Record CUDA benchmark diagnostics
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@@ -192,7 +192,7 @@ def detect_hardware() -> dict:
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}
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def benchmark_throughput(device_str: str = "cpu") -> float:
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def benchmark_throughput_checked(device_str: str = "cpu") -> tuple[float, bool, str | None]:
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"""
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Estimate compute throughput via a synthetic transformer GEMM benchmark.
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@@ -201,7 +201,8 @@ def benchmark_throughput(device_str: str = "cpu") -> float:
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The value is used as benchmark_tokens_per_sec in tracker registration for
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routing tiebreaks; it is not an absolute token rate.
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Falls back to 1.0 if torch is unavailable.
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Returns (score, ok, error). Score falls back to 1.0 when the requested
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device cannot run the benchmark.
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"""
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try:
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import torch # type: ignore[import]
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@@ -233,6 +234,12 @@ def benchmark_throughput(device_str: str = "cpu") -> float:
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_sync()
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elapsed = time.perf_counter() - t0
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return round(n_iters / max(elapsed, 1e-9), 2)
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except Exception:
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return 1.0
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return round(n_iters / max(elapsed, 1e-9), 2), True, None
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except Exception as exc:
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return 1.0, False, f"{type(exc).__name__}: {exc}"
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def benchmark_throughput(device_str: str = "cpu") -> float:
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"""Return only the numeric throughput index, preserving the legacy API."""
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score, _ok, _error = benchmark_throughput_checked(device_str)
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return score
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@@ -14,7 +14,7 @@ from pathlib import Path
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from typing import Any
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from .downloader import compute_shard_checksum, download_shard
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from .hardware import detect_hardware, benchmark_throughput
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from .hardware import detect_hardware, benchmark_throughput_checked
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from .relay_bridge import RelayHttpBridge, peer_id_from_wallet
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from .server import StubNodeServer
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from .torch_server import TorchNodeServer
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@@ -386,7 +386,18 @@ def run_startup(
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print(f" Memory budget: {memory_budget_mb / 1024:.1f} GB {memory_budget_source}", flush=True)
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print("Benchmarking compute...", flush=True)
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bench_tps = benchmark_throughput(device)
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if device != "cuda" and gpu_name:
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_cuda_score, cuda_ok, cuda_error = benchmark_throughput_checked("cuda")
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hw["cuda_benchmark_ok"] = cuda_ok
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if cuda_error:
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hw["cuda_benchmark_error"] = cuda_error
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if not cuda_ok:
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print(f" CUDA benchmark unavailable: {cuda_error}; using CPU benchmark", flush=True)
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bench_tps, bench_ok, bench_error = benchmark_throughput_checked(device)
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hw["benchmark_device"] = device
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hw["benchmark_ok"] = bench_ok
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if bench_error:
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hw["benchmark_error"] = bench_error
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device_label = "GPU" if device == "cuda" else "CPU"
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print(f" {device_label} throughput index: {bench_tps:,.0f}", flush=True)
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@@ -14,6 +14,6 @@ def _stub_benchmark_throughput(monkeypatch):
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"""
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try:
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import meshnet_node.startup as startup_mod
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monkeypatch.setattr(startup_mod, "benchmark_throughput", lambda _device: 999.0)
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monkeypatch.setattr(startup_mod, "benchmark_throughput_checked", lambda _device: (999.0, True, None))
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except ImportError:
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pass
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@@ -173,7 +173,7 @@ def test_benchmark_throughput_is_registered_in_payload(monkeypatch, tmp_path):
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monkeypatch.setattr(startup_mod, "detect_hardware",
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lambda: {"device": "cpu", "gpu_name": None, "vram_mb": 0, "ram_mb": 16384})
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monkeypatch.setattr(startup_mod, "benchmark_throughput", lambda _device: 42.5)
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monkeypatch.setattr(startup_mod, "benchmark_throughput_checked", lambda _device: (42.5, True, None))
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monkeypatch.setattr(startup_mod, "TorchNodeServer", lambda **_kw: FakeNode())
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monkeypatch.setattr(startup_mod, "_detect_num_layers", lambda _model_id: 24)
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monkeypatch.setattr(startup_mod, "RelayHttpBridge", None)
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@@ -193,6 +193,67 @@ def test_benchmark_throughput_is_registered_in_payload(monkeypatch, tmp_path):
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node.stop()
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assert captured.get("benchmark_tokens_per_sec") == 42.5
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assert captured["hardware_profile"]["benchmark_device"] == "cpu"
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assert captured["hardware_profile"]["benchmark_ok"] is True
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def test_cuda_benchmark_failure_is_registered_for_inventory_only_gpu(monkeypatch, tmp_path, capsys):
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import meshnet_node.startup as startup_mod
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captured: dict = {}
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class FakeNode:
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backend = None
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def start(self):
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return 7099
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def stop(self):
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pass
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def fake_benchmark(device):
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if device == "cuda":
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return 1.0, False, "AssertionError: Torch not compiled with CUDA enabled"
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return 55.0, True, None
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monkeypatch.setattr(
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startup_mod,
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"detect_hardware",
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lambda: {
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"device": "cpu",
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"gpu_name": "NVIDIA GeForce RTX 4060 Laptop GPU",
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"vram_mb": 8188,
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"dedicated_vram_mb": 8188,
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"shared_vram_mb": 40555,
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"ram_mb": 81111,
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"cuda_available": False,
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},
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)
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monkeypatch.setattr(startup_mod, "benchmark_throughput_checked", fake_benchmark)
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monkeypatch.setattr(startup_mod, "TorchNodeServer", lambda **_kw: FakeNode())
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monkeypatch.setattr(startup_mod, "_post_json",
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lambda _url, payload, timeout=10.0: (captured.update(payload) or {"node_id": "x"}))
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monkeypatch.setattr(startup_mod, "_start_heartbeat", lambda *a, **kw: None)
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node = run_startup(
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tracker_url="http://localhost:8080",
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model_id="Qwen/Qwen2.5-0.5B-Instruct",
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shard_start=0,
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shard_end=23,
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wallet_path=tmp_path / "wallet.json",
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)
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node.stop()
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output = capsys.readouterr().out
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assert "CUDA benchmark unavailable" in output
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assert "Hardware: CPU (CUDA inactive)" in output
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hw = captured["hardware_profile"]
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assert hw["cuda_benchmark_ok"] is False
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assert "Torch not compiled with CUDA enabled" in hw["cuda_benchmark_error"]
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assert hw["benchmark_device"] == "cpu"
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assert hw["benchmark_ok"] is True
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assert captured["ram_bytes"] == 81111 * 1024 * 1024
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assert captured["vram_bytes"] == 0
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def test_wallet_generates_new_keypair(tmp_path):
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