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import argparse
import contextlib
import fcntl
import hashlib
import importlib.util
import json
import os
from pathlib import Path
import statistics
import sys
import time
import traceback
from task_acceptance import compare_output
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--root", type=Path, default=Path(__file__).resolve().parents[1])
parser.add_argument(
"--data-root", type=Path, help="Optional materialized dataset with the same paths"
)
parser.add_argument("--lock-directory", type=Path, required=True)
parser.add_argument(
"--cache-directory", type=Path, help="Writable module and reference cache"
)
parser.add_argument("--definition", action="append")
parser.add_argument("--suite", choices=["first", "dev", "all"], default="first")
parser.add_argument("--only-default", action="store_true")
parser.add_argument("--report", required=True, type=Path)
parser.add_argument("--validation-only", action="store_true")
parser.add_argument("--scale-probe", action="store_true")
parser.add_argument("--gpu", required=True)
parser.add_argument("--workload", action="append")
parser.add_argument("--solution", action="append")
parser.add_argument(
"--candidate",
action="append",
default=[],
help="Extra run() module for comparing a candidate on one selected definition",
)
parser.add_argument("--reference-cache", action="store_true")
parser.add_argument("--graph-timing", action="store_true")
parser.add_argument(
"--strict-diagnostic",
action="store_true",
help="Also report the historical FP32 bound for GDN; does not affect task acceptance",
)
args = parser.parse_args()
SOURCE = args.root.resolve()
DATA = args.data_root.resolve() if args.data_root else SOURCE
CACHE = (
args.cache_directory.resolve()
if args.cache_directory
else SOURCE / "results/audit-cache"
)
GPU = args.gpu
LOCKS = args.lock_directory
LOCKS.mkdir(exist_ok=True, parents=True)
os.environ["CUDA_VISIBLE_DEVICES"] = GPU
stack = contextlib.ExitStack()
for suffix in ["", ".lock"]:
handle = stack.enter_context((LOCKS / (GPU + suffix)).open("a+"))
fcntl.flock(handle.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
import torch
from safetensors.torch import load_file
torch.set_num_threads(8)
torch.backends.cuda.matmul.allow_tf32 = False
torch._inductor.config.compile_threads = 1
def load_function(name, path):
identifier = "_audit_" + hashlib.sha256(name.encode()).hexdigest()[:20]
package = path.parent
spec = importlib.util.spec_from_file_location(
identifier, package / "__init__.py", submodule_search_locations=[str(package)]
)
if path.name != "main.py":
spec = importlib.util.spec_from_file_location(identifier, path)
module = importlib.util.module_from_spec(spec)
sys.modules[identifier] = module
spec.loader.exec_module(module)
if path.name == "main.py":
spec = importlib.util.spec_from_file_location(identifier + ".main", path)
child = importlib.util.module_from_spec(spec)
sys.modules[spec.name] = child
spec.loader.exec_module(child)
module = child
return module.run, getattr(module, "prepare", None)
def normalize(result, definition):
return (
list(result.values())
if isinstance(result, dict)
else list(result)
if isinstance(result, (tuple, list))
else [result]
)
def compare(actual, expected, definition, config):
values = normalize(actual, definition)
assert len(values) == len(expected)
metrics = {}
for name, value, reference in zip(definition["outputs"], values, expected):
metrics[name] = compare_output(value, reference, config["tolerance"])
return metrics
def measure(function, inputs, timing):
for _ in range(timing["warmup"]):
_result = function(*inputs)
torch.cuda.synchronize()
samples = []
for _ in range(timing["iters"] * timing["trials"]):
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
_result = function(*inputs)
end.record()
end.synchronize()
samples.append(start.elapsed_time(end) * 1000)
return {
"median_us": statistics.median(samples),
"samples_us": samples,
"method": "CUDA events over eager calls",
}
def measure_graph(function, inputs, timing, definition, config, expected):
for _ in range(max(3, timing["warmup"])):
result = function(*inputs)
torch.cuda.synchronize()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
for _ in range(timing["iters"]):
result = function(*inputs)
graph.replay()
torch.cuda.synchronize()
compare(result, expected, definition, config)
samples = []
for _ in range(timing["trials"]):
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
graph.replay()
end.record()
end.synchronize()
samples.append(start.elapsed_time(end) * 1000 / timing["iters"])
return {
"median_us": statistics.median(samples),
"samples_us": samples,
"method": "CUDA graph, iters calls per replay",
"calls_per_replay": timing["iters"],
}
definitions = {
json.loads(f.read_text())["name"]: json.loads(f.read_text())
for f in (SOURCE / "definitions").rglob("*.json")
}
solutions = {}
for f in (SOURCE / "solutions").rglob("*.json"):
s = json.loads(f.read_text())
solutions.setdefault(s["definition"], []).append(s)
rows_by_name = {
f.stem: [json.loads(l)["workload"] for l in f.read_text().splitlines()]
for f in (SOURCE / "workloads").rglob("*.jsonl")
}
args.report.parent.mkdir(exist_ok=True, parents=True)
failures = 0
with args.report.open("w") as report, torch.inference_mode():
def emit(row):
report.write(json.dumps(row) + "\n")
report.flush()
emit(
{
"type": "environment",
"gpu": torch.cuda.get_device_name(),
"torch": torch.__version__,
"suite": args.suite,
"pythonpath": os.environ.get("PYTHONPATH"),
"started_at": time.time(),
}
)
for name, definition in definitions.items():
if args.definition and name not in args.definition:
continue
config = json.loads((SOURCE / "tasks" / f"{name}.json").read_text())
rows = rows_by_name[name]
if args.suite == "dev":
rows = [row for row in rows if row["uuid"] in config["dev"]]
if args.suite == "first":
rows = rows[:1]
if args.workload:
rows = [row for row in rows if row["uuid"] in args.workload]
candidates = []
for solution in solutions[name]:
if args.only_default and solution["name"] == "sglang_" + name:
continue
digest = hashlib.sha256(
json.dumps(solution, sort_keys=True).encode()
).hexdigest()
package = CACHE / "modules" / digest
for source in solution["sources"]:
target = package / source["path"]
if not target.resolve().is_relative_to(package.resolve()):
raise ValueError("Source path escapes package")
target.parent.mkdir(parents=True, exist_ok=True)
target.write_text(source["content"])
(package / "__init__.py").touch()
entry = solution["spec"]["entry_point"].split("::")[0]
candidates.append((solution["name"], package / entry))
if args.candidate:
assert args.definition and len(args.definition) == 1
candidates.extend((Path(p).stem, Path(p)) for p in args.candidate)
if args.solution:
candidates = [(s, p) for s, p in candidates if s in args.solution]
ns = {}
exec(definition["reference"], ns)
reference = ns["run"]
for solution, path in candidates:
try:
function, prepare = load_function(solution, path)
except Exception:
failures += 1
emit(
{
"type": "load",
"definition": name,
"solution": solution,
"status": "failed",
"error": traceback.format_exc(),
}
)
print("LOAD FAILED", name, solution, flush=True)
continue
for row in rows:
inputs = []
saved = []
expected = []
result = {
"type": "workload",
"definition": name,
"solution": solution,
"uuid": row["uuid"],
"axes": row["axes"],
"solution_sha256": hashlib.sha256(path.read_bytes()).hexdigest(),
"definition_sha256": hashlib.sha256(
json.dumps(definition, sort_keys=True).encode()
).hexdigest(),
"task_sha256": hashlib.sha256(
json.dumps(config, sort_keys=True).encode()
).hexdigest(),
}
try:
for key in definition["inputs"]:
desc = row["inputs"][key]
value = (
desc["value"]
if desc["type"] == "scalar"
else load_file(str(DATA / desc["path"]), device="cuda")[
desc["tensor_key"]
]
)
inputs.append(value)
saved.append(
value.clone() if isinstance(value, torch.Tensor) else value
)
cache_key = hashlib.sha256(
json.dumps(
{
"reference": definition["reference"],
"row": row,
"torch": torch.__version__,
},
sort_keys=True,
).encode()
).hexdigest()
cache = CACHE / "references" / (cache_key + ".pt")
if args.reference_cache and cache.exists():
expected = [
value.to("cuda")
for value in torch.load(cache, weights_only=True)
]
else:
expected = [
value.clone()
for value in normalize(reference(*inputs), definition)
]
if args.reference_cache:
cache.parent.mkdir(exist_ok=True, parents=True)
torch.save([value.cpu() for value in expected], cache)
actual_result = function(*inputs)
result["errors"] = compare(
actual_result, expected, definition, config
)
if args.strict_diagnostic and name.startswith("gdn_"):
result["strict_fp32_diagnostic"] = {}
for key, value, target in zip(
definition["outputs"],
normalize(actual_result, definition),
expected,
):
if value.dtype == torch.float32:
close = torch.isclose(
value, target, rtol=1e-4, atol=2e-5
)
result["strict_fp32_diagnostic"][key] = {
"rtol": 1e-4,
"atol": 2e-5,
"mismatched_elements": int((~close).sum()),
"elements": close.numel(),
"passes": bool(close.all()),
}
for key, value, original in zip(
definition["inputs"], inputs, saved
):
if isinstance(value, torch.Tensor):
assert torch.equal(
value.contiguous().view(torch.uint8),
original.contiguous().view(torch.uint8),
), key + " input mutated"
if args.scale_probe and "scale" in definition["inputs"]:
index = list(definition["inputs"]).index("scale")
original = inputs[index]
inputs[index] = (
definition["axes"]["head_size"]["value"] ** -0.5
if original is None or original == 0
else original
) * 0.5
target = [
value.clone()
for value in normalize(reference(*inputs), definition)
]
result["scale_probe"] = compare(
function(*inputs), target, definition, config
)
inputs[index] = original
if not args.validation_only:
result["run_timing"] = measure(
function, inputs, config["timing"]
)
if prepare:
launch = prepare(*inputs)
result["prepared_timing"] = measure(
lambda *unused: launch(), inputs, config["timing"]
)
if args.graph_timing:
try:
launch_function = (
(lambda *unused: launch()) if prepare else function
)
result["graph_timing"] = measure_graph(
launch_function,
inputs,
config["timing"],
definition,
config,
expected,
)
except Exception:
result["graph_error"] = traceback.format_exc()
for key, value, original in zip(
definition["inputs"], inputs, saved
):
if isinstance(value, torch.Tensor):
assert torch.equal(
value.contiguous().view(torch.uint8),
original.contiguous().view(torch.uint8),
), key + " input mutated during timing"
result["inputs_unchanged"] = True
result["status"] = "passed"
except Exception:
failures += 1
result.update(status="failed", error=traceback.format_exc())
emit(result)
print(
result["status"],
name,
solution,
row["axes"],
result.get("run_timing", {}).get("median_us"),
flush=True,
)
inputs = saved = expected = actual_result = None
function = prepare = None
if failures:
raise SystemExit(f"{failures} failed cases; see {args.report}")
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