63c6750532* fix(studio/mmproj): block cross-family projectors in flat local GGUF dirs (#5347) When a flat local GGUF directory holds several unrelated models with their own mmproj siblings, detect_mmproj_file() returned the first projector it walked into. For the layout reported in #5347 (Qwen weights + a Gemma mmproj in the same dir) that meant llama-server was launched with --mmproj pointing at the Gemma projector, which fails to load and surfaces as a confusing crash. Disambiguation rules: - Drop candidates whose family token (qwen/gemma/llama/mistral/phi/...) disagrees with the model's family. Candidates with no recognised family token (e.g. the HF-convention 'mmproj-F16.gguf') are kept. - Among same-family candidates, prefer the one whose stem shares the longest prefix with the model (Qwen3.5-9B mmproj beats Qwen3.5-35B mmproj for a Qwen3.5-9B model). - If every candidate is dropped, return None — better than attaching a wrong projector and getting a server-launch failure. Tests cover the cross-family block, multi-candidate prefix tie-break, HF-convention 'mmproj-F16.gguf', unrecognised families, and the existing search_root walk. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio/mmproj: word-bounded family match, expanded token list, launcher guard Tighten the family-token detector to match only on word boundaries so substring collisions stop tagging false families: phi no longer matches sapphire, yi no longer matches yip, mimo no longer matches mimosa, and mistral does not bleed into ministral/magistral/devstral. Pick the token whose first occurrence is leftmost in the filename rather than the first hit in tuple order, so merge models disambiguate predictably (llama-phi tags llama; phi-llama tags phi). Expand _MODEL_FAMILY_TOKENS with the families an audit of the unsloth HF org turned up that the previous list missed: devstral, ministral, magistral (Mistral-derivative naming), nemotron, kimi, nanonets, cosmos, mimo, apriel, lfm. Without these, a flat local GGUF directory containing one of these weights plus an unrelated renamed projector still hit the original #5347 failure. Add mmproj_matches_model_family() and call it at the llama-server launch site in core/inference/llama_cpp.py. detect_mmproj_file already drops cross-family candidates at discovery time, but mmproj_path can also reach the launcher via config injection or future overrides; this guard keeps those paths from silently loading a known-wrong projector. Tests: 12 new cases covering substring rejection, leftmost-position selection, new family tokens, a new flat-dir Nemotron + Gemma rejection case, and the launcher-level guard. All 21 detect_mmproj_file tests and the existing 106 llama_cpp tests pass. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio/mmproj: pair via GGUF general.* metadata, not just filenames Real Unsloth vision GGUFs carry rich identity metadata that has been ignored by the discovery path. Every projector under the unsloth org has general.type='mmproj' plus general.base_model.0.repo_url pointing at the same upstream HF repo as its weight, and the equivalent basename, base_model.0.name, and base_model.0.organization fields. A flat-dir mismatch is therefore decidable from the headers alone, no matter how the user has renamed the files. Add utils/models/gguf_metadata.py with read_gguf_general_metadata(): a fast (~30 ms) header walk that pulls only the general.* string fields and skips everything else, cached by (resolved path, mtime_ns, size). Mirrors the parser shape already used by LlamaCppBackend._read_gguf_metadata so the format handling is consistent. is_mmproj_by_metadata() returns True/False/None from general.type, and pairing_score() returns 100 for an exact base_model URL match, 80 for basename plus organization match, 60 for basename only, -1 for definitive metadata disagreement, and 0 when neither side has enough metadata to decide. Rewire detect_mmproj_file() to a two-stage selector: 1. Detect projectors via metadata (general.type) when present, else fall back to the filename substring heuristic. This recovers headerless projectors AND projectors whose name does not contain 'mmproj' but whose header advertises one. 2. Score each candidate against the weight via pairing_score. Drop candidates with score -1 (definitive metadata disagreement). For candidates with score 0 (no usable metadata) fall back to the existing filename family-token check, dropping recognised-family mismatches. Pick the survivor with the highest (score, longest_prefix, -len(stem)) tuple, so a metadata URL match always wins over a filename-prefix match. Tests: 16 new cases. tests/test_gguf_metadata.py covers the parser (missing file, non-GGUF, string extraction, walking past arrays and uint32s, cache invalidation by mtime/size) and the score helpers. tests/test_detect_mmproj_file.py adds end-to-end cases that synthesise real on-disk GGUF headers: URL match wins over a longer-prefix sibling, URL mismatch returns None even when filenames match, a projector named 'vision-projector.gguf' is still discovered via general.type, and a 100-score header match outranks a near-perfect filename prefix on a headerless candidate. All 75 tests across detect_mmproj_file, gguf_metadata, llama_cpp load progress, cached gguf routes, trained model scan, and vision cache pass. * studio/mmproj: shorten comments and docstrings across the #5347 changes Trim verbose explanations to one-line statements of intent. The behaviour is unchanged: 161 tests across detect_mmproj_file, gguf_metadata, llama_cpp_load_progress (+ matrix), llama_server_args, llama_cpp_cache_aware_disk_check, trained_model_scan, and vision_cache all pass. * studio/mmproj: shorten remaining detect_mmproj_file body comments Trim the docstring and the dir-walking block comments inside detect_mmproj_file to one-liners. Behaviour unchanged; 44 mmproj + gguf_metadata + llama_cpp_load_progress tests pass. * studio/mmproj: cap gguf_metadata cache below ceiling on every insert The eviction branch popped exactly one entry when len >= max, so the cache size could only converge to the cap when entries were added slowly enough for natural growth. After a sandbox sim that reduced the cap mid-run, len stayed above the cap because each insert popped one and added one. Switch to a while loop so we evict until len is strictly below the cap before inserting. Steady-state behaviour at the default 4096 ceiling is unchanged. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com>
4487 lines
196 KiB
Python
4487 lines
196 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 os
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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 sys
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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, List, Optional
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from urllib.parse import urlparse
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import httpx
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from utils.native_path_leases import child_env_without_native_path_secret
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from utils.subprocess_compat import (
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windows_hidden_subprocess_kwargs as _windows_hidden_subprocess_kwargs,
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)
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logger = get_logger(__name__)
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# ── Pre-compiled patterns for plan-without-action re-prompt ──
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# Forward-looking intent signals that indicate the model is
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# describing what it *will* do rather than giving a final answer.
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_INTENT_SIGNAL = re.compile(
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r"(?i)("
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# Direct intent: "I'll ...", "I will ...", "Let me ...", "I am going to ..."
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# Handles both straight and curly apostrophes.
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# Excludes "I can", "I should", "I want to", "let's" which
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# appear frequently in direct answers / explanations.
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r"\b(i['\u2019](ll|m going to|m gonna)|i am (going to|gonna)|i will|i shall|let me|allow me)\b"
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r"|"
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# Step/plan framing: "First ...", "Step 1:", "Here's my plan"
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r"\b(?:first\b|step \d+:?|here['\u2019]?s (?:my |the |a )?(?:plan|approach))"
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r"|"
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# "Now I" / "Next I" patterns
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r"\b(?:now i|next i)\b"
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r")"
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)
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_MAX_REPROMPTS = 3
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# Without max_tokens, llama-server defaults to n_predict = n_ctx (up to
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# 262144 for Qwen3.5), producing many-minute zombie decodes when cancel
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# fails. t_max_predict_ms is a wall-clock backstop applied unconditionally,
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# but the llama.cpp README notes it ONLY fires after a newline has been
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# generated -- a model stuck in a long unbroken non-newline sequence is
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# unbounded by it. So we still want a token cap as the front-line limiter.
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#
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# The cap is the model's effective context length when we know it,
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# falling back to a generous floor when metadata is unavailable. 4096 was
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# too low: Qwen3 / gpt-oss reasoning traces routinely exceed it, and any
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# OpenAI-API caller that omits max_tokens (langchain, llama-index, raw
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# curl) sees responses silently truncated mid-sentence.
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_DEFAULT_MAX_TOKENS_FLOOR = 32768
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_DEFAULT_T_MAX_PREDICT_MS = 600_000 # 10 min
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_REPROMPT_MAX_CHARS = 2000
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# ── Pre-compiled patterns for GGUF shard detection ───────────
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_SHARD_FULL_RE = re.compile(r"^(.*)-(\d{5})-of-(\d{5})\.gguf$")
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_SHARD_RE = re.compile(r"^(.*)-\d{5}-of-\d{5}\.gguf$")
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# ── Sliding-window-pattern resolver ───────────────────────────
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# Resolves the per-layer SWA mask when a GGUF reports a sliding window
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# but no `sliding_window_pattern` field. Tier order in
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# `_resolve_swa_pattern`: GGUF metadata, on-disk cache, bootstrap dict
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# below, transformers introspection, HF Hub config.json, legacy 1/4
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# fallback. Period N means layer i is SWA iff `(i + 1) % N != 0`,
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# matching transformers. Skipped on purpose: phi3 (no key/val length
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# in GGUF, window >= ctx anyway), qwen2 family (converter strips
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# sliding_window when use_sliding_window=False), mistral v0.1/v0.2
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# (all-SWA can't be expressed as a period).
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_BOOTSTRAP_SWA_DEFAULTS: dict[str, int] = {
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"gemma2": 2, # Gemma2Config.sliding_window_pattern
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"gemma3": 6, # Gemma3TextConfig.sliding_window_pattern
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"gemma3n": 5, # text_config.layer_types: SWA*4 + FULL
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"gpt_oss": 2, # text_config.layer_types: alternating
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"cohere2": 4, # Cohere2Config.sliding_window_pattern
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}
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# Process-wide cache backed by JSON on disk. Values are int period or
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# list[bool] mask. Lazy-loaded.
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_SWA_CACHE: Optional[dict] = None
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_SWA_CACHE_LOCK = threading.Lock()
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def _swa_cache_path() -> Path:
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home = os.environ.get("UNSLOTH_STUDIO_HOME") or os.environ.get("STUDIO_HOME")
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base = Path(home) if home else Path.home() / ".unsloth" / "studio"
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return base / "swa_cache.json"
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def _load_swa_cache() -> dict:
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global _SWA_CACHE
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with _SWA_CACHE_LOCK:
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if _SWA_CACHE is not None:
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return _SWA_CACHE
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try:
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with open(_swa_cache_path()) as f:
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_SWA_CACHE = json.load(f)
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if not isinstance(_SWA_CACHE, dict):
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_SWA_CACHE = {}
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except (FileNotFoundError, json.JSONDecodeError, OSError):
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_SWA_CACHE = {}
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return _SWA_CACHE
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def _save_swa_cache(cache: dict) -> None:
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try:
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path = _swa_cache_path()
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path.parent.mkdir(parents = True, exist_ok = True)
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tmp = path.with_suffix(".json.tmp")
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with open(tmp, "w") as f:
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json.dump(cache, f, indent = 2, sort_keys = True)
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tmp.replace(path)
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except OSError:
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pass
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def _period_from_layer_types(layer_types: list) -> Optional[int]:
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"""Smallest period N where `(i+1) % N != 0` matches the SWA mask,
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or None if no fixed period fits."""
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if not layer_types:
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return None
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is_swa = ["full" not in str(t).lower() for t in layer_types]
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n = len(is_swa)
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for N in range(1, n + 1):
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if all(((i + 1) % N != 0) == is_swa[i] for i in range(n)):
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return N
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return None
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def _fetch_swa_entry_from_hf(repo_id: str) -> Optional[object]:
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try:
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from huggingface_hub import hf_hub_download
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cfg_path = hf_hub_download(repo_id, "config.json", repo_type = "model")
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with open(cfg_path) as f:
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cfg = json.load(f)
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except Exception:
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return None
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src = cfg.get("text_config") if isinstance(cfg.get("text_config"), dict) else cfg
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period = src.get("sliding_window_pattern")
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if isinstance(period, int) and period > 0:
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return period
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lt = src.get("layer_types")
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if isinstance(lt, list) and lt:
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return _period_from_layer_types(lt) or [
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"full" not in str(t).lower() for t in lt
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]
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return None
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def _arch_aliases(arch: str) -> tuple:
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# GGUF emits `falcon-h1`; HF model_type is `falcon_h1`. Normalise both ways.
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seen = []
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for a in (arch, arch.replace("-", "_"), arch.replace("_", "-")):
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if a and a not in seen:
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seen.append(a)
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return tuple(seen)
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def _swa_entry_from_config_obj(cfg) -> Optional[object]:
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src = getattr(cfg, "text_config", None) or cfg
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period = getattr(src, "sliding_window_pattern", None)
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if isinstance(period, int) and period > 0:
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return period
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lt = getattr(src, "layer_types", None)
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if isinstance(lt, list) and lt:
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return _period_from_layer_types(lt) or [
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"full" not in str(t).lower() for t in lt
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]
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return None
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_SWA_PATTERN_SOURCE_RE = re.compile(
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r"sliding_window_pattern\s*(?::\s*[\w\[\], ]*)?\s*=\s*(\d+)"
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)
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def _resolve_swa_entry_from_transformers(arch: str) -> Optional[object]:
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"""Default-instantiate the matching Config; on failure, regex-parse
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its source for `sliding_window_pattern = N`."""
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try:
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from transformers.models.auto.configuration_auto import (
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CONFIG_MAPPING,
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CONFIG_MAPPING_NAMES,
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)
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except Exception:
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return None
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cfg_class = None
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for alias in _arch_aliases(arch):
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if alias in CONFIG_MAPPING_NAMES:
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try:
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cfg_class = CONFIG_MAPPING[alias]
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break
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except Exception:
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cfg_class = None
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if cfg_class is None:
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return None
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try:
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if (entry := _swa_entry_from_config_obj(cfg_class())) is not None:
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return entry
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except Exception:
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pass
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import inspect
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candidates = [cfg_class]
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text_cfg_class = getattr(cfg_class, "sub_configs", {}).get("text_config")
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if text_cfg_class is not None:
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candidates.append(text_cfg_class)
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for cls in candidates:
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try:
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src = inspect.getsource(cls)
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except (OSError, TypeError):
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continue
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if m := _SWA_PATTERN_SOURCE_RE.search(src):
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period = int(m.group(1))
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if period > 0:
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return period
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return None
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def _resolve_swa_pattern(
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arch: Optional[str],
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n_layers: Optional[int],
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source_repo_candidates: tuple = (),
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*,
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allow_network: Optional[bool] = None,
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) -> Optional[list]:
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if not arch or not n_layers:
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return None
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if allow_network is None:
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allow_network = os.environ.get("UNSLOTH_STUDIO_OFFLINE", "0") not in (
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"1",
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"true",
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"True",
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"yes",
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)
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cache = _load_swa_cache()
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def _entry_to_mask(entry):
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if isinstance(entry, int) and entry > 0:
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return [(i + 1) % entry != 0 for i in range(n_layers)]
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if isinstance(entry, list) and entry:
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return [bool(entry[i % len(entry)]) for i in range(n_layers)]
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return None
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def _persist(entry):
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with _SWA_CACHE_LOCK:
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cache[arch] = entry
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_save_swa_cache(cache)
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if (entry := cache.get(arch)) is not None:
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if (mask := _entry_to_mask(entry)) is not None:
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return mask
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if (entry := _BOOTSTRAP_SWA_DEFAULTS.get(arch)) is not None:
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return _entry_to_mask(entry)
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entry = _resolve_swa_entry_from_transformers(arch)
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if entry is not None:
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_persist(entry)
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return _entry_to_mask(entry)
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# Tier 3: live HF fetch (with persistent caching of the result)
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if allow_network:
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for repo_id in source_repo_candidates:
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if not repo_id:
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continue
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entry = _fetch_swa_entry_from_hf(repo_id)
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if entry is not None:
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_persist(entry)
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||
return _entry_to_mask(entry)
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||
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||
return None
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||
|
||
|
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def _hf_repo_from_url(url: Optional[str]) -> Optional[str]:
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"""Strip `https://huggingface.co/owner/name(/...)` to `owner/name`."""
|
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if not url or "huggingface.co/" not in url:
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return None
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tail = url.split("huggingface.co/", 1)[1].rstrip("/")
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parts = tail.split("/")
|
||
if len(parts) < 2:
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return None
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return f"{parts[0]}/{parts[1]}"
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|
||
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# Model size extraction — lazy import to avoid pulling in transformers
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# at module level. See PR description for the full explanation.
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def _extract_model_size_b(model_id: str):
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from utils.models import extract_model_size_b
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||
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return extract_model_size_b(model_id)
|
||
|
||
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# ── Pre-compiled patterns for tool XML stripping ─────────────
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_TOOL_CLOSED_PATS = [
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re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL),
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re.compile(r"<function=\w+>.*?</function>", re.DOTALL),
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||
]
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_TOOL_ALL_PATS = _TOOL_CLOSED_PATS + [
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re.compile(r"<tool_call>.*$", re.DOTALL),
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re.compile(r"<function=\w+>.*$", re.DOTALL),
|
||
]
|
||
|
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# ── Pre-compiled patterns for tool-call XML parsing ──────────
|
||
_TC_JSON_START_RE = re.compile(r"<tool_call>\s*\{")
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_TC_FUNC_START_RE = re.compile(r"<function=(\w+)>\s*")
|
||
_TC_END_TAG_RE = re.compile(r"</tool_call>")
|
||
_TC_FUNC_CLOSE_RE = re.compile(r"\s*</function>\s*$")
|
||
_TC_PARAM_START_RE = re.compile(r"<parameter=(\w+)>\s*")
|
||
_TC_PARAM_CLOSE_RE = re.compile(r"\s*</parameter>\s*$")
|
||
|
||
|
||
_TOOL_TEMPLATE_MARKERS = (
|
||
"{%- if tools %}",
|
||
"{%- if tools -%}",
|
||
"{% if tools %}",
|
||
"{% if tools -%}",
|
||
'"role" == "tool"',
|
||
"'role' == 'tool'",
|
||
'message.role == "tool"',
|
||
"message.role == 'tool'",
|
||
)
|
||
|
||
|
||
def detect_reasoning_flags(
|
||
chat_template: Optional[str],
|
||
model_identifier: Optional[str] = None,
|
||
*,
|
||
log_source: Optional[str] = None,
|
||
) -> dict:
|
||
"""Classify a chat template's reasoning and tool-calling capabilities.
|
||
|
||
Returns a dict with the same five keys populated by the GGUF sniffer:
|
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``supports_reasoning``, ``reasoning_style``
|
||
(``"enable_thinking"`` | ``"reasoning_effort"``),
|
||
``reasoning_always_on``, ``supports_preserve_thinking``, and
|
||
``supports_tools``. Used by both the llama-server backend at load
|
||
time and the safetensors/transformers paths in ``routes/inference``
|
||
so the two agree on what the frontend will see.
|
||
"""
|
||
flags = {
|
||
"supports_reasoning": False,
|
||
"reasoning_style": "enable_thinking",
|
||
"reasoning_always_on": False,
|
||
"supports_preserve_thinking": False,
|
||
"supports_tools": False,
|
||
}
|
||
if not chat_template:
|
||
return flags
|
||
tpl = chat_template
|
||
prefix = f"{log_source}: " if log_source else ""
|
||
|
||
if "enable_thinking" in tpl:
|
||
flags["supports_reasoning"] = True
|
||
flags["reasoning_style"] = "enable_thinking"
|
||
logger.info(f"{prefix}model supports reasoning (enable_thinking)")
|
||
elif "reasoning_effort" in tpl:
|
||
# gpt-oss / Harmony templates use reasoning_effort
|
||
# ("low" | "medium" | "high") instead of a boolean.
|
||
flags["supports_reasoning"] = True
|
||
flags["reasoning_style"] = "reasoning_effort"
|
||
logger.info(f"{prefix}model supports reasoning (reasoning_effort)")
|
||
elif "thinking" in tpl:
|
||
# DeepSeek uses 'thinking' instead of 'enable_thinking'
|
||
normalized_id = (model_identifier or "").lower()
|
||
if "deepseek" in normalized_id:
|
||
flags["supports_reasoning"] = True
|
||
logger.info(f"{prefix}model supports reasoning (DeepSeek thinking)")
|
||
|
||
# Hardcoded <think> tags or reasoning_content in the template mean
|
||
# thinking is always on (no toggle to disable it).
|
||
if not flags["supports_reasoning"]:
|
||
if ("<think>" in tpl and "</think>" in tpl) or "reasoning_content" in tpl:
|
||
flags["supports_reasoning"] = True
|
||
flags["reasoning_always_on"] = True
|
||
logger.info(f"{prefix}model always reasons (<think> tags in template)")
|
||
|
||
# preserve_thinking is an independent kwarg on some Qwen templates
|
||
# that keeps historical <think> blocks in prior assistant turns.
|
||
if "preserve_thinking" in tpl:
|
||
flags["supports_preserve_thinking"] = True
|
||
logger.info(f"{prefix}model supports preserve_thinking")
|
||
|
||
if any(marker in tpl for marker in _TOOL_TEMPLATE_MARKERS):
|
||
flags["supports_tools"] = True
|
||
logger.info(f"{prefix}model supports tool calling")
|
||
|
||
return flags
|
||
|
||
|
||
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
|
||
# Set by _classify_gpu_offload after _wait_for_health.
|
||
self._gpu_offload_active: Optional[bool] = None
|
||
self._context_length: Optional[int] = None
|
||
self._effective_context_length: Optional[int] = None
|
||
self._max_context_length: Optional[int] = None
|
||
self._chat_template: Optional[str] = None
|
||
self._chat_template_override: Optional[str] = None
|
||
self._supports_reasoning: bool = False
|
||
self._reasoning_always_on: bool = False
|
||
self._reasoning_style: str = "enable_thinking"
|
||
self._supports_preserve_thinking: bool = False
|
||
self._supports_tools: bool = False
|
||
self._cache_type_kv: Optional[str] = None
|
||
self._reasoning_default: bool = True
|
||
self._speculative_type: Optional[str] = None
|
||
# KV-cache estimation fields (populated by _read_gguf_metadata)
|
||
self._n_layers: Optional[int] = None
|
||
self._n_kv_heads: Optional[int] = None
|
||
self._n_kv_heads_by_layer: Optional[list[int]] = None
|
||
self._n_heads: Optional[int] = None
|
||
self._embedding_length: Optional[int] = None
|
||
# Architecture-aware KV fields for 5-path estimation
|
||
self._kv_key_length: Optional[int] = None
|
||
self._kv_value_length: Optional[int] = None
|
||
self._sliding_window: Optional[int] = None
|
||
self._sliding_window_pattern: Optional[list[bool]] = None
|
||
self._full_attention_interval: Optional[int] = None
|
||
self._kv_lora_rank: Optional[int] = None
|
||
self._key_length_mla: Optional[int] = None
|
||
self._kv_key_length_swa: Optional[int] = None
|
||
self._kv_value_length_swa: Optional[int] = None
|
||
self._ssm_inner_size: Optional[int] = None
|
||
self._ssm_state_size: Optional[int] = None
|
||
# Last N layers reuse KV from earlier layers and don't allocate
|
||
# their own cache (Gemma 3n / Gemma 4: <arch>.attention.shared_kv_layers).
|
||
self._shared_kv_layers: Optional[int] = None
|
||
self._lock = threading.Lock()
|
||
self._stdout_lines: list[str] = []
|
||
self._stdout_thread: Optional[threading.Thread] = None
|
||
self._cancel_event = threading.Event()
|
||
self._api_key: Optional[str] = None
|
||
|
||
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 the effective context length the server is running at."""
|
||
return self._effective_context_length or self._context_length
|
||
|
||
@property
|
||
def max_context_length(self) -> Optional[int]:
|
||
"""Return the largest context that fits on this hardware at load time.
|
||
|
||
This is the "safe zone" threshold the UI renders warnings
|
||
against. For a model whose weights fit on some GPU subset, it
|
||
is the binary-search cap from ``_fit_context_to_vram`` for that
|
||
subset. For a model whose weights exceed 90% of every GPU
|
||
subset, it is the 4096 fallback -- the spec's default when the
|
||
model will not fit. The UI slider ceiling is
|
||
``native_context_length``; dragging above ``max_context_length``
|
||
triggers the "might be slower" warning.
|
||
"""
|
||
return self._max_context_length or self._context_length
|
||
|
||
@property
|
||
def native_context_length(self) -> Optional[int]:
|
||
"""Return the model's native context length from GGUF metadata."""
|
||
return self._context_length
|
||
|
||
def load_progress(self) -> Optional[dict]:
|
||
"""Return live model-load progress, or None if not loading.
|
||
|
||
While llama-server is warming up, its process is typically in
|
||
kernel state D (disk sleep) mmap'ing the weight shards into
|
||
page cache before pushing layers to VRAM. During that window
|
||
``/api/inference/status`` only reports ``loading``, which gives
|
||
the UI nothing to display besides a spinner that looks stuck
|
||
for minutes on large MoE models.
|
||
|
||
This method samples ``/proc/<pid>/status VmRSS`` against the
|
||
sum of the GGUF shard sizes so the UI can render a real bar
|
||
and compute rate / ETA. Returns ``None`` when no load is in
|
||
flight (no process, or process already healthy).
|
||
|
||
Shape::
|
||
|
||
{
|
||
"phase": "mmap" | "ready",
|
||
"bytes_loaded": int, # VmRSS of the llama-server
|
||
"bytes_total": int, # sum of shard file sizes
|
||
"fraction": float, # bytes_loaded / bytes_total, 0..1
|
||
}
|
||
|
||
Linux-only in the current implementation. On macOS/Windows the
|
||
equivalent would be a different API; this returns ``None`` on
|
||
platforms where ``/proc/<pid>/status`` is unavailable.
|
||
"""
|
||
proc = self._process
|
||
if proc is None:
|
||
return None
|
||
pid = proc.pid
|
||
if pid is None:
|
||
return None
|
||
|
||
# Sum up shard sizes (primary + any extras sitting alongside).
|
||
bytes_total = 0
|
||
gguf_path = self._gguf_path
|
||
if gguf_path:
|
||
primary = Path(gguf_path)
|
||
try:
|
||
if primary.is_file():
|
||
bytes_total += primary.stat().st_size
|
||
except OSError:
|
||
pass
|
||
# Extra shards live alongside the primary with the same prefix
|
||
# before the shard index (e.g. ``-00001-of-00004.gguf``).
|
||
try:
|
||
parent = primary.parent
|
||
stem = primary.name
|
||
m = _SHARD_RE.match(stem)
|
||
prefix = m.group(1) if m else None
|
||
if prefix and parent.is_dir():
|
||
for sibling in parent.iterdir():
|
||
if (
|
||
sibling.is_file()
|
||
and sibling.name.startswith(prefix)
|
||
and sibling.name != stem
|
||
and sibling.suffix == ".gguf"
|
||
):
|
||
try:
|
||
bytes_total += sibling.stat().st_size
|
||
except OSError:
|
||
pass
|
||
except OSError:
|
||
pass
|
||
|
||
# Read VmRSS from /proc/<pid>/status. Kilobytes on Linux.
|
||
bytes_loaded = 0
|
||
try:
|
||
with open(f"/proc/{pid}/status", "r", encoding = "utf-8") as f:
|
||
for line in f:
|
||
if line.startswith("VmRSS:"):
|
||
kb = int(line.split()[1])
|
||
bytes_loaded = kb * 1024
|
||
break
|
||
except (FileNotFoundError, PermissionError, ValueError, OSError):
|
||
return None
|
||
|
||
phase = "ready" if self._healthy else "mmap"
|
||
fraction = 0.0
|
||
if bytes_total > 0:
|
||
fraction = min(1.0, bytes_loaded / bytes_total)
|
||
return {
|
||
"phase": phase,
|
||
"bytes_loaded": bytes_loaded,
|
||
"bytes_total": bytes_total,
|
||
"fraction": round(fraction, 4),
|
||
}
|
||
|
||
@property
|
||
def chat_template(self) -> Optional[str]:
|
||
return self._chat_template
|
||
|
||
@property
|
||
def chat_template_override(self) -> Optional[str]:
|
||
return self._chat_template_override
|
||
|
||
@property
|
||
def supports_reasoning(self) -> bool:
|
||
return self._supports_reasoning
|
||
|
||
@property
|
||
def reasoning_always_on(self) -> bool:
|
||
return self._reasoning_always_on
|
||
|
||
@property
|
||
def reasoning_style(self) -> str:
|
||
return self._reasoning_style
|
||
|
||
@property
|
||
def supports_preserve_thinking(self) -> bool:
|
||
return self._supports_preserve_thinking
|
||
|
||
@property
|
||
def reasoning_default(self) -> bool:
|
||
return self._reasoning_default
|
||
|
||
def _reasoning_kwargs(self, enable_thinking: bool) -> dict:
|
||
if self._reasoning_style == "reasoning_effort":
|
||
return {"reasoning_effort": "high" if enable_thinking else "low"}
|
||
return {"enable_thinking": enable_thinking}
|
||
|
||
def _request_reasoning_kwargs(
|
||
self,
|
||
enable_thinking: Optional[bool],
|
||
reasoning_effort: Optional[str] = None,
|
||
preserve_thinking: Optional[bool] = None,
|
||
) -> Optional[dict]:
|
||
"""Build chat_template_kwargs from per-request reasoning fields.
|
||
|
||
Produces a merged dict covering the active model's reasoning style
|
||
(``enable_thinking`` or ``reasoning_effort``) plus the independent
|
||
``preserve_thinking`` kwarg when the template supports it.
|
||
"""
|
||
kwargs: dict = {}
|
||
# Always-on reasoning models hardcode <think> tags in their template
|
||
# and do not consume enable_thinking / reasoning_effort -- skip.
|
||
if self._supports_reasoning and not self._reasoning_always_on:
|
||
if self._reasoning_style == "reasoning_effort":
|
||
if reasoning_effort in ("low", "medium", "high"):
|
||
kwargs["reasoning_effort"] = reasoning_effort
|
||
elif enable_thinking is not None:
|
||
kwargs["reasoning_effort"] = "high" if enable_thinking else "low"
|
||
else:
|
||
if enable_thinking is not None:
|
||
kwargs["enable_thinking"] = enable_thinking
|
||
if self._supports_preserve_thinking and preserve_thinking is not None:
|
||
kwargs["preserve_thinking"] = preserve_thinking
|
||
return kwargs or None
|
||
|
||
@property
|
||
def supports_tools(self) -> bool:
|
||
return self._supports_tools
|
||
|
||
@property
|
||
def cache_type_kv(self) -> Optional[str]:
|
||
return self._cache_type_kv
|
||
|
||
@property
|
||
def speculative_type(self) -> Optional[str]:
|
||
return self._speculative_type
|
||
|
||
# ── 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. Match installer layout: env-mode -> $STUDIO_HOME/llama.cpp;
|
||
# default/HOME-redirect -> ~/.unsloth/llama.cpp (sibling of studio).
|
||
legacy_llama = Path.home() / ".unsloth" / "llama.cpp"
|
||
try:
|
||
from utils.paths.storage_roots import studio_root as _sr # noqa: WPS433
|
||
|
||
_resolved_sr = _sr()
|
||
_legacy_studio = Path.home() / ".unsloth" / "studio"
|
||
try:
|
||
_is_legacy = _resolved_sr.resolve() == _legacy_studio.resolve()
|
||
except (OSError, ValueError):
|
||
_is_legacy = _resolved_sr == _legacy_studio
|
||
if _is_legacy:
|
||
search_roots = [legacy_llama]
|
||
else:
|
||
# why: _kill_orphaned_servers excludes the legacy root in custom
|
||
# mode; discovery must match so we never spawn a server we then
|
||
# refuse to clean up. UNSLOTH_LLAMA_CPP_PATH (handled earlier)
|
||
# is the explicit way to share a build across roots.
|
||
search_roots = [_resolved_sr / "llama.cpp"]
|
||
except (ImportError, OSError, ValueError):
|
||
search_roots = [legacy_llama]
|
||
_seen_roots: set[str] = set()
|
||
_unique_roots: list[Path] = []
|
||
for r in search_roots:
|
||
k = str(r)
|
||
if k not in _seen_roots:
|
||
_seen_roots.add(k)
|
||
_unique_roots.append(r)
|
||
for unsloth_home in _unique_roots:
|
||
home_root = unsloth_home / binary_name
|
||
if home_root.is_file():
|
||
return str(home_root)
|
||
home_linux = unsloth_home / "build" / "bin" / binary_name
|
||
if home_linux.is_file():
|
||
return str(home_linux)
|
||
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."""
|
||
main = Path(model_path)
|
||
total = main.stat().st_size
|
||
|
||
# Check for split shards (e.g., model-00001-of-00003.gguf)
|
||
m = _SHARD_FULL_RE.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.
|
||
|
||
Order:
|
||
1. ``nvidia-smi`` (NVIDIA CUDA hosts) -- respects
|
||
``CUDA_VISIBLE_DEVICES``.
|
||
2. ``torch.cuda.mem_get_info`` -- universal fallback that
|
||
works on AMD ROCm too because the HIP runtime
|
||
reuses the entire ``torch.cuda.*`` namespace. Covers the
|
||
AMD case for issue #5106 (nvidia-smi-only probe silently
|
||
returned [] on AMD hosts) and also rescues NVIDIA hosts
|
||
where ``nvidia-smi`` is missing from PATH.
|
||
|
||
Returns list of (gpu_index, free_mib) sorted by index. Empty
|
||
list if no supported GPU is reachable.
|
||
"""
|
||
import os
|
||
|
||
# ── NVIDIA via nvidia-smi ────────────────────────────────────
|
||
try:
|
||
result = subprocess.run(
|
||
[
|
||
"nvidia-smi",
|
||
"--query-gpu=index,memory.free",
|
||
"--format=csv,noheader,nounits",
|
||
],
|
||
capture_output = True,
|
||
text = True,
|
||
timeout = 10,
|
||
env = child_env_without_native_path_secret(),
|
||
**_windows_hidden_subprocess_kwargs(),
|
||
)
|
||
if result.returncode == 0:
|
||
allowed: Optional[set[int]] = None
|
||
cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
|
||
if cvd is not None:
|
||
try:
|
||
# `if x.strip()` filters trailing-comma masks like
|
||
# "0,1," which would otherwise raise ValueError on
|
||
# an empty token. An explicitly empty mask (CVD="")
|
||
# yields an empty `allowed` set so all GPUs are
|
||
# filtered out, matching the codebase convention.
|
||
allowed = set(
|
||
int(x.strip()) for x in cvd.split(",") if x.strip()
|
||
)
|
||
except ValueError:
|
||
pass
|
||
gpus: list[tuple[int, int]] = []
|
||
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))
|
||
# Match the docstring's sort-by-id guarantee. nvidia-smi
|
||
# almost always returns sorted output, but driver order
|
||
# is not formally guaranteed.
|
||
gpus.sort(key = lambda g: g[0])
|
||
if gpus:
|
||
return gpus
|
||
except Exception as e:
|
||
logger.debug(f"nvidia-smi probe failed: {e}")
|
||
|
||
# ── Torch fallback (covers AMD ROCm and missing nvidia-smi) ──
|
||
try:
|
||
import torch
|
||
|
||
if not hasattr(torch, "cuda") or not torch.cuda.is_available():
|
||
return []
|
||
if not hasattr(torch.cuda, "mem_get_info"):
|
||
return []
|
||
# torch.cuda enumerates GPUs RELATIVE to the visibility mask.
|
||
# On NVIDIA builds the mask is CUDA_VISIBLE_DEVICES; on AMD
|
||
# ROCm builds it is HIP_VISIBLE_DEVICES (or ROCR_VISIBLE_DEVICES
|
||
# if HIP is unset). Downstream we feed these IDs back into the
|
||
# llama-server subprocess as CVD, so we must translate visible
|
||
# ordinals back to physical indices first; otherwise launching
|
||
# with ``CUDA_VISIBLE_DEVICES=2,3`` would get rewritten to
|
||
# ``CUDA_VISIBLE_DEVICES=0,1`` and target the wrong GPUs.
|
||
physical_ids: Optional[list[int]] = None
|
||
# Match the codebase convention in
|
||
# ``utils/hardware/hardware.py::_get_parent_visible_gpu_spec``:
|
||
# treat an explicitly empty mask (``HIP_VISIBLE_DEVICES=""``)
|
||
# as "set to no GPUs" rather than falling through to the next
|
||
# var. ``or`` would coerce empty string to falsy and silently
|
||
# promote the wrong source.
|
||
if getattr(torch.version, "hip", None) is not None:
|
||
hip_v = os.environ.get("HIP_VISIBLE_DEVICES")
|
||
rocr_v = os.environ.get("ROCR_VISIBLE_DEVICES")
|
||
cvd = (
|
||
hip_v
|
||
if hip_v is not None
|
||
else rocr_v
|
||
if rocr_v is not None
|
||
else os.environ.get("CUDA_VISIBLE_DEVICES")
|
||
)
|
||
else:
|
||
cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
|
||
if cvd is not None:
|
||
try:
|
||
# Empty mask (CVD="") yields an empty list so the
|
||
# below loop produces no GPUs, consistent with the
|
||
# nvidia-smi path and utils/hardware/hardware.py.
|
||
physical_ids = [int(x.strip()) for x in cvd.split(",") if x.strip()]
|
||
except ValueError:
|
||
physical_ids = None
|
||
gpus = []
|
||
for ordinal in range(torch.cuda.device_count()):
|
||
free_bytes, _total_bytes = torch.cuda.mem_get_info(ordinal)
|
||
idx = (
|
||
physical_ids[ordinal]
|
||
if physical_ids is not None and ordinal < len(physical_ids)
|
||
else ordinal
|
||
)
|
||
gpus.append((idx, free_bytes // (1024 * 1024)))
|
||
# Match the nvidia-smi path's docstring guarantee of sorted-by-id.
|
||
return sorted(gpus, key = lambda g: g[0])
|
||
except Exception as e:
|
||
logger.debug(f"torch GPU probe failed: {e}")
|
||
return []
|
||
|
||
# Free-VRAM fraction at which Studio pins the GPU directly instead
|
||
# of deferring to ``--fit on``. 5% headroom covers CUDA context +
|
||
# compute buffers; 0.90 was too conservative and dropped 91-94%
|
||
# fits to CPU offload (#5106). The fork's --fit on still catches
|
||
# the truly-too-large case.
|
||
_GPU_PIN_VRAM_FRACTION = 0.95
|
||
|
||
@staticmethod
|
||
def _windows_pip_nvidia_dll_dirs(prefix: str) -> list[str]:
|
||
"""Return DLL dirs from pip-installed CUDA wheels under
|
||
``<prefix>/Lib/site-packages/`` so llama-server.exe can load
|
||
``cudart64_X.dll`` / ``cublas64_X.dll`` without a system CUDA
|
||
toolkit. Mirrors the Linux ``nvidia/cu*/lib`` LD_LIBRARY_PATH
|
||
block, with parity for the Windows-specific wheel layouts seen
|
||
in the wild. Covered patterns:
|
||
* ``nvidia/<pkg>/bin`` -- legacy modular wheels
|
||
(``nvidia-cuda-runtime-cu12``, ``nvidia-cublas-cu12``, etc.).
|
||
* ``nvidia/<pkg>/bin/x86_64`` and ``.../bin/x64`` -- current
|
||
CUDA 13 wheel layout used by the unsuffixed
|
||
``nvidia-cuda-runtime`` / ``nvidia-cublas`` packages, which
|
||
ship under ``nvidia/cu13/bin/x86_64/`` (#5106).
|
||
* ``nvidia/<pkg>/Library/bin`` (and arch subdirs) -- conda-
|
||
style wheel repacks.
|
||
* ``torch/lib`` -- PyTorch's own CUDA-bundled Windows wheel,
|
||
which can ship ``cudart64_*.dll`` directly here instead of
|
||
as separate ``nvidia-*`` wheels. The install-side helper
|
||
``python_runtime_dirs`` in ``install_llama_prebuilt.py``
|
||
covers this path for the same reason.
|
||
|
||
Walks the tree with ``Path.iterdir`` rather than ``glob.glob``
|
||
so the resolver is safe against Windows paths containing
|
||
``[`` or ``]`` (valid in usernames; would otherwise be
|
||
interpreted as a glob character class and silently miss
|
||
existing dirs)."""
|
||
site_packages = Path(prefix) / "Lib" / "site-packages"
|
||
out: list[str] = []
|
||
seen: set[str] = set()
|
||
|
||
def _add(path: Path) -> None:
|
||
if not path.is_dir():
|
||
return
|
||
key = os.path.normcase(os.path.abspath(str(path)))
|
||
if key in seen:
|
||
return
|
||
seen.add(key)
|
||
out.append(str(path))
|
||
|
||
nvidia_root = site_packages / "nvidia"
|
||
if nvidia_root.is_dir():
|
||
for pkg_dir in nvidia_root.iterdir():
|
||
if not pkg_dir.is_dir():
|
||
continue
|
||
# Order matters for PATH search: arch-specific subdirs
|
||
# first so the explicit cudart64_X.dll location wins
|
||
# over a sibling ``bin`` that might be empty.
|
||
for sub in (
|
||
pkg_dir / "bin" / "x86_64",
|
||
pkg_dir / "bin" / "x64",
|
||
pkg_dir / "bin",
|
||
pkg_dir / "Library" / "bin" / "x86_64",
|
||
pkg_dir / "Library" / "bin" / "x64",
|
||
pkg_dir / "Library" / "bin",
|
||
):
|
||
_add(sub)
|
||
_add(site_packages / "torch" / "lib")
|
||
return out
|
||
|
||
@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 estimated VRAM and free memory.
|
||
|
||
``model_size_bytes`` should include both model weights and estimated
|
||
KV cache. The ``_GPU_PIN_VRAM_FRACTION`` threshold provides headroom
|
||
for compute buffers, CUDA context, and other runtime overhead.
|
||
|
||
Returns (gpu_indices, use_fit):
|
||
- ([1], False) model fits on 1 GPU at the headroom threshold
|
||
- ([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)
|
||
usable_fraction = LlamaCppBackend._GPU_PIN_VRAM_FRACTION
|
||
|
||
# Sort GPUs by free memory descending
|
||
ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
|
||
|
||
# Try fitting on 1 GPU at the usable-VRAM threshold.
|
||
if ranked[0][1] * usable_fraction >= 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 * usable_fraction
|
||
if cumulative >= model_size_mib:
|
||
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."""
|
||
if self._n_layers is None:
|
||
return False
|
||
# MLA: kv_lora_rank is sufficient (K-only cache)
|
||
if self._kv_lora_rank is not None:
|
||
return True
|
||
# New-style: need both explicit key AND value dimensions
|
||
if self._kv_key_length is not None and self._kv_value_length is not None:
|
||
return True
|
||
# Legacy: need embedding_length + a head count (scalar or per-layer).
|
||
return self._embedding_length is not None and (
|
||
self._n_kv_heads is not None
|
||
or self._n_heads is not None
|
||
or self._n_kv_heads_by_layer is not None
|
||
)
|
||
|
||
def _kv_heads_for_layer(self, layer_idx: int, fallback: int) -> int:
|
||
if self._n_kv_heads_by_layer is not None and layer_idx < len(
|
||
self._n_kv_heads_by_layer
|
||
):
|
||
return self._n_kv_heads_by_layer[layer_idx]
|
||
return fallback
|
||
|
||
def _estimate_kv_cache_bytes(
|
||
self,
|
||
n_ctx: int,
|
||
cache_type_kv: Optional[str] = None,
|
||
*,
|
||
swa_full: bool = False,
|
||
n_parallel: int = 1,
|
||
kv_unified: bool = True,
|
||
ctx_checkpoints: int = 0,
|
||
) -> int:
|
||
"""Estimate KV cache VRAM for a given context length.
|
||
|
||
Uses 5-path architecture-aware estimation:
|
||
1. MLA -- compressed KV latent + RoPE, K-only (no separate V)
|
||
2. Hybrid -- only attention layers need KV (Mamba layers don't)
|
||
3. SWA -- sliding-window layers cache min(ctx, window) tokens
|
||
4. GQA -- standard full KV with explicit key/value dimensions
|
||
5. Legacy -- fallback using embed // n_heads
|
||
|
||
Server-flag knobs (mirror llama-server's CLI):
|
||
swa_full -- ``--swa-full``: force SWA layers to cache the
|
||
full ``n_ctx`` (collapses path 3 to path 4
|
||
sizing for the SWA layers).
|
||
n_parallel -- ``--parallel``: number of server slots.
|
||
Verified empirically against llama-server:
|
||
non-SWA layers stay constant (cells split
|
||
across slots), SWA layers scale linearly
|
||
(per-slot window).
|
||
kv_unified -- ``--kv-unified`` (default on): retained for
|
||
API forward-compat. Currently a no-op for
|
||
memory math because the unified buffer total
|
||
matches per-slot buffers in measured cases.
|
||
ctx_checkpoints -- ``--ctx-checkpoints``: SWA snapshot count per
|
||
slot (PR #15293). Each snapshot stores one
|
||
sliding-window of state per SWA layer.
|
||
|
||
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]
|
||
# Gemma 3n / Gemma 4 reuse KV from earlier layers in the last
|
||
# ``shared_kv_layers`` blocks -- those don't allocate their own
|
||
# cache. Floor at 1 so a misconfigured GGUF can't zero out KV.
|
||
shared = self._shared_kv_layers or 0
|
||
n_layers_kv = max(1, n_layers - shared)
|
||
n_kv = self._n_kv_heads or self._n_heads or 1 # type: ignore[assignment]
|
||
|
||
# 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)
|
||
|
||
slots = max(1, n_parallel)
|
||
|
||
# Path 1: MLA (DeepSeek-V2/V3, GLM-4.7, GLM-5, Kimi-K2.5)
|
||
# MLA stores one compressed KV latent per token/layer (shared across heads).
|
||
# V is reconstructed from the latent on the fly -- no separate V cache.
|
||
# key_length = kv_lora_rank + rope_dim (the full compressed representation).
|
||
# MLA GGUFs set head_count_kv=1; default to 1 if absent to avoid
|
||
# falling back to n_heads (e.g., 128 for DeepSeek-V3) which would 128x.
|
||
if self._kv_lora_rank is not None:
|
||
n_kv_mla = self._n_kv_heads or 1
|
||
rope_dim = self._key_length_mla or 64
|
||
key_len = self._kv_key_length or (self._kv_lora_rank + rope_dim)
|
||
return int(n_layers_kv * n_ctx * n_kv_mla * key_len * bpe)
|
||
|
||
key_len = self._kv_key_length
|
||
val_len = self._kv_value_length
|
||
|
||
# Path 2: Hybrid Mamba/Attention (Qwen3.5-27B, Qwen3.5-35B-A3B)
|
||
# Only 1 in N layers is attention; the rest are Mamba (no KV cache).
|
||
if (
|
||
self._ssm_inner_size is not None
|
||
and self._full_attention_interval is not None
|
||
):
|
||
fai = self._full_attention_interval
|
||
n_attn = -(-n_layers // fai) if fai > 0 else n_layers # ceiling division
|
||
if key_len is not None and val_len is not None:
|
||
return int(n_attn * n_ctx * n_kv * (key_len + val_len) * bpe)
|
||
head_dim = self._embedding_length // self._n_heads if self._n_heads else 128 # type: ignore[operator]
|
||
return int(n_attn * n_ctx * n_kv * 2 * head_dim * bpe)
|
||
|
||
# Path 3: Sliding window (Gemma 2/3/3n/4, gpt-oss, Cohere2 ...).
|
||
# Pattern is filled in by the resolver at parse time; if absent,
|
||
# falls through to the legacy 1/4-global heuristic below.
|
||
# Per-layer-type ``--parallel N`` accounting (verified empirically
|
||
# against ``llama-server``):
|
||
# * non-SWA layers: total cells = n_ctx, partitioned across
|
||
# slots -> total memory CONSTANT in slots.
|
||
# * SWA layers: per-slot cells = 2 * sliding_window
|
||
# (capped at n_ctx and at per_slot_ctx
|
||
# when ctx is split among many slots) ->
|
||
# total memory grows LINEARLY in slots.
|
||
# ``--swa-full`` forces full n_ctx for SWA layers instead.
|
||
# ``--ctx-checkpoints N`` adds N snapshots per SWA layer per slot.
|
||
if (
|
||
self._sliding_window is not None
|
||
and self._sliding_window > 0
|
||
and key_len is not None
|
||
and val_len is not None
|
||
):
|
||
swa = self._sliding_window
|
||
per_slot_ctx = max(1, n_ctx // slots)
|
||
# ``--swa-full`` makes SWA layers cache the full context just
|
||
# like non-SWA: cells get partitioned across slots, so per-slot
|
||
# cells = per_slot_ctx and the slots*per-slot product collapses
|
||
# back to the constant ``n_ctx`` total. Otherwise SWA caches
|
||
# 2*sliding_window per slot, clamped at the per-slot ctx.
|
||
swa_cells_per_slot = (
|
||
per_slot_ctx if swa_full else min(n_ctx, 2 * swa, per_slot_ctx)
|
||
)
|
||
key_len_swa = self._kv_key_length_swa or key_len
|
||
val_len_swa = self._kv_value_length_swa or val_len
|
||
if self._sliding_window_pattern is not None:
|
||
global_bytes = 0.0 # constant across slots
|
||
swa_bytes_per_slot = 0.0 # multiplied by slots
|
||
checkpoint_extra_per_slot = 0.0
|
||
# Iterate only over layers that allocate their own KV;
|
||
# the trailing ``shared`` layers reuse earlier caches.
|
||
for layer_idx in range(n_layers_kv):
|
||
layer_n_kv = self._kv_heads_for_layer(layer_idx, n_kv)
|
||
is_swa = (
|
||
layer_idx < len(self._sliding_window_pattern)
|
||
and self._sliding_window_pattern[layer_idx]
|
||
)
|
||
if is_swa:
|
||
swa_bytes_per_slot += (
|
||
swa_cells_per_slot
|
||
* layer_n_kv
|
||
* (key_len_swa + val_len_swa)
|
||
* bpe
|
||
)
|
||
if ctx_checkpoints > 0 and not swa_full:
|
||
checkpoint_extra_per_slot += (
|
||
ctx_checkpoints
|
||
* swa
|
||
* layer_n_kv
|
||
* (key_len_swa + val_len_swa)
|
||
* bpe
|
||
)
|
||
else:
|
||
global_bytes += n_ctx * layer_n_kv * (key_len + val_len) * bpe
|
||
return int(
|
||
global_bytes
|
||
+ slots * (swa_bytes_per_slot + checkpoint_extra_per_slot)
|
||
)
|
||
n_global = max(1, n_layers_kv // 4)
|
||
n_swa = n_layers_kv - n_global
|
||
kv_per_token = n_kv * (key_len + val_len) * bpe
|
||
kv_per_token_swa = n_kv * (key_len_swa + val_len_swa) * bpe
|
||
global_bytes = n_global * n_ctx * kv_per_token
|
||
swa_bytes_per_slot = n_swa * swa_cells_per_slot * kv_per_token_swa
|
||
checkpoint_extra_per_slot = (
|
||
ctx_checkpoints * n_swa * swa * kv_per_token_swa
|
||
if ctx_checkpoints > 0 and not swa_full
|
||
else 0.0
|
||
)
|
||
return int(
|
||
global_bytes + slots * (swa_bytes_per_slot + checkpoint_extra_per_slot)
|
||
)
|
||
|
||
# Path 4: Standard GQA with explicit key/value dimensions
|
||
if key_len is not None and val_len is not None:
|
||
return int(n_layers_kv * n_ctx * n_kv * (key_len + val_len) * bpe)
|
||
|
||
# Path 5: Legacy fallback (old GGUFs without explicit dimensions)
|
||
head_dim = self._embedding_length // self._n_heads if self._n_heads else 128 # type: ignore[operator]
|
||
return int(2 * n_kv * head_dim * n_layers_kv * 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,
|
||
*,
|
||
swa_full: bool = False,
|
||
n_parallel: int = 1,
|
||
kv_unified: bool = True,
|
||
ctx_checkpoints: int = 0,
|
||
kv_on_gpu: bool = True,
|
||
) -> int:
|
||
"""Return the largest context length that fits in GPU VRAM.
|
||
|
||
Uses 90% of available VRAM as the ctx-fit budget. Tighter than
|
||
``_GPU_PIN_VRAM_FRACTION`` on purpose: over-promising context
|
||
OOMs at runtime, while pinning conservatively just defers to
|
||
--fit on. If the weights alone don't fit, returns
|
||
``requested_ctx`` unchanged.
|
||
|
||
``kv_on_gpu`` mirrors ``--kv-offload`` (default on). When False
|
||
the KV cache lives in CPU RAM and doesn't compete with weights
|
||
for VRAM; the requested context is honored verbatim. The other
|
||
keyword args mirror ``_estimate_kv_cache_bytes``.
|
||
"""
|
||
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
|
||
|
||
# KV lives off-GPU: no VRAM accounting needed for the cache itself.
|
||
if not kv_on_gpu:
|
||
return requested_ctx
|
||
|
||
kv_kwargs = dict(
|
||
swa_full = swa_full,
|
||
n_parallel = n_parallel,
|
||
kv_unified = kv_unified,
|
||
ctx_checkpoints = ctx_checkpoints,
|
||
)
|
||
|
||
budget_bytes = available_mib * 1024 * 1024 * 0.90
|
||
model_footprint = model_size_bytes
|
||
|
||
# Check if requested context already fits
|
||
kv = self._estimate_kv_cache_bytes(requested_ctx, cache_type_kv, **kv_kwargs)
|
||
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, **kv_kwargs)
|
||
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.
|
||
"""
|
||
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
|
||
variants: dict[str, list[str]] = {}
|
||
for f in gguf_files:
|
||
m = _SHARD_RE.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)
|
||
|
||
@staticmethod
|
||
def _gguf_read_array_value(f, atype: int, alen: int) -> Optional[list]:
|
||
if atype == 4: # UINT32
|
||
return [struct.unpack("<I", f.read(4))[0] for _ in range(alen)]
|
||
if atype == 5: # INT32
|
||
return [struct.unpack("<i", f.read(4))[0] for _ in range(alen)]
|
||
if atype == 7: # BOOL
|
||
return [struct.unpack("<?", f.read(1))[0] for _ in range(alen)]
|
||
|
||
for _ in range(alen):
|
||
LlamaCppBackend._gguf_skip_value(f, atype)
|
||
return None
|
||
|
||
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._reasoning_style = "enable_thinking"
|
||
self._reasoning_default = True
|
||
self._supports_preserve_thinking = False
|
||
self._supports_tools = False
|
||
self._n_layers = None
|
||
self._n_kv_heads = None
|
||
self._n_kv_heads_by_layer = None
|
||
self._n_heads = None
|
||
self._embedding_length = None
|
||
self._kv_key_length = None
|
||
self._kv_value_length = None
|
||
self._sliding_window = None
|
||
self._sliding_window_pattern = None
|
||
self._full_attention_interval = None
|
||
self._kv_lora_rank = None
|
||
self._key_length_mla = None
|
||
self._kv_key_length_swa = None
|
||
self._kv_value_length_swa = None
|
||
self._ssm_inner_size = None
|
||
self._ssm_state_size = None
|
||
self._shared_kv_layers = None
|
||
|
||
try:
|
||
WANTED = {
|
||
"general.architecture",
|
||
"tokenizer.chat_template",
|
||
# Source-repo hints for the SWA resolver's HF fallback.
|
||
"general.source.huggingface.repository",
|
||
"general.source.url",
|
||
"general.source.repo_url",
|
||
"general.base_model.0.repo_url",
|
||
"general.base_model.0.organization",
|
||
"general.base_model.0.name",
|
||
"general.basename",
|
||
"general.organization",
|
||
"general.size_label",
|
||
"general.finetune",
|
||
}
|
||
# Additional arch-specific keys are added dynamically once
|
||
# we know the architecture name.
|
||
arch_keys: dict[str, str] = {} # gguf_key -> attribute name
|
||
arch = None
|
||
sliding_window_pattern_period: Optional[int] = None
|
||
general: dict[str, str] = {}
|
||
|
||
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):
|
||
# Tolerate truncated input (e.g., a partial header
|
||
# fetched via HTTP byte-range): bail out gracefully
|
||
# so the resolver fallback still runs on whatever
|
||
# we did manage to parse.
|
||
try:
|
||
key_len_bytes = f.read(8)
|
||
if len(key_len_bytes) < 8:
|
||
break
|
||
key_len = struct.unpack("<Q", key_len_bytes)[0]
|
||
key_bytes = f.read(key_len)
|
||
if len(key_bytes) < key_len:
|
||
break
|
||
key = key_bytes.decode("utf-8")
|
||
vtype_bytes = f.read(4)
|
||
if len(vtype_bytes) < 4:
|
||
break
|
||
vtype = struct.unpack("<I", vtype_bytes)[0]
|
||
except (struct.error, UnicodeDecodeError):
|
||
break
|
||
|
||
try:
|
||
if key in WANTED or key in arch_keys:
|
||
if vtype == 8: # STRING
|
||
slen = struct.unpack("<Q", f.read(8))[0]
|
||
val_s = f.read(slen).decode("utf-8")
|
||
if (
|
||
key.startswith("general.")
|
||
and key != "general.architecture"
|
||
):
|
||
general[key] = val_s
|
||
if key == "general.architecture":
|
||
arch = val_s
|
||
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",
|
||
f"{arch}.attention.key_length": "kv_key_length",
|
||
f"{arch}.attention.value_length": "kv_value_length",
|
||
f"{arch}.attention.sliding_window": "sliding_window",
|
||
f"{arch}.attention.sliding_window_pattern": "sliding_window_pattern",
|
||
f"{arch}.full_attention_interval": "full_attention_interval",
|
||
f"{arch}.attention.kv_lora_rank": "kv_lora_rank",
|
||
f"{arch}.attention.key_length_mla": "key_length_mla",
|
||
f"{arch}.attention.key_length_swa": "kv_key_length_swa",
|
||
f"{arch}.attention.value_length_swa": "kv_value_length_swa",
|
||
f"{arch}.attention.shared_kv_layers": "shared_kv_layers",
|
||
f"{arch}.ssm.inner_size": "ssm_inner_size",
|
||
f"{arch}.ssm.state_size": "ssm_state_size",
|
||
}
|
||
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:
|
||
if attr == "sliding_window_pattern":
|
||
sliding_window_pattern_period = val_i
|
||
else:
|
||
setattr(self, f"_{attr}", val_i)
|
||
elif vtype == 9: # ARRAY
|
||
atype = struct.unpack("<I", f.read(4))[0]
|
||
alen = struct.unpack("<Q", f.read(8))[0]
|
||
val_a = self._gguf_read_array_value(f, atype, alen)
|
||
attr = arch_keys.get(key)
|
||
if attr == "n_kv_heads" and val_a is not None:
|
||
self._n_kv_heads_by_layer = [int(x) for x in val_a]
|
||
if self._n_kv_heads is None and val_a:
|
||
self._n_kv_heads = max(int(x) for x in val_a)
|
||
elif (
|
||
attr == "sliding_window_pattern"
|
||
and val_a is not None
|
||
):
|
||
self._sliding_window_pattern = [
|
||
bool(x) for x in val_a
|
||
]
|
||
sliding_window_pattern_period = None
|
||
else:
|
||
self._gguf_skip_value(f, vtype)
|
||
else:
|
||
self._gguf_skip_value(f, vtype)
|
||
except (struct.error, UnicodeDecodeError):
|
||
# Truncated input (e.g., HTTP byte-range fetch
|
||
# of just the GGUF header); break so the
|
||
# resolver fallback still runs on what we have.
|
||
break
|
||
|
||
# Expand a scalar period straight from the GGUF first.
|
||
if (
|
||
self._sliding_window_pattern is None
|
||
and sliding_window_pattern_period
|
||
and self._n_layers
|
||
):
|
||
self._sliding_window_pattern = [
|
||
(i + 1) % sliding_window_pattern_period != 0
|
||
for i in range(self._n_layers)
|
||
]
|
||
|
||
# Otherwise hand off to the resolver (cache / bootstrap /
|
||
# transformers / HF). See `_resolve_swa_pattern`.
|
||
if (
|
||
self._sliding_window_pattern is None
|
||
and self._sliding_window
|
||
and self._n_layers
|
||
):
|
||
hf_repo_candidates = (
|
||
general.get("general.source.huggingface.repository"),
|
||
_hf_repo_from_url(general.get("general.source.url")),
|
||
_hf_repo_from_url(general.get("general.source.repo_url")),
|
||
_hf_repo_from_url(general.get("general.base_model.0.repo_url")),
|
||
(
|
||
f"{general['general.base_model.0.organization']}/"
|
||
f"{general['general.base_model.0.name']}".replace(" ", "-")
|
||
if general.get("general.base_model.0.organization")
|
||
and general.get("general.base_model.0.name")
|
||
else None
|
||
),
|
||
(
|
||
f"{general['general.organization']}/"
|
||
f"{general['general.basename']}".replace(" ", "-")
|
||
if general.get("general.organization")
|
||
and general.get("general.basename")
|
||
else None
|
||
),
|
||
)
|
||
self._sliding_window_pattern = _resolve_swa_pattern(
|
||
arch,
|
||
self._n_layers,
|
||
hf_repo_candidates,
|
||
)
|
||
|
||
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
|
||
flags = detect_reasoning_flags(
|
||
self._chat_template,
|
||
self._model_identifier,
|
||
log_source = "GGUF metadata",
|
||
)
|
||
self._supports_reasoning = flags["supports_reasoning"]
|
||
self._reasoning_style = flags["reasoning_style"]
|
||
self._reasoning_always_on = flags["reasoning_always_on"]
|
||
self._supports_preserve_thinking = flags["supports_preserve_thinking"]
|
||
self._supports_tools = flags["supports_tools"]
|
||
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:
|
||
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]
|
||
m = _SHARD_FULL_RE.match(gguf_filename)
|
||
if m:
|
||
prefix = m.group(1)
|
||
total = m.group(3)
|
||
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, try_to_load_from_cache
|
||
|
||
path_infos = list(get_paths_info(hf_repo, all_gguf_files, token = hf_token))
|
||
total_bytes = sum((p.size or 0) for p in path_infos)
|
||
|
||
# Subtract bytes already present in the HF cache so we only
|
||
# preflight against what we actually have to download. Without
|
||
# this, re-loading a cached large model (e.g. MiniMax-M2.7-GGUF
|
||
# at 131 GB) fails cold whenever free disk is below the full
|
||
# weight footprint, even though nothing needs downloading.
|
||
already_cached_bytes = 0
|
||
for p in path_infos:
|
||
if not p.size:
|
||
continue
|
||
try:
|
||
cached_path = try_to_load_from_cache(hf_repo, p.path)
|
||
except Exception:
|
||
cached_path = None
|
||
if isinstance(cached_path, str) and os.path.exists(cached_path):
|
||
try:
|
||
on_disk = os.path.getsize(cached_path)
|
||
except OSError:
|
||
on_disk = 0
|
||
# Count as satisfied only when the full blob is present.
|
||
if on_disk >= p.size:
|
||
already_cached_bytes += p.size
|
||
|
||
total_download_bytes = max(0, total_bytes - already_cached_bytes)
|
||
|
||
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)
|
||
cached_gb = already_cached_bytes / (1024**3)
|
||
|
||
logger.info(
|
||
f"GGUF download: {total_gb:.1f} GB needed "
|
||
f"({cached_gb:.1f} GB already cached), "
|
||
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
|
||
_m = _SHARD_RE.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 f.lower().endswith("-f16.gguf"):
|
||
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,
|
||
speculative_type: Optional[str] = None,
|
||
n_threads: Optional[int] = None,
|
||
n_gpu_layers: Optional[int] = None, # Accepted for caller compat, unused
|
||
n_parallel: int = 1,
|
||
extra_args: Optional[List[str]] = None,
|
||
) -> 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
|
||
gpus: list[tuple[int, int]] = []
|
||
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,
|
||
n_parallel = n_parallel,
|
||
)
|
||
kv = self._estimate_kv_cache_bytes(
|
||
capped, cache_type_kv, n_parallel = n_parallel
|
||
)
|
||
total_mib = (model_size + kv) / (1024 * 1024)
|
||
if total_mib <= pool_mib * 0.90:
|
||
best_cap = max(best_cap, capped)
|
||
if best_cap > 0:
|
||
max_available_ctx = best_cap
|
||
else:
|
||
# Weights exceed 90% of every GPU subset's free
|
||
# memory, so there is no fitting context. Anchor
|
||
# the UI's "safe zone" threshold at 4096 (the
|
||
# spec's default when the model cannot fit) so
|
||
# the ctx slider shows the "might be slower"
|
||
# warning as soon as the user drags above the
|
||
# fallback default instead of never.
|
||
max_available_ctx = min(4096, native_ctx_for_cap)
|
||
|
||
if explicit_ctx:
|
||
# Honor the user's requested context verbatim. If it
|
||
# fits, pin GPUs and skip --fit; if it doesn't, ship
|
||
# -c <user_ctx> --fit on and let llama-server flex
|
||
# -ngl (CPU layer offload). The UI is expected to
|
||
# have surfaced the "might be slower" warning before
|
||
# the user submitted a ctx above the fit ceiling.
|
||
requested_total = model_size + self._estimate_kv_cache_bytes(
|
||
effective_ctx, cache_type_kv, n_parallel = n_parallel
|
||
)
|
||
gpu_indices, use_fit = self._select_gpus(requested_total, gpus)
|
||
# No silent shrink: effective_ctx stays == n_ctx.
|
||
else:
|
||
# Auto context: prefer fewer GPUs, cap context
|
||
# to fit. Same headroom threshold as
|
||
# _select_gpus (#5106).
|
||
ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
|
||
pin_fraction = self._GPU_PIN_VRAM_FRACTION
|
||
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,
|
||
n_parallel = n_parallel,
|
||
)
|
||
kv = self._estimate_kv_cache_bytes(
|
||
capped, cache_type_kv, n_parallel = n_parallel
|
||
)
|
||
total_mib = (model_size + kv) / (1024 * 1024)
|
||
if total_mib <= pool_mib * pin_fraction:
|
||
effective_ctx = capped
|
||
gpu_indices = sorted(idx for idx, _ in subset)
|
||
use_fit = False
|
||
break
|
||
else:
|
||
# Native ctx doesn't fit. Drop to 4096 and
|
||
# re-check before deferring to --fit on:
|
||
# a model that overflows at 131k may pin
|
||
# comfortably with a 4096 KV cache (#5106).
|
||
effective_ctx = min(4096, effective_ctx)
|
||
if effective_ctx > 0:
|
||
for n_gpus in range(1, len(ranked) + 1):
|
||
subset = ranked[:n_gpus]
|
||
pool_mib = sum(free for _, free in subset)
|
||
kv = self._estimate_kv_cache_bytes(
|
||
effective_ctx,
|
||
cache_type_kv,
|
||
n_parallel = n_parallel,
|
||
)
|
||
total_mib = (model_size + kv) / (1024 * 1024)
|
||
if total_mib <= pool_mib * pin_fraction:
|
||
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 use_fit and not explicit_ctx:
|
||
# Weights don't fit on any subset. Default the UI to
|
||
# 4096 so the slider doesn't land on an unusable native
|
||
# context. --fit on will flex -ngl at runtime.
|
||
effective_ctx = (
|
||
min(4096, effective_ctx) if effective_ctx > 0 else 4096
|
||
)
|
||
|
||
if effective_ctx < original_ctx:
|
||
kv_est = self._estimate_kv_cache_bytes(
|
||
effective_ctx, cache_type_kv, n_parallel = n_parallel
|
||
)
|
||
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, n_parallel = n_parallel
|
||
)
|
||
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",
|
||
str(n_parallel),
|
||
"--flash-attn",
|
||
"on", # Force flash attention for speed
|
||
# Error out at n_ctx instead of silently rotating the KV cache; frontend catches it and points the user at "Context Length".
|
||
"--no-context-shift",
|
||
]
|
||
|
||
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"])
|
||
|
||
# -1 = llama.cpp auto-detect (physical cores). Pass explicitly so we
|
||
# do not inherit llama-server's internal default, which has historically
|
||
# varied (hardware concurrency incl. hyperthreads on some builds).
|
||
cmd.extend(["--threads", str(n_threads if n_threads is not None else -1)])
|
||
|
||
# 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
|
||
|
||
# Speculative decoding (n-gram self-speculation, zero VRAM cost)
|
||
# ngram-mod: ~16 MB shared hash pool, constant memory/complexity,
|
||
# variable draft lengths. Helps most when the model repeats
|
||
# existing text (code refactoring, summarization, reasoning).
|
||
# For general chat with low repetition, overhead is ~5 ms.
|
||
#
|
||
# Benchmarks from upstream llama.cpp speculative-decoding PRs:
|
||
# Scenario | Without | With | Speedup
|
||
# gpt-oss-120b code refactor | 181 t/s | 446 t/s | 2.5x
|
||
# Qwen3-235B offloaded | 12 t/s | 21 t/s | 1.8x
|
||
# gpt-oss-120b repeat (92% accept)| 181 t/s | 814 t/s | 4.5x
|
||
#
|
||
# Params from llama.cpp docs (docs/speculative.md):
|
||
# --spec-ngram-size-n 24 (small n not recommended)
|
||
# --draft-min 48 --draft-max 64 (MoEs need long drafts;
|
||
# dense models can reduce these)
|
||
# ref: https://github.com/ggml-org/llama.cpp/blob/master/docs/speculative.md
|
||
# ref: https://github.com/ggml-org/llama.cpp/pull/19164
|
||
# ref: https://github.com/ggml-org/llama.cpp/pull/18471
|
||
# ``"default"`` -> let llama-server pick a sensible spec
|
||
# config via ``--spec-default``. Explicit type names are
|
||
# passed through with the manual draft tuning we've shipped
|
||
# historically so power users keep their overrides.
|
||
_valid_spec_types = {"ngram-simple", "ngram-mod"}
|
||
normalized_spec = (
|
||
speculative_type.lower().strip() if speculative_type else None
|
||
)
|
||
if normalized_spec and normalized_spec != "off" and not is_vision:
|
||
if normalized_spec == "default":
|
||
cmd.append("--spec-default")
|
||
self._speculative_type = "default"
|
||
elif normalized_spec in _valid_spec_types:
|
||
cmd.extend(["--spec-type", normalized_spec])
|
||
if normalized_spec == "ngram-mod":
|
||
cmd.extend(
|
||
[
|
||
"--spec-ngram-size-n",
|
||
"24",
|
||
"--draft-min",
|
||
"48",
|
||
"--draft-max",
|
||
"64",
|
||
]
|
||
)
|
||
self._speculative_type = normalized_spec
|
||
else:
|
||
self._speculative_type = None
|
||
else:
|
||
self._speculative_type = None
|
||
|
||
# Apply custom chat template override if provided
|
||
self._chat_template_override = chat_template_override
|
||
if chat_template_override:
|
||
import tempfile
|
||
|
||
flags = detect_reasoning_flags(
|
||
chat_template_override,
|
||
self._model_identifier,
|
||
log_source = "GGUF chat template override",
|
||
)
|
||
self._supports_reasoning = flags["supports_reasoning"]
|
||
self._reasoning_style = flags["reasoning_style"]
|
||
self._reasoning_always_on = flags["reasoning_always_on"]
|
||
self._supports_preserve_thinking = flags["supports_preserve_thinking"]
|
||
self._supports_tools = flags["supports_tools"]
|
||
|
||
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.
|
||
# Qwen3.5/3.6 models below 9B (0.8B, 2B, 4B) disable thinking by default.
|
||
# Only 9B and larger enable thinking.
|
||
# Always-on templates ignore the kwarg entirely, so skip.
|
||
if self._supports_reasoning and not self._reasoning_always_on:
|
||
thinking_default = True
|
||
mid = (model_identifier or "").lower()
|
||
if "qwen3.5" in mid or "qwen3.6" in mid:
|
||
size_val = _extract_model_size_b(mid)
|
||
if size_val is not None and size_val < 9:
|
||
thinking_default = False
|
||
self._reasoning_default = thinking_default
|
||
reasoning_kw = self._reasoning_kwargs(thinking_default)
|
||
cmd.extend(
|
||
[
|
||
"--chat-template-kwargs",
|
||
json.dumps(reasoning_kw),
|
||
]
|
||
)
|
||
logger.info(f"Reasoning model: {reasoning_kw} by default")
|
||
|
||
if mmproj_path:
|
||
if not Path(mmproj_path).is_file():
|
||
logger.warning(f"mmproj file not found: {mmproj_path}")
|
||
else:
|
||
# #5347 guard for paths that bypass detect_mmproj_file.
|
||
from utils.models.model_config import (
|
||
mmproj_matches_model_family,
|
||
)
|
||
|
||
if not mmproj_matches_model_family(model_path, mmproj_path):
|
||
logger.warning(
|
||
f"Skipping mmproj with mismatched family: "
|
||
f"model={Path(model_path).name}, "
|
||
f"mmproj={Path(mmproj_path).name}"
|
||
)
|
||
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
|
||
|
||
# User-supplied pass-through args go last so llama.cpp's
|
||
# last-wins flag parsing lets the user override Studio's
|
||
# auto-set tier-2 flags (e.g. --cache-type-k, --spec-type).
|
||
# The route layer has already validated this list against
|
||
# the managed-flag denylist via validate_extra_args().
|
||
if extra_args:
|
||
cmd.extend(str(a) for a in extra_args)
|
||
logger.info(
|
||
f"Appending user extra args to llama-server: {list(extra_args)}"
|
||
)
|
||
|
||
_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 = child_env_without_native_path_secret()
|
||
binary_dir = str(Path(binary).parent)
|
||
|
||
if sys.platform == "win32":
|
||
# CUDA DLLs (cudart64_X.dll, cublas64_X.dll, etc.) must
|
||
# be on PATH. Order: binary_dir, torch's pip-installed
|
||
# nvidia wheels, then a system CUDA toolkit. Pip wheels
|
||
# are the canonical source per Studio's install design
|
||
# (mirrors the Linux LD_LIBRARY_PATH block below) and
|
||
# CUDA_PATH covers users with a system toolkit. #5106.
|
||
path_dirs = [binary_dir]
|
||
path_dirs.extend(self._windows_pip_nvidia_dll_dirs(sys.prefix))
|
||
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). On ROCm, llama-server (and any torch
|
||
# in the subprocess) honors HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES;
|
||
# narrowing only CUDA_VISIBLE_DEVICES leaves an AMD child seeing
|
||
# the full HIP/ROCR set the parent inherited.
|
||
if gpu_indices is not None:
|
||
pinned = ",".join(str(i) for i in gpu_indices)
|
||
env["CUDA_VISIBLE_DEVICES"] = pinned
|
||
try:
|
||
import torch as _torch
|
||
|
||
if getattr(_torch.version, "hip", None) is not None:
|
||
env["HIP_VISIBLE_DEVICES"] = pinned
|
||
env["ROCR_VISIBLE_DEVICES"] = pinned
|
||
except Exception as e:
|
||
logger.debug(
|
||
"Failed to set ROCm visibility env vars for child: %s", e
|
||
)
|
||
|
||
# Defensive kill: if a concurrent load slipped past Phase 1
|
||
# (because its `self._process` was None at the time) and
|
||
# already stored a Popen handle here, drop that orphan
|
||
# before we overwrite the reference. See issue #5161.
|
||
self._kill_process()
|
||
|
||
self._stdout_lines = []
|
||
self._process = subprocess.Popen(
|
||
cmd,
|
||
stdout = subprocess.PIPE,
|
||
stderr = subprocess.STDOUT,
|
||
text = True,
|
||
env = env,
|
||
**_windows_hidden_subprocess_kwargs(),
|
||
)
|
||
|
||
# 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()
|
||
|
||
# Store the resolved on-disk path, not the caller's kwarg. In
|
||
# HF mode the caller passes gguf_path=None and the real path
|
||
# (``model_path``) is what llama-server is actually mmap'ing.
|
||
# Downstream consumers (load_progress, log lines, etc.) need
|
||
# the path that exists on disk.
|
||
self._gguf_path = model_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()
|
||
_gguf = gguf_path or ""
|
||
_is_ollama = (
|
||
".studio_links" in _gguf
|
||
or os.sep + "ollama_links" + os.sep in _gguf
|
||
or os.sep + ".cache" + os.sep + "ollama" + os.sep in _gguf
|
||
or (self._model_identifier or "").startswith("ollama/")
|
||
)
|
||
# Only show the Ollama-specific message when the server
|
||
# output indicates a GGUF compatibility issue, not for
|
||
# unrelated failures like OOM or missing binaries.
|
||
if _is_ollama:
|
||
_output = "\n".join(self._stdout_lines[-50:]).lower()
|
||
_gguf_compat_hints = (
|
||
"key not found",
|
||
"unknown model architecture",
|
||
"failed to load model",
|
||
)
|
||
if any(h in _output for h in _gguf_compat_hints):
|
||
raise RuntimeError(
|
||
"Some Ollama models do not work with llama.cpp. "
|
||
"Try a different model, or use this model directly through Ollama instead."
|
||
)
|
||
raise RuntimeError(
|
||
"llama-server failed to start. "
|
||
"Check that the GGUF file is valid and you have enough memory."
|
||
)
|
||
|
||
self._healthy = True
|
||
|
||
# Catch silent CPU fallback when GPU was intended (#5106).
|
||
self._gpu_offload_active = self._classify_gpu_offload(
|
||
gpu_indices is not None or use_fit, gpus or []
|
||
)
|
||
if self._gpu_offload_active is False:
|
||
logger.warning(
|
||
"llama-server appears to have loaded the model entirely "
|
||
"on CPU even though Studio detected at least one GPU. "
|
||
"This usually means the prebuilt binary's GPU backend "
|
||
"failed to load -- on Windows, cudart64_X.dll / "
|
||
"cublas64_X.dll could not be resolved. Reinstall the "
|
||
"Studio llama.cpp prebuilt or install a matching CUDA "
|
||
"toolkit (issue unslothai/unsloth#5106).",
|
||
)
|
||
|
||
logger.info(
|
||
f"llama-server ready on port {self._port} "
|
||
f"for model '{model_identifier}'"
|
||
)
|
||
return True
|
||
|
||
def _classify_gpu_offload(
|
||
self,
|
||
expected_gpu: bool,
|
||
detected_gpus: list[tuple[int, int]],
|
||
) -> Optional[bool]:
|
||
"""True if a GPU model buffer was allocated, False if only CPU
|
||
buffers landed despite GPU intent, None when there's no signal
|
||
(no GPU detected, no buffer-size lines, etc.)."""
|
||
if not detected_gpus or not expected_gpu:
|
||
return None
|
||
# llama-server logs one ``... model buffer size = N MiB`` line
|
||
# per backend buffer; CUDA0 / ROCm0 / Metal / Vulkan0 /
|
||
# OpenCL0 / SYCL0 are GPU, CPU / CPU_Mapped are not.
|
||
gpu_markers = ("CUDA", "ROCm", "Metal", "Vulkan", "OpenCL", "SYCL")
|
||
saw_buffer_line = False
|
||
saw_gpu_buffer = False
|
||
for line in self._stdout_lines:
|
||
if "model buffer size" not in line:
|
||
continue
|
||
saw_buffer_line = True
|
||
if any(marker in line for marker in gpu_markers):
|
||
saw_gpu_buffer = True
|
||
break
|
||
if not saw_buffer_line:
|
||
return None
|
||
return saw_gpu_buffer
|
||
|
||
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._chat_template_override = None
|
||
self._supports_reasoning = False
|
||
self._reasoning_always_on = False
|
||
self._reasoning_style = "enable_thinking"
|
||
self._reasoning_default = True
|
||
self._supports_preserve_thinking = False
|
||
self._supports_tools = False
|
||
self._cache_type_kv = None
|
||
self._speculative_type = None
|
||
self._n_layers = None
|
||
self._n_kv_heads = None
|
||
self._n_kv_heads_by_layer = None
|
||
self._n_heads = None
|
||
self._embedding_length = None
|
||
self._kv_key_length = None
|
||
self._kv_value_length = None
|
||
self._sliding_window = None
|
||
self._sliding_window_pattern = None
|
||
self._full_attention_interval = None
|
||
self._kv_lora_rank = None
|
||
self._key_length_mla = None
|
||
self._kv_key_length_swa = None
|
||
self._kv_value_length_swa = None
|
||
self._ssm_inner_size = None
|
||
self._ssm_state_size = None
|
||
self._shared_kv_layers = 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] = []
|
||
|
||
# Env-mode custom root (mirrors _find_llama_server_binary).
|
||
_is_custom_root = False
|
||
try:
|
||
from utils.paths.storage_roots import studio_root as _sr # noqa: WPS433
|
||
|
||
_resolved_sr = _sr()
|
||
_legacy_studio = Path.home() / ".unsloth" / "studio"
|
||
try:
|
||
_is_custom_root = _resolved_sr.resolve() != _legacy_studio.resolve()
|
||
except (OSError, ValueError):
|
||
_is_custom_root = _resolved_sr != _legacy_studio
|
||
if _is_custom_root:
|
||
install_roots.append(_resolved_sr / "llama.cpp")
|
||
except (ImportError, OSError, ValueError):
|
||
pass
|
||
|
||
# Primary install dir (default mode only). Env-mode skips this so
|
||
# a custom-root Studio cannot kill a concurrent default-install
|
||
# Studio's llama-server (same OS user, different install).
|
||
if not _is_custom_root:
|
||
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,
|
||
env = child_env_without_native_path_secret(),
|
||
)
|
||
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.
|
||
"""
|
||
tool_calls = []
|
||
|
||
# Pattern 1: JSON inside <tool_call> tags.
|
||
# Use balanced-brace extraction that skips braces inside JSON strings.
|
||
for m in _TC_JSON_START_RE.finditer(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(_TC_FUNC_START_RE.finditer(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 = _TC_END_TAG_RE.search(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 = _TC_FUNC_CLOSE_RE.sub("", 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(_TC_PARAM_START_RE.finditer(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 = _TC_PARAM_CLOSE_RE.sub("", 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 = _TC_PARAM_CLOSE_RE.sub("", 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,
|
||
reasoning_effort: Optional[str] = None,
|
||
preserve_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 / reasoning_effort / preserve_thinking per-request
|
||
_reasoning_kw = self._request_reasoning_kwargs(
|
||
enable_thinking, reasoning_effort, preserve_thinking
|
||
)
|
||
if _reasoning_kw is not None:
|
||
payload["chat_template_kwargs"] = _reasoning_kw
|
||
# Default cap to the model's effective context length when known,
|
||
# otherwise the conservative floor. The wall-clock backstop below
|
||
# keeps a stuck model from running indefinitely either way.
|
||
payload["max_tokens"] = (
|
||
max_tokens
|
||
if max_tokens is not None
|
||
else (self._effective_context_length or _DEFAULT_MAX_TOKENS_FLOOR)
|
||
)
|
||
payload["t_max_predict_ms"] = _DEFAULT_T_MAX_PREDICT_MS
|
||
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, limits = httpx.Limits(max_keepalive_connections = 0)
|
||
) 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,
|
||
reasoning_effort: Optional[str] = None,
|
||
preserve_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: ..."/"Reading: ..."} -- 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
|
||
|
||
def _strip_tool_markup(text: str, *, final: bool = False) -> str:
|
||
if not auto_heal_tool_calls:
|
||
return text
|
||
patterns = _TOOL_ALL_PATS if final else _TOOL_CLOSED_PATS
|
||
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)
|
||
|
||
# ── Re-prompt on plan-without-action ─────────────────
|
||
# When the model describes what it intends to do (forward-looking
|
||
# language) without actually calling a tool, re-prompt once.
|
||
# Only triggers on responses that signal intent/planning -- a
|
||
# direct answer like "4" or "Hello!" will not match.
|
||
# Pattern is compiled once at module level (_INTENT_SIGNAL).
|
||
_reprompt_count = 0
|
||
|
||
# Reserve extra iterations for re-prompts so they don't
|
||
# consume the caller's tool-call budget. Only add the
|
||
# extra slot when tool iterations are actually allowed.
|
||
_extra = _MAX_REPROMPTS if max_tool_iterations > 0 else 0
|
||
for iteration in range(max_tool_iterations + _extra):
|
||
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",
|
||
}
|
||
_reasoning_kw = self._request_reasoning_kwargs(
|
||
enable_thinking, reasoning_effort, preserve_thinking
|
||
)
|
||
if _reasoning_kw is not None:
|
||
payload["chat_template_kwargs"] = _reasoning_kw
|
||
payload["max_tokens"] = (
|
||
max_tokens
|
||
if max_tokens is not None
|
||
else (self._effective_context_length or _DEFAULT_MAX_TOKENS_FLOOR)
|
||
)
|
||
payload["t_max_predict_ms"] = _DEFAULT_T_MAX_PREDICT_MS
|
||
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,
|
||
limits = httpx.Limits(max_keepalive_connections = 0),
|
||
) 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: {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:
|
||
# ── Re-prompt on plan-without-action ──
|
||
# If the model described what it intends to do
|
||
# (forward-looking language) without calling any
|
||
# tool, nudge it to act. Only fires once per
|
||
# request and only on short responses that
|
||
# contain intent signals -- a direct answer
|
||
# like "4" or "Hello!" won't trigger this.
|
||
# Use content if available, otherwise fall back
|
||
# to reasoning text (reasoning-only stalls).
|
||
_stripped = content_accum.strip()
|
||
if not _stripped:
|
||
_stripped = reasoning_accum.strip()
|
||
if (
|
||
tools
|
||
and _reprompt_count < _MAX_REPROMPTS
|
||
and 0 < len(_stripped) < _REPROMPT_MAX_CHARS
|
||
and _INTENT_SIGNAL.search(_stripped)
|
||
):
|
||
_reprompt_count += 1
|
||
logger.info(
|
||
f"Re-prompt {_reprompt_count}/{_MAX_REPROMPTS}: "
|
||
f"model responded without calling tools "
|
||
f"({len(_stripped)} chars)"
|
||
)
|
||
conversation.append(
|
||
{
|
||
"role": "assistant",
|
||
"content": _stripped,
|
||
}
|
||
)
|
||
conversation.append(
|
||
{
|
||
"role": "user",
|
||
"content": (
|
||
"STOP. Do NOT write code or explain. "
|
||
"You MUST call a tool NOW. "
|
||
"Call web_search or python immediately."
|
||
),
|
||
}
|
||
)
|
||
# Accumulate tokens and timing from this iteration
|
||
_fu_r = _iter_usage or {}
|
||
_accumulated_completion_tokens += _fu_r.get(
|
||
"completion_tokens", 0
|
||
)
|
||
_it_r = _iter_timings or {}
|
||
_accumulated_predicted_ms += _it_r.get("predicted_ms", 0)
|
||
_accumulated_predicted_n += _it_r.get("predicted_n", 0)
|
||
yield {"type": "status", "text": ""}
|
||
continue
|
||
|
||
# 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":
|
||
_ws_url = (arguments.get("url") or "").strip()
|
||
if _ws_url:
|
||
_parsed = urlparse(_ws_url)
|
||
if _parsed.scheme in ("http", "https") and _parsed.hostname:
|
||
_ws_host = _parsed.hostname
|
||
if _ws_host.startswith("www."):
|
||
_ws_host = _ws_host[4:]
|
||
status_text = f"Reading: {_ws_host}"
|
||
else:
|
||
status_text = "Reading page..."
|
||
else:
|
||
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))
|
||
# Strip image sentinel before feeding result to the LLM
|
||
# (the full result with sentinel is still yielded via
|
||
# tool_end so the frontend can extract image paths).
|
||
_result_content = result
|
||
if "\n__IMAGES__:" in _result_content:
|
||
_result_content = _result_content.rsplit("\n__IMAGES__:", 1)[0]
|
||
if _is_error:
|
||
_result_content = (
|
||
_result_content + "\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,
|
||
}
|
||
_reasoning_kw = self._request_reasoning_kwargs(
|
||
enable_thinking, reasoning_effort, preserve_thinking
|
||
)
|
||
if _reasoning_kw is not None:
|
||
stream_payload["chat_template_kwargs"] = _reasoning_kw
|
||
stream_payload["max_tokens"] = (
|
||
max_tokens
|
||
if max_tokens is not None
|
||
else (self._effective_context_length or _DEFAULT_MAX_TOKENS_FLOOR)
|
||
)
|
||
stream_payload["t_max_predict_ms"] = _DEFAULT_T_MAX_PREDICT_MS
|
||
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, limits = httpx.Limits(max_keepalive_connections = 0)
|
||
) 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("<audio_soft_token>")) == 1:
|
||
return "audio_vlm"
|
||
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", "")
|
||
)
|