852755cc92
Reset _supports_reasoning, _supports_tools, _chat_template, and _context_length at the top of _read_gguf_metadata so flags from a previously loaded model do not carry over. Without this, loading a reasoning model (eg Qwen3.5-4B) then switching to a non-reasoning model (eg Qwen3-4B-Instruct-2507) would keep supports_reasoning=True from the first model, causing the UI to show "Thought for 0 seconds" and passing --chat-template-kwargs enable_thinking to a model whose chat template does not support it. Also update the default helper GGUF from Qwen3-4B-Instruct-2507-GGUF to Qwen3.5-4B-GGUF to match the frontend fallback auto-load model.
1786 lines
69 KiB
Python
1786 lines
69 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
|
||
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
|
||
|
||
"""
|
||
llama-server inference backend for GGUF models.
|
||
|
||
Manages a llama-server subprocess and proxies chat completions
|
||
through its OpenAI-compatible /v1/chat/completions endpoint.
|
||
"""
|
||
|
||
import atexit
|
||
import json
|
||
import struct
|
||
import structlog
|
||
from loggers import get_logger
|
||
import shutil
|
||
import signal
|
||
import socket
|
||
import subprocess
|
||
import threading
|
||
import time
|
||
from pathlib import Path
|
||
from typing import Generator, Optional
|
||
|
||
import httpx
|
||
|
||
logger = get_logger(__name__)
|
||
|
||
|
||
class LlamaCppBackend:
|
||
"""
|
||
Manages a llama-server subprocess for GGUF model inference.
|
||
|
||
Lifecycle:
|
||
1. load_model() — starts llama-server with the GGUF file
|
||
2. generate_chat_completion() — proxies to /v1/chat/completions, streams back
|
||
3. unload_model() — terminates llama-server subprocess
|
||
"""
|
||
|
||
def __init__(self):
|
||
self._process: Optional[subprocess.Popen] = None
|
||
self._port: Optional[int] = None
|
||
self._model_identifier: Optional[str] = None
|
||
self._gguf_path: Optional[str] = None
|
||
self._hf_repo: Optional[str] = None
|
||
self._hf_variant: Optional[str] = None
|
||
self._is_vision: bool = False
|
||
self._healthy = False
|
||
self._context_length: Optional[int] = None
|
||
self._chat_template: Optional[str] = None
|
||
self._supports_reasoning: bool = False
|
||
self._supports_tools: bool = False
|
||
self._cache_type_kv: Optional[str] = None
|
||
self._reasoning_default: bool = True
|
||
self._lock = threading.Lock()
|
||
self._stdout_lines: list[str] = []
|
||
self._stdout_thread: Optional[threading.Thread] = None
|
||
self._cancel_event = threading.Event()
|
||
|
||
self._kill_orphaned_servers()
|
||
atexit.register(self._cleanup)
|
||
|
||
# ── Properties ────────────────────────────────────────────────
|
||
|
||
@property
|
||
def is_loaded(self) -> bool:
|
||
return self._process is not None and self._healthy
|
||
|
||
@property
|
||
def is_active(self) -> bool:
|
||
"""True if a llama-server process exists (loading or loaded)."""
|
||
return self._process is not None
|
||
|
||
@property
|
||
def base_url(self) -> str:
|
||
return f"http://127.0.0.1:{self._port}"
|
||
|
||
@property
|
||
def model_identifier(self) -> Optional[str]:
|
||
return self._model_identifier
|
||
|
||
@property
|
||
def is_vision(self) -> bool:
|
||
return self._is_vision
|
||
|
||
@property
|
||
def hf_variant(self) -> Optional[str]:
|
||
return self._hf_variant
|
||
|
||
@property
|
||
def context_length(self) -> Optional[int]:
|
||
return self._context_length
|
||
|
||
@property
|
||
def chat_template(self) -> Optional[str]:
|
||
return self._chat_template
|
||
|
||
@property
|
||
def supports_reasoning(self) -> bool:
|
||
return self._supports_reasoning
|
||
|
||
@property
|
||
def reasoning_default(self) -> bool:
|
||
return self._reasoning_default
|
||
|
||
@property
|
||
def supports_tools(self) -> bool:
|
||
return self._supports_tools
|
||
|
||
@property
|
||
def cache_type_kv(self) -> Optional[str]:
|
||
return self._cache_type_kv
|
||
|
||
# ── Binary discovery ──────────────────────────────────────────
|
||
|
||
@staticmethod
|
||
def _find_llama_server_binary() -> Optional[str]:
|
||
"""
|
||
Locate the llama-server binary.
|
||
|
||
Search order:
|
||
1. LLAMA_SERVER_PATH environment variable (direct path to binary)
|
||
1b. UNSLOTH_LLAMA_CPP_PATH env var (custom llama.cpp install dir)
|
||
2. ~/.unsloth/llama.cpp/llama-server (make build, root dir)
|
||
3. ~/.unsloth/llama.cpp/build/bin/llama-server (cmake build, Linux)
|
||
4. ~/.unsloth/llama.cpp/build/bin/Release/llama-server.exe (cmake build, Windows)
|
||
5. ./llama.cpp/llama-server (legacy: make build, root dir)
|
||
6. ./llama.cpp/build/bin/llama-server (legacy: cmake in-tree build)
|
||
7. llama-server on PATH (system install)
|
||
8. ./bin/llama-server (legacy: extracted binary)
|
||
"""
|
||
import os
|
||
import sys
|
||
|
||
binary_name = "llama-server.exe" if sys.platform == "win32" else "llama-server"
|
||
|
||
# 1. Env var — direct path to binary
|
||
env_path = os.environ.get("LLAMA_SERVER_PATH")
|
||
if env_path and Path(env_path).is_file():
|
||
return env_path
|
||
|
||
# 1b. UNSLOTH_LLAMA_CPP_PATH — custom llama.cpp install directory
|
||
custom_llama_cpp = os.environ.get("UNSLOTH_LLAMA_CPP_PATH")
|
||
if custom_llama_cpp:
|
||
custom_dir = Path(custom_llama_cpp)
|
||
# Root dir (make builds)
|
||
root_bin = custom_dir / binary_name
|
||
if root_bin.is_file():
|
||
return str(root_bin)
|
||
# build/bin/ (cmake builds on Linux)
|
||
cmake_bin = custom_dir / "build" / "bin" / binary_name
|
||
if cmake_bin.is_file():
|
||
return str(cmake_bin)
|
||
# build/bin/Release/ (cmake builds on Windows)
|
||
if sys.platform == "win32":
|
||
win_bin = custom_dir / "build" / "bin" / "Release" / binary_name
|
||
if win_bin.is_file():
|
||
return str(win_bin)
|
||
|
||
# 2–4. ~/.unsloth/llama.cpp (primary — setup.sh / setup.ps1 build here)
|
||
unsloth_home = Path.home() / ".unsloth" / "llama.cpp"
|
||
# Root dir (make builds copy binaries here)
|
||
home_root = unsloth_home / binary_name
|
||
if home_root.is_file():
|
||
return str(home_root)
|
||
# build/bin/ (cmake builds on Linux)
|
||
home_linux = unsloth_home / "build" / "bin" / binary_name
|
||
if home_linux.is_file():
|
||
return str(home_linux)
|
||
|
||
# 3. Windows MSVC build has Release subdir
|
||
if sys.platform == "win32":
|
||
home_win = unsloth_home / "build" / "bin" / "Release" / binary_name
|
||
if home_win.is_file():
|
||
return str(home_win)
|
||
|
||
# 5–6. Legacy: in-tree build (older setup.sh / setup.ps1 versions)
|
||
project_root = Path(__file__).resolve().parents[4]
|
||
# Root dir (make builds)
|
||
root_path = project_root / "llama.cpp" / binary_name
|
||
if root_path.is_file():
|
||
return str(root_path)
|
||
# build/bin/ (cmake builds)
|
||
build_path = project_root / "llama.cpp" / "build" / "bin" / binary_name
|
||
if build_path.is_file():
|
||
return str(build_path)
|
||
if sys.platform == "win32":
|
||
win_path = (
|
||
project_root / "llama.cpp" / "build" / "bin" / "Release" / binary_name
|
||
)
|
||
if win_path.is_file():
|
||
return str(win_path)
|
||
|
||
# 7. System PATH
|
||
system_path = shutil.which("llama-server")
|
||
if system_path:
|
||
return system_path
|
||
|
||
# 8. Legacy: extracted to bin/
|
||
bin_path = project_root / "bin" / binary_name
|
||
if bin_path.is_file():
|
||
return str(bin_path)
|
||
|
||
return None
|
||
|
||
# ── GPU allocation ────────────────────────────────────────────
|
||
|
||
@staticmethod
|
||
def _get_gguf_size_bytes(model_path: str) -> int:
|
||
"""Get total GGUF size in bytes, including split shards."""
|
||
import re
|
||
|
||
main = Path(model_path)
|
||
total = main.stat().st_size
|
||
|
||
# Check for split shards (e.g., model-00001-of-00003.gguf)
|
||
shard_pat = re.compile(r"^(.*)-(\d{5})-of-(\d{5})\.gguf$")
|
||
m = shard_pat.match(main.name)
|
||
if m:
|
||
prefix, _, num_total = m.group(1), m.group(2), m.group(3)
|
||
sibling_pat = re.compile(
|
||
r"^"
|
||
+ re.escape(prefix)
|
||
+ r"-\d{5}-of-"
|
||
+ re.escape(num_total)
|
||
+ r"\.gguf$"
|
||
)
|
||
for sibling in main.parent.iterdir():
|
||
if sibling != main and sibling_pat.match(sibling.name):
|
||
total += sibling.stat().st_size
|
||
|
||
return total
|
||
|
||
@staticmethod
|
||
def _get_gpu_free_memory() -> list[tuple[int, int]]:
|
||
"""Query free memory per GPU via nvidia-smi.
|
||
|
||
Returns list of (gpu_index, free_mib) sorted by index.
|
||
Respects CUDA_VISIBLE_DEVICES if set.
|
||
Returns empty list if nvidia-smi is not available.
|
||
"""
|
||
import os
|
||
|
||
try:
|
||
result = subprocess.run(
|
||
[
|
||
"nvidia-smi",
|
||
"--query-gpu=index,memory.free",
|
||
"--format=csv,noheader,nounits",
|
||
],
|
||
capture_output = True,
|
||
text = True,
|
||
timeout = 10,
|
||
)
|
||
if result.returncode != 0:
|
||
return []
|
||
|
||
# Parse which GPUs are allowed by existing CUDA_VISIBLE_DEVICES
|
||
allowed = None
|
||
cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
|
||
if cvd is not None and cvd.strip():
|
||
try:
|
||
allowed = set(int(x.strip()) for x in cvd.split(","))
|
||
except ValueError:
|
||
pass # Non-numeric (e.g., "GPU-uuid"), ignore filter
|
||
|
||
gpus = []
|
||
for line in result.stdout.strip().splitlines():
|
||
parts = line.split(",")
|
||
if len(parts) == 2:
|
||
idx = int(parts[0].strip())
|
||
free_mib = int(parts[1].strip())
|
||
if allowed is not None and idx not in allowed:
|
||
continue
|
||
gpus.append((idx, free_mib))
|
||
return gpus
|
||
except Exception:
|
||
return []
|
||
|
||
@staticmethod
|
||
def _select_gpus(
|
||
model_size_bytes: int,
|
||
gpus: list[tuple[int, int]],
|
||
) -> tuple[Optional[list[int]], bool]:
|
||
"""Pick GPU(s) for a model based on file size and free memory.
|
||
|
||
Uses GGUF file size as a rough proxy for VRAM usage (actual usage
|
||
is higher due to KV cache and compute buffers, but 70% threshold
|
||
accounts for that).
|
||
|
||
Returns (gpu_indices, use_fit):
|
||
- ([1], False) model fits on 1 GPU at 70% of free
|
||
- ([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)
|
||
if ranked[0][1] * 0.70 >= model_size_mib:
|
||
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:
|
||
selected.append(idx)
|
||
cumulative += free_mib * 0.70
|
||
if cumulative >= model_size_mib:
|
||
return sorted(selected), False
|
||
|
||
# Model is too large even for all GPUs, let --fit handle it
|
||
return None, True
|
||
|
||
# ── 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 and chat_template from a GGUF file's KV 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._supports_tools = False
|
||
|
||
try:
|
||
WANTED = {"general.architecture", "tokenizer.chat_template"}
|
||
arch = None
|
||
ctx_key = None
|
||
|
||
with open(gguf_path, "rb") as f:
|
||
magic = struct.unpack("<I", f.read(4))[0]
|
||
if magic != 0x46554747: # b"GGUF" as little-endian u32
|
||
return
|
||
_version = struct.unpack("<I", f.read(4))[0]
|
||
_tensor_count, kv_count = struct.unpack("<QQ", f.read(16))
|
||
|
||
for _ in range(kv_count):
|
||
key_len = struct.unpack("<Q", f.read(8))[0]
|
||
key = f.read(key_len).decode("utf-8")
|
||
vtype = struct.unpack("<I", f.read(4))[0]
|
||
|
||
if key in WANTED or (ctx_key and key == ctx_key):
|
||
# Read this value
|
||
if vtype == 8: # STRING
|
||
slen = struct.unpack("<Q", f.read(8))[0]
|
||
val_s = f.read(slen).decode("utf-8")
|
||
if key == "general.architecture":
|
||
arch = val_s
|
||
ctx_key = f"{arch}.context_length"
|
||
elif key == "tokenizer.chat_template":
|
||
self._chat_template = val_s
|
||
elif vtype == 4: # UINT32
|
||
val_i = struct.unpack("<I", f.read(4))[0]
|
||
if ctx_key and key == ctx_key:
|
||
self._context_length = val_i
|
||
elif vtype == 10: # UINT64
|
||
val_i = struct.unpack("<Q", f.read(8))[0]
|
||
if ctx_key and key == ctx_key:
|
||
self._context_length = val_i
|
||
else:
|
||
self._gguf_skip_value(f, vtype)
|
||
else:
|
||
self._gguf_skip_value(f, vtype)
|
||
|
||
if self._context_length:
|
||
logger.info(f"GGUF metadata: context_length={self._context_length}")
|
||
if self._chat_template:
|
||
logger.info(
|
||
f"GGUF metadata: chat_template={len(self._chat_template)} chars"
|
||
)
|
||
# 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)"
|
||
)
|
||
# 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 and free memory
|
||
try:
|
||
model_size = self._get_gguf_size_bytes(model_path)
|
||
gpus = self._get_gpu_free_memory()
|
||
gpu_indices, use_fit = self._select_gpus(model_size, gpus)
|
||
logger.info(
|
||
f"GGUF size: {model_size / (1024**3):.1f} GB, "
|
||
f"GPUs free: {gpus}, selected: {gpu_indices}, fit: {use_fit}"
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"GPU selection failed ({e}), using --fit on")
|
||
gpu_indices, use_fit = None, True
|
||
|
||
cmd = [
|
||
binary,
|
||
"-m",
|
||
model_path,
|
||
"--port",
|
||
str(self._port),
|
||
"-c",
|
||
"0", # 0 = use model's native context size
|
||
"--parallel",
|
||
"1", # Single-user studio, saves VRAM
|
||
"--flash-attn",
|
||
"on", # Force flash attention for speed
|
||
]
|
||
|
||
if use_fit:
|
||
cmd.extend(["--fit", "on"])
|
||
|
||
if n_threads is not None:
|
||
cmd.extend(["--threads", str(n_threads)])
|
||
|
||
# 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 small models (0.8B, 2B, 4B, 9B) disable thinking by default
|
||
# per Qwen's recommendation. Larger models default to thinking ON.
|
||
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 <= 2:
|
||
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}")
|
||
|
||
logger.info(f"Starting llama-server: {' '.join(cmd)}")
|
||
|
||
# Set library paths so llama-server can find its shared libs and CUDA DLLs
|
||
import os
|
||
import sys
|
||
|
||
env = os.environ.copy()
|
||
binary_dir = str(Path(binary).parent)
|
||
|
||
if sys.platform == "win32":
|
||
# On Windows, CUDA DLLs (cublas64_12.dll, cudart64_12.dll, etc.)
|
||
# must be on PATH. Add CUDA_PATH\bin if available.
|
||
path_dirs = [binary_dir]
|
||
cuda_path = os.environ.get("CUDA_PATH", "")
|
||
if cuda_path:
|
||
cuda_bin = os.path.join(cuda_path, "bin")
|
||
if os.path.isdir(cuda_bin):
|
||
path_dirs.append(cuda_bin)
|
||
# Some CUDA installs put DLLs in bin\x64
|
||
cuda_bin_x64 = os.path.join(cuda_path, "bin", "x64")
|
||
if os.path.isdir(cuda_bin_x64):
|
||
path_dirs.append(cuda_bin_x64)
|
||
existing_path = env.get("PATH", "")
|
||
env["PATH"] = ";".join(path_dirs) + ";" + existing_path
|
||
else:
|
||
# Linux: set LD_LIBRARY_PATH for shared libs next to the binary
|
||
# and CUDA runtime libs (libcudart, libcublas, etc.)
|
||
import platform
|
||
|
||
lib_dirs = [binary_dir]
|
||
_arch = platform.machine() # x86_64, aarch64, etc.
|
||
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
|
||
self._hf_variant = hf_variant
|
||
self._is_vision = is_vision
|
||
self._model_identifier = model_identifier
|
||
|
||
# Wait for llama-server to become healthy
|
||
if not self._wait_for_health(timeout = 120.0):
|
||
self._kill_process()
|
||
raise RuntimeError(
|
||
"llama-server failed to start. "
|
||
"Check that the GGUF file is valid and you have enough memory."
|
||
)
|
||
|
||
self._healthy = True
|
||
|
||
logger.info(
|
||
f"llama-server ready on port {self._port} "
|
||
f"for model '{model_identifier}'"
|
||
)
|
||
return True
|
||
|
||
def unload_model(self) -> bool:
|
||
"""Terminate the llama-server subprocess and cancel any in-flight download."""
|
||
self._cancel_event.set()
|
||
with self._lock:
|
||
self._kill_process()
|
||
logger.info(f"Unloaded GGUF model: {self._model_identifier}")
|
||
self._model_identifier = None
|
||
self._gguf_path = None
|
||
self._hf_repo = None
|
||
self._hf_variant = None
|
||
self._is_vision = False
|
||
self._is_audio = False
|
||
self._audio_type = None
|
||
self._port = None
|
||
self._healthy = False
|
||
self._context_length = None
|
||
self._chat_template = None
|
||
self._supports_reasoning = False
|
||
self._supports_tools = False
|
||
self._cache_type_kv = 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 binary lives under ~/.unsloth/llama.cpp/
|
||
to avoid terminating unrelated llama-server instances on the machine.
|
||
"""
|
||
import os
|
||
import signal
|
||
|
||
try:
|
||
# Use pgrep with full command match to identify studio-managed servers
|
||
result = subprocess.run(
|
||
["pgrep", "-a", "-f", "llama-server"],
|
||
capture_output = True,
|
||
text = True,
|
||
timeout = 5,
|
||
)
|
||
if result.returncode != 0:
|
||
return
|
||
for line in result.stdout.strip().splitlines():
|
||
parts = line.strip().split(None, 1)
|
||
if len(parts) < 2:
|
||
continue
|
||
pid = int(parts[0])
|
||
cmdline = parts[1]
|
||
if pid == os.getpid():
|
||
continue
|
||
# Only kill if it's a studio-managed server (lives under .unsloth/)
|
||
if ".unsloth/" not in cmdline and "unsloth" not in cmdline.lower():
|
||
continue
|
||
try:
|
||
os.kill(pid, signal.SIGKILL)
|
||
logger.info(f"Killed orphaned llama-server process (pid={pid})")
|
||
except ProcessLookupError:
|
||
pass
|
||
except PermissionError:
|
||
pass
|
||
except Exception:
|
||
pass
|
||
|
||
def _cleanup(self):
|
||
"""atexit handler to ensure llama-server is terminated."""
|
||
self._kill_process()
|
||
|
||
def _wait_for_health(self, timeout: float = 120.0, interval: float = 0.5) -> bool:
|
||
"""
|
||
Poll llama-server's /health endpoint until it responds 200.
|
||
|
||
Also monitors subprocess for early exit/crash.
|
||
"""
|
||
deadline = time.monotonic() + timeout
|
||
url = f"http://127.0.0.1:{self._port}/health"
|
||
|
||
while time.monotonic() < deadline:
|
||
# Check if process crashed
|
||
if self._process.poll() is not None:
|
||
# Give the drain thread a moment to collect final output
|
||
if self._stdout_thread is not None:
|
||
self._stdout_thread.join(timeout = 2)
|
||
output = "\n".join(self._stdout_lines[-50:])
|
||
logger.error(
|
||
f"llama-server exited with code {self._process.returncode}. "
|
||
f"Output: {output[:2000]}"
|
||
)
|
||
return False
|
||
|
||
try:
|
||
resp = httpx.get(url, timeout = 2.0)
|
||
if resp.status_code == 200:
|
||
return True
|
||
except (httpx.ConnectError, httpx.TimeoutException):
|
||
pass
|
||
|
||
time.sleep(interval)
|
||
|
||
logger.error(f"llama-server health check timed out after {timeout}s")
|
||
return False
|
||
|
||
# ── Message building (OpenAI format) ──────────────────────────
|
||
|
||
@staticmethod
|
||
def _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 </tool_call> tag is optional (models sometimes omit it).
|
||
"""
|
||
import re
|
||
|
||
tool_calls = []
|
||
# Pattern 1: JSON inside <tool_call> tags (closing tag optional)
|
||
for match in re.finditer(
|
||
r"<tool_call>\s*(\{.*?\})\s*(?:</tool_call>)?", content, re.DOTALL
|
||
):
|
||
try:
|
||
obj = json.loads(match.group(1))
|
||
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>
|
||
# Closing </tool_call> optional
|
||
if not tool_calls:
|
||
for match in re.finditer(
|
||
r"<tool_call>\s*<function=(\w+)>(.*?)</function>\s*(?:</tool_call>)?",
|
||
content,
|
||
re.DOTALL,
|
||
):
|
||
func_name = match.group(1)
|
||
params_text = match.group(2)
|
||
arguments = {}
|
||
for param_match in re.finditer(
|
||
r"<parameter=(\w+)>\s*(.*?)\s*</parameter>",
|
||
params_text,
|
||
re.DOTALL,
|
||
):
|
||
arguments[param_match.group(1)] = param_match.group(2)
|
||
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) ────────────────────────
|
||
|
||
def generate_chat_completion(
|
||
self,
|
||
messages: list[dict],
|
||
image_b64: Optional[str] = None,
|
||
temperature: float = 0.7,
|
||
top_p: float = 0.9,
|
||
top_k: int = 40,
|
||
min_p: float = 0.0,
|
||
max_tokens: Optional[int] = None,
|
||
repetition_penalty: float = 1.0,
|
||
stop: Optional[list[str]] = None,
|
||
cancel_event: Optional[threading.Event] = None,
|
||
enable_thinking: Optional[bool] = None,
|
||
) -> Generator[str, None, None]:
|
||
"""
|
||
Send a chat completion request to llama-server and stream tokens back.
|
||
|
||
Uses /v1/chat/completions — llama-server handles chat template
|
||
application and vision (multimodal image_url parts) natively.
|
||
|
||
Yields cumulative text (matching InferenceBackend's convention).
|
||
"""
|
||
if not self.is_loaded:
|
||
raise RuntimeError("llama-server is not loaded")
|
||
|
||
openai_messages = self._build_openai_messages(messages, image_b64)
|
||
|
||
payload = {
|
||
"messages": openai_messages,
|
||
"stream": True,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"top_k": top_k if top_k >= 0 else 0,
|
||
"min_p": min_p,
|
||
"repeat_penalty": repetition_penalty,
|
||
}
|
||
# 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
|
||
|
||
url = f"{self.base_url}/v1/chat/completions"
|
||
cumulative = ""
|
||
in_thinking = False
|
||
|
||
try:
|
||
with httpx.Client(timeout = None) as client:
|
||
with client.stream("POST", url, json = payload) as response:
|
||
if response.status_code != 200:
|
||
error_body = response.read().decode()
|
||
raise RuntimeError(
|
||
f"llama-server returned {response.status_code}: {error_body}"
|
||
)
|
||
|
||
buffer = ""
|
||
has_content_tokens = False
|
||
reasoning_text = ""
|
||
for raw_chunk in response.iter_text():
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
break
|
||
|
||
buffer += raw_chunk
|
||
while "\n" in buffer:
|
||
line, buffer = buffer.split("\n", 1)
|
||
line = line.strip()
|
||
|
||
if not line:
|
||
continue
|
||
if line == "data: [DONE]":
|
||
if in_thinking:
|
||
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
|
||
return
|
||
if not line.startswith("data: "):
|
||
continue
|
||
|
||
try:
|
||
data = json.loads(line[6:])
|
||
choices = data.get("choices", [])
|
||
if choices:
|
||
delta = choices[0].get("delta", {})
|
||
|
||
# Handle reasoning/thinking tokens
|
||
# llama-server sends these as "reasoning_content"
|
||
# Wrap in <think> tags for the frontend parser
|
||
reasoning = delta.get("reasoning_content", "")
|
||
if reasoning:
|
||
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]}"
|
||
)
|
||
|
||
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 = 0.7,
|
||
top_p: float = 0.9,
|
||
top_k: int = 40,
|
||
min_p: float = 0.0,
|
||
max_tokens: Optional[int] = None,
|
||
repetition_penalty: float = 1.0,
|
||
stop: Optional[list[str]] = None,
|
||
cancel_event: Optional[threading.Event] = None,
|
||
enable_thinking: Optional[bool] = None,
|
||
max_tool_iterations: int = 5,
|
||
) -> 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"
|
||
|
||
for iteration in range(max_tool_iterations):
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
return
|
||
|
||
# Build payload for non-streaming tool detection pass
|
||
payload = {
|
||
"messages": conversation,
|
||
"stream": False,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"top_k": top_k if top_k >= 0 else 0,
|
||
"min_p": min_p,
|
||
"repeat_penalty": repetition_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:
|
||
with httpx.Client(timeout = None) as client:
|
||
resp = client.post(url, json = payload)
|
||
if resp.status_code != 200:
|
||
raise RuntimeError(
|
||
f"llama-server returned {resp.status_code}: {resp.text}"
|
||
)
|
||
data = resp.json()
|
||
except httpx.ConnectError:
|
||
raise RuntimeError("Lost connection to llama-server")
|
||
|
||
choices = data.get("choices", [])
|
||
if not choices:
|
||
return
|
||
|
||
choice = choices[0]
|
||
finish_reason = choice.get("finish_reason", "")
|
||
message = choice.get("message", {})
|
||
|
||
# If model wants to call tools
|
||
tool_calls = message.get("tool_calls")
|
||
|
||
# Fallback: detect tool calls embedded as XML/text in content
|
||
# Some models output <tool_call> XML instead of structured tool_calls
|
||
content_text = message.get("content", "") or ""
|
||
if not tool_calls and "<tool_call>" in content_text:
|
||
tool_calls = self._parse_tool_calls_from_text(content_text)
|
||
if tool_calls:
|
||
# Strip the tool call markup from content
|
||
import re
|
||
|
||
content_text = re.sub(
|
||
r"<tool_call>.*?(?:</tool_call>|$)",
|
||
"",
|
||
content_text,
|
||
flags = re.DOTALL,
|
||
).strip()
|
||
logger.info(
|
||
f"Parsed {len(tool_calls)} tool call(s) from content text"
|
||
)
|
||
|
||
if finish_reason == "tool_calls" or (tool_calls and len(tool_calls) > 0):
|
||
# Append the assistant message with tool_calls to conversation
|
||
assistant_msg = {"role": "assistant", "content": content_text}
|
||
if tool_calls:
|
||
assistant_msg["tool_calls"] = tool_calls
|
||
conversation.append(assistant_msg)
|
||
|
||
# Execute each tool call
|
||
for tc in tool_calls or []:
|
||
func = tc.get("function", {})
|
||
tool_name = func.get("name", "")
|
||
raw_args = func.get("arguments", {})
|
||
|
||
# Handle arguments as either string or dict
|
||
if isinstance(raw_args, str):
|
||
try:
|
||
arguments = json.loads(raw_args)
|
||
except (json.JSONDecodeError, ValueError):
|
||
arguments = {"query": raw_args}
|
||
else:
|
||
arguments = raw_args
|
||
|
||
# Yield status update
|
||
query_text = arguments.get("query", tool_name)
|
||
yield {"type": "status", "text": f"Searching: {query_text}"}
|
||
|
||
# Execute the tool
|
||
result = execute_tool(tool_name, arguments)
|
||
|
||
# Append tool result to conversation
|
||
tool_msg = {
|
||
"role": "tool",
|
||
"name": tool_name,
|
||
"content": result,
|
||
}
|
||
tool_call_id = tc.get("id")
|
||
if tool_call_id:
|
||
tool_msg["tool_call_id"] = tool_call_id
|
||
conversation.append(tool_msg)
|
||
|
||
# Continue the loop to let model respond with context
|
||
continue
|
||
|
||
# No tool calls -- model answered directly.
|
||
# If no tools were executed at all, just yield the content
|
||
# from this response instead of making a redundant second request.
|
||
if iteration == 0 and content_text:
|
||
yield {"type": "status", "text": ""}
|
||
yield {"type": "content", "text": content_text}
|
||
return
|
||
|
||
# Tools were called in previous iterations; do a final
|
||
# streaming pass so the model can synthesize a response
|
||
# incorporating the tool results.
|
||
break
|
||
|
||
# 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,
|
||
}
|
||
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
|
||
|
||
cumulative = ""
|
||
in_thinking = False
|
||
has_content_tokens = False
|
||
reasoning_text = ""
|
||
|
||
try:
|
||
with httpx.Client(timeout = None) as client:
|
||
with client.stream("POST", url, json = stream_payload) as response:
|
||
if response.status_code != 200:
|
||
error_body = response.read().decode()
|
||
raise RuntimeError(
|
||
f"llama-server returned {response.status_code}: {error_body}"
|
||
)
|
||
|
||
buffer = ""
|
||
for raw_chunk in response.iter_text():
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
break
|
||
|
||
buffer += raw_chunk
|
||
while "\n" in buffer:
|
||
line, buffer = buffer.split("\n", 1)
|
||
line = line.strip()
|
||
|
||
if not line:
|
||
continue
|
||
if line == "data: [DONE]":
|
||
if in_thinking:
|
||
if has_content_tokens:
|
||
cumulative += "</think>"
|
||
yield {"type": "content", "text": cumulative}
|
||
else:
|
||
cumulative = reasoning_text
|
||
yield {"type": "content", "text": cumulative}
|
||
return
|
||
if not line.startswith("data: "):
|
||
continue
|
||
|
||
try:
|
||
chunk_data = json.loads(line[6:])
|
||
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
|
||
yield {"type": "content", "text": cumulative}
|
||
except json.JSONDecodeError:
|
||
logger.debug(
|
||
f"Skipping malformed SSE line: {line[:100]}"
|
||
)
|
||
|
||
except httpx.ConnectError:
|
||
raise RuntimeError("Lost connection to llama-server")
|
||
except Exception as e:
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
return
|
||
raise
|
||
|
||
# ── TTS support ────────────────────────────────────────────
|
||
|
||
def detect_audio_type(self) -> Optional[str]:
|
||
"""Detect audio/TTS codec by probing the loaded model's vocabulary."""
|
||
if not self.is_loaded:
|
||
return None
|
||
try:
|
||
with httpx.Client(timeout = 10) as client:
|
||
|
||
def _detok(tid: int) -> str:
|
||
r = client.post(
|
||
f"{self.base_url}/detokenize", json = {"tokens": [tid]}
|
||
)
|
||
return r.json().get("content", "") if r.status_code == 200 else ""
|
||
|
||
def _tok(text: str) -> list[int]:
|
||
r = client.post(
|
||
f"{self.base_url}/tokenize",
|
||
json = {"content": text, "add_special": False},
|
||
)
|
||
return r.json().get("tokens", []) if r.status_code == 200 else []
|
||
|
||
# Check codec-specific tokens (not generic ones that may exist in non-audio models)
|
||
if "<custom_token_" in _detok(128258) and "<custom_token_" in _detok(
|
||
128259
|
||
):
|
||
return "snac"
|
||
if len(_tok("<|AUDIO|>")) == 1 and len(_tok("<|audio_eos|>")) == 1:
|
||
return "csm"
|
||
if len(_tok("<|startoftranscript|>")) == 1:
|
||
return "whisper"
|
||
if (
|
||
len(_tok("<|bicodec_semantic_0|>")) == 1
|
||
and len(_tok("<|bicodec_global_0|>")) == 1
|
||
):
|
||
return "bicodec"
|
||
if len(_tok("<|c1_0|>")) == 1 and len(_tok("<|c2_0|>")) == 1:
|
||
return "dac"
|
||
except Exception as e:
|
||
logger.debug(f"Audio type detection failed: {e}")
|
||
return None
|
||
|
||
# Prompt format per codec: (template, stop_tokens, needs_token_ids)
|
||
# Matches prompts in InferenceBackend._generate_snac/bicodec/dac
|
||
_TTS_PROMPTS = {
|
||
"snac": (
|
||
"<custom_token_3>{text}<|eot_id|><custom_token_4>",
|
||
["<custom_token_2>"],
|
||
True,
|
||
),
|
||
"bicodec": (
|
||
"<|task_tts|><|start_content|>{text}<|end_content|><|start_global_token|>",
|
||
["<|im_end|>", "</s>"],
|
||
False,
|
||
),
|
||
"dac": (
|
||
"<|im_start|>\n<|text_start|>{text}<|text_end|>\n<|audio_start|><|global_features_start|>\n",
|
||
["<|im_end|>", "<|audio_end|>"],
|
||
False,
|
||
),
|
||
}
|
||
|
||
_codec_mgr = None # Shared AudioCodecManager instance
|
||
|
||
def init_audio_codec(self, audio_type: str) -> None:
|
||
"""Load the audio codec at model load time (mirrors non-GGUF path)."""
|
||
import torch
|
||
from core.inference.audio_codecs import AudioCodecManager
|
||
|
||
if LlamaCppBackend._codec_mgr is None:
|
||
LlamaCppBackend._codec_mgr = AudioCodecManager()
|
||
|
||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
model_repo_path = None
|
||
|
||
# BiCodec needs a repo with BiCodec/ weights — download canonical SparkTTS
|
||
if audio_type == "bicodec":
|
||
from huggingface_hub import snapshot_download
|
||
import os
|
||
|
||
repo_path = snapshot_download(
|
||
"unsloth/Spark-TTS-0.5B", local_dir = "Spark-TTS-0.5B"
|
||
)
|
||
model_repo_path = os.path.abspath(repo_path)
|
||
|
||
LlamaCppBackend._codec_mgr.load_codec(
|
||
audio_type, device, model_repo_path = model_repo_path
|
||
)
|
||
logger.info(f"Loaded audio codec for GGUF TTS: {audio_type}")
|
||
|
||
def generate_audio_response(
|
||
self,
|
||
text: str,
|
||
audio_type: str,
|
||
temperature: float = 0.6,
|
||
top_p: float = 0.95,
|
||
top_k: int = 50,
|
||
min_p: float = 0.0,
|
||
max_new_tokens: int = 2048,
|
||
repetition_penalty: float = 1.1,
|
||
) -> tuple:
|
||
"""
|
||
Generate TTS audio via llama-server /completion + codec decoding.
|
||
Returns (wav_bytes, sample_rate).
|
||
"""
|
||
if audio_type not in self._TTS_PROMPTS:
|
||
raise RuntimeError(f"GGUF TTS does not support '{audio_type}' codec.")
|
||
|
||
tpl, stop, need_ids = self._TTS_PROMPTS[audio_type]
|
||
|
||
payload: dict = {
|
||
"prompt": tpl.format(text = text),
|
||
"stream": False,
|
||
"n_predict": max_new_tokens,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"top_k": top_k if top_k >= 0 else 0,
|
||
"min_p": min_p,
|
||
"repeat_penalty": repetition_penalty,
|
||
}
|
||
if stop:
|
||
payload["stop"] = stop
|
||
if need_ids:
|
||
payload["n_probs"] = 1
|
||
|
||
with httpx.Client(timeout = httpx.Timeout(300, connect = 10)) as client:
|
||
resp = client.post(f"{self.base_url}/completion", json = payload)
|
||
if resp.status_code != 200:
|
||
raise RuntimeError(
|
||
f"llama-server returned {resp.status_code}: {resp.text}"
|
||
)
|
||
|
||
data = resp.json()
|
||
token_ids = (
|
||
[p["id"] for p in data.get("completion_probabilities", []) if "id" in p]
|
||
if need_ids
|
||
else None
|
||
)
|
||
|
||
import torch
|
||
|
||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
return LlamaCppBackend._codec_mgr.decode(
|
||
audio_type, device, token_ids = token_ids, text = data.get("content", "")
|
||
)
|