#!/usr/bin/env python3
"""
llama.cpp router benchmark — WM (GTX 1080 Ti) edition → writes a results ledger for tuning.

This is the NVIDIA port of the fl (RX 7600 XT) bench.py. Same flow, different failure mode:
an NVIDIA overcommit does NOT spill to system RAM and freeze the desktop — cudaMalloc fails
and the load errors out cleanly. So the guards here protect the NUMBERS, not the PC:
  - records the BASELINE VRAM after unloading anything of ours (the desktop alone holds 3.5–4.5 GB;
    a browser/game on top skews every margin — the header says when a resident model was unloaded
    first, so "7.5 GB baseline" can't masquerade as the desktop again)
  - ensures a CLEAN card before EVERY load: resident models are force-unloaded via the
    router API (POST /models/unload), then VRAM is verified with nvidia-smi — the poll covers
    driver reclaim lag, older builds without the endpoint (sleep-idle fallback) and desktop
    apps the router doesn't own. Aborts if the card never drains.
  - checks free VRAM right after a model mounts; too tight (--min-free-gb) → records a ⚠ row
    and skips generating (a preset that barely loads on a clean bench will fail on a busy
    desktop); the sweep continues after the drain-wait
  - refuses to sweep multiple models blind (no nvidia-smi) unless --no-vram-ok
(There is no GTT column: that is an amdgpu concept. The gtt hooks are kept as no-ops so the
 fl script and this one stay diffable.)

Per model it records decode/prefill t/s, TTFT, VRAM used/free, and the preset's knobs
(ctx, n-cpu-moe, spec-type) parsed from config.ini. For spec/MTP presets the draft
acceptance % is appended to the spec column, read from the response's `timings` object.

Output: bench-results.md (latest run, overwritten) + bench-history.md (every run, appended)
— both next to this script. Run ON this box against its own localhost router. Stdlib only.

  ./bench.py                            # all models the server lists
  ./bench.py -m id1,id2                 # only these
  ./bench.py -x some-id                 # skip some (e.g. not downloaded yet)
  ./bench.py -n 512 --ctx 12000         # longer gen + a long-context decode column
Compare a change: edit config.ini → restart `llamaserver` → re-run → diff runs in bench-history.md.
"""
import argparse, json, os, re, subprocess, sys, time, urllib.request, urllib.error
from datetime import datetime

HERE = os.path.dirname(os.path.abspath(__file__))
_smi_warned = False


def gpu_mem(kind="vram"):
    """(used_bytes, total_bytes) from nvidia-smi for 'vram'; (None, None) for 'gtt' (no such
    thing on NVIDIA — kept so the fl flow stays identical) or if nvidia-smi is unavailable."""
    global _smi_warned
    if kind != "vram":
        return None, None
    try:
        out = subprocess.run(["nvidia-smi", "--query-gpu=memory.used,memory.total",
                              "--format=csv,noheader,nounits"],
                             capture_output=True, text=True, timeout=15).stdout
        used_mib, total_mib = (int(x.strip()) for x in out.strip().splitlines()[0].split(","))
        return used_mib * 1024 * 1024, total_mib * 1024 * 1024
    except Exception:
        if not _smi_warned:
            _smi_warned = True
            print("  (nvidia-smi not readable here — VRAM columns and guards are OFF)", file=sys.stderr)
        return None, None


def with_retries(fn, retries, wait, label=""):
    """Retry on 5xx/connection errors (model still mounting). A deterministic 'failed to
    load' from the router is NOT retried — that's a broken preset, not a slow mount."""
    last = None
    for i in range(retries + 1):
        try:
            return fn()
        except urllib.error.HTTPError as e:
            if e.code < 500:              # 4xx = real client error
                raise
            body = ""
            try:
                body = e.read().decode("utf-8", "ignore")
            except Exception:
                pass
            if "failed to load" in body:  # deterministic → don't burn retries
                raise RuntimeError("model failed to load — check the llamaserver terminal (VRAM? path?)") from None
            last = e
        except (urllib.error.URLError, ConnectionError, TimeoutError) as e:
            last = e
        if i < retries:
            print(f"    … {label} not ready ({last}); waiting {wait}s (try {i + 1}/{retries})", file=sys.stderr)
            time.sleep(wait)
    raise last


def _open(url, key, payload=None, timeout=600):
    data = json.dumps(payload).encode() if payload is not None else None
    hdrs = {"Authorization": f"Bearer {key}"}
    if data:
        hdrs["Content-Type"] = "application/json"
    return urllib.request.urlopen(urllib.request.Request(url, data=data, headers=hdrs), timeout=timeout)


def list_models(base, key):
    d = json.load(_open(base.rstrip("/") + "/models", key))
    return [m["id"] for m in d.get("data", [])]


def force_unload(base, key):
    """Ask the router to unload every resident model (POST /models/unload on the ROOT api,
    not /v1). Harmless no-op on builds without the endpoint — callers fall back to polling."""
    root = re.sub(r"/v1/?$", "", base.rstrip("/"))
    try:
        d = json.load(_open(root + "/models", key, timeout=30))
    except Exception as e:
        print(f"    (router /models not readable: {e} — relying on sleep-idle drain)", file=sys.stderr)
        return
    for m in d.get("data", []):
        if m.get("status", "loaded") != "loaded":   # builds without 'status': try them all
            continue
        try:
            _open(root + "/models/unload", key, {"model": m["id"]}, timeout=30).read()
        except Exception:
            pass                                    # not loaded / endpoint missing — poll covers it


def parse_config(path):
    """{preset: {ctx, ncpumoe, spec}} from the router config.ini."""
    sections, sec = {}, None
    try:
        lines = open(path, encoding="utf-8").read().splitlines()
    except OSError:
        return {}
    for ln in lines:
        s = ln.strip()
        if s.startswith("[") and s.endswith("]"):
            sec = s[1:-1]; sections[sec] = {}
        elif sec and "=" in s and not s.startswith("#"):
            k, v = s.split("=", 1)
            sections[sec][k.strip()] = v.split("#", 1)[0].strip()
    return {sec: {"ctx": kv.get("ctx-size", "-"),
                  "ncpumoe": kv.get("n-cpu-moe", "-"),
                  "spec": kv.get("spec-type", "-") or "-"}
            for sec, kv in sections.items()}


def run(base, key, model, prompt, n, temp, timeout):
    payload = {"model": model, "messages": [{"role": "user", "content": prompt}],
               "max_tokens": n, "temperature": temp, "stream": True,
               "stream_options": {"include_usage": True},
               # llama.cpp extension: makes the server embed its own timings in the stream →
               # the table shows the SAME predicted_per_second as the llama.cpp web UI
               # (without it we fall back to wall-clock estimates, which read lower).
               "timings_per_token": True}
    # ttft = first ANSWER token (delta.content) — for a thinking model that is the end of the think
    # block, which is what the user waits for. t_first = first token of any kind (reasoning_content
    # counts): the end of prefill, used for the wall-clock fallbacks. A thinking model that spends the
    # whole gen budget reasoning never sets ttft → returned as None, rendered 'all-think' (NOT 0.00).
    t0 = time.perf_counter(); ttft = None; t_first = None; ntok = 0; usage = None; timings = None
    for raw in _open(base.rstrip("/") + "/chat/completions", key, payload, timeout):
        line = raw.decode("utf-8", "ignore").strip()
        if not line.startswith("data:"):
            continue
        body = line[5:].strip()
        if body == "[DONE]":
            break
        try:
            c = json.loads(body)
        except ValueError:
            continue
        ch = c.get("choices") or [{}]
        delta = ch[0].get("delta", {}) if ch else {}
        if delta.get("content") or delta.get("reasoning_content"):
            if t_first is None:
                t_first = time.perf_counter() - t0
            if delta.get("content") and ttft is None:
                ttft = time.perf_counter() - t0
            ntok += 1
        if c.get("usage"):
            usage = c["usage"]
        if c.get("timings"):
            timings = c["timings"]
    total = time.perf_counter() - t0
    comp = (usage or {}).get("completion_tokens") or ntok
    ptok = (usage or {}).get("prompt_tokens")
    gen_s = total - (t_first or total)
    tg = comp / gen_s if gen_s > 0 else 0.0
    pp = (ptok / t_first) if (ptok and t_first) else None
    acc = None
    if timings:  # llama.cpp's own numbers are authoritative when the router forwards them
        tg = timings.get("predicted_per_second", tg)
        pp = timings.get("prompt_per_second", pp)
        ptok = timings.get("prompt_n", ptok)
        dn, da = timings.get("draft_n"), timings.get("draft_n_accepted")
        if dn:
            acc = 100.0 * (da or 0) / dn
    return {"tg": tg, "pp": pp, "ttft": ttft, "ptok": ptok or 0, "acc": acc}


def wait_drain(drain_gb, window_s, label="", base=None, key=None):
    """Force-unload resident models via the router API, then poll until VRAM used drops
    below drain_gb (card is clean) or the window expires."""
    used, _ = gpu_mem()
    if base and used is not None and used > drain_gb * 1e9:
        force_unload(base, key)
    end = time.time() + window_s
    announced = False
    while used is not None and used > drain_gb * 1e9 and time.time() < end:
        if not announced:
            print(f"    … waiting for VRAM to drain before {label} ({gb(used)} used)", file=sys.stderr)
            announced = True
        time.sleep(5)
        used, _ = gpu_mem()
    return used


def gb(b):
    return f"{b / 1e9:.1f} GB" if b else "-"


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("-u", "--url", default="http://127.0.0.1:11343/v1")
    ap.add_argument("-k", "--key", default="no-key-required")
    ap.add_argument("-m", "--models", default="all", help="comma-separated ids, or 'all'")
    ap.add_argument("-x", "--skip", default="", help="comma-separated ids to skip")
    ap.add_argument("-n", "--tokens", type=int, default=256)
    ap.add_argument("-t", "--temp", type=float, default=0.3)
    ap.add_argument("--ctx", type=int, default=0, help="also record decode at ~this many prompt tokens")
    ap.add_argument("--timeout", type=int, default=600, help="per-request stall timeout (s)")
    ap.add_argument("--config", default=os.path.join(HERE, "config.ini"))
    ap.add_argument("--out", default=os.path.join(HERE, "bench-results.md"))
    ap.add_argument("--history", default=os.path.join(HERE, "bench-history.md"))
    ap.add_argument("--retries", type=int, default=4, help="retries while a model is still mounting")
    ap.add_argument("--retry-wait", type=int, default=15, help="seconds between retries")
    ap.add_argument("--drain-gb", type=float, default=5.5,
                    help="VRAM considered 'clean' below this (the desktop alone uses 3.5–4.5 GB here)")
    ap.add_argument("--min-free-gb", type=float, default=0.8,
                    help="flag a model that leaves less VRAM free than this after loading (busy-desktop risk)")
    ap.add_argument("--max-gtt-gb", type=float, default=2.0,
                    help="(inert on NVIDIA — kept for fl parity)")
    ap.add_argument("--unload-wait", type=int, default=360,
                    help="after a too-tight model, wait up to this long for it to unload before continuing "
                         "(sleep-idle-seconds keeps models resident up to 120s here)")
    ap.add_argument("--no-vram-ok", action="store_true",
                    help="allow a multi-model sweep even though nvidia-smi (and thus the guards) is unavailable")
    a = ap.parse_args()

    models = list_models(a.url, a.key) if a.models == "all" else [m.strip() for m in a.models.split(",")]
    skip = {s.strip() for s in a.skip.split(",") if s.strip()}
    models = [m for m in models if m not in skip]
    cfg = parse_config(a.config)

    used0, total_vram = gpu_mem("vram")
    if total_vram is None and len(models) > 1 and not a.no_vram_ok:
        sys.exit("ABORT: nvidia-smi is not readable here, so the VRAM guards can't work.\n"
                 "Run this ON the GPU box, bench a single model with -m <id>, or pass --no-vram-ok to accept the risk.")
    # The BASELINE must be the desktop, not a resident model: the 2026-10-19 sweep started while pi's
    # gemma was still mounted and recorded "baseline 7.5 GB" for a ~4 GB desktop. Drain first (force-
    # unload + poll, same as before every model), record the result; keep the pre-drain figure so the
    # header says which it was.
    resident0 = None
    if used0 is not None and used0 > a.drain_gb * 1e9:
        resident0 = used0
        used0 = wait_drain(a.drain_gb, a.unload_wait, "the baseline", a.url, a.key)
        if used0 is not None and used0 > a.drain_gb * 1e9:
            print(f"⚠ baseline VRAM used is {gb(used0)} with nothing of ours resident — desktop apps "
                  f"(browser/electron/game) hold it. Margins below are for THIS desktop; ideally close "
                  f"GPU apps and re-run (or raise --drain-gb if this is normal).", file=sys.stderr)

    task = "Write a Python function that merges two sorted lists, with a short docstring and one example."
    filler = ("The quick brown fox jumps over the lazy dog. " * max(1, a.ctx // 9)) if a.ctx else None

    cols = ["model", "cfg ctx", "n-cpu-moe", "spec", "decode t/s", "prefill t/s", "TTFT s", "VRAM used", "VRAM free"]
    if a.ctx:
        cols.append(f"decode@{a.ctx // 1000}k")

    latest = open(a.out, "w", encoding="utf-8")
    hist = open(a.history, "a", encoding="utf-8")
    baseline = gb(used0)
    if resident0 is not None and used0 is not None and resident0 - used0 > 0.5e9:
        baseline += f" (was {gb(resident0)} with a resident model, unloaded first)"
    meta = (f"_Run {datetime.now():%Y-%m-%d %H:%M} · server `{a.url}` · VRAM total {gb(total_vram)} · "
            f"baseline used {baseline} · gen {a.tokens} tok · guard: free≥{a.min_free_gb} GB_")

    def emit(line, both=True):
        print(line)
        latest.write(line + "\n"); latest.flush()
        if both:
            hist.write(line + "\n"); hist.flush()

    latest.write("# llama.cpp benchmark results (latest run)\n\n")
    hist.write(f"\n## run {datetime.now():%Y-%m-%d %H:%M}\n\n")
    emit(meta + "\n")
    emit("| " + " | ".join(cols) + " |")
    emit("|" + "|".join(["---"] * len(cols)) + "|")

    for m in models:
        c = cfg.get(m, {})
        base_cells = [m, c.get("ctx", "-"), c.get("ncpumoe", "-"), c.get("spec", "-")]
        # A CLEAN card before EVERY load (incl. the first): force-unload via the router API,
        # then verify with nvidia-smi — double residency = bogus numbers (or a failed load).
        u = wait_drain(a.drain_gb, a.unload_wait, m, a.url, a.key)
        if u is not None and u > a.drain_gb * 1e9:
            emit("")
            emit(f"_⚠ aborted before {m}: VRAM still {gb(u)} used after {a.unload_wait}s — something "
                 f"won't unload (leftover model / GPU apps). If that's your normal desktop, raise --drain-gb._")
            print(f"\n⚠ aborted: card not clean ({gb(u)} used).", file=sys.stderr)
            break
        try:
            # warmup — retries wait out the mount; 'failed to load' aborts immediately
            with_retries(lambda: run(a.url, a.key, m, "hi", 8, a.temp, a.timeout), a.retries, a.retry_wait, m)
            used, _ = gpu_mem("vram")
            free = (total_vram - used) if (total_vram and used) else None
            reason = None
            if free is not None and free < a.min_free_gb * 1e9:
                reason = f"only {gb(free)} VRAM free"
            if reason:
                # Too tight — do NOT generate (allocates more). Record the row; the sweep continues
                # once this model has fully unloaded (see below).
                hint = ("raise n-cpu-moe" if c.get("ncpumoe", "-") != "-"
                        else "lower ctx / n-gpu-layers / smaller quant")
                cells = base_cells + [f"⚠ {reason} — SKIPPED ({hint})", "", "", gb(used), gb(free)]
                if a.ctx:
                    cells.append("")
            else:
                r = with_retries(lambda: run(a.url, a.key, m, task, a.tokens, a.temp, a.timeout),
                                 a.retries, a.retry_wait, m)
                if r.get("acc") is not None:
                    base_cells[3] = f"{base_cells[3]} ({r['acc']:.0f}% acc)"
                cells = base_cells + [f"{r['tg']:.1f}", (f"{r['pp']:.0f}" if r['pp'] else "-"),
                                      (f"{r['ttft']:.2f}" if r['ttft'] is not None else "all-think"),
                                      gb(used), gb(free)]
                if a.ctx:
                    rc = with_retries(lambda: run(a.url, a.key, m, filler + "\n\n" + task, a.tokens, a.temp, a.timeout),
                                      a.retries, a.retry_wait, m)
                    cells.append(f"{rc['tg']:.1f}")
        except Exception as e:
            err = f"HTTP {e.code} (check server log)" if isinstance(e, urllib.error.HTTPError) else f"ERROR {e}"
            cells = base_cells + [err] + [""] * (len(cols) - len(base_cells) - 1)
        emit("| " + " | ".join(str(x) for x in cells) + " |")
        # (no post-row wait needed: the pre-load drain above protects the next iteration,
        #  including after ⚠-tight rows where generation was skipped)

    latest.write("\n_Tuning hints: **VRAM free** = headroom to raise `ctx-size` or lower `n-cpu-moe` "
                 "(more experts on GPU → faster). Low decode t/s on an offloaded MoE → lower `n-cpu-moe` "
                 "if free allows, or pick a smaller/K-quant file (CPU experts are bandwidth-bound). "
                 "High **baseline used** invalidates the run. For spec/MTP models the acceptance % is in the spec column. "
                 "**TTFT** = time to the first ANSWER token (for thinking models: the think block); `all-think` = the whole "
                 "gen budget was reasoning and no answer arrived (decode t/s is still valid — raise `-n` or use the "
                 "-low/-instant alias). Prefill t/s at the ~30-token prompt is fixed-overhead noise; use `--ctx` for a real number._\n")
    latest.close(); hist.close()
    print(f"\nwrote {a.out} (+ appended {a.history})")


if __name__ == "__main__":
    main()