windows: one-command benchmark submission block (fixes #139)

windows/README.md

@@ -129,6 +129,27 @@ Context to keep when reading these numbers:
 - Same-condition A/B against unpatched vanilla llama.cpp (vendor tree with the patch bands skipped) shows the patch set costs nothing (8B: 41.5 vs 39.5 decode; 35B: 27.2 vs 27.4 — within machine drift).
 - Expect day-to-day drift of a few percent on this class of machine; compare like-for-like, same day, same process if you benchmark.
 
+### 2.1 Community benchmark submission (Hardware Benchmarks #142)
+
+To add a Windows row to the Hardware Benchmarks discussion (#142), build the
+engine and download a tier as in §1, then run:
+
+    python tools/r3_bench.py --tier 8b --submission --reporter @you
+
+The script discards the short warmup, runs 2 timed reps (`--reps N` to
+override) on the ~3k-token long-context prompt (same construction as the
+Python bench's BENCH_LONG), and prints a ready-to-paste submission block —
+the mean of the timed reps, with `device` / `chip` / `gpu` (the Vulkan device
+from the engine log) / `ram_gb` / `os` / `engine_commit` (vendor.llama.pin)
+filled in automatically. `--json` additionally prints the block as a single
+JSON line for aggregation, and `benchmarks/r3/bench_<tier>_submission.json`
+stores the per-rep detail.
+
+On the memory field: `peak_active_gib` on Windows is the engine process's
+peak working set during the timed reps (OS-level high-water mark). Windows
+has no allocator-level counter, so this is not directly comparable with the
+python-mlx column's `mlx.core.get_peak_memory()`.
+
 ---
 
 ## 3. Technical Details

windows/tools/r3_bench.py

@@ -8,9 +8,16 @@
 window when compared against the reference — cross-window comparison is always
 suspect (measure on the same day / same process, or treat numbers as shape-only).
 
+Community submission mode (Hardware Benchmarks discussion #142, issue #139):
+`--submission` discards the same short warmup, then runs `reps` timed reps
+(default 2) on the ~3k-token long-context prompt, averages the numbers, and
+prints the ready-to-paste submission block; `--json` prints the same data as
+JSON for aggregation.
+
 Usage: python tools/r3_bench.py --tier 35b [--reps 3]
+       python tools/r3_bench.py --tier 8b --submission --reporter @you
 """
-import argparse, json, os, re, subprocess, sys, time
+import argparse, ctypes, json, os, re, subprocess, sys, time
 
 ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
@@ -44,6 +51,21 @@ def models_root(tier):
 
 
 PROMPT = ("混合专家模型（MoE）通过稀疏激活把多个专家网络与门控路由组合起来：" * 12)
+
+# Long-context prompt for the submission block (~3k tokens after tokenization):
+# identical construction and text to the Python bench's BENCH_LONG (see
+# python/examples/bench.py), so prefill numbers on both runtimes refer to the
+# same measured work. Short prompts only measure fixed overhead.
+_PARA_ZH = '\u6d41\u5f0f\u63a8\u7406\u6846\u67b6\u7684\u8bbe\u8ba1\u8981\u70b9\u5728\u4e8e\u628a\u78c1\u76d8\u5e26\u5bbd\u3001\u7f13\u5b58\u5c42\u7ea7\u4e0e\u8ba1\u7b97\u6838\u5fc3\u4e09\u8005\u91cd\u53e0\u8d77\u6765\u3002\u6df7\u5408\u4e13\u5bb6\u6a21\u578b\u6bcf\u4e00\u5c42\u53ea\u5728\u5c11\u6570\u4e13\u5bb6\u4e0a\u6fc0\u6d3b\uff0c\u8def\u7531\u5668\u7ed9\u51fa\u7684\u7a00\u758f\u9009\u62e9\u6070\u597d\u4e3a\u6309\u9700\u52a0\u8f7d\u63d0\u4f9b\u4e86\u5929\u7136\u7684\u6279\u7c92\u5ea6\u3002\u82e5\u80fd\u5728\u524d\u4e00\u6b65\u7684\u9690\u72b6\u6001\u57fa\u7840\u4e0a\u9884\u5224\u4e0b\u4e00\u6b65\u7684\u4e13\u5bb6\u96c6\u5408\uff0c\u56fa\u6001\u786c\u76d8\u7684\u8bfb\u53d6\u5ef6\u8fdf\u5c31\u80fd\u88ab\u8ba1\u7b97\u5b8c\u5168\u63a9\u76d6\uff0c\u8fd9\u5bf9\u8fb9\u7f18\u8bbe\u5907\u4e0a\u7684\u5927\u6a21\u578b\u90e8\u7f72\u5177\u6709\u76f4\u63a5\u610f\u4e49\u3002'
+_PARA_EN = 'The design of a streaming inference framework hinges on overlapping disk bandwidth, cache hierarchy, and compute. A mixture-of-experts layer activates only a handful of experts per token, and the router sparsity provides a natural granularity for on-demand loading. If the expert set of the next step can be predicted from the previous hidden state, SSD read latency hides behind compute entirely, which matters for large-model deployment on edge hardware.'
+_LONG_HEADER = '\u8bf7\u4ed4\u7ec6\u9605\u8bfb\u4ee5\u4e0b\u6750\u6599\u5e76\u7528\u4e00\u53e5\u8bdd\u603b\u7ed3\u5176\u6838\u5fc3\u601d\u60f3\uff1a'
+
+
+def long_prompt():
+    paras = [_PARA_ZH if i % 2 == 0 else _PARA_EN for i in range(40)]
+    return _LONG_HEADER + " ".join(paras)
+
+
 PAT = {
     "prompt": re.compile(r"prompt eval time =.*?,\s*([\d.]+) tokens per second"),
     "eval": re.compile(r"(?<!prompt )eval time =.*?,\s*([\d.]+) tokens per second"),
@@ -53,35 +75,198 @@ def models_root(tier):
 }
 
 
-def one(gguf, lora, n, tier):
+# ---------------------------------------------------------------- metadata --
+def cpu_name():
+    """CPU marketing name from the registry (no wmic/sysinfo dependency)."""
+    try:
+        import winreg
+        k = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE,
+                           r"HARDWARE\DESCRIPTION\System\CentralProcessor\0")
+        name, _ = winreg.QueryValueEx(k, "ProcessorNameString")
+        winreg.CloseKey(k)
+        return " ".join(str(name).split())
+    except OSError:
+        return None
+
+
+def total_ram_gb():
+    try:
+        class MEMORYSTATUSEX(ctypes.Structure):
+            _fields_ = [("dwLength", ctypes.c_uint),
+                        ("dwMemoryLoad", ctypes.c_uint),
+                        ("ullTotalPhys", ctypes.c_uint64),
+                        ("ullAvailPhys", ctypes.c_uint64),
+                        ("ullTotalPageFile", ctypes.c_uint64),
+                        ("ullAvailPageFile", ctypes.c_uint64),
+                        ("ullTotalVirtual", ctypes.c_uint64),
+                        ("ullAvailVirtual", ctypes.c_uint64),
+                        ("ullAvailExtendedVirtual", ctypes.c_uint64)]
+        st = MEMORYSTATUSEX()
+        st.dwLength = ctypes.sizeof(st)
+        if not ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(st)):
+            return None
+        return st.ullTotalPhys / (1024 ** 3)
+    except (OSError, AttributeError):
+        return None
+
+
+def windows_build():
+    v = sys.getwindowsversion()
+    label = "11" if v.build >= 22000 else "10"
+    return f"Windows {label} (build {v.build})"
+
+
+def machine_name():
+    """Computer name (device) from the registry; a human brand is not exposed by winreg."""
+    try:
+        import winreg
+        k = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE,
+                           r"SYSTEM\CurrentControlSet\Control\ComputerName\ComputerName")
+        name, _ = winreg.QueryValueEx(k, "ComputerName")
+        winreg.CloseKey(k)
+        return str(name)
+    except OSError:
+        return None
+
+
+def engine_commit():
+    """Head of vendor.llama.pin at the repo root (the pinned llama.cpp fork)."""
+    pin = os.path.join(ROOT, "..", "vendor.llama.pin")
+    try:
+        with open(pin, encoding="utf-8") as f:
+            return f.read().splitlines()[0].strip()
+    except OSError:
+        return None
+
+
+def vulkan_device(err):
+    """Vulkan device name from the engine's own startup log, e.g:
+    'ggml_vulkan: 0 = AMD Radeon RX 9070 GRE (vendor) | uma: ...'."""
+    for line in err.splitlines():
+        if not re.search(r"vulkan", line, re.I):
+            continue
+        m = re.search(r"\d+\s*=\s*([^\n|()]+)", line)
+        if m:
+            return m.group(1).strip()
+        m = re.search(r"Device\s*\d*\s*[:=]\s*([^\n|(),]+)", line, re.I)
+        if m:
+            return m.group(1).strip()
+    return None
+
+
+def open_handle(pid):
+    """Engine-process handle for the memory probe (Windows only; None otherwise)."""
+    if sys.platform != "win32":
+        return None
+    for _ in range(10):                      # the child may still be spawning
+        h = ctypes.windll.kernel32.OpenProcess(0x0410, False, pid)  # QUERY_INFORMATION|VM_READ
+        if h:
+            return h
+        time.sleep(0.01)
+    return None
+
+
+def peak_working_set_gib(handle):
+    """Peak process working set (GiB) — the OS keeps the high-water mark while
+    any handle to the process is open, so probe after communicate(), then close."""
+    if not handle:
+        return None
+    try:
+        class PMC(ctypes.Structure):
+            _fields_ = [("cb", ctypes.c_uint),
+                        ("PageFaultCount", ctypes.c_uint),
+                        ("PeakWorkingSetSize", ctypes.c_size_t),
+                        ("WorkingSetSize", ctypes.c_size_t),
+                        ("QuotaPeakPagedPoolUsage", ctypes.c_size_t),
+                        ("QuotaPagedPoolUsage", ctypes.c_size_t),
+                        ("QuotaPeakNonPagedPoolUsage", ctypes.c_size_t),
+                        ("QuotaNonPagedPoolUsage", ctypes.c_size_t),
+                        ("PagefileUsage", ctypes.c_size_t),
+                        ("PeakPagefileUsage", ctypes.c_size_t)]
+        pmc = PMC()
+        pmc.cb = ctypes.sizeof(pmc)
+        if not ctypes.WinDLL("psapi").GetProcessMemoryInfo(handle, ctypes.byref(pmc), pmc.cb):
+            return None
+        return pmc.PeakWorkingSetSize / (1024 ** 3)
+    except OSError:
+        return None
+
+
+def one(gguf, lora, n, tier, prompt=None):
     cmd = [os.path.join(SERVER_BIN, "llama-completion.exe"), "-m", gguf,
            "-ngl", "99", "-cmoe", "--temp", "0", "-n", str(n), "--seed", "7",
-           "-no-cnv", "--no-display-prompt", "-p", PROMPT]
+           "-no-cnv", "--no-display-prompt", "-p", prompt or PROMPT]
     if lora:
         cmd[3:3] = ["--lora", lora]
     t0 = time.time()
-    r = subprocess.run(cmd, capture_output=True, text=True, encoding="utf8", errors="replace")
-    err = r.stderr or ""
-    out = {"wall_s": round(time.time() - t0, 1)}
+    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+                         text=True, encoding="utf8", errors="replace")
+    h = open_handle(p.pid)
+    _, err = p.communicate()
+    peak = peak_working_set_gib(h)           # probed after wait while the handle is live
+    if h:
+        ctypes.windll.kernel32.CloseHandle(h)
+    out = {"wall_s": round(time.time() - t0, 1),
+           "peak_working_set_gib": round(peak, 2) if peak is not None else None,
+           "gpu": vulkan_device(err or "")}
     for k, pat in PAT.items():
-        m = pat.search(err)
+        m = pat.search(err or "")
         if m:
             out[k] = float(m.group(1))
-    ev = re.search(r"n_ctx = (\d+)", err)
+    ev = re.search(r"n_ctx = (\d+)", err or "")
     out["n_ctx"] = int(ev.group(1)) if ev else None
-    if "error" in err.lower() and "eval time" not in err:
+    if "error" in (err or "").lower() and "eval time" not in err:
         out["error"] = [l for l in err.splitlines() if "error" in l.lower()][:2]
     if "total_ms" in out and "prompt_ms" in out:
         dec_ms = out["total_ms"] - out["prompt_ms"]        # total = generation end-to-end, excludes load (verified)
         out["decode_tps_derived"] = round(n * 1000 / dec_ms, 2) if dec_ms > 0 else None
     return out
 
 
+def submission_block(tier, reporter, notes, rows):
+    """Ready-to-paste Hardware Benchmarks block; numbers are the mean of the timed reps."""
+    evs = [r["eval"] for r in rows if "eval" in r]
+    prs = [r["prompt"] for r in rows if "prompt" in r]
+    peaks = [r["peak_working_set_gib"] for r in rows if r.get("peak_working_set_gib") is not None]
+    gpus = sorted({r["gpu"] for r in rows if r.get("gpu")})
+    dev, chip = machine_name(), cpu_name()
+    ram = total_ram_gb()
+    data = {
+        "tier": f"edge0-{tier}",
+        "runtime": "windows",
+        "device": dev or "(unknown)",
+        "chip": chip or "(unknown)",
+        "gpu": gpus[0] if gpus else "(not detected)",
+        "ram_gb": round(ram) if ram else None,
+        "os": windows_build(),
+        "decode_tok_s": round(sum(evs) / len(evs), 2) if evs else None,
+        "prefill_tok_s": round(sum(prs) / len(prs), 2) if prs else None,
+        "peak_active_gib": round(max(peaks), 2) if peaks else None,
+        "engine_commit": engine_commit(),
+        "reporter": reporter or "",
+        "notes": notes or "",
+    }
+    return data
+
+
+def print_block(d):
+    for k, v in d.items():
+        print(f"{k:<16} {'' if v is None else v}".rstrip())
+
+
 def main():
     ap = argparse.ArgumentParser()
     ap.add_argument("--tier", choices=["35b", "8b"], required=True)
-    ap.add_argument("--reps", type=int, default=3)
+    ap.add_argument("--reps", type=int, default=None,
+                    help="timed reps (default: 2 with --submission, 3 otherwise)")
+    ap.add_argument("--submission", action="store_true",
+                    help="print the Hardware Benchmarks submission block (long-context prefill)")
+    ap.add_argument("--json", action="store_true",
+                    help="with --submission, also print the block as JSON")
+    ap.add_argument("--reporter", default="", help="@handle for the submission block")
+    ap.add_argument("--notes", default="", help="free-form notes field")
     a = ap.parse_args()
+    reps = a.reps or (2 if a.submission else 3)
     exe = os.path.join(SERVER_BIN, "llama-completion.exe")
     if not os.path.isfile(exe):
         raise SystemExit(f"llama-completion.exe not found in {SERVER_BIN} "
@@ -90,10 +275,30 @@ def main():
     gguf = os.path.join(md, f"edge0-{a.tier}-gguf", f"edge0-{a.tier}.gguf")
     lora = os.path.join(md, f"edge0-{a.tier}",
                         f"lora_edge0_{a.tier}-gguf.gguf")
+    if a.submission:
+        warm = one(gguf, lora, 64, a.tier)          # warmup (page cache + mmap fault-in), discarded
+        print(f"warm(discard): {warm}", flush=True)
+        longp = long_prompt()
+        rows = []
+        for i in range(reps):
+            time.sleep(5)
+            m = one(gguf, lora, 256, a.tier, prompt=longp)
+            m["rep"] = i
+            rows.append(m)
+            print(f"rep{i}: {m}", flush=True)
+        data = submission_block(a.tier, a.reporter, a.notes, rows)
+        outp = os.path.join(ROOT, "benchmarks", "r3", f"bench_{a.tier}_submission.json")
+        os.makedirs(os.path.dirname(outp), exist_ok=True)
+        with open(outp, "w", encoding="utf8") as f:
+            json.dump({**data, "timed_reps": rows}, f, ensure_ascii=False, indent=1)
+        print_block(data)
+        if a.json:
+            print("SUBMIT_JSON " + json.dumps(data, ensure_ascii=False))
+        return
     rows = []
     warm = one(gguf, lora, 64, a.tier)          # warmup (page cache + mmap fault-in), discarded
     print(f"warm(discard): {warm}", flush=True)
-    for i in range(a.reps):
+    for i in range(reps):
         time.sleep(5)
         m = one(gguf, lora, 256, a.tier)
         m["rep"] = i