d63cc57e1e* fix: clear tool status badge immediately after tool execution The tool status timer badge (Searching 1s, 2s...) persisted after tool calls finished because the status clear event was only sent at the start of the next generation iteration, not after tool execution completed. Backend: yield status clear after all tools finish in the agentic loop iteration, before continue starts the next generation pass. Frontend: debounce badge visibility by 300ms so sub-second tool calls dont flash the badge. * Fix debounce regression for consecutive tool calls Only apply the 300ms show-delay when transitioning from idle to tool-active. When switching between consecutive tools in the same turn (e.g. web_search -> python), keep the badge visible immediately so it does not flicker or disappear during multi-tool runs. * Delay wasActiveRef reset to bridge inter-iteration tool gaps The backend emits a status-clear event between tool iterations, which was resetting wasActiveRef immediately and causing the next tool to be re-debounced (300ms hidden gap between consecutive tools in the same turn). Now the ref reset is delayed by 500ms so a follow-up tool within the same agentic turn shows the badge immediately, while a genuinely new turn still gets the debounce. * Use thread lifecycle to track tool-run boundaries Replace the 500ms wall-clock timeout with the actual thread.isRunning state to determine when wasActiveRef should reset. This properly handles all cases: - Consecutive tools within the same run stay visible without flicker - The badge hides only when the thread run actually ends - New turns always get a fresh 300ms debounce on the first tool - No heuristic timeout that can misfire on slow or fast inference * Consolidate wasActiveRef reset into single effect Removes the separate isThreadRunning effect to avoid a race where the ref resets before the tool-status effect reads it (when isThreadRunning flips to false before setToolStatus(null) from the adapter's finally block). Now wasActiveRef resets only when both toolStatus is null AND the thread run has ended, eliminating any flicker on the last tool of a run. * Simplify debounce: use visible state instead of ref tracking Drop wasActiveRef entirely and use the visible state as the debounce gate. When the badge is not yet on screen, debounce for 300ms before showing. When already visible from a prior tool, keep showing immediately. This correctly handles all cases: - All fast tools (<300ms) are suppressed, not just the first - Consecutive tools after the badge is shown stay visible - Badge persists across inter-iteration clears while thread runs - New turns get a fresh debounce after visible resets --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com>
3086 lines
131 KiB
Python
3086 lines
131 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 contextlib
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import json
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import re
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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 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._effective_context_length: Optional[int] = None
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self._max_context_length: Optional[int] = None
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self._chat_template: Optional[str] = None
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self._supports_reasoning: bool = False
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self._reasoning_always_on: bool = False
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self._supports_tools: bool = False
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self._cache_type_kv: Optional[str] = None
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self._reasoning_default: bool = True
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# KV-cache estimation fields (populated by _read_gguf_metadata)
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self._n_layers: Optional[int] = None
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self._n_kv_heads: Optional[int] = None
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self._n_heads: Optional[int] = None
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self._embedding_length: Optional[int] = 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._api_key: Optional[str] = None
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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 the effective context length the server is running at."""
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return self._effective_context_length or self._context_length
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@property
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def max_context_length(self) -> Optional[int]:
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"""Return the maximum context currently available on this hardware."""
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return self._max_context_length or 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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@property
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def supports_reasoning(self) -> bool:
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return self._supports_reasoning
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@property
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def reasoning_always_on(self) -> bool:
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return self._reasoning_always_on
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@property
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def reasoning_default(self) -> bool:
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return self._reasoning_default
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@property
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def supports_tools(self) -> bool:
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return self._supports_tools
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@property
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def cache_type_kv(self) -> Optional[str]:
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return self._cache_type_kv
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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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|
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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 as e:
|
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logger.debug(f"Failed to query GPU free memory via nvidia-smi: {e}")
|
||
return []
|
||
|
||
@staticmethod
|
||
def _select_gpus(
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model_size_bytes: int,
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gpus: list[tuple[int, int]],
|
||
) -> tuple[Optional[list[int]], bool]:
|
||
"""Pick GPU(s) for a model based on estimated VRAM and free memory.
|
||
|
||
``model_size_bytes`` should include both model weights and estimated
|
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KV cache. The 70% threshold provides headroom for compute buffers,
|
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CUDA context, and other runtime overhead.
|
||
|
||
Returns (gpu_indices, use_fit):
|
||
- ([1], False) model fits on 1 GPU at 70% of free
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||
- ([1, 2], False) model needs 2 GPUs
|
||
- (None, True) model too large, let --fit handle it
|
||
"""
|
||
if not gpus:
|
||
return None, True
|
||
|
||
model_size_mib = model_size_bytes / (1024 * 1024)
|
||
|
||
# Sort GPUs by free memory descending
|
||
ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
|
||
|
||
# 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
|
||
|
||
# Try fitting on N GPUs (accumulate free memory from most-free)
|
||
cumulative = 0
|
||
selected = []
|
||
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
|
||
|
||
# Model is too large even for all GPUs, let --fit handle it
|
||
logger.debug(
|
||
"Model does not fit in available GPU memory, falling back to --fit",
|
||
model_size_mib = round(model_size_mib, 2),
|
||
ranked_gpus = ranked,
|
||
)
|
||
return None, True
|
||
|
||
# ── KV cache VRAM estimation ─────────────────────────────────────
|
||
|
||
def _can_estimate_kv(self) -> bool:
|
||
"""True if we have enough GGUF metadata to estimate KV cache size."""
|
||
return (
|
||
self._n_layers is not None
|
||
and self._embedding_length is not None
|
||
and (self._n_kv_heads is not None or self._n_heads is not None)
|
||
)
|
||
|
||
def _estimate_kv_cache_bytes(
|
||
self, n_ctx: int, cache_type_kv: Optional[str] = None
|
||
) -> int:
|
||
"""Estimate KV cache VRAM for a given context length.
|
||
|
||
Returns 0 if metadata is insufficient for estimation.
|
||
"""
|
||
if not self._can_estimate_kv() or n_ctx <= 0:
|
||
return 0
|
||
|
||
n_layers = self._n_layers # type: ignore[assignment]
|
||
n_kv_heads = self._n_kv_heads or self._n_heads # type: ignore[assignment]
|
||
head_dim = self._embedding_length // self._n_heads if self._n_heads else 128 # type: ignore[operator]
|
||
|
||
# Bytes per element depends on KV cache quantization
|
||
bpe = {
|
||
"f32": 4.0,
|
||
"f16": 2.0,
|
||
"bf16": 2.0,
|
||
"q8_0": 34 / 32,
|
||
"q5_1": 0.75,
|
||
"q5_0": 0.6875,
|
||
"q4_1": 0.625,
|
||
"q4_0": 0.5625,
|
||
"iq4_nl": 0.5625,
|
||
}.get(cache_type_kv or "f16", 2.0)
|
||
|
||
# K + V caches: 2 * n_kv_heads * head_dim * n_layers * n_ctx * bpe
|
||
return int(2 * n_kv_heads * head_dim * n_layers * n_ctx * bpe)
|
||
|
||
def _fit_context_to_vram(
|
||
self,
|
||
requested_ctx: int,
|
||
available_mib: int,
|
||
model_size_bytes: int,
|
||
cache_type_kv: Optional[str] = None,
|
||
min_ctx: int = 4096,
|
||
) -> int:
|
||
"""Return the largest context length that fits in GPU VRAM.
|
||
|
||
Uses 70% of available VRAM as the budget (matching _select_gpus
|
||
threshold -- 30% reserved for compute buffers, CUDA context,
|
||
scratch space, flash-attn workspace, etc.).
|
||
If the model weights alone don't fit, returns min_ctx unchanged.
|
||
"""
|
||
if not self._can_estimate_kv():
|
||
logger.debug(
|
||
"Skipping context fit because KV cache metadata is unavailable",
|
||
requested_ctx = requested_ctx,
|
||
available_mib = available_mib,
|
||
)
|
||
return requested_ctx
|
||
|
||
budget_bytes = available_mib * 1024 * 1024 * 0.70
|
||
model_footprint = model_size_bytes
|
||
|
||
# Check if requested context already fits
|
||
kv = self._estimate_kv_cache_bytes(requested_ctx, cache_type_kv)
|
||
if model_footprint + kv <= budget_bytes:
|
||
return requested_ctx
|
||
|
||
# Model weights alone exceed budget -- can't help by reducing ctx.
|
||
# Return requested_ctx unchanged; --fit will handle VRAM management.
|
||
if model_footprint >= budget_bytes:
|
||
logger.debug(
|
||
"Model footprint exceeds GPU budget before KV cache",
|
||
requested_ctx = requested_ctx,
|
||
available_mib = available_mib,
|
||
model_size_gb = round(model_footprint / (1024**3), 2),
|
||
)
|
||
return requested_ctx
|
||
|
||
# Binary search for max context that fits
|
||
remaining = budget_bytes - model_footprint
|
||
effective_min = min(min_ctx, requested_ctx)
|
||
lo, hi = effective_min, requested_ctx
|
||
best = effective_min
|
||
while lo <= hi:
|
||
mid = (lo + hi) // 2
|
||
kv = self._estimate_kv_cache_bytes(mid, cache_type_kv)
|
||
if kv <= remaining:
|
||
best = mid
|
||
lo = mid + 1
|
||
else:
|
||
hi = mid - 1
|
||
|
||
# Round down to nearest 256 for alignment, but never exceed requested_ctx
|
||
best = (best // 256) * 256
|
||
best = max(effective_min, best)
|
||
best = min(best, requested_ctx)
|
||
return best
|
||
|
||
# ── Variant fallback ────────────────────────────────────────────
|
||
|
||
@staticmethod
|
||
def _find_smallest_fitting_variant(
|
||
hf_repo: str,
|
||
free_bytes: int,
|
||
hf_token: Optional[str] = None,
|
||
) -> Optional[tuple[str, int]]:
|
||
"""Find the smallest GGUF variant (including all shards) that fits.
|
||
|
||
Groups split shards by variant prefix and sums their sizes.
|
||
For example, UD-Q4_K_XL with 9 shards of 50 GB each = 450 GB total.
|
||
|
||
Returns (first_shard_filename, total_size_bytes) or None if nothing fits.
|
||
"""
|
||
import re
|
||
|
||
try:
|
||
from huggingface_hub import get_paths_info, list_repo_files
|
||
|
||
files = list_repo_files(hf_repo, token = hf_token)
|
||
gguf_files = [
|
||
f for f in files if f.endswith(".gguf") and "mmproj" not in f.lower()
|
||
]
|
||
if not gguf_files:
|
||
return None
|
||
|
||
# Get sizes for all GGUF files
|
||
path_infos = list(get_paths_info(hf_repo, gguf_files, token = hf_token))
|
||
size_map = {p.path: (p.size or 0) for p in path_infos}
|
||
|
||
# Group files by variant: shards share a prefix before -NNNNN-of-NNNNN
|
||
shard_pat = re.compile(r"^(.*)-\d{5}-of-\d{5}\.gguf$")
|
||
variants: dict[str, list[str]] = {}
|
||
for f in gguf_files:
|
||
m = shard_pat.match(f)
|
||
key = m.group(1) if m else f
|
||
variants.setdefault(key, []).append(f)
|
||
|
||
# Sum shard sizes per variant, track the first shard (for download)
|
||
variant_sizes: list[tuple[str, int, list[str]]] = []
|
||
for key, shard_files in variants.items():
|
||
total = sum(size_map.get(f, 0) for f in shard_files)
|
||
first = sorted(shard_files)[0]
|
||
variant_sizes.append((first, total, shard_files))
|
||
|
||
# Sort by total size ascending and pick the smallest that fits
|
||
variant_sizes.sort(key = lambda x: x[1])
|
||
for first_file, total_size, _ in variant_sizes:
|
||
if total_size > 0 and total_size <= free_bytes:
|
||
return first_file, total_size
|
||
|
||
return None
|
||
except Exception:
|
||
return None
|
||
|
||
# ── Port allocation ───────────────────────────────────────────
|
||
|
||
@staticmethod
|
||
def _find_free_port() -> int:
|
||
"""Find an available TCP port."""
|
||
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
|
||
s.bind(("127.0.0.1", 0))
|
||
return s.getsockname()[1]
|
||
|
||
# ── Stdout drain (prevents pipe deadlock on Windows) ─────────
|
||
|
||
def _drain_stdout(self):
|
||
"""
|
||
Read lines from the subprocess stdout in a background thread.
|
||
|
||
This prevents a pipe-buffer deadlock on Windows where the default
|
||
pipe buffer is only ~4 KB. Without draining, llama-server blocks
|
||
on writes and never becomes healthy.
|
||
"""
|
||
try:
|
||
for line in self._process.stdout:
|
||
line = line.rstrip()
|
||
if line:
|
||
self._stdout_lines.append(line)
|
||
logger.debug(f"[llama-server] {line}")
|
||
except (ValueError, OSError):
|
||
# Pipe closed — process is terminating
|
||
pass
|
||
|
||
# GGUF KV type sizes for fast skipping
|
||
_GGUF_TYPE_SIZE = {
|
||
0: 1,
|
||
1: 1,
|
||
2: 2,
|
||
3: 2,
|
||
4: 4,
|
||
5: 4,
|
||
6: 4,
|
||
7: 1,
|
||
10: 8,
|
||
11: 8,
|
||
12: 8,
|
||
}
|
||
|
||
@staticmethod
|
||
def _gguf_skip_value(f, vtype: int) -> None:
|
||
"""Skip a GGUF KV value without reading it."""
|
||
sz = LlamaCppBackend._GGUF_TYPE_SIZE.get(vtype)
|
||
if sz is not None:
|
||
f.seek(sz, 1)
|
||
elif vtype == 8: # STRING
|
||
slen = struct.unpack("<Q", f.read(8))[0]
|
||
f.seek(slen, 1)
|
||
elif vtype == 9: # ARRAY
|
||
atype = struct.unpack("<I", f.read(4))[0]
|
||
alen = struct.unpack("<Q", f.read(8))[0]
|
||
elem_sz = LlamaCppBackend._GGUF_TYPE_SIZE.get(atype)
|
||
if elem_sz is not None:
|
||
f.seek(elem_sz * alen, 1)
|
||
elif atype == 8:
|
||
for _ in range(alen):
|
||
slen = struct.unpack("<Q", f.read(8))[0]
|
||
f.seek(slen, 1)
|
||
else:
|
||
for _ in range(alen):
|
||
LlamaCppBackend._gguf_skip_value(f, atype)
|
||
|
||
def _read_gguf_metadata(self, gguf_path: str) -> None:
|
||
"""Read context_length, architecture params, and chat_template from a GGUF header.
|
||
|
||
Parses only the KV pairs we need (~30ms even for multi-GB files).
|
||
For split GGUFs, metadata is always in shard 1.
|
||
"""
|
||
# Reset metadata from any previously loaded model so stale flags
|
||
# (eg _supports_reasoning) do not carry over when switching models.
|
||
self._context_length = None
|
||
self._chat_template = None
|
||
self._supports_reasoning = False
|
||
self._reasoning_always_on = False
|
||
self._supports_tools = False
|
||
self._n_layers = None
|
||
self._n_kv_heads = None
|
||
self._n_heads = None
|
||
self._embedding_length = None
|
||
|
||
try:
|
||
WANTED = {"general.architecture", "tokenizer.chat_template"}
|
||
# Additional arch-specific keys are added dynamically once
|
||
# we know the architecture name.
|
||
arch_keys: dict[str, str] = {} # gguf_key -> attribute name
|
||
arch = 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
|
||
_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 key in arch_keys:
|
||
# 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
|
||
# Register arch-specific keys to look for
|
||
arch_keys = {
|
||
f"{arch}.context_length": "context_length",
|
||
f"{arch}.block_count": "n_layers",
|
||
f"{arch}.attention.head_count_kv": "n_kv_heads",
|
||
f"{arch}.attention.head_count": "n_heads",
|
||
f"{arch}.embedding_length": "embedding_length",
|
||
}
|
||
elif key == "tokenizer.chat_template":
|
||
self._chat_template = val_s
|
||
elif vtype in (4, 10): # UINT32 or UINT64
|
||
val_i = (
|
||
struct.unpack("<I", f.read(4))[0]
|
||
if vtype == 4
|
||
else struct.unpack("<Q", f.read(8))[0]
|
||
)
|
||
attr = arch_keys.get(key)
|
||
if attr:
|
||
setattr(self, f"_{attr}", 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"
|
||
)
|
||
# Detect thinking/reasoning support from chat template
|
||
tpl = self._chat_template
|
||
if "enable_thinking" in tpl:
|
||
self._supports_reasoning = True
|
||
logger.info(
|
||
"GGUF metadata: model supports reasoning (enable_thinking)"
|
||
)
|
||
elif "thinking" in tpl:
|
||
# DeepSeek uses 'thinking' instead of 'enable_thinking'
|
||
normalized_id = (self._model_identifier or "").lower()
|
||
if "deepseek" in normalized_id:
|
||
self._supports_reasoning = True
|
||
logger.info(
|
||
"GGUF metadata: model supports reasoning (DeepSeek thinking)"
|
||
)
|
||
# Models with hardcoded <think> tags or reasoning_content
|
||
# in their chat template always produce thinking output
|
||
# (no toggle to disable it).
|
||
if not self._supports_reasoning:
|
||
if (
|
||
"<think>" in tpl
|
||
and "</think>" in tpl
|
||
or "reasoning_content" in tpl
|
||
):
|
||
self._supports_reasoning = True
|
||
self._reasoning_always_on = True
|
||
logger.info(
|
||
"GGUF metadata: model always reasons (<think> tags in template)"
|
||
)
|
||
# Detect tool calling support from chat template
|
||
tool_markers = [
|
||
"{%- if tools %}",
|
||
"{% if tools %}",
|
||
'"role" == "tool"',
|
||
"'role' == 'tool'",
|
||
'message.role == "tool"',
|
||
"message.role == 'tool'",
|
||
]
|
||
if any(marker in tpl for marker in tool_markers):
|
||
self._supports_tools = True
|
||
logger.info("GGUF metadata: model supports tool calling")
|
||
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}")
|
||
|
||
gguf_label = f"{hf_repo}/{gguf_filename}" + (
|
||
f" (+{len(gguf_extra_shards)} shards)" if gguf_extra_shards else ""
|
||
)
|
||
logger.info(f"Resolving GGUF: {gguf_label}")
|
||
try:
|
||
if self._cancel_event.is_set():
|
||
raise RuntimeError("Cancelled")
|
||
dl_start = time.monotonic()
|
||
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"Resolving 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}"
|
||
)
|
||
|
||
dl_elapsed = time.monotonic() - dl_start
|
||
if dl_elapsed < 2.0:
|
||
logger.info(f"GGUF resolved from cache: {local_path}")
|
||
else:
|
||
logger.info(f"GGUF downloaded in {dl_elapsed:.1f}s: {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,
|
||
chat_template_override: Optional[str] = None,
|
||
cache_type_kv: Optional[str] = None,
|
||
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")
|
||
|
||
# Set identifier early so _read_gguf_metadata can use it for DeepSeek detection
|
||
self._model_identifier = model_identifier
|
||
|
||
# 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 + estimated KV cache.
|
||
# Seed safe defaults before GPU probing so the except path
|
||
# still has valid state to publish.
|
||
effective_ctx = n_ctx if n_ctx > 0 else (self._context_length or 0)
|
||
max_available_ctx = self._context_length or effective_ctx
|
||
try:
|
||
model_size = self._get_gguf_size_bytes(model_path)
|
||
gpus = self._get_gpu_free_memory()
|
||
|
||
# Resolve effective context: 0 means let llama-server use the
|
||
# model's native length. Only expand to a known native length
|
||
# if metadata is available; otherwise preserve 0 as a sentinel.
|
||
if n_ctx > 0:
|
||
effective_ctx = n_ctx
|
||
elif self._context_length is not None:
|
||
effective_ctx = self._context_length
|
||
else:
|
||
effective_ctx = 0
|
||
original_ctx = effective_ctx
|
||
# Default UI ceiling to the model's native context length.
|
||
# GPU/VRAM-fit logic below may shrink this if hardware is limited.
|
||
max_available_ctx = self._context_length or effective_ctx
|
||
|
||
# Auto-cap context to fit in GPU VRAM and select GPUs.
|
||
#
|
||
# Two policies depending on whether the user set n_ctx:
|
||
#
|
||
# Explicit n_ctx (user chose a context length):
|
||
# Honor it. Try the full requested context with _select_gpus
|
||
# (which uses as many GPUs as needed). Only cap if it doesn't
|
||
# fit on any GPU combination.
|
||
#
|
||
# Auto n_ctx=0 (model's native context):
|
||
# Prefer fewer GPUs with reduced context over more GPUs,
|
||
# since multi-GPU is slower and the user didn't ask for a
|
||
# specific context length.
|
||
gpu_indices, use_fit = None, True
|
||
explicit_ctx = n_ctx > 0
|
||
|
||
if gpus and self._can_estimate_kv() and effective_ctx > 0:
|
||
# Compute the largest hardware-aware cap from the model's
|
||
# native context across all usable GPU subsets (for UI
|
||
# bounds), independent of the currently requested context.
|
||
native_ctx_for_cap = self._context_length or effective_ctx
|
||
if native_ctx_for_cap > 0:
|
||
ranked_for_cap = sorted(gpus, key = lambda g: g[1], reverse = True)
|
||
best_cap = 0
|
||
for n_gpus in range(1, len(ranked_for_cap) + 1):
|
||
subset = ranked_for_cap[:n_gpus]
|
||
pool_mib = sum(free for _, free in subset)
|
||
capped = self._fit_context_to_vram(
|
||
native_ctx_for_cap,
|
||
pool_mib,
|
||
model_size,
|
||
cache_type_kv,
|
||
)
|
||
kv = self._estimate_kv_cache_bytes(capped, cache_type_kv)
|
||
total_mib = (model_size + kv) / (1024 * 1024)
|
||
if total_mib <= pool_mib * 0.70:
|
||
best_cap = max(best_cap, capped)
|
||
if best_cap > 0:
|
||
max_available_ctx = best_cap
|
||
|
||
if explicit_ctx:
|
||
# Try to honor the user's requested context exactly.
|
||
requested_total = model_size + self._estimate_kv_cache_bytes(
|
||
effective_ctx, cache_type_kv
|
||
)
|
||
gpu_indices, use_fit = self._select_gpus(requested_total, gpus)
|
||
|
||
# Full context doesn't fit anywhere -- cap it on the
|
||
# best GPU subset we can find (fewest GPUs first).
|
||
if use_fit:
|
||
ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
|
||
for n_gpus in range(1, len(ranked) + 1):
|
||
subset = ranked[:n_gpus]
|
||
pool_mib = sum(free for _, free in subset)
|
||
capped = self._fit_context_to_vram(
|
||
effective_ctx,
|
||
pool_mib,
|
||
model_size,
|
||
cache_type_kv,
|
||
)
|
||
kv = self._estimate_kv_cache_bytes(
|
||
capped, cache_type_kv
|
||
)
|
||
total_mib = (model_size + kv) / (1024 * 1024)
|
||
if total_mib <= pool_mib * 0.70:
|
||
effective_ctx = capped
|
||
gpu_indices = sorted(idx for idx, _ in subset)
|
||
use_fit = False
|
||
break
|
||
else:
|
||
# Auto context: prefer fewer GPUs, cap context to fit.
|
||
ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
|
||
for n_gpus in range(1, len(ranked) + 1):
|
||
subset = ranked[:n_gpus]
|
||
pool_mib = sum(free for _, free in subset)
|
||
capped = self._fit_context_to_vram(
|
||
effective_ctx,
|
||
pool_mib,
|
||
model_size,
|
||
cache_type_kv,
|
||
)
|
||
kv = self._estimate_kv_cache_bytes(capped, cache_type_kv)
|
||
total_mib = (model_size + kv) / (1024 * 1024)
|
||
if total_mib <= pool_mib * 0.70:
|
||
effective_ctx = capped
|
||
gpu_indices = sorted(idx for idx, _ in subset)
|
||
use_fit = False
|
||
break
|
||
|
||
elif gpus:
|
||
# Can't estimate KV -- fall back to file-size-only check.
|
||
# Without KV estimation we cannot prove a hardware cap, so
|
||
# keep the ceiling at the native context (already the default).
|
||
logger.debug(
|
||
"Falling back to file-size-only GPU selection",
|
||
model_size_gb = round(model_size / (1024**3), 2),
|
||
)
|
||
gpu_indices, use_fit = self._select_gpus(model_size, gpus)
|
||
|
||
if effective_ctx < original_ctx:
|
||
kv_est = self._estimate_kv_cache_bytes(effective_ctx, cache_type_kv)
|
||
logger.info(
|
||
f"Context auto-reduced: {original_ctx} -> {effective_ctx} "
|
||
f"(model: {model_size / (1024**3):.1f} GB, "
|
||
f"est. KV cache: {kv_est / (1024**3):.1f} GB)"
|
||
)
|
||
|
||
kv_cache_bytes = self._estimate_kv_cache_bytes(
|
||
effective_ctx, cache_type_kv
|
||
)
|
||
logger.info(
|
||
f"GGUF size: {model_size / (1024**3):.1f} GB, "
|
||
f"est. KV cache: {kv_cache_bytes / (1024**3):.1f} GB, "
|
||
f"context: {effective_ctx}, "
|
||
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
|
||
effective_ctx = n_ctx # fall back to original
|
||
|
||
cmd = [
|
||
binary,
|
||
"-m",
|
||
model_path,
|
||
"--port",
|
||
str(self._port),
|
||
"-c",
|
||
str(effective_ctx) if effective_ctx > 0 else "0",
|
||
"--parallel",
|
||
"1", # Single-user studio, saves VRAM
|
||
"--flash-attn",
|
||
"on", # Force flash attention for speed
|
||
]
|
||
|
||
if use_fit:
|
||
cmd.extend(["--fit", "on"])
|
||
elif gpu_indices is not None:
|
||
# Model fits on selected GPU(s) -- offload all layers
|
||
cmd.extend(["-ngl", "-1"])
|
||
|
||
if n_threads is not None:
|
||
cmd.extend(["--threads", str(n_threads)])
|
||
|
||
# Always enable Jinja chat template rendering for proper template support
|
||
cmd.extend(["--jinja"])
|
||
|
||
# KV cache data type
|
||
_valid_cache_types = {
|
||
"f16",
|
||
"bf16",
|
||
"q8_0",
|
||
"q4_0",
|
||
"q4_1",
|
||
"q5_0",
|
||
"q5_1",
|
||
"iq4_nl",
|
||
"f32",
|
||
}
|
||
if cache_type_kv and cache_type_kv in _valid_cache_types:
|
||
cmd.extend(
|
||
["--cache-type-k", cache_type_kv, "--cache-type-v", cache_type_kv]
|
||
)
|
||
self._cache_type_kv = cache_type_kv
|
||
logger.info(f"KV cache type: {cache_type_kv}")
|
||
else:
|
||
self._cache_type_kv = None
|
||
|
||
# Apply custom chat template override if provided
|
||
if chat_template_override:
|
||
import tempfile
|
||
|
||
self._chat_template_file = tempfile.NamedTemporaryFile(
|
||
mode = "w",
|
||
suffix = ".jinja",
|
||
delete = False,
|
||
prefix = "unsloth_chat_template_",
|
||
)
|
||
self._chat_template_file.write(chat_template_override)
|
||
self._chat_template_file.close()
|
||
cmd.extend(["--chat-template-file", self._chat_template_file.name])
|
||
logger.info(
|
||
f"Using custom chat template file: {self._chat_template_file.name}"
|
||
)
|
||
|
||
# For reasoning models, set default thinking mode.
|
||
# Qwen3.5 models below 9B (0.8B, 2B, 4B) disable thinking by default.
|
||
# Only 9B and larger enable thinking.
|
||
if self._supports_reasoning:
|
||
import re
|
||
|
||
thinking_default = True
|
||
mid = (model_identifier or "").lower()
|
||
if "qwen3.5" in mid:
|
||
# Extract size like "0.8b", "4b", "35b" etc.
|
||
size_match = re.search(r"(\d+\.?\d*)\s*b", mid)
|
||
if size_match:
|
||
size_val = float(size_match.group(1))
|
||
if size_val < 9:
|
||
thinking_default = False
|
||
self._reasoning_default = thinking_default
|
||
cmd.extend(
|
||
[
|
||
"--chat-template-kwargs",
|
||
json.dumps({"enable_thinking": thinking_default}),
|
||
]
|
||
)
|
||
logger.info(
|
||
f"Reasoning model: enable_thinking={thinking_default} by default"
|
||
)
|
||
|
||
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}")
|
||
|
||
# Option C: add --api-key for direct client access when enabled
|
||
import os as _os
|
||
import secrets as _secrets
|
||
|
||
if _os.getenv("UNSLOTH_DIRECT_STREAM", "0") == "1":
|
||
self._api_key = _secrets.token_urlsafe(32)
|
||
cmd.extend(["--api-key", self._api_key])
|
||
logger.info("llama-server started with --api-key for direct streaming")
|
||
else:
|
||
self._api_key = None
|
||
|
||
_log_cmd = list(cmd)
|
||
if "--api-key" in _log_cmd:
|
||
_ki = _log_cmd.index("--api-key") + 1
|
||
if _ki < len(_log_cmd):
|
||
_log_cmd[_ki] = "<redacted>"
|
||
logger.info(f"Starting llama-server: {' '.join(_log_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
|
||
# and CUDA runtime libs (libcudart, libcublas, etc.)
|
||
import platform
|
||
|
||
lib_dirs = [binary_dir]
|
||
_arch = platform.machine() # x86_64, aarch64, etc.
|
||
|
||
# Pip-installed nvidia CUDA runtime libs (e.g. torch's
|
||
# bundled cuda-bindings). The prebuilt llama.cpp binary
|
||
# links against libcudart.so.13 / libcublas.so.13 which
|
||
# live here, not in /usr/local/cuda.
|
||
import glob as _glob
|
||
|
||
for _nv_pattern in [
|
||
os.path.join(
|
||
sys.prefix,
|
||
"lib",
|
||
"python*",
|
||
"site-packages",
|
||
"nvidia",
|
||
"cu*",
|
||
"lib",
|
||
),
|
||
os.path.join(
|
||
sys.prefix,
|
||
"lib",
|
||
"python*",
|
||
"site-packages",
|
||
"nvidia",
|
||
"cudnn",
|
||
"lib",
|
||
),
|
||
os.path.join(
|
||
sys.prefix,
|
||
"lib",
|
||
"python*",
|
||
"site-packages",
|
||
"nvidia",
|
||
"nvjitlink",
|
||
"lib",
|
||
),
|
||
]:
|
||
for _nv_dir in _glob.glob(_nv_pattern):
|
||
if os.path.isdir(_nv_dir):
|
||
lib_dirs.append(_nv_dir)
|
||
|
||
for cuda_lib in [
|
||
"/usr/local/cuda/lib64",
|
||
f"/usr/local/cuda/targets/{_arch}-linux/lib",
|
||
# Fallback CUDA compat paths (e.g. binary built with
|
||
# CUDA 12 on a system where default /usr/local/cuda
|
||
# points to CUDA 13+).
|
||
"/usr/local/cuda-12/lib64",
|
||
"/usr/local/cuda-12.8/lib64",
|
||
f"/usr/local/cuda-12/targets/{_arch}-linux/lib",
|
||
f"/usr/local/cuda-12.8/targets/{_arch}-linux/lib",
|
||
]:
|
||
if os.path.isdir(cuda_lib):
|
||
lib_dirs.append(cuda_lib)
|
||
existing_ld = env.get("LD_LIBRARY_PATH", "")
|
||
new_ld = ":".join(lib_dirs)
|
||
env["LD_LIBRARY_PATH"] = (
|
||
f"{new_ld}:{existing_ld}" if existing_ld else new_ld
|
||
)
|
||
|
||
# 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
|
||
# For local GGUF files, extract variant from filename if not provided
|
||
if hf_variant:
|
||
self._hf_variant = hf_variant
|
||
elif gguf_path:
|
||
try:
|
||
from utils.models.model_config import _extract_quant_label
|
||
|
||
self._hf_variant = _extract_quant_label(gguf_path)
|
||
except Exception:
|
||
self._hf_variant = None
|
||
else:
|
||
self._hf_variant = None
|
||
self._is_vision = is_vision
|
||
self._model_identifier = model_identifier
|
||
|
||
# Store the effective (possibly capped) context separately.
|
||
# Do NOT overwrite _context_length -- it holds the model's native
|
||
# context length from GGUF metadata and is used for display/info.
|
||
self._effective_context_length = (
|
||
effective_ctx if effective_ctx > 0 else self._context_length
|
||
)
|
||
self._max_context_length = (
|
||
max_available_ctx
|
||
if max_available_ctx > 0
|
||
else self._effective_context_length
|
||
)
|
||
|
||
# Wait for llama-server to become healthy
|
||
if not self._wait_for_health(timeout = 600.0):
|
||
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._effective_context_length = None
|
||
self._max_context_length = None
|
||
self._chat_template = None
|
||
self._supports_reasoning = False
|
||
self._reasoning_always_on = False
|
||
self._supports_tools = False
|
||
self._cache_type_kv = None
|
||
self._n_layers = None
|
||
self._n_kv_heads = None
|
||
self._n_heads = None
|
||
self._embedding_length = None
|
||
# Clean up temp chat template file
|
||
if hasattr(self, "_chat_template_file") and self._chat_template_file:
|
||
try:
|
||
import os
|
||
|
||
os.unlink(self._chat_template_file.name)
|
||
except Exception:
|
||
pass
|
||
self._chat_template_file = 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 resolved binary lives under a known
|
||
Studio install directory (or matches an exact env-var override)
|
||
to avoid terminating unrelated llama-server instances.
|
||
|
||
Mirrors every location that _find_llama_server_binary() can
|
||
return from so that orphans from any supported install path
|
||
are still cleaned up.
|
||
|
||
Uses psutil for cross-platform support (Linux, macOS, Windows).
|
||
Falls back to pgrep + /proc/<pid>/exe on Linux when psutil is
|
||
not installed.
|
||
"""
|
||
import os
|
||
import signal
|
||
import sys
|
||
|
||
try:
|
||
# -- Build the ownership allowlist --------------------------------
|
||
# Two kinds of matches:
|
||
# exact_binaries -- env var overrides (exact path match only)
|
||
# install_roots -- directory trees that are Studio-owned
|
||
# (binary must be *under* one of these)
|
||
install_roots: list[Path] = []
|
||
|
||
# Primary install dir (setup.sh / prebuilt installer)
|
||
install_roots.append(Path.home() / ".unsloth" / "llama.cpp")
|
||
|
||
# Legacy in-tree build dirs (older setup.sh versions)
|
||
project_root = Path(__file__).resolve().parents[4]
|
||
install_roots.append(project_root / "llama.cpp")
|
||
|
||
# Legacy: extracted binary
|
||
install_roots.append(project_root / "bin")
|
||
|
||
# UNSLOTH_LLAMA_CPP_PATH env var (custom install dir)
|
||
custom_dir = os.environ.get("UNSLOTH_LLAMA_CPP_PATH")
|
||
if custom_dir:
|
||
install_roots.append(Path(custom_dir))
|
||
|
||
# LLAMA_SERVER_PATH env var (exact binary path)
|
||
exact_binaries: list[Path] = []
|
||
env_binary = os.environ.get("LLAMA_SERVER_PATH")
|
||
if env_binary:
|
||
try:
|
||
exact_binaries.append(Path(env_binary).resolve())
|
||
except OSError:
|
||
pass
|
||
|
||
# Resolve all roots so is_relative_to works reliably
|
||
resolved_roots: list[Path] = []
|
||
for root in install_roots:
|
||
try:
|
||
resolved_roots.append(root.resolve())
|
||
except OSError:
|
||
pass
|
||
|
||
my_pid = os.getpid()
|
||
|
||
# -- Enumerate processes -------------------------------------------
|
||
# Prefer psutil (cross-platform). Fall back to pgrep + /proc on
|
||
# Linux when psutil is not installed.
|
||
try:
|
||
import psutil
|
||
|
||
has_psutil = True
|
||
except ImportError:
|
||
has_psutil = False
|
||
|
||
if has_psutil:
|
||
for proc in psutil.process_iter(["pid", "name", "exe"]):
|
||
try:
|
||
if proc.info["pid"] == my_pid:
|
||
continue
|
||
|
||
name = proc.info.get("name") or ""
|
||
if not name.lower().startswith("llama-server"):
|
||
continue
|
||
|
||
exe = proc.info.get("exe")
|
||
if not exe:
|
||
continue
|
||
|
||
exe_path = Path(exe).resolve()
|
||
|
||
# Check ownership: exact binary match OR binary is
|
||
# under a known install root (proper ancestry, not
|
||
# substring).
|
||
is_ours = exe_path in exact_binaries or any(
|
||
exe_path.is_relative_to(root) for root in resolved_roots
|
||
)
|
||
if not is_ours:
|
||
continue
|
||
|
||
proc.kill()
|
||
logger.info(
|
||
f"Killed orphaned llama-server process "
|
||
f"(pid={proc.info['pid']})"
|
||
)
|
||
except (
|
||
psutil.NoSuchProcess,
|
||
psutil.AccessDenied,
|
||
psutil.ZombieProcess,
|
||
):
|
||
pass
|
||
else:
|
||
# -- Fallback: pgrep + /proc/<pid>/exe (Linux only) -----------
|
||
if sys.platform != "linux":
|
||
return
|
||
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])
|
||
if pid == my_pid:
|
||
continue
|
||
|
||
# Resolve the actual executable. /proc/<pid>/exe is a
|
||
# symlink to the real binary and avoids all cmdline-
|
||
# parsing ambiguities (spaces in paths, argv rewriting).
|
||
# Fall back to the first cmdline token when /proc is
|
||
# unavailable.
|
||
proc_exe = Path(f"/proc/{pid}/exe")
|
||
try:
|
||
binary = proc_exe.resolve(strict = True)
|
||
except (OSError, ValueError):
|
||
cmdline = parts[1]
|
||
token = cmdline.split()[0] if cmdline.strip() else ""
|
||
if not token:
|
||
continue
|
||
binary = Path(token).resolve(strict = False)
|
||
|
||
owned = binary in exact_binaries or any(
|
||
binary.is_relative_to(root) for root in resolved_roots
|
||
)
|
||
if not owned:
|
||
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:
|
||
logger.warning("Error during orphan server cleanup", exc_info = True)
|
||
|
||
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 _parse_tool_calls_from_text(content: str) -> list[dict]:
|
||
"""
|
||
Parse tool calls from XML markup in content text.
|
||
|
||
Handles formats like:
|
||
<tool_call>{"name":"web_search","arguments":{"query":"..."}}</tool_call>
|
||
<tool_call><function=web_search><parameter=query>...</parameter></function></tool_call>
|
||
Closing tags (</tool_call>, </function>, </parameter>) are all optional
|
||
since models frequently omit them.
|
||
"""
|
||
import re
|
||
|
||
tool_calls = []
|
||
|
||
# Pattern 1: JSON inside <tool_call> tags.
|
||
# Use balanced-brace extraction that skips braces inside JSON strings.
|
||
for m in re.finditer(r"<tool_call>\s*\{", content):
|
||
brace_start = m.end() - 1 # position of the opening {
|
||
depth, i = 0, brace_start
|
||
in_string = False
|
||
while i < len(content):
|
||
ch = content[i]
|
||
if in_string:
|
||
if ch == "\\" and i + 1 < len(content):
|
||
i += 2 # skip escaped character
|
||
continue
|
||
if ch == '"':
|
||
in_string = False
|
||
elif ch == '"':
|
||
in_string = True
|
||
elif ch == "{":
|
||
depth += 1
|
||
elif ch == "}":
|
||
depth -= 1
|
||
if depth == 0:
|
||
break
|
||
i += 1
|
||
if depth == 0:
|
||
json_str = content[brace_start : i + 1]
|
||
try:
|
||
obj = json.loads(json_str)
|
||
tc = {
|
||
"id": f"call_{len(tool_calls)}",
|
||
"type": "function",
|
||
"function": {
|
||
"name": obj.get("name", ""),
|
||
"arguments": obj.get("arguments", {}),
|
||
},
|
||
}
|
||
if isinstance(tc["function"]["arguments"], dict):
|
||
tc["function"]["arguments"] = json.dumps(
|
||
tc["function"]["arguments"]
|
||
)
|
||
tool_calls.append(tc)
|
||
except (json.JSONDecodeError, ValueError):
|
||
pass
|
||
|
||
# Pattern 2: XML-style <function=name><parameter=key>value</parameter></function>
|
||
# All closing tags optional -- models frequently omit </parameter>,
|
||
# </function>, and/or </tool_call>.
|
||
if not tool_calls:
|
||
# Step 1: Find all <function=name> positions and extract their bodies.
|
||
# Body boundary: use only </tool_call> or next <function= as hard
|
||
# boundaries. We avoid using </function> as a boundary because
|
||
# code parameter values can contain that literal string.
|
||
# After extracting, we trim a trailing </function> if present.
|
||
func_starts = list(re.finditer(r"<function=(\w+)>\s*", content))
|
||
for idx, fm in enumerate(func_starts):
|
||
func_name = fm.group(1)
|
||
body_start = fm.end()
|
||
# Hard boundaries: next <function= tag or </tool_call>
|
||
next_func = (
|
||
func_starts[idx + 1].start()
|
||
if idx + 1 < len(func_starts)
|
||
else len(content)
|
||
)
|
||
end_tag = re.search(r"</tool_call>", content[body_start:])
|
||
if end_tag:
|
||
body_end = body_start + end_tag.start()
|
||
else:
|
||
body_end = len(content)
|
||
body_end = min(body_end, next_func)
|
||
body = content[body_start:body_end]
|
||
# Trim trailing </function> if present (it's the real closing tag)
|
||
body = re.sub(r"\s*</function>\s*$", "", body)
|
||
|
||
# Step 2: Extract parameters from body.
|
||
# For single-parameter functions (the common case: code, command,
|
||
# query), use body end as the only boundary to avoid false matches
|
||
# on </parameter> inside code strings.
|
||
arguments = {}
|
||
param_starts = list(re.finditer(r"<parameter=(\w+)>\s*", body))
|
||
if len(param_starts) == 1:
|
||
# Single parameter: value is everything from after the tag
|
||
# to end of body, trimming any trailing </parameter>.
|
||
pm = param_starts[0]
|
||
val = body[pm.end() :]
|
||
val = re.sub(r"\s*</parameter>\s*$", "", val)
|
||
arguments[pm.group(1)] = val.strip()
|
||
else:
|
||
for pidx, pm in enumerate(param_starts):
|
||
param_name = pm.group(1)
|
||
val_start = pm.end()
|
||
# Value ends at next <parameter= or end of body
|
||
next_param = (
|
||
param_starts[pidx + 1].start()
|
||
if pidx + 1 < len(param_starts)
|
||
else len(body)
|
||
)
|
||
val = body[val_start:next_param]
|
||
# Trim trailing </parameter> if present
|
||
val = re.sub(r"\s*</parameter>\s*$", "", val)
|
||
arguments[param_name] = val.strip()
|
||
|
||
tc = {
|
||
"id": f"call_{len(tool_calls)}",
|
||
"type": "function",
|
||
"function": {
|
||
"name": func_name,
|
||
"arguments": json.dumps(arguments),
|
||
},
|
||
}
|
||
tool_calls.append(tc)
|
||
|
||
return tool_calls
|
||
|
||
@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) ────────────────────────
|
||
|
||
@staticmethod
|
||
def _iter_text_cancellable(
|
||
response: "httpx.Response",
|
||
cancel_event: Optional[threading.Event] = None,
|
||
) -> Generator[str, None, None]:
|
||
"""Iterate over an httpx streaming response with cancel support.
|
||
|
||
Checks cancel_event between chunks and on ReadTimeout. The
|
||
cancel watcher in _stream_with_retry also calls response.close()
|
||
on cancel, which unblocks iter_text() once the response exists.
|
||
During normal streaming llama-server sends tokens frequently,
|
||
so the cancel check between chunks is the primary mechanism.
|
||
"""
|
||
text_iter = response.iter_text()
|
||
while True:
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
response.close()
|
||
return
|
||
try:
|
||
chunk = next(text_iter)
|
||
yield chunk
|
||
except StopIteration:
|
||
return
|
||
except httpx.ReadTimeout:
|
||
# No data within the timeout window -- just loop back
|
||
# and re-check cancel_event.
|
||
continue
|
||
|
||
@staticmethod
|
||
@contextlib.contextmanager
|
||
def _stream_with_retry(
|
||
client: "httpx.Client",
|
||
url: str,
|
||
payload: dict,
|
||
cancel_event: Optional[threading.Event] = None,
|
||
headers: Optional[dict] = None,
|
||
):
|
||
"""Open an httpx streaming POST with cancel support.
|
||
|
||
Sends the request once with a long read timeout (120 s) so
|
||
prompt processing (prefill) can finish without triggering a
|
||
retry storm. The previous 0.5 s timeout caused duplicate POST
|
||
requests every half second, forcing llama-server to restart
|
||
processing each time.
|
||
|
||
A background watcher thread provides cancel by closing the
|
||
response when cancel_event is set. Limitation: httpx does not
|
||
allow interrupting a blocked read from another thread before
|
||
the response object exists, so cancel during the initial
|
||
header wait (prefill phase) only takes effect once headers
|
||
arrive. After that, response.close() unblocks reads promptly.
|
||
In practice llama-server prefill is 1-5 s for typical prompts,
|
||
during which cancel is deferred -- still much better than the
|
||
old retry storm which made prefill slower.
|
||
"""
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
raise GeneratorExit
|
||
|
||
# Background watcher: close the response if cancel is requested.
|
||
# Only effective after response headers arrive (httpx limitation).
|
||
_cancel_closed = threading.Event()
|
||
_response_ref: list = [None]
|
||
|
||
def _cancel_watcher():
|
||
while not _cancel_closed.is_set():
|
||
if cancel_event.wait(timeout = 0.3):
|
||
# Cancel requested. Keep polling until the response object
|
||
# exists so we can close it, or until the main thread
|
||
# finishes on its own (_cancel_closed is set in finally).
|
||
while not _cancel_closed.is_set():
|
||
r = _response_ref[0]
|
||
if r is not None:
|
||
try:
|
||
r.close()
|
||
return
|
||
except Exception as e:
|
||
logger.debug(
|
||
f"Error closing response in cancel watcher: {e}"
|
||
)
|
||
# Response not created yet -- wait briefly and retry
|
||
_cancel_closed.wait(timeout = 0.1)
|
||
return
|
||
|
||
watcher = None
|
||
if cancel_event is not None:
|
||
watcher = threading.Thread(
|
||
target = _cancel_watcher, daemon = True, name = "prefill-cancel"
|
||
)
|
||
watcher.start()
|
||
|
||
try:
|
||
# Long read timeout so prefill (prompt processing) can finish
|
||
# without triggering a retry storm. Cancel during both
|
||
# prefill and streaming is handled by the watcher thread
|
||
# which closes the response, unblocking any httpx read.
|
||
prefill_timeout = httpx.Timeout(
|
||
connect = 30,
|
||
read = 120.0,
|
||
write = 10,
|
||
pool = 10,
|
||
)
|
||
with client.stream(
|
||
"POST",
|
||
url,
|
||
json = payload,
|
||
timeout = prefill_timeout,
|
||
headers = headers,
|
||
) as response:
|
||
_response_ref[0] = response
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
raise GeneratorExit
|
||
yield response
|
||
return
|
||
except (httpx.ReadError, httpx.RemoteProtocolError, httpx.CloseError):
|
||
# Response was closed by the cancel watcher
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
raise GeneratorExit
|
||
raise
|
||
finally:
|
||
_cancel_closed.set()
|
||
|
||
def generate_chat_completion(
|
||
self,
|
||
messages: list[dict],
|
||
image_b64: Optional[str] = None,
|
||
temperature: float = 0.6,
|
||
top_p: float = 0.95,
|
||
top_k: int = 20,
|
||
min_p: float = 0.01,
|
||
max_tokens: Optional[int] = None,
|
||
repetition_penalty: float = 1.0,
|
||
presence_penalty: float = 0.0,
|
||
stop: Optional[list[str]] = None,
|
||
cancel_event: Optional[threading.Event] = None,
|
||
enable_thinking: Optional[bool] = None,
|
||
) -> Generator[str | dict, 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,
|
||
"presence_penalty": presence_penalty,
|
||
}
|
||
# Pass enable_thinking per-request for reasoning models
|
||
if self._supports_reasoning and enable_thinking is not None:
|
||
payload["chat_template_kwargs"] = {"enable_thinking": enable_thinking}
|
||
if max_tokens is not None:
|
||
payload["max_tokens"] = max_tokens
|
||
if stop:
|
||
payload["stop"] = stop
|
||
payload["stream_options"] = {"include_usage": True}
|
||
|
||
url = f"{self.base_url}/v1/chat/completions"
|
||
cumulative = ""
|
||
in_thinking = False
|
||
_stream_done = False
|
||
_metadata_usage = None
|
||
_metadata_timings = None
|
||
|
||
try:
|
||
# _stream_with_retry uses a 120 s read timeout so prefill
|
||
# can finish. Cancel during streaming is handled by the
|
||
# watcher thread (closes the response on cancel_event).
|
||
stream_timeout = httpx.Timeout(connect = 10, read = 0.5, write = 10, pool = 10)
|
||
_auth_headers = (
|
||
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
|
||
)
|
||
with httpx.Client(timeout = stream_timeout) as client:
|
||
with self._stream_with_retry(
|
||
client,
|
||
url,
|
||
payload,
|
||
cancel_event,
|
||
headers = _auth_headers,
|
||
) as response:
|
||
if response.status_code != 200:
|
||
error_body = response.read().decode()
|
||
raise RuntimeError(
|
||
f"llama-server returned {response.status_code}: {error_body}"
|
||
)
|
||
|
||
buffer = ""
|
||
has_content_tokens = False
|
||
reasoning_text = ""
|
||
for raw_chunk in self._iter_text_cancellable(
|
||
response, cancel_event
|
||
):
|
||
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:
|
||
if has_content_tokens:
|
||
# Real thinking + content: close the tag
|
||
cumulative += "</think>"
|
||
yield cumulative
|
||
else:
|
||
# Only reasoning_content, no content tokens:
|
||
# the model put its entire reply in reasoning
|
||
# (e.g. Qwen3 always-think mode). Show it
|
||
# as the main response, not as a thinking block.
|
||
cumulative = reasoning_text
|
||
yield cumulative
|
||
_stream_done = True
|
||
break # exit inner while
|
||
if not line.startswith("data: "):
|
||
continue
|
||
|
||
try:
|
||
data = json.loads(line[6:])
|
||
# Capture server timings/usage from final chunks
|
||
_chunk_timings = data.get("timings")
|
||
if _chunk_timings:
|
||
_metadata_timings = _chunk_timings
|
||
_chunk_usage = data.get("usage")
|
||
if _chunk_usage:
|
||
_metadata_usage = _chunk_usage
|
||
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:
|
||
reasoning_text += reasoning
|
||
if not in_thinking:
|
||
cumulative += "<think>"
|
||
in_thinking = True
|
||
cumulative += reasoning
|
||
yield cumulative
|
||
|
||
token = delta.get("content", "")
|
||
if token:
|
||
has_content_tokens = True
|
||
if in_thinking:
|
||
cumulative += "</think>"
|
||
in_thinking = False
|
||
cumulative += token
|
||
yield cumulative
|
||
except json.JSONDecodeError:
|
||
logger.debug(
|
||
f"Skipping malformed SSE line: {line[:100]}"
|
||
)
|
||
if _stream_done:
|
||
break # exit outer for
|
||
if _metadata_usage or _metadata_timings:
|
||
yield {
|
||
"type": "metadata",
|
||
"usage": _metadata_usage,
|
||
"timings": _metadata_timings,
|
||
}
|
||
|
||
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
|
||
|
||
# ── Tool-calling agentic loop ──────────────────────────────
|
||
|
||
def generate_chat_completion_with_tools(
|
||
self,
|
||
messages: list[dict],
|
||
tools: list[dict],
|
||
temperature: float = 0.6,
|
||
top_p: float = 0.95,
|
||
top_k: int = 20,
|
||
min_p: float = 0.01,
|
||
max_tokens: Optional[int] = None,
|
||
repetition_penalty: float = 1.0,
|
||
presence_penalty: float = 0.0,
|
||
stop: Optional[list[str]] = None,
|
||
cancel_event: Optional[threading.Event] = None,
|
||
enable_thinking: Optional[bool] = None,
|
||
max_tool_iterations: int = 25,
|
||
auto_heal_tool_calls: bool = True,
|
||
tool_call_timeout: int = 300,
|
||
session_id: Optional[str] = None,
|
||
) -> Generator[dict, None, None]:
|
||
"""
|
||
Agentic loop: let the model call tools, execute them, and continue.
|
||
|
||
Yields dicts with:
|
||
{"type": "status", "text": "Searching: ..."} -- tool status updates
|
||
{"type": "content", "text": "token"} -- streamed content tokens (cumulative)
|
||
{"type": "reasoning", "text": "token"} -- streamed reasoning tokens (cumulative)
|
||
"""
|
||
from core.inference.tools import execute_tool
|
||
|
||
if not self.is_loaded:
|
||
raise RuntimeError("llama-server is not loaded")
|
||
|
||
conversation = list(messages)
|
||
url = f"{self.base_url}/v1/chat/completions"
|
||
_accumulated_completion_tokens = 0
|
||
_accumulated_predicted_ms = 0.0
|
||
_accumulated_predicted_n = 0
|
||
|
||
# ── Shared patterns for stripping tool XML from streamed content ──
|
||
import re as _re_tool
|
||
|
||
_TOOL_CLOSED_PATTERNS = [
|
||
_re_tool.compile(r"<tool_call>.*?</tool_call>", _re_tool.DOTALL),
|
||
_re_tool.compile(r"<function=\w+>.*?</function>", _re_tool.DOTALL),
|
||
]
|
||
_TOOL_ALL_PATTERNS = _TOOL_CLOSED_PATTERNS + [
|
||
_re_tool.compile(r"<tool_call>.*$", _re_tool.DOTALL),
|
||
_re_tool.compile(r"<function=\w+>.*$", _re_tool.DOTALL),
|
||
]
|
||
|
||
def _strip_tool_markup(text: str, *, final: bool = False) -> str:
|
||
if not auto_heal_tool_calls:
|
||
return text
|
||
patterns = _TOOL_ALL_PATTERNS if final else _TOOL_CLOSED_PATTERNS
|
||
for pat in patterns:
|
||
text = pat.sub("", text)
|
||
return text.strip() if final else text
|
||
|
||
# XML prefixes that signal a tool call in content.
|
||
# Empty when auto_heal is disabled so the buffer never
|
||
# speculatively holds content for XML detection.
|
||
_TOOL_XML_SIGNALS = (
|
||
("<tool_call>", "<function=") if auto_heal_tool_calls else ()
|
||
)
|
||
_MAX_BUFFER_CHARS = 32
|
||
|
||
# ── Duplicate tool-call detection ────────────────────────
|
||
# Track recent (tool_name, arguments) hashes to detect loops
|
||
# where the model repeats the exact same call. Retries after
|
||
# a transient failure are allowed (only block when the previous
|
||
# identical call succeeded).
|
||
_tool_call_history: list[tuple[str, bool]] = [] # (key, failed)
|
||
|
||
for iteration in range(max_tool_iterations):
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
return
|
||
|
||
# Build payload -- stream: True so we detect tool signals
|
||
# in the first 1-2 chunks without a non-streaming penalty.
|
||
payload = {
|
||
"messages": conversation,
|
||
"stream": True,
|
||
"stream_options": {"include_usage": 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,
|
||
"presence_penalty": presence_penalty,
|
||
"tools": tools,
|
||
"tool_choice": "auto",
|
||
}
|
||
if self._supports_reasoning and enable_thinking is not None:
|
||
payload["chat_template_kwargs"] = {"enable_thinking": enable_thinking}
|
||
if max_tokens is not None:
|
||
payload["max_tokens"] = max_tokens
|
||
if stop:
|
||
payload["stop"] = stop
|
||
|
||
try:
|
||
_auth_headers = (
|
||
{"Authorization": f"Bearer {self._api_key}"}
|
||
if self._api_key
|
||
else None
|
||
)
|
||
|
||
# ── Speculative buffer state machine ──────────────────
|
||
# BUFFERING: accumulating content, checking for tool signals
|
||
# STREAMING: no tool detected, yielding tokens to caller
|
||
# DRAINING: tool signal found, silently consuming rest
|
||
_S_BUFFERING = 0
|
||
_S_STREAMING = 1
|
||
_S_DRAINING = 2
|
||
|
||
detect_state = _S_BUFFERING
|
||
content_buffer = "" # Raw content held during BUFFERING
|
||
content_accum = "" # All content tokens (for tool parsing)
|
||
reasoning_accum = ""
|
||
cumulative_display = "" # Cumulative text yielded (with <think>)
|
||
in_thinking = False
|
||
has_content_tokens = False
|
||
tool_calls_acc = {} # Structured delta.tool_calls fragments
|
||
has_structured_tc = False
|
||
_iter_usage = None
|
||
_iter_timings = None
|
||
_stream_done = False
|
||
_last_emitted = ""
|
||
|
||
stream_timeout = httpx.Timeout(
|
||
connect = 10,
|
||
read = 0.5,
|
||
write = 10,
|
||
pool = 10,
|
||
)
|
||
with httpx.Client(timeout = stream_timeout) as client:
|
||
with self._stream_with_retry(
|
||
client,
|
||
url,
|
||
payload,
|
||
cancel_event,
|
||
headers = _auth_headers,
|
||
) as response:
|
||
if response.status_code != 200:
|
||
error_body = response.read().decode()
|
||
raise RuntimeError(
|
||
f"llama-server returned {response.status_code}: "
|
||
f"{error_body}"
|
||
)
|
||
|
||
raw_buf = ""
|
||
for raw_chunk in self._iter_text_cancellable(
|
||
response,
|
||
cancel_event,
|
||
):
|
||
raw_buf += raw_chunk
|
||
while "\n" in raw_buf:
|
||
line, raw_buf = raw_buf.split("\n", 1)
|
||
line = line.strip()
|
||
|
||
if not line:
|
||
continue
|
||
if line == "data: [DONE]":
|
||
# Flush thinking state for STREAMING
|
||
if detect_state == _S_STREAMING and in_thinking:
|
||
if has_content_tokens:
|
||
cumulative_display += "</think>"
|
||
yield {
|
||
"type": "content",
|
||
"text": _strip_tool_markup(
|
||
cumulative_display,
|
||
final = True,
|
||
),
|
||
}
|
||
else:
|
||
cumulative_display = reasoning_accum
|
||
yield {
|
||
"type": "content",
|
||
"text": cumulative_display,
|
||
}
|
||
_stream_done = True
|
||
break # exit inner while
|
||
if not line.startswith("data: "):
|
||
continue
|
||
|
||
try:
|
||
chunk_data = json.loads(line[6:])
|
||
_ct = chunk_data.get("timings")
|
||
if _ct:
|
||
_iter_timings = _ct
|
||
_cu = chunk_data.get("usage")
|
||
if _cu:
|
||
_iter_usage = _cu
|
||
|
||
choices = chunk_data.get("choices", [])
|
||
if not choices:
|
||
continue
|
||
|
||
delta = choices[0].get("delta", {})
|
||
|
||
# ── Structured tool_calls ──
|
||
tc_deltas = delta.get("tool_calls")
|
||
if tc_deltas:
|
||
# Once visible content has been
|
||
# emitted, do not reclassify this
|
||
# turn as a tool call.
|
||
if _last_emitted:
|
||
continue
|
||
has_structured_tc = True
|
||
detect_state = _S_DRAINING
|
||
for tc_d in tc_deltas:
|
||
idx = tc_d.get("index", 0)
|
||
if idx not in tool_calls_acc:
|
||
tool_calls_acc[idx] = {
|
||
"id": tc_d.get("id", f"call_{idx}"),
|
||
"type": "function",
|
||
"function": {
|
||
"name": "",
|
||
"arguments": "",
|
||
},
|
||
}
|
||
elif tc_d.get("id"):
|
||
# Update ID if real one
|
||
# arrives on a later delta
|
||
tool_calls_acc[idx]["id"] = tc_d["id"]
|
||
func = tc_d.get("function", {})
|
||
if func.get("name"):
|
||
tool_calls_acc[idx]["function"][
|
||
"name"
|
||
] += func["name"]
|
||
if func.get("arguments"):
|
||
tool_calls_acc[idx]["function"][
|
||
"arguments"
|
||
] += func["arguments"]
|
||
continue
|
||
|
||
# ── Reasoning tokens ──
|
||
# Only yield in STREAMING state. In BUFFERING
|
||
# and DRAINING, accumulate silently so we don't
|
||
# corrupt the consumer's prev_text tracker
|
||
# (routes/inference.py never resets prev_text
|
||
# between tool iterations).
|
||
reasoning = delta.get("reasoning_content", "")
|
||
if reasoning:
|
||
reasoning_accum += reasoning
|
||
if detect_state == _S_STREAMING:
|
||
if not in_thinking:
|
||
cumulative_display += "<think>"
|
||
in_thinking = True
|
||
cumulative_display += reasoning
|
||
yield {
|
||
"type": "content",
|
||
"text": cumulative_display,
|
||
}
|
||
|
||
# ── Content tokens ──
|
||
token = delta.get("content", "")
|
||
if token:
|
||
has_content_tokens = True
|
||
content_accum += token
|
||
|
||
if detect_state == _S_DRAINING:
|
||
pass # accumulate silently
|
||
|
||
elif detect_state == _S_STREAMING:
|
||
if in_thinking:
|
||
cumulative_display += "</think>"
|
||
in_thinking = False
|
||
cumulative_display += token
|
||
cleaned = _strip_tool_markup(
|
||
cumulative_display,
|
||
)
|
||
if len(cleaned) > len(_last_emitted):
|
||
_last_emitted = cleaned
|
||
yield {
|
||
"type": "content",
|
||
"text": cleaned,
|
||
}
|
||
|
||
elif detect_state == _S_BUFFERING:
|
||
content_buffer += token
|
||
stripped_buf = content_buffer.lstrip()
|
||
if not stripped_buf:
|
||
continue
|
||
|
||
# Check tool signal prefixes
|
||
is_prefix = False
|
||
is_match = False
|
||
for sig in _TOOL_XML_SIGNALS:
|
||
if stripped_buf.startswith(sig):
|
||
is_match = True
|
||
break
|
||
if sig.startswith(stripped_buf):
|
||
is_prefix = True
|
||
break
|
||
|
||
if is_match:
|
||
detect_state = _S_DRAINING
|
||
elif (
|
||
is_prefix
|
||
and len(stripped_buf)
|
||
< _MAX_BUFFER_CHARS
|
||
):
|
||
pass # keep buffering
|
||
else:
|
||
# Not a tool -- flush buffer
|
||
detect_state = _S_STREAMING
|
||
# Flush any reasoning accumulated
|
||
# during BUFFERING phase
|
||
if reasoning_accum:
|
||
cumulative_display += "<think>"
|
||
cumulative_display += (
|
||
reasoning_accum
|
||
)
|
||
cumulative_display += "</think>"
|
||
cumulative_display += content_buffer
|
||
cleaned = _strip_tool_markup(
|
||
cumulative_display,
|
||
)
|
||
if len(cleaned) > len(_last_emitted):
|
||
_last_emitted = cleaned
|
||
yield {
|
||
"type": "content",
|
||
"text": cleaned,
|
||
}
|
||
|
||
except json.JSONDecodeError:
|
||
logger.debug(
|
||
f"Skipping malformed SSE line: " f"{line[:100]}"
|
||
)
|
||
if _stream_done:
|
||
break # exit outer for
|
||
|
||
# ── Resolve BUFFERING at stream end ──
|
||
if detect_state == _S_BUFFERING:
|
||
stripped_buf = content_buffer.lstrip()
|
||
if (
|
||
stripped_buf
|
||
and auto_heal_tool_calls
|
||
and any(s in stripped_buf for s in _TOOL_XML_SIGNALS)
|
||
):
|
||
detect_state = _S_DRAINING
|
||
elif content_accum or reasoning_accum:
|
||
detect_state = _S_STREAMING
|
||
if content_buffer:
|
||
# Flush any reasoning accumulated first
|
||
if reasoning_accum:
|
||
cumulative_display += "<think>"
|
||
cumulative_display += reasoning_accum
|
||
cumulative_display += "</think>"
|
||
cumulative_display += content_buffer
|
||
yield {
|
||
"type": "content",
|
||
"text": _strip_tool_markup(
|
||
cumulative_display,
|
||
final = True,
|
||
),
|
||
}
|
||
elif reasoning_accum and not has_content_tokens:
|
||
# Reasoning-only response (no content tokens):
|
||
# show reasoning as plain text, matching
|
||
# the final streaming pass behavior for
|
||
# models that put everything in reasoning.
|
||
cumulative_display = reasoning_accum
|
||
yield {
|
||
"type": "content",
|
||
"text": cumulative_display,
|
||
}
|
||
else:
|
||
return
|
||
|
||
# ── STREAMING path: no tool call ──
|
||
if detect_state == _S_STREAMING:
|
||
# Safety net: check for XML tool signals in content.
|
||
# The route layer resets prev_text on tool_start, so
|
||
# post-tool synthesis streams correctly even if
|
||
# content was already emitted before the tool XML.
|
||
_safety_tc = None
|
||
if auto_heal_tool_calls and any(
|
||
s in content_accum for s in _TOOL_XML_SIGNALS
|
||
):
|
||
_safety_tc = self._parse_tool_calls_from_text(
|
||
content_accum,
|
||
)
|
||
if not _safety_tc:
|
||
# Content was already streamed. Yield metadata.
|
||
yield {"type": "status", "text": ""}
|
||
_fu = _iter_usage or {}
|
||
_fc = _fu.get("completion_tokens", 0)
|
||
_fp = _fu.get("prompt_tokens", 0)
|
||
_tc = _fc + _accumulated_completion_tokens
|
||
if (
|
||
_iter_usage
|
||
or _iter_timings
|
||
or _accumulated_completion_tokens
|
||
):
|
||
_mt = dict(_iter_timings) if _iter_timings else {}
|
||
if _accumulated_predicted_ms or _accumulated_predicted_n:
|
||
_mt["predicted_ms"] = (
|
||
_mt.get("predicted_ms", 0)
|
||
+ _accumulated_predicted_ms
|
||
)
|
||
_tn = (
|
||
_mt.get("predicted_n", 0) + _accumulated_predicted_n
|
||
)
|
||
_mt["predicted_n"] = _tn
|
||
_tms = _mt["predicted_ms"]
|
||
if _tms > 0:
|
||
_mt["predicted_per_second"] = _tn / (_tms / 1000.0)
|
||
yield {
|
||
"type": "metadata",
|
||
"usage": {
|
||
"prompt_tokens": _fp,
|
||
"completion_tokens": _tc,
|
||
"total_tokens": _fp + _tc,
|
||
},
|
||
"timings": _mt,
|
||
}
|
||
return
|
||
|
||
# Safety net caught tool XML -- treat as tool call
|
||
tool_calls = _safety_tc
|
||
content_text = _strip_tool_markup(
|
||
content_accum,
|
||
final = True,
|
||
)
|
||
logger.info(
|
||
f"Safety net: parsed {len(tool_calls)} tool call(s) "
|
||
f"from streamed content"
|
||
)
|
||
else:
|
||
# ── DRAINING path: assemble tool_calls ──
|
||
tool_calls = None
|
||
content_text = content_accum
|
||
if has_structured_tc:
|
||
# Filter out incomplete fragments (e.g. from
|
||
# truncation by max_tokens or disconnect).
|
||
tool_calls = [
|
||
tool_calls_acc[i]
|
||
for i in sorted(tool_calls_acc)
|
||
if (
|
||
tool_calls_acc[i]
|
||
.get("function", {})
|
||
.get("name", "")
|
||
.strip()
|
||
)
|
||
] or None
|
||
if (
|
||
not tool_calls
|
||
and auto_heal_tool_calls
|
||
and any(s in content_accum for s in _TOOL_XML_SIGNALS)
|
||
):
|
||
tool_calls = self._parse_tool_calls_from_text(
|
||
content_accum,
|
||
)
|
||
if tool_calls and not has_structured_tc:
|
||
content_text = _strip_tool_markup(
|
||
content_text,
|
||
final = True,
|
||
)
|
||
if tool_calls:
|
||
logger.info(
|
||
f"Parsed {len(tool_calls)} tool call(s) from "
|
||
f"{'structured delta' if has_structured_tc else 'content text'}"
|
||
)
|
||
if not tool_calls:
|
||
# DRAINING but no tool calls (false positive).
|
||
# Merge accumulated metrics from prior tool
|
||
# iterations so they are not silently dropped.
|
||
yield {"type": "status", "text": ""}
|
||
if content_accum:
|
||
# Strip leaked tool-call XML before yielding
|
||
content_accum = _strip_tool_markup(
|
||
content_accum, final = True
|
||
)
|
||
if content_accum:
|
||
yield {"type": "content", "text": content_accum}
|
||
_fu = _iter_usage or {}
|
||
_fc = _fu.get("completion_tokens", 0)
|
||
_fp = _fu.get("prompt_tokens", 0)
|
||
_tc = _fc + _accumulated_completion_tokens
|
||
if (
|
||
_iter_usage
|
||
or _iter_timings
|
||
or _accumulated_completion_tokens
|
||
):
|
||
_mt = dict(_iter_timings) if _iter_timings else {}
|
||
if _accumulated_predicted_ms or _accumulated_predicted_n:
|
||
_mt["predicted_ms"] = (
|
||
_mt.get("predicted_ms", 0)
|
||
+ _accumulated_predicted_ms
|
||
)
|
||
_tn = (
|
||
_mt.get("predicted_n", 0) + _accumulated_predicted_n
|
||
)
|
||
_mt["predicted_n"] = _tn
|
||
_tms = _mt["predicted_ms"]
|
||
if _tms > 0:
|
||
_mt["predicted_per_second"] = _tn / (_tms / 1000.0)
|
||
yield {
|
||
"type": "metadata",
|
||
"usage": {
|
||
"prompt_tokens": _fp,
|
||
"completion_tokens": _tc,
|
||
"total_tokens": _fp + _tc,
|
||
},
|
||
"timings": _mt,
|
||
}
|
||
return
|
||
|
||
# ── Execute tool calls ──
|
||
_accumulated_completion_tokens += (_iter_usage or {}).get(
|
||
"completion_tokens", 0
|
||
)
|
||
_it = _iter_timings or {}
|
||
_accumulated_predicted_ms += _it.get("predicted_ms", 0)
|
||
_accumulated_predicted_n += _it.get("predicted_n", 0)
|
||
|
||
assistant_msg = {"role": "assistant", "content": content_text}
|
||
if tool_calls:
|
||
assistant_msg["tool_calls"] = tool_calls
|
||
conversation.append(assistant_msg)
|
||
|
||
for tc in tool_calls or []:
|
||
func = tc.get("function", {})
|
||
tool_name = func.get("name", "")
|
||
raw_args = func.get("arguments", {})
|
||
|
||
if isinstance(raw_args, str):
|
||
try:
|
||
arguments = json.loads(raw_args)
|
||
except (json.JSONDecodeError, ValueError):
|
||
if auto_heal_tool_calls:
|
||
arguments = {"query": raw_args}
|
||
else:
|
||
arguments = {"raw": raw_args}
|
||
else:
|
||
arguments = raw_args
|
||
|
||
if tool_name == "web_search":
|
||
status_text = f"Searching: {arguments.get('query', '')}"
|
||
elif tool_name == "python":
|
||
preview = (
|
||
(arguments.get("code") or "").strip().split("\n")[0][:60]
|
||
)
|
||
status_text = (
|
||
f"Running Python: {preview}"
|
||
if preview
|
||
else "Running Python..."
|
||
)
|
||
elif tool_name == "terminal":
|
||
cmd_preview = (arguments.get("command") or "")[:60]
|
||
status_text = (
|
||
f"Running: {cmd_preview}"
|
||
if cmd_preview
|
||
else "Running command..."
|
||
)
|
||
else:
|
||
status_text = f"Calling: {tool_name}"
|
||
yield {"type": "status", "text": status_text}
|
||
|
||
yield {
|
||
"type": "tool_start",
|
||
"tool_name": tool_name,
|
||
"tool_call_id": tc.get("id", ""),
|
||
"arguments": arguments,
|
||
}
|
||
|
||
# ── Duplicate call detection ──────────────
|
||
# str(dict) is stable here: arguments always comes from
|
||
# json.loads on the same model output within one request,
|
||
# so insertion order is deterministic (Python 3.7+).
|
||
_tc_key = tool_name + str(arguments)
|
||
_prev = _tool_call_history[-1] if _tool_call_history else None
|
||
if _prev and _prev[0] == _tc_key and not _prev[1]:
|
||
result = (
|
||
"You already made this exact call. "
|
||
"Do not repeat the same tool call. "
|
||
"Try a different approach: fetch a URL "
|
||
"from previous results, use Python to "
|
||
"process data you already have, or "
|
||
"provide your final answer now."
|
||
)
|
||
else:
|
||
_effective_timeout = (
|
||
None if tool_call_timeout >= 9999 else tool_call_timeout
|
||
)
|
||
result = execute_tool(
|
||
tool_name,
|
||
arguments,
|
||
cancel_event = cancel_event,
|
||
timeout = _effective_timeout,
|
||
session_id = session_id,
|
||
)
|
||
|
||
yield {
|
||
"type": "tool_end",
|
||
"tool_name": tool_name,
|
||
"tool_call_id": tc.get("id", ""),
|
||
"result": result,
|
||
}
|
||
|
||
# Nudge model to try a different approach on errors
|
||
_error_prefixes = (
|
||
"Error",
|
||
"Search failed",
|
||
"Execution error",
|
||
"Blocked:",
|
||
"Exit code",
|
||
"Failed to fetch",
|
||
"Failed to resolve",
|
||
"No query provided",
|
||
)
|
||
_is_error = isinstance(result, str) and result.lstrip().startswith(
|
||
_error_prefixes
|
||
)
|
||
_tool_call_history.append((_tc_key, _is_error))
|
||
_result_content = result
|
||
if _is_error:
|
||
_result_content = (
|
||
result + "\n\nThe tool call encountered an issue. "
|
||
"Please try a different approach or rephrase your request."
|
||
)
|
||
|
||
tool_msg = {
|
||
"role": "tool",
|
||
"name": tool_name,
|
||
"content": _result_content,
|
||
}
|
||
tool_call_id = tc.get("id")
|
||
if tool_call_id:
|
||
tool_msg["tool_call_id"] = tool_call_id
|
||
conversation.append(tool_msg)
|
||
|
||
# Clear tool status badge before next generation iteration
|
||
yield {"type": "status", "text": ""}
|
||
# Continue the loop to let model respond with context
|
||
continue
|
||
|
||
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
|
||
|
||
# ── Tool iteration cap reached -- synthesize final answer ──
|
||
# The model used all iterations without producing a final text
|
||
# response. Inject a nudge so the final streaming pass produces
|
||
# a useful answer instead of continuing to request tools.
|
||
if max_tool_iterations > 0:
|
||
conversation.append(
|
||
{
|
||
"role": "user",
|
||
"content": (
|
||
"You have used all available tool calls. Based on "
|
||
"everything you have found so far, provide your final "
|
||
"answer now. Do not call any more tools."
|
||
),
|
||
}
|
||
)
|
||
|
||
# Clear status
|
||
yield {"type": "status", "text": ""}
|
||
|
||
# Final streaming pass with the full conversation context
|
||
stream_payload = {
|
||
"messages": conversation,
|
||
"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,
|
||
"presence_penalty": presence_penalty,
|
||
}
|
||
if self._supports_reasoning and enable_thinking is not None:
|
||
stream_payload["chat_template_kwargs"] = {
|
||
"enable_thinking": enable_thinking
|
||
}
|
||
if max_tokens is not None:
|
||
stream_payload["max_tokens"] = max_tokens
|
||
if stop:
|
||
stream_payload["stop"] = stop
|
||
stream_payload["stream_options"] = {"include_usage": True}
|
||
|
||
cumulative = ""
|
||
_last_emitted = ""
|
||
in_thinking = False
|
||
has_content_tokens = False
|
||
reasoning_text = ""
|
||
_metadata_usage = None
|
||
_metadata_timings = None
|
||
_stream_done = False
|
||
|
||
try:
|
||
stream_timeout = httpx.Timeout(connect = 10, read = 0.5, write = 10, pool = 10)
|
||
_auth_headers = (
|
||
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
|
||
)
|
||
with httpx.Client(timeout = stream_timeout) as client:
|
||
with self._stream_with_retry(
|
||
client,
|
||
url,
|
||
stream_payload,
|
||
cancel_event,
|
||
headers = _auth_headers,
|
||
) 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 self._iter_text_cancellable(
|
||
response, cancel_event
|
||
):
|
||
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:
|
||
if has_content_tokens:
|
||
cumulative += "</think>"
|
||
yield {
|
||
"type": "content",
|
||
"text": _strip_tool_markup(
|
||
cumulative, final = True
|
||
),
|
||
}
|
||
else:
|
||
cumulative = reasoning_text
|
||
yield {"type": "content", "text": cumulative}
|
||
_stream_done = True
|
||
break # exit inner while
|
||
if not line.startswith("data: "):
|
||
continue
|
||
|
||
try:
|
||
chunk_data = json.loads(line[6:])
|
||
# Capture server timings/usage from final chunks
|
||
_chunk_timings = chunk_data.get("timings")
|
||
if _chunk_timings:
|
||
_metadata_timings = _chunk_timings
|
||
_chunk_usage = chunk_data.get("usage")
|
||
if _chunk_usage:
|
||
_metadata_usage = _chunk_usage
|
||
choices = chunk_data.get("choices", [])
|
||
if choices:
|
||
delta = choices[0].get("delta", {})
|
||
|
||
reasoning = delta.get("reasoning_content", "")
|
||
if reasoning:
|
||
reasoning_text += reasoning
|
||
if not in_thinking:
|
||
cumulative += "<think>"
|
||
in_thinking = True
|
||
cumulative += reasoning
|
||
yield {"type": "content", "text": cumulative}
|
||
|
||
token = delta.get("content", "")
|
||
if token:
|
||
has_content_tokens = True
|
||
if in_thinking:
|
||
cumulative += "</think>"
|
||
in_thinking = False
|
||
cumulative += token
|
||
cleaned = _strip_tool_markup(cumulative)
|
||
# Only emit when cleaned text grows (monotonic).
|
||
if len(cleaned) > len(_last_emitted):
|
||
_last_emitted = cleaned
|
||
yield {"type": "content", "text": cleaned}
|
||
except json.JSONDecodeError:
|
||
logger.debug(
|
||
f"Skipping malformed SSE line: {line[:100]}"
|
||
)
|
||
if _stream_done:
|
||
break # exit outer for
|
||
_final_usage = _metadata_usage or {}
|
||
_final_completion = _final_usage.get("completion_tokens", 0)
|
||
_final_prompt = _final_usage.get("prompt_tokens", 0)
|
||
_total_completion = (
|
||
_final_completion + _accumulated_completion_tokens
|
||
)
|
||
if _metadata_usage or _metadata_timings:
|
||
_merged_timings = (
|
||
dict(_metadata_timings) if _metadata_timings else {}
|
||
)
|
||
if _accumulated_predicted_ms or _accumulated_predicted_n:
|
||
_merged_timings["predicted_ms"] = (
|
||
_merged_timings.get("predicted_ms", 0)
|
||
+ _accumulated_predicted_ms
|
||
)
|
||
_total_predicted_n = (
|
||
_merged_timings.get("predicted_n", 0)
|
||
+ _accumulated_predicted_n
|
||
)
|
||
_merged_timings["predicted_n"] = _total_predicted_n
|
||
_total_predicted_ms = _merged_timings["predicted_ms"]
|
||
if _total_predicted_ms > 0:
|
||
_merged_timings["predicted_per_second"] = (
|
||
_total_predicted_n / (_total_predicted_ms / 1000.0)
|
||
)
|
||
yield {
|
||
"type": "metadata",
|
||
"usage": {
|
||
"prompt_tokens": _final_prompt,
|
||
"completion_tokens": _total_completion,
|
||
"total_tokens": _final_prompt + _total_completion,
|
||
},
|
||
"timings": _merged_timings,
|
||
}
|
||
|
||
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:
|
||
_auth_headers = (
|
||
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
|
||
)
|
||
with httpx.Client(timeout = 10, headers = _auth_headers) 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
|
||
|
||
_auth_headers = (
|
||
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
|
||
)
|
||
with httpx.Client(
|
||
timeout = httpx.Timeout(300, connect = 10), headers = _auth_headers
|
||
) 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", "")
|
||
)
|