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fix(hwfit): honor manual "metal" backend in the hardware simulator (#1090)
The Cookbook's manual hardware simulator ("what if I had this setup") let users
pick a backend, but _apply_manual_hardware only accepted cuda/rocm/cpu_x86/
cpu_arm and silently coerced anything else to cuda. So selecting Apple/Metal
simulated a CUDA box instead — and ranked safetensors-only repos a Mac can't
serve, even though the rest of hwfit (services.hwfit.fit, the serve-command
generation) already supports Metal as GGUF-only via llama.cpp/Ollama.
Add "metal" to the accepted backends (now a named _MANUAL_BACKENDS set, kept a
subset of what fit.py understands) and set unified_memory=True for it — Apple
Silicon shares one memory pool with the GPU — while clearing that flag for the
discrete (cuda/rocm) and CPU backends. _apply_manual_hardware is lifted to
module scope so it is directly unit-testable; both route call sites are
unchanged.
Adds tests/test_hwfit_manual_backend.py, including an end-to-end check that a
simulated Metal box only recommends GGUF-servable models.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Shaw
+93
-76
@@ -4,85 +4,102 @@ from copy import deepcopy
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from fastapi import APIRouter
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# Backends the manual hardware simulator accepts. Must stay a subset of what
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# services.hwfit.fit understands so a simulated box ranks like a real one:
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# "metal" routes through the Apple-Silicon path (GGUF-only, llama.cpp/Ollama),
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# the CPU backends through the RAM/offload path, cuda/rocm through vLLM.
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_MANUAL_BACKENDS = {"cuda", "rocm", "metal", "cpu_x86", "cpu_arm"}
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def _apply_manual_hardware(system, manual_mode="", manual_gpu_count="", manual_vram_gb="", manual_ram_gb="", manual_backend=""):
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"""Manual hardware is a "what if I had this setup" simulator —
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REPLACES the detected hardware entirely instead of adding to it.
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The previous additive behavior averaged the manual VRAM across
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all GPUs (base + manual), which meant adding "1× 400 GB" on top
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of "2× 70 GB" only nudged the per-GPU cap from 70 to 180 GB
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(= 540 / 3), so GGUF models bigger than that still didn't surface
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— exactly the "cap stuck at detected level" bug the user hit.
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"""
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manual_mode = (manual_mode or "").lower()
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if manual_mode not in {"gpu", "ram"}:
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return system
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try:
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override_ram_gb = float(manual_ram_gb) if manual_ram_gb else 0
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except ValueError:
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override_ram_gb = 0
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override_ram_gb = max(0.0, override_ram_gb)
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if override_ram_gb:
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# Replace RAM, don't add. The number in the field is the
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# TOTAL system memory the user wants to simulate.
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system["available_ram_gb"] = round(override_ram_gb, 1)
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system["total_ram_gb"] = round(override_ram_gb, 1)
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system["manual_hardware"] = True
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if manual_mode == "ram":
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# RAM-only simulation — wipe GPU entirely so the ranker uses
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# CPU/RAM paths.
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system["has_gpu"] = False
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system["gpu_name"] = None
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system["gpu_vram_gb"] = 0
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system["gpu_count"] = 0
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system["gpus"] = []
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system["gpu_groups"] = []
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system["backend"] = "cpu_x86"
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system.pop("unified_memory", None)
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return system
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try:
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count = int(manual_gpu_count) if manual_gpu_count else 1
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except ValueError:
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count = 1
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try:
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vram_each = float(manual_vram_gb) if manual_vram_gb else 8.0
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except ValueError:
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vram_each = 8.0
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count = max(1, min(count, 16))
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vram_each = max(1.0, vram_each)
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backend = (manual_backend or system.get("backend") or "cuda").lower()
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if backend not in _MANUAL_BACKENDS:
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backend = "cuda"
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total_vram = round(vram_each * count, 1)
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gpu_name = f"Simulated {backend.upper()} GPU" + (f" × {count}" if count > 1 else "")
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system["has_gpu"] = True
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system["gpu_name"] = gpu_name
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system["gpu_vram_gb"] = total_vram
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system["gpu_count"] = count
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system["gpus"] = [
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{"index": i, "name": gpu_name, "vram_gb": vram_each}
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for i in range(count)
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]
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# Single homogeneous pool — vram_each here is the ACTUAL per-GPU
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# VRAM the user entered, not an average. That's the whole point:
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# raising vram_each lifts the per-GPU cap (GGUF, tensor-parallel
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# math) all the way up, not just by a small fraction.
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system["gpu_groups"] = [{
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"name": gpu_name,
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"vram_each": vram_each,
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"count": count,
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"indices": list(range(count)),
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"vram_total": total_vram,
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}]
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system["homogeneous"] = True
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system["backend"] = backend
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# Apple Silicon shares one unified memory pool with the GPU; flag it so
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# the API/UI report it the way real Metal detection does. Discrete GPUs
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# (cuda/rocm) and the CPU backends carry separate VRAM, so clear any
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# stale flag a previous detection left on the dict.
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if backend == "metal":
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system["unified_memory"] = True
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else:
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system.pop("unified_memory", None)
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return system
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def setup_hwfit_routes():
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router = APIRouter(prefix="/api/hwfit", tags=["hwfit"])
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def _apply_manual_hardware(system, manual_mode="", manual_gpu_count="", manual_vram_gb="", manual_ram_gb="", manual_backend=""):
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"""Manual hardware is a "what if I had this setup" simulator —
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REPLACES the detected hardware entirely instead of adding to it.
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The previous additive behavior averaged the manual VRAM across
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all GPUs (base + manual), which meant adding "1× 400 GB" on top
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of "2× 70 GB" only nudged the per-GPU cap from 70 to 180 GB
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(= 540 / 3), so GGUF models bigger than that still didn't surface
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— exactly the "cap stuck at detected level" bug the user hit.
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"""
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manual_mode = (manual_mode or "").lower()
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if manual_mode not in {"gpu", "ram"}:
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return system
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try:
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override_ram_gb = float(manual_ram_gb) if manual_ram_gb else 0
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except ValueError:
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override_ram_gb = 0
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override_ram_gb = max(0.0, override_ram_gb)
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if override_ram_gb:
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# Replace RAM, don't add. The number in the field is the
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# TOTAL system memory the user wants to simulate.
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system["available_ram_gb"] = round(override_ram_gb, 1)
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system["total_ram_gb"] = round(override_ram_gb, 1)
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system["manual_hardware"] = True
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if manual_mode == "ram":
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# RAM-only simulation — wipe GPU entirely so the ranker uses
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# CPU/RAM paths.
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system["has_gpu"] = False
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system["gpu_name"] = None
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system["gpu_vram_gb"] = 0
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system["gpu_count"] = 0
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system["gpus"] = []
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system["gpu_groups"] = []
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system["backend"] = "cpu_x86"
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return system
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try:
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count = int(manual_gpu_count) if manual_gpu_count else 1
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except ValueError:
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count = 1
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try:
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vram_each = float(manual_vram_gb) if manual_vram_gb else 8.0
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except ValueError:
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vram_each = 8.0
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count = max(1, min(count, 16))
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vram_each = max(1.0, vram_each)
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backend = (manual_backend or system.get("backend") or "cuda").lower()
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if backend not in {"cuda", "rocm", "cpu_x86", "cpu_arm"}:
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backend = "cuda"
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total_vram = round(vram_each * count, 1)
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gpu_name = f"Simulated {backend.upper()} GPU" + (f" × {count}" if count > 1 else "")
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system["has_gpu"] = True
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system["gpu_name"] = gpu_name
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system["gpu_vram_gb"] = total_vram
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system["gpu_count"] = count
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system["gpus"] = [
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{"index": i, "name": gpu_name, "vram_gb": vram_each}
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for i in range(count)
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]
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# Single homogeneous pool — vram_each here is the ACTUAL per-GPU
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# VRAM the user entered, not an average. That's the whole point:
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# raising vram_each lifts the per-GPU cap (GGUF, tensor-parallel
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# math) all the way up, not just by a small fraction.
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system["gpu_groups"] = [{
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"name": gpu_name,
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"vram_each": vram_each,
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"count": count,
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"indices": list(range(count)),
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"vram_total": total_vram,
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}]
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system["homogeneous"] = True
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system["backend"] = backend
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return system
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@router.get("/system")
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def get_system(host: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False):
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"""Detect and return current system hardware info. Pass host=user@server for remote.
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