| """Auto-validation gate for canonical kernels: the correct implementation must |
| survive every suite (T0 capped at 2 trials) before it may serve as a mutation |
| substrate. Exits nonzero on any failure so the pipeline aborts. |
| |
| Usage: python3 validate_ext.py [problem ...] (default: extended problems only) |
| """ |
| import ctypes |
| import sys |
|
|
| import torch |
|
|
| import kernels_def as K |
| import problems_ext |
| from nvrtc_tce import compile_ptx, device_source |
| from screen_mutants import NvrtcKernel, compare, kernel_name, launch_spec, ref_fn_for |
|
|
| EXT = ["leakyrelu", "gelu", "sigmoid", "logsoftmax", "matmul_mk", "matmul_ta", |
| "mean", "max", "rmsnorm"] |
|
|
|
|
| def main(): |
| problems = sys.argv[1:] or EXT |
| torch.zeros(1, device="cuda") |
| cuda_lib = ctypes.CDLL("libcuda.so.1") |
| failures = 0 |
| for problem in problems: |
| src = device_source(K.PROBLEMS[problem]["cuda"]).replace( |
| "__global__ void", 'extern "C" __global__ void') |
| ptx = compile_ptx(src, f"{problem}.cu") |
| kern = NvrtcKernel(cuda_lib, ptx, kernel_name(problem)) |
| ref_fn = ref_fn_for(problem) |
| for sname, n_trials, builder in K.PROBLEMS[problem]["suites"](): |
| for trial in range(min(n_trials, 2)): |
| inputs = builder(trial) |
| gpu_in = [t.cuda() for t in inputs] |
| with torch.no_grad(): |
| ref = ref_fn(*gpu_in) |
| out = kern.launch(problem, gpu_in, torch) |
| r = compare(ref, out, torch) |
| status = "OK " if r == "survived" else "FAIL" |
| if r != "survived": |
| failures += 1 |
| diff = (ref - out).abs().nan_to_num(nan=float("inf")).max() |
| print(f"[validate] {status} {problem}/{sname} t{trial}: {r} " |
| f"max_diff={float(diff):.4g}", flush=True) |
| else: |
| print(f"[validate] {status} {problem}/{sname} t{trial}", flush=True) |
| del gpu_in, ref, out |
| torch.cuda.empty_cache() |
| kern.unload() |
| print(f"[validate] failures: {failures}", flush=True) |
| sys.exit(1 if failures else 0) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
