quant"
|
||
hf_repo: Optional[str] = None,
|
||
hf_variant: Optional[str] = None,
|
||
hf_token: Optional[str] = None,
|
||
# Common
|
||
model_identifier: str,
|
||
is_vision: bool = False,
|
||
n_ctx: int = 4096,
|
||
n_threads: Optional[int] = None,
|
||
n_gpu_layers: Optional[int] = None, # Accepted for caller compat, unused
|
||
) -> bool:
|
||
"""
|
||
Start llama-server with a GGUF model.
|
||
|
||
Two modes:
|
||
- Local: ``gguf_path="/path/to/model.gguf"`` → uses ``-m``
|
||
- HF: ``hf_repo="unsloth/gemma-3-4b-it-GGUF", hf_variant="Q4_K_M"`` → uses ``-hf``
|
||
|
||
In HF mode, llama-server handles downloading, caching, and
|
||
auto-loading mmproj files for vision models.
|
||
|
||
Returns True if server started and health check passed.
|
||
"""
|
||
self._cancel_event.clear()
|
||
|
||
# ── Phase 1: kill old process (under lock, fast) ──────────
|
||
with self._lock:
|
||
self._kill_process()
|
||
|
||
binary = self._find_llama_server_binary()
|
||
if not binary:
|
||
raise RuntimeError(
|
||
"llama-server binary not found. "
|
||
"Run setup.sh to build it, install llama.cpp, "
|
||
"or set LLAMA_SERVER_PATH environment variable."
|
||
)
|
||
|
||
# ── Phase 2: download (NO lock held, so cancel can proceed) ──
|
||
if hf_repo:
|
||
model_path = self._download_gguf(
|
||
hf_repo = hf_repo,
|
||
hf_variant = hf_variant,
|
||
hf_token = hf_token,
|
||
)
|
||
# Auto-download mmproj for vision models
|
||
if is_vision and not mmproj_path:
|
||
mmproj_path = self._download_mmproj(
|
||
hf_repo = hf_repo,
|
||
hf_token = hf_token,
|
||
)
|
||
elif gguf_path:
|
||
if not Path(gguf_path).is_file():
|
||
raise FileNotFoundError(f"GGUF file not found: {gguf_path}")
|
||
model_path = gguf_path
|
||
else:
|
||
raise ValueError("Either gguf_path or hf_repo must be provided")
|
||
|
||
# Read GGUF metadata (context_length, chat_template) -- fast, header only
|
||
self._read_gguf_metadata(model_path)
|
||
|
||
# Check cancel after download
|
||
if self._cancel_event.is_set():
|
||
logger.info("Load cancelled after download phase")
|
||
return False
|
||
|
||
# ── Phase 3: start llama-server (under lock) ──────────────
|
||
with self._lock:
|
||
# Re-check cancel inside lock
|
||
if self._cancel_event.is_set():
|
||
logger.info("Load cancelled before server start")
|
||
return False
|
||
|
||
self._port = self._find_free_port()
|
||
|
||
# Select GPU(s) based on model size and free memory
|
||
try:
|
||
model_size = self._get_gguf_size_bytes(model_path)
|
||
gpus = self._get_gpu_free_memory()
|
||
gpu_indices, use_fit = self._select_gpus(model_size, gpus)
|
||
logger.info(
|
||
f"GGUF size: {model_size / (1024**3):.1f} GB, "
|
||
f"GPUs free: {gpus}, selected: {gpu_indices}, fit: {use_fit}"
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"GPU selection failed ({e}), using --fit on")
|
||
gpu_indices, use_fit = None, True
|
||
|
||
cmd = [
|
||
binary,
|
||
"-m",
|
||
model_path,
|
||
"--port",
|
||
str(self._port),
|
||
"-c",
|
||
"0", # 0 = use model's native context size
|
||
"--parallel",
|
||
"1", # Single-user studio, saves VRAM
|
||
"--flash-attn",
|
||
"on", # Force flash attention for speed
|
||
]
|
||
|
||
if use_fit:
|
||
cmd.extend(["--fit", "on"])
|
||
|
||
if n_threads is not None:
|
||
cmd.extend(["--threads", str(n_threads)])
|
||
|
||
if mmproj_path:
|
||
if not Path(mmproj_path).is_file():
|
||
logger.warning(f"mmproj file not found: {mmproj_path}")
|
||
else:
|
||
cmd.extend(["--mmproj", mmproj_path])
|
||
logger.info(f"Using mmproj for vision: {mmproj_path}")
|
||
|
||
logger.info(f"Starting llama-server: {' '.join(cmd)}")
|
||
|
||
# Set library paths so llama-server can find its shared libs and CUDA DLLs
|
||
import os
|
||
import sys
|
||
|
||
env = os.environ.copy()
|
||
binary_dir = str(Path(binary).parent)
|
||
|
||
if sys.platform == "win32":
|
||
# On Windows, CUDA DLLs (cublas64_12.dll, cudart64_12.dll, etc.)
|
||
# must be on PATH. Add CUDA_PATH\bin if available.
|
||
path_dirs = [binary_dir]
|
||
cuda_path = os.environ.get("CUDA_PATH", "")
|
||
if cuda_path:
|
||
cuda_bin = os.path.join(cuda_path, "bin")
|
||
if os.path.isdir(cuda_bin):
|
||
path_dirs.append(cuda_bin)
|
||
# Some CUDA installs put DLLs in bin\x64
|
||
cuda_bin_x64 = os.path.join(cuda_path, "bin", "x64")
|
||
if os.path.isdir(cuda_bin_x64):
|
||
path_dirs.append(cuda_bin_x64)
|
||
existing_path = env.get("PATH", "")
|
||
env["PATH"] = ";".join(path_dirs) + ";" + existing_path
|
||
else:
|
||
# Linux: set LD_LIBRARY_PATH for shared libs next to the binary
|
||
existing_ld = env.get("LD_LIBRARY_PATH", "")
|
||
env["LD_LIBRARY_PATH"] = (
|
||
f"{binary_dir}:{existing_ld}" if existing_ld else binary_dir
|
||
)
|
||
|
||
# Pin to selected GPU(s) via CUDA_VISIBLE_DEVICES
|
||
if gpu_indices is not None:
|
||
env["CUDA_VISIBLE_DEVICES"] = ",".join(str(i) for i in gpu_indices)
|
||
|
||
self._stdout_lines = []
|
||
self._process = subprocess.Popen(
|
||
cmd,
|
||
stdout = subprocess.PIPE,
|
||
stderr = subprocess.STDOUT,
|
||
text = True,
|
||
env = env,
|
||
)
|
||
|
||
# Start background thread to drain stdout and prevent pipe deadlock
|
||
self._stdout_thread = threading.Thread(
|
||
target = self._drain_stdout, daemon = True, name = "llama-stdout"
|
||
)
|
||
self._stdout_thread.start()
|
||
|
||
self._gguf_path = gguf_path
|
||
self._hf_repo = hf_repo
|
||
self._hf_variant = hf_variant
|
||
self._is_vision = is_vision
|
||
self._model_identifier = model_identifier
|
||
|
||
# Wait for llama-server to become healthy
|
||
if not self._wait_for_health(timeout = 120.0):
|
||
self._kill_process()
|
||
raise RuntimeError(
|
||
"llama-server failed to start. "
|
||
"Check that the GGUF file is valid and you have enough memory."
|
||
)
|
||
|
||
self._healthy = True
|
||
|
||
logger.info(
|
||
f"llama-server ready on port {self._port} "
|
||
f"for model '{model_identifier}'"
|
||
)
|
||
return True
|
||
|
||
def unload_model(self) -> bool:
|
||
"""Terminate the llama-server subprocess and cancel any in-flight download."""
|
||
self._cancel_event.set()
|
||
with self._lock:
|
||
self._kill_process()
|
||
logger.info(f"Unloaded GGUF model: {self._model_identifier}")
|
||
self._model_identifier = None
|
||
self._gguf_path = None
|
||
self._hf_repo = None
|
||
self._hf_variant = None
|
||
self._is_vision = False
|
||
self._is_audio = False
|
||
self._audio_type = None
|
||
self._port = None
|
||
self._healthy = False
|
||
self._context_length = None
|
||
self._chat_template = None
|
||
# Free audio codec GPU memory
|
||
if LlamaCppBackend._codec_mgr is not None:
|
||
LlamaCppBackend._codec_mgr.unload()
|
||
LlamaCppBackend._codec_mgr = None
|
||
import torch
|
||
|
||
if torch.cuda.is_available():
|
||
torch.cuda.empty_cache()
|
||
return True
|
||
|
||
def _kill_process(self):
|
||
"""Terminate the subprocess if running."""
|
||
if self._process is None:
|
||
return
|
||
try:
|
||
self._process.terminate()
|
||
self._process.wait(timeout = 5)
|
||
except subprocess.TimeoutExpired:
|
||
logger.warning("llama-server did not exit on SIGTERM, sending SIGKILL")
|
||
self._process.kill()
|
||
self._process.wait(timeout = 5)
|
||
except Exception as e:
|
||
logger.warning(f"Error killing llama-server process: {e}")
|
||
finally:
|
||
self._process = None
|
||
if self._stdout_thread is not None:
|
||
self._stdout_thread.join(timeout = 2)
|
||
self._stdout_thread = None
|
||
|
||
@staticmethod
|
||
def _kill_orphaned_servers():
|
||
"""Kill orphaned llama-server processes started by studio.
|
||
|
||
Only kills processes whose binary lives under ~/.unsloth/llama.cpp/
|
||
to avoid terminating unrelated llama-server instances on the machine.
|
||
"""
|
||
import os
|
||
import signal
|
||
|
||
try:
|
||
# Use pgrep with full command match to identify studio-managed servers
|
||
result = subprocess.run(
|
||
["pgrep", "-a", "-f", "llama-server"],
|
||
capture_output = True,
|
||
text = True,
|
||
timeout = 5,
|
||
)
|
||
if result.returncode != 0:
|
||
return
|
||
for line in result.stdout.strip().splitlines():
|
||
parts = line.strip().split(None, 1)
|
||
if len(parts) < 2:
|
||
continue
|
||
pid = int(parts[0])
|
||
cmdline = parts[1]
|
||
if pid == os.getpid():
|
||
continue
|
||
# Only kill if it's a studio-managed server (lives under .unsloth/)
|
||
if ".unsloth/" not in cmdline and "unsloth" not in cmdline.lower():
|
||
continue
|
||
try:
|
||
os.kill(pid, signal.SIGKILL)
|
||
logger.info(f"Killed orphaned llama-server process (pid={pid})")
|
||
except ProcessLookupError:
|
||
pass
|
||
except PermissionError:
|
||
pass
|
||
except Exception:
|
||
pass
|
||
|
||
def _cleanup(self):
|
||
"""atexit handler to ensure llama-server is terminated."""
|
||
self._kill_process()
|
||
|
||
def _wait_for_health(self, timeout: float = 120.0, interval: float = 0.5) -> bool:
|
||
"""
|
||
Poll llama-server's /health endpoint until it responds 200.
|
||
|
||
Also monitors subprocess for early exit/crash.
|
||
"""
|
||
deadline = time.monotonic() + timeout
|
||
url = f"http://127.0.0.1:{self._port}/health"
|
||
|
||
while time.monotonic() < deadline:
|
||
# Check if process crashed
|
||
if self._process.poll() is not None:
|
||
# Give the drain thread a moment to collect final output
|
||
if self._stdout_thread is not None:
|
||
self._stdout_thread.join(timeout = 2)
|
||
output = "\n".join(self._stdout_lines[-50:])
|
||
logger.error(
|
||
f"llama-server exited with code {self._process.returncode}. "
|
||
f"Output: {output[:2000]}"
|
||
)
|
||
return False
|
||
|
||
try:
|
||
resp = httpx.get(url, timeout = 2.0)
|
||
if resp.status_code == 200:
|
||
return True
|
||
except (httpx.ConnectError, httpx.TimeoutException):
|
||
pass
|
||
|
||
time.sleep(interval)
|
||
|
||
logger.error(f"llama-server health check timed out after {timeout}s")
|
||
return False
|
||
|
||
# ── Message building (OpenAI format) ──────────────────────────
|
||
|
||
@staticmethod
|
||
def _build_openai_messages(
|
||
messages: list[dict],
|
||
image_b64: Optional[str] = None,
|
||
) -> list[dict]:
|
||
"""
|
||
Build OpenAI-format messages, optionally injecting an image_url
|
||
content part into the last user message for vision models.
|
||
|
||
If no image is provided, returns messages as-is.
|
||
"""
|
||
if not image_b64:
|
||
return messages
|
||
|
||
# Find the last user message and convert to multimodal content parts
|
||
result = [msg.copy() for msg in messages]
|
||
last_user_idx = None
|
||
for i, msg in enumerate(result):
|
||
if msg["role"] == "user":
|
||
last_user_idx = i
|
||
|
||
if last_user_idx is not None:
|
||
text_content = result[last_user_idx].get("content", "")
|
||
result[last_user_idx]["content"] = [
|
||
{"type": "text", "text": text_content},
|
||
{
|
||
"type": "image_url",
|
||
"image_url": {
|
||
"url": f"data:image/png;base64,{image_b64}",
|
||
},
|
||
},
|
||
]
|
||
|
||
return result
|
||
|
||
# ── Generation (proxy to llama-server) ────────────────────────
|
||
|
||
def generate_chat_completion(
|
||
self,
|
||
messages: list[dict],
|
||
image_b64: Optional[str] = None,
|
||
temperature: float = 0.7,
|
||
top_p: float = 0.9,
|
||
top_k: int = 40,
|
||
min_p: float = 0.0,
|
||
max_tokens: Optional[int] = None,
|
||
repetition_penalty: float = 1.0,
|
||
stop: Optional[list[str]] = None,
|
||
cancel_event: Optional[threading.Event] = None,
|
||
) -> Generator[str, None, None]:
|
||
"""
|
||
Send a chat completion request to llama-server and stream tokens back.
|
||
|
||
Uses /v1/chat/completions — llama-server handles chat template
|
||
application and vision (multimodal image_url parts) natively.
|
||
|
||
Yields cumulative text (matching InferenceBackend's convention).
|
||
"""
|
||
if not self.is_loaded:
|
||
raise RuntimeError("llama-server is not loaded")
|
||
|
||
openai_messages = self._build_openai_messages(messages, image_b64)
|
||
|
||
payload = {
|
||
"messages": openai_messages,
|
||
"stream": True,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"top_k": top_k if top_k >= 0 else 0,
|
||
"min_p": min_p,
|
||
"repeat_penalty": repetition_penalty,
|
||
}
|
||
if max_tokens is not None:
|
||
payload["max_tokens"] = max_tokens
|
||
if stop:
|
||
payload["stop"] = stop
|
||
|
||
url = f"{self.base_url}/v1/chat/completions"
|
||
cumulative = ""
|
||
in_thinking = False
|
||
|
||
try:
|
||
with httpx.Client(timeout = None) as client:
|
||
with client.stream("POST", url, json = payload) as response:
|
||
if response.status_code != 200:
|
||
error_body = response.read().decode()
|
||
raise RuntimeError(
|
||
f"llama-server returned {response.status_code}: {error_body}"
|
||
)
|
||
|
||
buffer = ""
|
||
for raw_chunk in response.iter_text():
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
break
|
||
|
||
buffer += raw_chunk
|
||
while "\n" in buffer:
|
||
line, buffer = buffer.split("\n", 1)
|
||
line = line.strip()
|
||
|
||
if not line:
|
||
continue
|
||
if line == "data: [DONE]":
|
||
if in_thinking:
|
||
cumulative += "</think>"
|
||
yield cumulative
|
||
return
|
||
if not line.startswith("data: "):
|
||
continue
|
||
|
||
try:
|
||
data = json.loads(line[6:])
|
||
choices = data.get("choices", [])
|
||
if choices:
|
||
delta = choices[0].get("delta", {})
|
||
|
||
# Handle reasoning/thinking tokens
|
||
# llama-server sends these as "reasoning_content"
|
||
# Wrap in <think> tags for the frontend parser
|
||
reasoning = delta.get("reasoning_content", "")
|
||
if reasoning:
|
||
if not in_thinking:
|
||
cumulative += "<think>"
|
||
in_thinking = True
|
||
cumulative += reasoning
|
||
yield cumulative
|
||
|
||
token = delta.get("content", "")
|
||
if token:
|
||
if in_thinking:
|
||
cumulative += "</think>"
|
||
in_thinking = False
|
||
cumulative += token
|
||
yield cumulative
|
||
except json.JSONDecodeError:
|
||
logger.debug(
|
||
f"Skipping malformed SSE line: {line[:100]}"
|
||
)
|
||
|
||
except httpx.ConnectError:
|
||
raise RuntimeError("Lost connection to llama-server")
|
||
except Exception as e:
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
return
|
||
raise
|
||
|
||
# ── TTS support ────────────────────────────────────────────
|
||
|
||
def detect_audio_type(self) -> Optional[str]:
|
||
"""Detect audio/TTS codec by probing the loaded model's vocabulary."""
|
||
if not self.is_loaded:
|
||
return None
|
||
try:
|
||
with httpx.Client(timeout = 10) as client:
|
||
|
||
def _detok(tid: int) -> str:
|
||
r = client.post(
|
||
f"{self.base_url}/detokenize", json = {"tokens": [tid]}
|
||
)
|
||
return r.json().get("content", "") if r.status_code == 200 else ""
|
||
|
||
def _tok(text: str) -> list[int]:
|
||
r = client.post(
|
||
f"{self.base_url}/tokenize",
|
||
json = {"content": text, "add_special": False},
|
||
)
|
||
return r.json().get("tokens", []) if r.status_code == 200 else []
|
||
|
||
# Check codec-specific tokens (not generic ones that may exist in non-audio models)
|
||
if "<custom_token_" in _detok(128258) and "<custom_token_" in _detok(
|
||
128259
|
||
):
|
||
return "snac"
|
||
if len(_tok("<|AUDIO|>")) == 1 and len(_tok("<|audio_eos|>")) == 1:
|
||
return "csm"
|
||
if len(_tok("<|startoftranscript|>")) == 1:
|
||
return "whisper"
|
||
if (
|
||
len(_tok("<|bicodec_semantic_0|>")) == 1
|
||
and len(_tok("<|bicodec_global_0|>")) == 1
|
||
):
|
||
return "bicodec"
|
||
if len(_tok("<|c1_0|>")) == 1 and len(_tok("<|c2_0|>")) == 1:
|
||
return "dac"
|
||
except Exception as e:
|
||
logger.debug(f"Audio type detection failed: {e}")
|
||
return None
|
||
|
||
# Prompt format per codec: (template, stop_tokens, needs_token_ids)
|
||
# Matches prompts in InferenceBackend._generate_snac/bicodec/dac
|
||
_TTS_PROMPTS = {
|
||
"snac": (
|
||
"<custom_token_3>{text}<|eot_id|><custom_token_4>",
|
||
["<custom_token_2>"],
|
||
True,
|
||
),
|
||
"bicodec": (
|
||
"<|task_tts|><|start_content|>{text}<|end_content|><|start_global_token|>",
|
||
["<|im_end|>", "</s>"],
|
||
False,
|
||
),
|
||
"dac": (
|
||
"<|im_start|>\n<|text_start|>{text}<|text_end|>\n<|audio_start|><|global_features_start|>\n",
|
||
["<|im_end|>", "<|audio_end|>"],
|
||
False,
|
||
),
|
||
}
|
||
|
||
_codec_mgr = None # Shared AudioCodecManager instance
|
||
|
||
def init_audio_codec(self, audio_type: str) -> None:
|
||
"""Load the audio codec at model load time (mirrors non-GGUF path)."""
|
||
import torch
|
||
from core.inference.audio_codecs import AudioCodecManager
|
||
|
||
if LlamaCppBackend._codec_mgr is None:
|
||
LlamaCppBackend._codec_mgr = AudioCodecManager()
|
||
|
||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
model_repo_path = None
|
||
|
||
# BiCodec needs a repo with BiCodec/ weights — download canonical SparkTTS
|
||
if audio_type == "bicodec":
|
||
from huggingface_hub import snapshot_download
|
||
import os
|
||
|
||
repo_path = snapshot_download(
|
||
"unsloth/Spark-TTS-0.5B", local_dir = "Spark-TTS-0.5B"
|
||
)
|
||
model_repo_path = os.path.abspath(repo_path)
|
||
|
||
LlamaCppBackend._codec_mgr.load_codec(
|
||
audio_type, device, model_repo_path = model_repo_path
|
||
)
|
||
logger.info(f"Loaded audio codec for GGUF TTS: {audio_type}")
|
||
|
||
def generate_audio_response(
|
||
self,
|
||
text: str,
|
||
audio_type: str,
|
||
temperature: float = 0.6,
|
||
top_p: float = 0.95,
|
||
top_k: int = 50,
|
||
min_p: float = 0.0,
|
||
max_new_tokens: int = 2048,
|
||
repetition_penalty: float = 1.1,
|
||
) -> tuple:
|
||
"""
|
||
Generate TTS audio via llama-server /completion + codec decoding.
|
||
Returns (wav_bytes, sample_rate).
|
||
"""
|
||
if audio_type not in self._TTS_PROMPTS:
|
||
raise RuntimeError(f"GGUF TTS does not support '{audio_type}' codec.")
|
||
|
||
tpl, stop, need_ids = self._TTS_PROMPTS[audio_type]
|
||
|
||
payload: dict = {
|
||
"prompt": tpl.format(text = text),
|
||
"stream": False,
|
||
"n_predict": max_new_tokens,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"top_k": top_k if top_k >= 0 else 0,
|
||
"min_p": min_p,
|
||
"repeat_penalty": repetition_penalty,
|
||
}
|
||
if stop:
|
||
payload["stop"] = stop
|
||
if need_ids:
|
||
payload["n_probs"] = 1
|
||
|
||
with httpx.Client(timeout = httpx.Timeout(300, connect = 10)) as client:
|
||
resp = client.post(f"{self.base_url}/completion", json = payload)
|
||
if resp.status_code != 200:
|
||
raise RuntimeError(
|
||
f"llama-server returned {resp.status_code}: {resp.text}"
|
||
)
|
||
|
||
data = resp.json()
|
||
token_ids = (
|
||
[p["id"] for p in data.get("completion_probabilities", []) if "id" in p]
|
||
if need_ids
|
||
else None
|
||
)
|
||
|
||
import torch
|
||
|
||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
return LlamaCppBackend._codec_mgr.decode(
|
||
audio_type, device, token_ids = token_ids, text = data.get("content", "")
|
||
)
|