File size: 8,728 Bytes
22741d9 65cd644 22741d9 65cd644 22741d9 65cd644 22741d9 65cd644 22741d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | #!/usr/bin/env python3
"""Cheap HF Jobs boot/log/runtime smoke for HYDRA/Feather images.
This command is intentionally non-training and non-secret-printing. It exists so
we can verify that an HF image starts, emits logs, sees the requested runtime
environment, and carries the checkpoint symbols needed by the real training
entrypoint before spending on data prep or training.
"""
from __future__ import annotations
import importlib
import json
import os
import sys
from pathlib import Path
SAFE_ENV_KEYS = [
"FEATHER_GPU_PROFILE",
"FEATHER_HF_FLAVOR",
"FEATHER_RUNTIME_MODE",
"FEATHER_HF_STRICT_RUNTIME_PREFLIGHT",
"HYDRA_RUNTIME_PROFILE",
"HYDRA_STRICT_OPTIMAL_COMPONENTS",
"HYDRA_USE_NEMOTRON",
"HYDRA_NEMOTRON_SINGLE_CONFIG",
"HYDRA_LOCAL_SHARDS_ONLY",
"HYDRA_TARGET_SHARDS",
"HYDRA_TIME_BUDGET",
"HYDRA_CKPT_INTERVAL",
"HYDRA_EVAL_TOKENS",
"HYDRA_HYENA_LAYERS",
"HYDRA_FORCE_HTM_CPU",
"HYDRA_HTM_FUSED",
"HYDRA_HTM_BATCHED_FUSED",
"HYDRA_DISABLE_FUSED_SDR_TRITON",
"HYDRA_TOKEN_CACHE_GB",
"HYDRA_DISABLE_TOKEN_CACHE",
"HYDRA_HTM_STRICT_SCALE_FREE",
"HYDRA_HTM_REGION_POOL_SIZE",
"HYDRA_HTM_CHUNK_B",
"HTM_CUDA_ARCH",
"TORCH_CUDA_ARCH_LIST",
]
def _repo_candidates() -> list[Path]:
here = Path(__file__).resolve()
return [
Path("/workspace/feather"),
Path("/app"),
here.parents[1] if len(here.parents) > 1 else here.parent,
]
def ensure_repo_on_path() -> None:
for candidate in _repo_candidates():
if (candidate / "hydra").exists() and str(candidate) not in sys.path:
sys.path.insert(0, str(candidate))
print(f"[boot_smoke] repo_path={candidate}", flush=True)
return
print("[boot_smoke] repo_path=<not-found>; using existing sys.path", flush=True)
def safe_env_summary() -> dict[str, str]:
return {key: os.environ[key] for key in SAFE_ENV_KEYS if key in os.environ}
def _truthy_env(name: str) -> bool:
return os.environ.get(name, "0").strip().lower() in {"1", "true", "yes", "on"}
def strict_optimal_preflight_requested() -> bool:
return (
_truthy_env("FEATHER_HF_STRICT_RUNTIME_PREFLIGHT")
or os.environ.get("HYDRA_STRICT_OPTIMAL_COMPONENTS", "0") == "1"
or os.environ.get("HYDRA_RUNTIME_PROFILE", "").strip().lower() == "optimal-strict"
)
def _import_required_module(module_name: str):
try:
module = importlib.import_module(module_name)
except Exception as exc:
print(f"[strict_preflight] {module_name}=failed {type(exc).__name__}: {exc}", flush=True)
return None
print(f"[strict_preflight] {module_name}=ok", flush=True)
return module
def run_strict_optimal_preflight() -> int:
"""Fail before training if the strict-optimal A10G fast path is unavailable.
This is intentionally a runtime/image preflight, not a CPU fallback. It
verifies the same strict fast-path surfaces that otherwise fail only after a
paid trainer has finished build/provenance setup.
"""
print("[strict_preflight] phase=start", flush=True)
failures: list[str] = []
torch = _import_required_module("torch")
if torch is None:
failures.append("torch_import")
else:
try:
cuda_available = bool(torch.cuda.is_available())
device_count = int(torch.cuda.device_count()) if cuda_available else 0
device_name = torch.cuda.get_device_name(0) if cuda_available and device_count else "<none>"
if not cuda_available or device_count < 1:
failures.append("torch_cuda")
print(
f"[strict_preflight] torch_cuda=failed cuda_available={int(cuda_available)} device_count={device_count}",
flush=True,
)
else:
print(
f"[strict_preflight] torch_cuda=ok device_count={device_count} device0={device_name}",
flush=True,
)
except Exception as exc:
failures.append("torch_cuda")
print(f"[strict_preflight] torch_cuda=failed {type(exc).__name__}: {exc}", flush=True)
triton = _import_required_module("triton")
if triton is None:
failures.append("triton_import")
else:
try:
active = triton.runtime.driver.active
device = active.get_current_device()
print(f"[strict_preflight] triton_driver=ok device={device}", flush=True)
except Exception as exc:
failures.append("triton_driver")
print(f"[strict_preflight] triton_driver=failed {type(exc).__name__}: {exc}", flush=True)
mamba = _import_required_module("mamba_ssm")
if mamba is None or not hasattr(mamba, "Mamba3"):
failures.append("mamba")
print("[strict_preflight] mamba=missing Mamba3", flush=True)
else:
print("[strict_preflight] mamba=ok Mamba3=True", flush=True)
fused_sdr = None
for module_name in ("subsystems.fused_sdr_project",):
try:
module = importlib.import_module(module_name)
except Exception as exc:
print(f"[strict_preflight] fused_sdr_candidate={module_name} failed {type(exc).__name__}: {exc}", flush=True)
continue
if hasattr(module, "FusedSDRProject"):
fused_sdr = (module_name, module)
break
if fused_sdr is None:
failures.append("fused_sdr")
print("[strict_preflight] fused_sdr=missing FusedSDRProject", flush=True)
else:
module_name, _module = fused_sdr
print(f"[strict_preflight] fused_sdr=ok module={module_name}", flush=True)
htm = _import_required_module("htm_rust")
if htm is None:
failures.append("htm_rust")
else:
has_region = hasattr(htm, "HTMRegion")
has_gpu = hasattr(htm, "HTMRegionGpu")
has_fused = hasattr(htm, "step_batch_fused_cuda")
if not (has_region and has_gpu and has_fused):
failures.append("htm_rust")
print(
"[strict_preflight] htm_rust=failed "
f"HTMRegion={has_region} HTMRegionGpu={has_gpu} step_batch_fused_cuda={has_fused}",
flush=True,
)
else:
print(
"[strict_preflight] htm_rust=ok "
f"HTMRegion={has_region} HTMRegionGpu={has_gpu} step_batch_fused_cuda={has_fused}",
flush=True,
)
if failures:
print(f"[strict_preflight] phase=failed failures={','.join(failures)}", flush=True)
return 5
print("[strict_preflight] phase=done", flush=True)
return 0
def main() -> int:
print("[boot_smoke] phase=start", flush=True)
ensure_repo_on_path()
print(f"[boot_smoke] python={sys.version.split()[0]} executable={sys.executable}", flush=True)
print(f"[boot_smoke] env={json.dumps(safe_env_summary(), sort_keys=True)}", flush=True)
try:
torch = importlib.import_module("torch")
cuda_available = bool(torch.cuda.is_available())
device_count = int(torch.cuda.device_count()) if cuda_available else 0
device_name = torch.cuda.get_device_name(0) if cuda_available and device_count else "<none>"
print(
f"[boot_smoke] torch={torch.__version__} cuda_available={int(cuda_available)} "
f"device_count={device_count} device0={device_name}",
flush=True,
)
except Exception as exc: # pragma: no cover - depends on image contents
print(f"[boot_smoke] torch_import_failed={type(exc).__name__}: {exc}", flush=True)
return 2
if strict_optimal_preflight_requested():
rc = run_strict_optimal_preflight()
if rc != 0:
return rc
try:
training = importlib.import_module("hydra.training")
required = ["LATEST_CKPT", "PRETRAIN_FINAL_CKPT", "save_ckpt", "maybe_resume_ckpt"]
missing = [name for name in required if not hasattr(training, name)]
if missing:
print(f"[boot_smoke] training_contract=missing {missing}", flush=True)
return 3
print(
"[boot_smoke] training_contract=ok "
f"LATEST_CKPT={getattr(training, 'LATEST_CKPT')} "
f"PRETRAIN_FINAL_CKPT={getattr(training, 'PRETRAIN_FINAL_CKPT')}",
flush=True,
)
except Exception as exc: # pragma: no cover - depends on image contents
print(f"[boot_smoke] training_import_failed={type(exc).__name__}: {exc}", flush=True)
return 4
print("[boot_smoke] phase=done", flush=True)
return 0
if __name__ == "__main__":
raise SystemExit(main())
|