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Shared Modal app for evaluating Triton kernels on cloud GPUs.
Scoring: score = score_scale / geom_mean_runtime_us.
Usage:
Set GPUMODE_USE_MODAL=true and GPUMODE_MODAL_GPU=H100 (or A100, L40S, T4, H200)
in environment variables, then call eval functions from evaluators.
"""
import modal
app = modal.App("gpu-mode-triton-eval")
cuda_image = (
modal.Image.debian_slim(python_version="3.11")
.pip_install(
"torch>=2.2.0",
"triton>=3.0.0",
"numpy",
)
)
def _eval_triton_impl(
submission_code: str,
reference_code: str,
test_cases: list,
benchmark_cases: list,
score_scale: float = 3000.0,
bench_use_cuda_events: bool = True,
bench_rel_error: float = 0.001,
bench_wall_timeout_ns: float = 120e9,
bench_no_grad: bool = False,
bench_max_repeats: int = 100,
bench_max_time_ns: float = 10e9,
bench_warmup_style: str = 'tiny_benchmark',
) -> dict:
"""
Core evaluation logic that runs inside a Modal GPU container.
Returns dict with: combined_score, correctness, geom_mean_us, error
"""
import os
import sys
import gc
import copy
import math
import time
import contextlib
import dataclasses
import tempfile
# Help with memory fragmentation for large models (MLA bs=128)
os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
import importlib.util
import traceback
import torch
import torch.cuda
def clone_data(data):
if isinstance(data, tuple):
return tuple(clone_data(x) for x in data)
elif isinstance(data, list):
return [clone_data(x) for x in data]
elif isinstance(data, dict):
return {k: clone_data(v) for k, v in data.items()}
elif isinstance(data, torch.Tensor):
return data.clone()
elif dataclasses.is_dataclass(data) and not isinstance(data, type):
fields = {f.name: clone_data(getattr(data, f.name)) for f in dataclasses.fields(data)}
return type(data)(**fields)
elif isinstance(data, torch.nn.Module):
cloned = copy.deepcopy(data)
if hasattr(data, 'seq_len'):
cloned.seq_len = data.seq_len
return cloned
return data
def stats(durations):
n = len(durations)
avg = sum(durations) / n
if n > 1:
var = sum((x - avg) ** 2 for x in durations) / (n - 1)
std = math.sqrt(var)
err = std / math.sqrt(n)
else:
std, err = 0.0, 0.0
return {"runs": n, "mean": avg, "std": std, "err": err}
tmpdir = tempfile.mkdtemp()
try:
ref_path = os.path.join(tmpdir, "reference.py")
sub_path = os.path.join(tmpdir, "submission.py")
with open(ref_path, "w") as f:
f.write(reference_code)
with open(sub_path, "w") as f:
f.write(submission_code)
sys.path.insert(0, tmpdir)
spec = importlib.util.spec_from_file_location("reference", ref_path)
reference = importlib.util.module_from_spec(spec)
spec.loader.exec_module(reference)
generate_input = reference.generate_input
check_implementation = reference.check_implementation
spec = importlib.util.spec_from_file_location("submission", sub_path)
submission = importlib.util.module_from_spec(spec)
spec.loader.exec_module(submission)
custom_kernel = submission.custom_kernel
# Correctness tests (use no_grad to reduce memory from autograd)
for i, test_args in enumerate(test_cases):
data = generate_input(**test_args)
data_copy = clone_data(data)
torch.cuda.synchronize()
with torch.no_grad():
output = custom_kernel(data)
torch.cuda.synchronize()
# Aggressively free GPU memory before ref kernel runs
del data
gc.collect()
torch.cuda.empty_cache()
passed, msg = check_implementation(data_copy, output)
del data_copy, output
gc.collect()
torch.cuda.empty_cache()
if not passed:
return {"combined_score": 0.0, "correctness": 0.0,
"error": f"Test {i} failed: {msg}"}
# Warmup
wb = benchmark_cases[0]
if bench_warmup_style == 'timed_calls':
wdata = generate_input(**wb)
start = time.perf_counter()
while time.perf_counter() - start < 0.2:
custom_kernel(wdata)
torch.cuda.synchronize()
else:
# tiny_benchmark: quick run to trigger compilation
wdata = generate_input(**wb)
for _ in range(3):
custom_kernel(wdata)
torch.cuda.synchronize()
# Benchmarks — collect mean runtimes in nanoseconds
ctx = torch.no_grad() if bench_no_grad else contextlib.nullcontext()
bench_means_ns = []
for bench_args in benchmark_cases:
data = generate_input(**bench_args)
data_copy = clone_data(data)
# Correctness check
with ctx:
output = custom_kernel(data)
torch.cuda.synchronize()
# Aggressively free GPU memory before ref kernel runs
del data
gc.collect()
torch.cuda.empty_cache()
passed, msg = check_implementation(data_copy, output)
del data_copy, output
gc.collect()
torch.cuda.empty_cache()
if not passed:
return {"combined_score": 0.0, "correctness": 1.0,
"error": f"Benchmark correctness: {msg}"}
# Regenerate data for timed runs (was freed during correctness check)
data = generate_input(**bench_args)
# Timed runs
durations_ns = []
bm_start = time.perf_counter_ns()
with ctx:
for t in range(bench_max_repeats):
torch.cuda.synchronize()
if bench_use_cuda_events:
s = torch.cuda.Event(enable_timing=True)
e = torch.cuda.Event(enable_timing=True)
s.record()
output = custom_kernel(data)
e.record()
torch.cuda.synchronize()
duration_ns = s.elapsed_time(e) * 1e6 # ms -> ns
else:
start_ns = time.perf_counter_ns()
output = custom_kernel(data)
torch.cuda.synchronize()
duration_ns = time.perf_counter_ns() - start_ns
del output
durations_ns.append(duration_ns)
if t > 1:
st = stats(durations_ns)
if st["mean"] > 0 and st["err"] / st["mean"] < bench_rel_error:
break
if st["mean"] * st["runs"] > bench_max_time_ns:
break
if bench_wall_timeout_ns is not None and \
(time.perf_counter_ns() - bm_start) > bench_wall_timeout_ns:
break
bench_means_ns.append(stats(durations_ns)["mean"])
# Scoring: geometric mean → microseconds → score
means_seconds = [ns / 1e9 for ns in bench_means_ns]
geom_mean_s = math.pow(math.prod(means_seconds), 1.0 / len(means_seconds))
geom_mean_us = geom_mean_s * 1e6
score = score_scale / geom_mean_us
bench_means_us = [ns / 1e3 for ns in bench_means_ns]
return {
"combined_score": score,
"correctness": 1.0,
"geom_mean_us": geom_mean_us,
"bench_means_us": bench_means_us,
}
except Exception as e:
return {"combined_score": 0.0, "correctness": 0.0,
"error": f"{e}\n{traceback.format_exc()}"}
finally:
sys.path.remove(tmpdir)
import shutil
shutil.rmtree(tmpdir, ignore_errors=True)
@app.function(image=cuda_image, gpu="H100", timeout=600)
def eval_triton_h100(**kwargs) -> dict:
return _eval_triton_impl(**kwargs)
@app.function(image=cuda_image, gpu="A100", timeout=600)
def eval_triton_a100(**kwargs) -> dict:
return _eval_triton_impl(**kwargs)
@app.function(image=cuda_image, gpu="L40S", timeout=600)
def eval_triton_l40s(**kwargs) -> dict:
return _eval_triton_impl(**kwargs)
@app.function(image=cuda_image, gpu="T4", timeout=600)
def eval_triton_t4(**kwargs) -> dict:
return _eval_triton_impl(**kwargs)
@app.function(image=cuda_image, gpu="H200", timeout=600)
def eval_triton_h200(**kwargs) -> dict:
return _eval_triton_impl(**kwargs)
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