run_benchmark.py

import torch


torch.set_float32_matmul_precision("high")

import sys  # noqa: E402


sys.path.append(".")
from utils.benchmarking_utils import (  # noqa: E402
    benchmark_fn,
    create_parser,
    generate_csv_dict,
    write_to_csv,
)
from utils.pipeline_utils import load_pipeline  # noqa: E402


def run_inference(pipe, args):
    _ = pipe(
        prompt=args.prompt,
        num_inference_steps=args.num_inference_steps,
        num_images_per_prompt=args.batch_size,
    )


def main(args) -> dict:
    pipeline = load_pipeline(
        ckpt=args.ckpt,
        compile_unet=args.compile_unet,
        compile_vae=args.compile_vae,
        no_sdpa=args.no_sdpa,
        no_bf16=args.no_bf16,
        upcast_vae=args.upcast_vae,
        enable_fused_projections=args.enable_fused_projections,
        do_quant=args.do_quant,
        compile_mode=args.compile_mode,
        change_comp_config=args.change_comp_config,
        device=args.device,
    )

    # Warmup.
    run_inference(pipeline, args)
    run_inference(pipeline, args)
    run_inference(pipeline, args)

    time = benchmark_fn(run_inference, pipeline, args)  # in seconds.

    data_dict = generate_csv_dict(
        pipeline_cls=str(pipeline.__class__.__name__),
        args=args,
        time=time,
    )
    img = pipeline(
        prompt=args.prompt,
        num_inference_steps=args.num_inference_steps,
        num_images_per_prompt=args.batch_size,
    ).images[0]

    return data_dict, img


if __name__ == "__main__":
    parser = create_parser()
    args = parser.parse_args()
    print(args)

    data_dict, img = main(args)

    name = (
        args.ckpt.replace("/", "_")
        + f"bf16@{not args.no_bf16}-sdpa@{not args.no_sdpa}-bs@{args.batch_size}-fuse@{args.enable_fused_projections}-upcast_vae@{args.upcast_vae}-steps@{args.num_inference_steps}-unet@{args.compile_unet}-vae@{args.compile_vae}-mode@{args.compile_mode}-change_comp_config@{args.change_comp_config}-do_quant@{args.do_quant}-tag@{args.tag}-device@{args.device}.csv"
    )
    img.save(f'{name.replace(".csv", "")}.jpeg')
    write_to_csv(name, data_dict)