odysseus/routes/hwfit_routes.py

import re
from copy import deepcopy

from fastapi import APIRouter


def setup_hwfit_routes():
    router = APIRouter(prefix="/api/hwfit", tags=["hwfit"])

    def _apply_manual_hardware(system, manual_mode="", manual_gpu_count="", manual_vram_gb="", manual_ram_gb="", manual_backend=""):
        """Manual hardware is a "what if I had this setup" simulator —
        REPLACES the detected hardware entirely instead of adding to it.

        The previous additive behavior averaged the manual VRAM across
        all GPUs (base + manual), which meant adding "1× 400 GB" on top
        of "2× 70 GB" only nudged the per-GPU cap from 70 to 180 GB
        (= 540 / 3), so GGUF models bigger than that still didn't surface
        — exactly the "cap stuck at detected level" bug the user hit.
        """
        manual_mode = (manual_mode or "").lower()
        if manual_mode not in {"gpu", "ram"}:
            return system

        try:
            override_ram_gb = float(manual_ram_gb) if manual_ram_gb else 0
        except ValueError:
            override_ram_gb = 0
        override_ram_gb = max(0.0, override_ram_gb)
        if override_ram_gb:
            # Replace RAM, don't add. The number in the field is the
            # TOTAL system memory the user wants to simulate.
            system["available_ram_gb"] = round(override_ram_gb, 1)
            system["total_ram_gb"] = round(override_ram_gb, 1)
        system["manual_hardware"] = True

        if manual_mode == "ram":
            # RAM-only simulation — wipe GPU entirely so the ranker uses
            # CPU/RAM paths.
            system["has_gpu"] = False
            system["gpu_name"] = None
            system["gpu_vram_gb"] = 0
            system["gpu_count"] = 0
            system["gpus"] = []
            system["gpu_groups"] = []
            system["backend"] = "cpu_x86"
            return system

        try:
            count = int(manual_gpu_count) if manual_gpu_count else 1
        except ValueError:
            count = 1
        try:
            vram_each = float(manual_vram_gb) if manual_vram_gb else 8.0
        except ValueError:
            vram_each = 8.0
        count = max(1, min(count, 16))
        vram_each = max(1.0, vram_each)
        backend = (manual_backend or system.get("backend") or "cuda").lower()
        if backend not in {"cuda", "rocm", "cpu_x86", "cpu_arm"}:
            backend = "cuda"
        total_vram = round(vram_each * count, 1)
        gpu_name = f"Simulated {backend.upper()} GPU" + (f" × {count}" if count > 1 else "")
        system["has_gpu"] = True
        system["gpu_name"] = gpu_name
        system["gpu_vram_gb"] = total_vram
        system["gpu_count"] = count
        system["gpus"] = [
            {"index": i, "name": gpu_name, "vram_gb": vram_each}
            for i in range(count)
        ]
        # Single homogeneous pool — vram_each here is the ACTUAL per-GPU
        # VRAM the user entered, not an average. That's the whole point:
        # raising vram_each lifts the per-GPU cap (GGUF, tensor-parallel
        # math) all the way up, not just by a small fraction.
        system["gpu_groups"] = [{
            "name": gpu_name,
            "vram_each": vram_each,
            "count": count,
            "indices": list(range(count)),
            "vram_total": total_vram,
        }]
        system["homogeneous"] = True
        system["backend"] = backend
        return system

    @router.get("/system")
    def get_system(host: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False):
        """Detect and return current system hardware info. Pass host=user@server for remote.
        fresh=true bypasses the per-host cache (the Rescan button)."""
        from services.hwfit.hardware import detect_system
        return detect_system(host=host, ssh_port=ssh_port, platform=platform, fresh=fresh)

    @router.get("/models")
    def get_models(use_case: str = "", sort: str = "score", limit: int = 50, search: str = "", host: str = "", quant: str = "", gpu_count: str = "", gpu_group: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False, manual_mode: str = "", manual_gpu_count: str = "", manual_vram_gb: str = "", manual_ram_gb: str = "", manual_backend: str = "", ignore_detected_gpu: bool = False, ignore_detected_ram: bool = False):
        """Rank LLM models against detected hardware and return scored results.
        gpu_count: override GPU count (0 = CPU only, 1-N = simulate N GPUs of the
            active group). gpu_group: index into system.gpu_groups (the homogeneous
            pools) to target — empty/auto = the largest pool. vLLM can only
            tensor-parallel across identical GPUs, so we never mix pools.
        fresh=true bypasses the hardware-detection cache."""
        from services.hwfit.hardware import detect_system
        from services.hwfit.fit import rank_models
        from services.hwfit.models import get_models, model_catalog_path
        system = deepcopy(detect_system(host=host, ssh_port=ssh_port, platform=platform, fresh=fresh))
        if system.get("error"):
            return {"system": system, "models": [], "error": system["error"]}
        if not get_models():
            return {
                "system": system,
                "models": [],
                "error": f"Model catalog missing or empty: {model_catalog_path()}",
            }

        if ignore_detected_gpu:
            system["has_gpu"] = False
            system["gpu_name"] = None
            system["gpu_vram_gb"] = 0
            system["gpu_count"] = 0
            system["gpus"] = []
            system["gpu_groups"] = []
        if ignore_detected_ram:
            system["available_ram_gb"] = 0
            system["total_ram_gb"] = 0

        system = _apply_manual_hardware(system, manual_mode, manual_gpu_count, manual_vram_gb, manual_ram_gb, manual_backend)

        # Keep the raw detection around so the UI can still show the box's full
        # GPU complement even while we rank against one homogeneous pool.
        system["detected_gpu_vram_gb"] = system.get("gpu_vram_gb")
        system["detected_gpu_count"] = system.get("gpu_count")

        groups = system.get("gpu_groups") or []
        # Resolve the target homogeneous pool. Default (auto) = the largest pool,
        # which for a uniform box is simply "all the GPUs" — no behaviour change.
        grp = None
        if groups:
            try:
                gidx = int(gpu_group) if gpu_group != "" else 0
            except ValueError:
                gidx = 0
            if 0 <= gidx < len(groups):
                grp = groups[gidx]

        def _apply_group(g, n):
            n = max(1, min(n, g["count"]))
            system["gpu_count"] = n
            system["gpu_vram_gb"] = round(g["vram_each"] * n, 1)
            system["gpu_name"] = g["name"]
            system["active_group"] = {**g, "use_count": n}

        if gpu_count != "":
            n = int(gpu_count)
            if n == 0:
                # RAM-only mode: rank against system memory, offload allowed.
                system["has_gpu"] = False
                system["gpu_vram_gb"] = 0
                system["gpu_count"] = 0
                system["gpu_only"] = False
                system.pop("active_group", None)
            elif grp:
                _apply_group(grp, n)
                system["gpu_only"] = True
            else:
                # No per-GPU detail (older detection) — assume uniform split.
                single_vram = (system.get("gpu_vram_gb") or 0) / (system.get("gpu_count") or 1)
                system["gpu_count"] = max(1, n)
                system["gpu_vram_gb"] = round(single_vram * max(1, n), 1)
                system["gpu_only"] = True
        elif grp:
            # No explicit count, but we still pin to one pool so heterogeneous
            # boxes rank against a real mixable group, not a fictional VRAM sum.
            # gpu_only stays off here so the default view still surfaces offload.
            _apply_group(grp, grp["count"])

        results = rank_models(system, use_case=use_case or None, limit=limit, search=search or None, sort=sort, quant=quant or None)
        return {"system": system, "models": results}

    @router.get("/profiles")
    def get_serve_profiles(model: str = "", host: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False, serve_weights_gb: float = 0.0, serve_quant: str = ""):
        """Compute llama.cpp serve profiles (Quality/Balanced/Speed) for `model`
        against the detected hardware on `host` (or local). Returns concrete
        flags (n_gpu_layers, n_cpu_moe, cache_type, ctx) the serve UI can apply.

        `model` is matched against the catalog by name; if it's not in the
        catalog (e.g. an ad-hoc HF repo), pass enough hints via a minimal synthetic
        entry isn't possible here, so we return [] and the UI keeps manual flags.
        """
        from services.hwfit.hardware import detect_system
        from services.hwfit.models import get_models
        from services.hwfit.profiles import compute_serve_profiles
        system = detect_system(host=host, ssh_port=ssh_port, platform=platform, fresh=fresh)
        if system.get("error"):
            return {"system": system, "profiles": [], "error": system["error"]}
        catalog = {m.get("name"): m for m in (get_models() or [])}

        def _norm(s):
            # Normalize for matching: drop org/ prefix, a trailing -GGUF/-gguf
            # marker, and any quant tag, lowercase. So "DeepSeek-Coder-V2-Lite-
            # Instruct-GGUF" (a local folder name) matches catalog entry
            # "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct".
            s = (s or "").lower().strip()
            s = s.split("/")[-1]                     # drop org prefix
            s = re.sub(r"[-_.]?gguf$", "", s)        # drop trailing gguf marker
            s = re.sub(r"[-_.](q\d[^/]*|iq\d[^/]*|fp8|bf16|f16|awq[^/]*|gptq[^/]*)$", "", s)
            return s

        m = catalog.get(model)
        if m is None and model:
            want = _norm(model)
            for name, entry in catalog.items():
                nn = _norm(name)
                if nn and (nn == want or want.endswith(nn) or nn.endswith(want)):
                    m = entry
                    break
        if m is None:
            return {"system": system, "profiles": [], "error": "model not in catalog"}
        # Surface the model's trained context limit so the serve UI can clamp a
        # user-typed context down to it (asking for ctx > n_ctx_train overflows
        # and, with a quantized KV cache, can crash the GPU).
        model_ctx_max = 0
        for k in ("context_length", "max_position_embeddings", "n_ctx_train", "context"):
            v = m.get(k)
            if isinstance(v, (int, float)) and v > 0:
                model_ctx_max = int(v)
                break
        return {
            "system": system,
            "profiles": compute_serve_profiles(
                system, m,
                serve_weights_gb=(serve_weights_gb or None),
                serve_quant=(serve_quant or None),
            ),
            "model_ctx_max": model_ctx_max,
        }

    @router.get("/image-models")
    def get_image_models(sort: str = "fit", search: str = "", host: str = "", gpu_count: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False, manual_mode: str = "", manual_gpu_count: str = "", manual_vram_gb: str = "", manual_ram_gb: str = "", manual_backend: str = "", ignore_detected_gpu: bool = False, ignore_detected_ram: bool = False):
        """Rank image generation models against detected hardware."""
        from services.hwfit.hardware import detect_system
        from services.hwfit.image_models import rank_image_models
        system = deepcopy(detect_system(host=host, ssh_port=ssh_port, platform=platform, fresh=fresh))
        if system.get("error"):
            return {"system": system, "models": [], "error": system["error"]}
        if ignore_detected_gpu:
            system["has_gpu"] = False
            system["gpu_name"] = None
            system["gpu_vram_gb"] = 0
            system["gpu_count"] = 0
            system["gpus"] = []
            system["gpu_groups"] = []
        if ignore_detected_ram:
            system["available_ram_gb"] = 0
            system["total_ram_gb"] = 0
        system = _apply_manual_hardware(system, manual_mode, manual_gpu_count, manual_vram_gb, manual_ram_gb, manual_backend)
        # Image models use a single GPU — always use per-GPU VRAM
        gpu_vrams = [float(g.get("vram_gb") or 0) for g in (system.get("gpus") or []) if isinstance(g, dict)]
        single_vram = max(gpu_vrams) if gpu_vrams else ((system.get("gpu_vram_gb") or 0) / max(system.get("gpu_count") or 1, 1))
        system["gpu_vram_gb"] = single_vram
        system["gpu_count"] = 1 if single_vram > 0 else 0
        results = rank_image_models(system, search=search or None, sort=sort)
        return {"system": system, "models": results}

    return router