* Cookbook: Engine filter + intelligent hardware-computed serve profiles Two related Cookbook serving improvements for accurate, hardware-aware model serving (especially on consumer GPUs that can only run GGUF/llama.cpp). Engine filter - New "Engine" dropdown (All / llama.cpp / vLLM / SGLang) beside the quant picker. Pure client-side view filter over the fetched list via the same _detectBackend() the serve commands use, so what you filter to is exactly what would launch. Re-renders from cache (no refetch). Empty-state message + the instant-cache-paint path account for it too. Intelligent serve profiles (Quality / Balanced / Speed) - services/hwfit/profiles.py: compute_serve_profiles() turns detected VRAM + model size into concrete llama.cpp flags (n_gpu_layers, n_cpu_moe, cache-type, context). Encodes the by-hand tuning: a too-big MoE offloads experts to CPU instead of failing; a model that fits stays fully on GPU; quant tracks profile intent; vision models keep image-encoder headroom. Reuses models.py VRAM math so filtering and serving agree on what fits. Pure/deterministic (no t/s claims — partial-offload speed isn't reliably predictable; fit is what's computed). - /api/hwfit/profiles endpoint returns the profiles + the model's trained context limit, with loose name matching (strips org/ prefix, -GGUF suffix, quant tag) so a local GGUF folder name resolves to its catalog entry. - _buildServeCmd (llama.cpp) now emits --n-cpu-moe / --flash-attn / --cache-type-k/v when set, with llama-cpp-python fallback equivalents. It previously only set -ngl/-c, which is why it OOM'd or ran slow. - Serve panel: profile chips that fill the fields on click, plus CPU-MoE / KV Cache / Flash Attn fields. Context is clamped to the model's trained limit (and an absolute 1M sanity ceiling) on type/blur/profile-load and at launch — fixes a crash where a stale 256k/16M preset + quantized KV cache caused an amdgpu ErrorDeviceLost. Tests: tests/test_serve_profiles.py (7) — offload vs full-GPU fit, never exceed VRAM, context cap, launchable flags, vision headroom, no-GPU empty. Checks: py_compile + node --check pass; pytest test_serve_profiles + test_hwfit_amd green; verified live on an RDNA4 box (gfx1200) — Balanced lands ~ncm18 q4 128k, matching hand-tuning. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Cookbook: make column-header sorting discoverable (incl. Newest) Sorting in Cookbook is via clickable column headers (pewds' design), but the headers had no visual cue that they're interactive — so sorting in general, and the Newest sort on the Model header specifically, was undiscoverable. - Style sortable headers as interactive: pointer cursor, hover underline, and the active sort column bolded/highlighted. There was no CSS for .hwfit-sortable / .hwfit-sort-active at all; this helps every existing sort, not just Newest. - The Model column header sorts by release_date (newest first), reusing the existing header-click sort wiring and the "newest" SORT_KEY. No new sort control — uses the existing column-header paradigm. Checks: node --check passes. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Cookbook serve profiles: keep the on-disk file's quant fixed (don't propose Q6/Q2) In the Serve tab the model is a specific GGUF file already on disk, so its quant can't change — but the profiles were suggesting "Quality · Q6_K" / "Speed · Q2_K" as if you could re-quantize it. That's meaningless when serving a fixed file. - compute_serve_profiles gains serve_weights_gb / serve_quant. When set (SERVE mode), the quant is locked to the file's and profiles differ only in the real serving knobs — n_cpu_moe, KV-cache type, context. _weights_gb / _cpu_moe_for_budget use the file's actual size instead of a quant-derived estimate. DOWNLOAD mode (no override) still varies the quant to show download options. - /api/hwfit/profiles accepts serve_weights_gb & serve_quant. - The Serve panel parses the file's size (from m.size "20.6 GB") and quant (from the repo/file name) and passes them, so profiles match what's actually served. Result for a 20.6 GB Q4_K_M file: all three profiles stay Q4_K_M and differ by KV/ctx/offload (Quality q8 KV 128k ncm21, Balanced q4 128k ncm17, Speed q4 32k ncm15) — no nonsensical quant changes. Tests: test_serve_mode_keeps_fixed_quant. Full serve-profile suite green (9). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Cookbook serve: Vision toggle (auto-find mmproj) + live VRAM/RAM-spillover monitor Two serve-panel additions: 1. **Vision toggle.** A "Vision" checkbox that serves the model with its multimodal projector so it can read images. The mmproj path is resolved at runtime (find mmproj-*.gguf next to the model), so dropping an mmproj file in the model folder makes the toggle just work; `--mmproj … --image-max-tokens 1024` (native) / `--clip_model_path` (llama-cpp-python) only when on + found. 2. **Live GPU-memory monitor.** A readout that polls /api/cookbook/gpus every 4s while the panel is open and shows VRAM used/total/%, free, and — crucially on a discrete card — **RAM spillover** (AMD gtt_used_mb), with a plain-language health hint: green/healthy, amber/tight, red/"spilled to RAM — slow (raise CPU MoE or lower context)". Surfaces gtt_used_mb from the gpus endpoint (previously read for total only and discarded for 'used'). Lets you see at a glance whether a config fits VRAM (fast) or is paging to system RAM over PCIe (slow) instead of guessing. Checks: node --check + py_compile pass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
"""Intelligent llama.cpp serve profiles computed from hardware.
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Locks in that compute_serve_profiles() turns detected VRAM + model size into
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sane Quality/Balanced/Speed flag sets: a too-big MoE offloads experts to CPU
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(n_cpu_moe > 0) instead of failing, a model that fits stays fully on GPU
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(n_cpu_moe == 0), context shrinks before giving up, and quant choice tracks the
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profile intent.
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"""
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from services.hwfit.profiles import compute_serve_profiles
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_QWEN_35B_MOE = {
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"name": "Qwen3.6-35B-A3B",
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"parameter_count": "35B",
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"is_moe": True,
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"active_parameters": 3_000_000_000,
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"num_hidden_layers": 48,
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}
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_DENSE_8B = {
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"name": "Qwen3-8B",
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"parameter_count": "8B",
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"is_moe": False,
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"num_hidden_layers": 36,
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}
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def _sys(vram, family="rdna"):
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return {"backend": "rocm", "gpu_vram_gb": vram, "gpu_family": family}
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def test_big_moe_on_small_card_offloads_not_fails():
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"""A 35B MoE can't hold its weights on 16 GB, so the Quality profile must
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offload experts to CPU (n_cpu_moe > 0) rather than be dropped."""
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profs = compute_serve_profiles(_sys(15.9), _QWEN_35B_MOE)
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assert profs, "expected at least one profile"
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q = next(p for p in profs if p["key"] == "quality")
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assert q["n_cpu_moe"] > 0
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assert q["offloads"] is True
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assert q["cache_type"] == "q8_0" # quality uses the sharp KV cache
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assert q["est_vram_gb"] <= 16.0 # never exceeds the card
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def test_profiles_never_exceed_vram():
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"""Every profile's VRAM estimate must fit the detected card."""
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for vram in (8.0, 12.0, 16.0, 24.0):
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for p in compute_serve_profiles(_sys(vram), _QWEN_35B_MOE):
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assert p["est_vram_gb"] <= vram + 0.05, (vram, p)
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def test_small_model_stays_fully_on_gpu():
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"""A model whose weights fit must NOT offload — n_cpu_moe == 0 everywhere."""
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for p in compute_serve_profiles(_sys(15.9), _DENSE_8B):
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assert p["n_cpu_moe"] == 0
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assert p["offloads"] is False
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def test_speed_profile_is_lighter_than_quality():
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"""Speed trades quant/context for less offload than Quality."""
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profs = {p["key"]: p for p in compute_serve_profiles(_sys(15.9), _QWEN_35B_MOE)}
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if "speed" in profs and "quality" in profs:
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assert profs["speed"]["n_cpu_moe"] <= profs["quality"]["n_cpu_moe"]
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assert profs["speed"]["ctx"] <= profs["quality"]["ctx"]
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def test_flags_are_launchable():
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"""Each profile must carry the concrete llama.cpp flags the cmd builder needs."""
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for p in compute_serve_profiles(_sys(15.9), _QWEN_35B_MOE):
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assert p["n_gpu_layers"] == 999
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assert isinstance(p["n_cpu_moe"], int) and p["n_cpu_moe"] >= 0
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assert p["cache_type"] in ("q4_0", "q8_0", "f16")
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assert p["ctx"] >= 8192
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assert p["quant"]
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def test_context_capped_at_model_limit():
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"""Profiles must never propose more context than the model was trained for
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— over-asking triggers a training-context overflow and, with a quantized KV
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cache, a GPU OOM/device-lost crash."""
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small_ctx_model = dict(_QWEN_35B_MOE, name="X", context_length=32768)
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for p in compute_serve_profiles(_sys(15.9), small_ctx_model):
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assert p["ctx"] <= 32768, p
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def test_no_gpu_returns_empty():
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"""No VRAM detected → no GPU profiles (caller falls back to manual flags)."""
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assert compute_serve_profiles({"backend": "cpu_x86", "gpu_vram_gb": 0}, _QWEN_35B_MOE) == []
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def test_vision_model_leaves_encoder_headroom():
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"""A vision model must budget extra VRAM for the image encoder, so its
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estimate leaves more slack below the card than a text model would."""
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vis = dict(_QWEN_35B_MOE, name="Qwen3-VL-35B", is_multimodal=True)
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for p in compute_serve_profiles(_sys(15.9), vis):
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assert p["est_vram_gb"] <= 15.9 - 1.0 + 0.05 # ~1.1 GB encoder headroom
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def test_serve_mode_keeps_fixed_quant():
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"""Serving a specific GGUF file: the quant is fixed (the file's), so every
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profile must keep it and vary only the serving knobs (KV/ctx/offload) — not
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propose a different quant (which makes no sense for an on-disk file)."""
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profs = compute_serve_profiles(_sys(15.9), _QWEN_35B_MOE,
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serve_weights_gb=20.6, serve_quant="Q4_K_M")
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assert profs
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assert all(p["quant"] == "Q4_K_M" for p in profs), [p["quant"] for p in profs]
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# The knobs should still differ across profiles (KV type and/or context).
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kvs = {p["cache_type"] for p in profs}
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ctxs = {p["ctx"] for p in profs}
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assert len(kvs) > 1 or len(ctxs) > 1, "serve profiles are identical"
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# All must fit the card.
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assert all(p["est_vram_gb"] <= 16.0 for p in profs)
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