* Studio: GPU memory dropdown — llama.cpp --fit on and manual gpu-layers/cpu-moe * Studio: simplify GPU memory changes (reuse ParamSlider, GPU_LAYERS_ALL, loadedGpuMemoryFields helper) * Studio: GPU picker — choose which GPUs a GGUF model loads on (gpu_ids) * Studio: simplify GPU picker (share /api/system fetch, validate gpu_ids) * Studio: GPU picker review fixes (gate relative indices, no cross-model leak, validate, types) * Studio: group GPU controls under a collapsible GPU section * Studio: GPU feature review fixes (fix fit-ctx test, behavior-test the floor, comment accuracy) * Studio: make GPU a top-level settings section (not nested under Model) * Studio: flatten GPU controls into the Model section, group by GPU/context/generation * Studio: move GPU Memory to the bottom of Model with its dependent controls beneath it * Studio: move GPU Memory below Tensor Parallelism and GPUs below GPU Memory * Studio: tighten GPU Memory and GPU Layers tooltip copy * Studio: fix fit-mode context slider track-click, restore GPU Memory tooltip, shorten fit dropdown label * Studio: GPU Memory tooltip one mode per line, briefer * Studio: note HIP_VISIBLE_DEVICES (ROCm) in the GPUs picker tooltip * Studio: narrow the GPU Memory dropdown to fit the shortened label * Studio: use 'llama.cpp --fit' in the GPU Memory tooltip for consistency * Studio: allow Tensor Parallelism in Manual GPU mode * Studio: graduated MoE-on-CPU offload (--n-cpu-moe) replacing the all-or-nothing toggle * Studio: size the MoE-offload slider for staged (deferred-load) models * Studio: share one GGUF header walk for the context-length and MoE-count readers * Studio: size the GPU Layers slider for staged models (one staged-header read) * Studio: move Tensor Parallelism below the GPUs picker * Studio: GPU split (--tensor-split) per-GPU model share in Manual mode * Studio: tolerate whitespace in GPU split input, move it below GPU Layers * Studio: rename the GPU split control to "Split ratio" * Studio: Split ratio sends explicit even input; fix blank=free-VRAM (not even) copy * Studio: tighten llama.cpp --fit VRAM margin with --fit-target 512 * Studio: GPU memory review fixes (rollback re-baseline, single-GPU TP gate, accurate copy) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: move Split ratio below MoE Layers on CPU * Studio: address PR review (fix GPU-info hydration race, share fit context-length across load paths) * Studio: address codex review (manual single-GPU TP guard, GPU-aware spec defaults in fit/manual, GGUF-only context/preference) * Studio: address codex review round 2 (gpu_present seed, single-GPU tensor-split guard, staged manual-knob reset, strip inherited offload flags) * Studio: address codex review round 3 (strip inherited --n-cpu-moe, CPU-fallback warning in Manual mode) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: address codex review round 4 (preserve pinned fit context across a later Apply) * Studio: address codex review round 5 (honor GPU picker for diffusion GGUFs, clear fit pin on cross-model switch) * Studio: preserve the pending GPU Memory mode when staging a model * Studio: pin diffusion GPU device order and reset GPU-memory state for diffusion loads * Studio: address codex review round 6 (fit-Auto rollback context, preserve manual non-tensor split modes, persist GPU mode on load not select) * Studio: persist the applied GPU Memory mode, not the requested one (skip diffusion loads) * Studio: replace Manual-mode split-ratio field with per-GPU layer sliders * Studio: clarify per-GPU layer split hint for tensor-parallel mode * Studio: address codex review round 7 (allow GGUF gpu_ids past the legacy guard, replay GPU-memory fields on respawn) * Studio: address codex review round 8 (size the validate preflight like the load in fit mode, across both load paths) * Studio: skip the training-OOM guard for llama.cpp --fit GGUF loads (they spill to RAM) * Studio: drop the now-redundant compare-path validate sizing (the --fit guard skip makes it moot) * Studio: address codex review round 9 (keep the training guard for fit loads, forward gpu_ids to validate, strip inherited manual tensor-split) * Studio: address codex review round 10 (gate GPU-memory adoption on is_gguf, record manual knobs only in Manual mode) * Studio: handle diffusion GGUFs symmetrically in the GPU Memory controls (preserve the standing mode preference, hide the inapplicable mode/TP controls) * Studio: remember the GPU Memory settings per model * Studio: consolidate --fit mode and Manual mode into a single Manual mode * Studio: preserve the per-GPU layer split across GPU Layers changes * Studio: trim overly long GPU Memory comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * address GPU memory config review comments * trim redundant GPU memory tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Reconcile manual-mode TP drops with the #6659 drop-site invariants * Preserve quantized KV in manual --fit, charge GGUF companions in full, reconcile GPU pick on load * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Clear stale GPU baseline on non-GGUF loads so it can't read as dirty * Fix no-context-shift test for the conditional -c flag * Credit manual GPU-layer offload for cached HF GGUFs * Reset per-model load knobs on GGUF quant switch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Strip inherited tensor-split when manual ratio is cleared * Match auto-load validation to safetensors placement * Reset editable manual knobs after Auto GGUF loads * Record a single device for diffusion GPU picks * Reset per-model GPU knobs before applying saved settings * Address review comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Guard manual tensor splits and keep remembered context on auto-load * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Snapshot compare knobs, seed splits from free VRAM, flag zero-offload loads * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Exempt CPU-only loads from the guard floor and harden compare and reseed paths * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Reach full offload from the layers slider and charge extras drafters in the guard * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Warm the GPU device cache before pick reconciles and disable staged GPU controls * Align the training guard with inherited extras, spec mode, and compare targets * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Hide GPUs from companion-less zero-offload loads * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Size diffusion picks per device, own manual offload flags, reject XPU picks * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Drop tensor flags at zero layers and exempt CPU-pinned drafters * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Allowlist the zero-layer tensor parallel drop site * Keep validate and load guards on the same extras and refresh stale baselines * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Drop mismatched manual tensor splits before launch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Gate XPU picks on the real backend field and harden split and hydration paths * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Weight full GPUs as zero, clamp split shares, and refine the zero-layer mask gate * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Carry fit context across mode changes and align drafter and picker gates * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Catch variant switches, uncached diffusion repos, and text-only mmproj skips * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Check companions on the first device and size native and remote zero-layer loads * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Replace the training guard's precise VRAM modeling with a conservative bound * Baseline context pins on non-GGUF hydration and reprobe list-seeded staged GGUFs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Size manual splits by their largest share and preserve resolved context from Default * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Default-deny unsized required companions and price KV at the effective cache dtype * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Reserve MTP draft KV and MLA target-copy in the training guard * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Size tensor-parallel loads per device and show GPU controls for native GGUFs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Reserve MTP overhead for uncached remote GGUFs and the mmproj runtime factor * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Drop the training-coexistence VRAM estimation this PR added * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Gate remembered load settings to GGUF picks * Lock the remaining load-time controls during a staged load * Clear the stale native-path token on compare loads * Drop a stale guard reference from the zero-offload masking comment * Seed GPU baselines from the rollback response and drop never-emitted offload flags * Match validate's training guard to load and keep the native reload token * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Trim verbose GPU-memory comments * Thread the variants header walk off the event loop, honor device pins on zero-offload, and hold staged GPU edits * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Honor manual placement and classify pinned zero-offload loads * Close diffusion admission and status hydration gaps * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Check the actual diffusion GPU during training * Align staged baselines and manual reload dedupe * Fix GGUF placement and rollback state * Harden manual GGUF placement boundaries * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove unused resolve_tensor_parallel import in llama_cpp.py The name is used only in llama_server_args.py, routes/inference.py, and tests, not in llama_cpp.py; the unused hoisted import trips the import-hoist verifier in the source-lint CI job. * Fix diffusion GPU dedup and training guard for non-numeric device tokens The diffusion runner drives only its single lowest device and the backend records that one device (self._gpu_ids = [sorted(gpu_ids)[0]]), but the reload dedupe compared it against the full requested list, so a multi-GPU pick that resolves to the same device forced a needless reload. Normalize the request the same way for a loaded diffusion model in both _already_in_target_state and the route _request_matches_loaded_settings. The chat-during-training coexistence guard called int() on the single-device token and hard-rejected when it could not parse. A non-numeric token (a CUDA UUID / MIG handle) now sizes against the whole visible pool like the GGUF guard instead of falsely blocking the load, and an empty token (a CPU-only runner such as a CPU diffusion GGUF) is allowed outright since it uses no GPU VRAM. * Tighten comments added by the GPU memory config changes * Harden GGUF placement from independent review: VRAM sizing, diffusion TP reset, tensor_split validation - Training coexistence guard: a single-device runner pinned through an unresolvable UUID/MIG token was sized against the aggregate visible-VRAM pool, so a load could pass on capacity it cannot use and then OOM active training. Size against the worst-case visible device (min free) instead, keeping the guard's documented default-deny contract. The empty-token (CPU-only runner) allow path is unchanged. - Diffusion startup: _start_diffusion_server now resets self._tensor_parallel to False alongside the other placement resets. A prior tensor-parallel chat load (process killed but not fully unload-reset) otherwise left /status misreporting tensor parallelism and made an identical diffusion re-Apply reload against the stale state. - tensor_split: reject negative / non-finite / all-zero splits up front. They were dropped at launch but still compared raw in the reload dedupe, so an identical Apply reloaded indefinitely. - Tests: the shared httpx stub was incomplete and, installed via setdefault before real httpx loaded, broke a combined pytest run (collection errors on httpx.Response). Import the real installed httpx instead. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: danielhanchen <unslothshared@gmail.com> Co-authored-by: danielhanchen <danielhanchen@gmail.com>
139 lines
5.3 KiB
Python
139 lines
5.3 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
|
|
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
|
|
|
|
"""``--no-context-shift`` launch-flag contract.
|
|
|
|
With llama-server's default context-shift behavior, the UI cannot tell the user
|
|
the KV cache was rotated -- earlier turns silently vanish from the conversation.
|
|
The Unsloth backend always passes ``--no-context-shift`` so the server returns a
|
|
clean error instead, and the chat adapter can point the user at the
|
|
``Context Length`` input in the settings panel.
|
|
|
|
This file statically reads the launch command: we ask ``LlamaCppBackend`` to
|
|
assemble its ``cmd`` list and assert the flag is present. Testing via the real
|
|
subprocess would need an actual GGUF on disk, out of scope for the fast suite.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import inspect
|
|
import sys
|
|
import types as _types
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Same external-dep stubs as the other llama_cpp tests.
|
|
# ---------------------------------------------------------------------------
|
|
|
|
_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
|
|
if _BACKEND_DIR not in sys.path:
|
|
sys.path.insert(0, _BACKEND_DIR)
|
|
|
|
_loggers_stub = _types.ModuleType("loggers")
|
|
_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
|
|
sys.modules.setdefault("loggers", _loggers_stub)
|
|
|
|
_structlog_stub = _types.ModuleType("structlog")
|
|
sys.modules.setdefault("structlog", _structlog_stub)
|
|
|
|
_httpx_stub = _types.ModuleType("httpx")
|
|
for _exc in (
|
|
"ConnectError",
|
|
"TimeoutException",
|
|
"ReadTimeout",
|
|
"ReadError",
|
|
"RemoteProtocolError",
|
|
"CloseError",
|
|
):
|
|
setattr(_httpx_stub, _exc, type(_exc, (Exception,), {}))
|
|
_httpx_stub.Timeout = type("T", (), {"__init__": lambda s, *a, **k: None})
|
|
_httpx_stub.Client = type(
|
|
"C",
|
|
(),
|
|
{
|
|
"__init__": lambda s, **kw: None,
|
|
"__enter__": lambda s: s,
|
|
"__exit__": lambda s, *a: None,
|
|
},
|
|
)
|
|
sys.modules.setdefault("httpx", _httpx_stub)
|
|
|
|
from core.inference import llama_cpp as llama_cpp_module
|
|
|
|
|
|
def _load_model_source() -> str:
|
|
"""Return the source of ``LlamaCppBackend.load_model``.
|
|
|
|
Using ``inspect.getsource`` instead of reading the file scopes the assertions
|
|
to the function that launches llama-server, so neither the presence nor the
|
|
location check can be fooled by a stray ``"--no-context-shift"`` elsewhere in
|
|
the module.
|
|
"""
|
|
return inspect.getsource(llama_cpp_module.LlamaCppBackend.load_model)
|
|
|
|
|
|
def test_no_context_shift_is_in_load_model():
|
|
"""The flag is part of the static launch-command template.
|
|
|
|
We check the source of ``load_model`` rather than mocking the whole call
|
|
chain (GPU probing, GGUF stat, etc.): the flag is a literal in one place and
|
|
any regression must delete it, which a text search catches.
|
|
"""
|
|
assert '"--no-context-shift"' in _load_model_source(), (
|
|
"llama-server must be launched with --no-context-shift so the "
|
|
"UI can surface a clean 'context full' error instead of silently "
|
|
"losing old turns to a KV-cache rotation."
|
|
)
|
|
|
|
|
|
def test_flag_sits_inside_the_base_cmd_list():
|
|
"""Pin the flag's location so a refactor can't move it into a branch that
|
|
only fires on some code paths.
|
|
|
|
We slice from ``cmd = [`` to the first ``]`` at the same indent. Since
|
|
``inspect.getsource`` gives the function its own string with no siblings, a
|
|
plain bracket search would also work -- anchoring on the trailing indent just
|
|
keeps the slice from wandering into a later expression if the opening literal
|
|
ever grows a trailing in-line comment.
|
|
"""
|
|
source = _load_model_source()
|
|
start = source.find("cmd = [")
|
|
assert start >= 0, "could not find the base cmd = [...] block"
|
|
# Find the first line containing only ``]`` (possibly indented).
|
|
rest = source[start:]
|
|
end_rel = -1
|
|
for line_start, line in _iter_lines_with_offset(rest):
|
|
if line_start == 0:
|
|
# Skip the opening ``cmd = [`` line itself.
|
|
continue
|
|
if line.strip() == "]":
|
|
end_rel = line_start
|
|
break
|
|
assert end_rel > 0, "could not find end of cmd = [...] block"
|
|
block = rest[:end_rel]
|
|
assert '"--no-context-shift"' in block, (
|
|
"--no-context-shift must be in the base cmd list, not in a "
|
|
"conditional branch -- otherwise some code paths would still "
|
|
"run with silent context shift enabled."
|
|
)
|
|
assert '"--flash-attn"' in block
|
|
# -c is emitted in the conditional right after the base list, not inside
|
|
# it: auto-fit (--fit on with no pinned context) must omit -c entirely,
|
|
# because "-c 0" pins the full native context and disables --fit's
|
|
# VRAM-based sizing. Pin that it still sits next to the base block so the
|
|
# context grouping stays intact.
|
|
after = rest[end_rel : end_rel + 1000]
|
|
assert '"-c"' in after, (
|
|
"-c must still be emitted in the conditional immediately after the "
|
|
"base cmd list (omitted only in auto-fit, where --fit sizes context)."
|
|
)
|
|
|
|
|
|
def _iter_lines_with_offset(text: str):
|
|
"""Yield (offset, line) pairs over ``text`` without losing offsets."""
|
|
offset = 0
|
|
for line in text.splitlines(keepends = True):
|
|
yield offset, line
|
|
offset += len(line)
|