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from __future__ import annotations
import os
import argparse
import subprocess
def print_and_write(outfile, line: str, flush: bool = False):
print(line, end="", flush=flush)
outfile.write(line)
if flush:
outfile.flush()
def main(args: argparse.Namespace) -> None:
assert len(args.gpu_ids) == 1
hf_token = os.environ["HF_TOKEN"]
if args.model.startswith("models/"):
outdir = f"{args.result_root}/{args.model[len('models/'):]}"
else:
outdir = f"{args.result_root}/{args.model}"
os.makedirs(outdir, exist_ok=True)
outfile = open(f"{outdir}/gpus{''.join(args.gpu_ids)}.out.txt", "w")
print_and_write(outfile, f"Benchmarking {args.model}\n")
print_and_write(outfile, f"Batch sizes: {args.batch_sizes}\n")
print_and_write(outfile, f"Power limits: {args.power_limits}\n")
print_and_write(outfile, f"Number of inference steps: {args.num_inference_steps}\n")
for batch_size in args.batch_sizes:
for power_limit in args.power_limits:
for num_inference_steps in args.num_inference_steps:
print_and_write(outfile, f"{batch_size=}, {power_limit=}, {num_inference_steps=}\n", flush=True)
with subprocess.Popen(
args=[
"docker", "run",
"--gpus", '"device=' + ','.join(args.gpu_ids) + '"',
"--cap-add", "SYS_ADMIN",
"--name", f"leaderboard-t2v-{''.join(args.gpu_ids)}",
"--rm",
"-v", "/data/leaderboard/hfcache:/root/.cache/huggingface",
"-v", f"{os.getcwd()}:/workspace/text-to-video",
"mlenergy/leaderboard:diffusion-t2v",
"--result-root", args.result_root,
"--batch-size", batch_size,
"--num-batches", "10",
"--power-limit", power_limit,
"--model", args.model,
"--dataset-path", args.dataset_path,
"--huggingface-token", hf_token,
"--num-inference-steps", num_inference_steps,
"--num-frames", args.num_frames,
],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
) as proc:
if proc.stdout:
i = 0
for line in proc.stdout:
print_and_write(outfile, line, flush=i % 50 == 0)
i += 1
# If proc exited with non-zero status, it's probably an OOM.
# Move on to the next batch size.
if proc.returncode != 0:
break
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, help="ID of the model to benchmark")
parser.add_argument("--result-root", type=str, help="Root directory to store the results")
parser.add_argument("--gpu-ids", type=str, nargs="+", help="GPU IDs to use")
parser.add_argument("--batch-sizes", type=str, nargs="+", default=["8", "4", "2", "1"], help="Batch sizes to benchmark")
parser.add_argument("--power-limits", type=str, nargs="+", default=["400", "300", "200"], help="Power limits to benchmark")
parser.add_argument("--num-inference-steps", type=str, nargs="+", default=["1", "2", "4", "8", "16", "25", "30", "40", "50"], help="Number of denoising steps")
parser.add_argument("--num-frames", type=str, required=True, help="Number of frames to generate")
parser.add_argument("--dataset-path", type=str, help="Path to the dataset JSON file.")
args = parser.parse_args()
main(args)
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