quanto-exps-2/benchmark.py

import argparse
from optimum.quanto import freeze, qfloat8, qint4, qint8, quantize
import torch
import json
import torch.utils.benchmark as benchmark
from diffusers import DiffusionPipeline
import gc


WARM_UP_ITERS = 5
PROMPT = "ghibli style, a fantasy landscape with castles"

TORCH_DTYPES = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}
QTYPES = {"fp8": qfloat8, "int8": qint8, "int4": qint4, "none": None}
# QTYPES = {"fp8": qfloat8, "int8": qint8, "none": None}

PREFIXES = {
 "stabilityai/stable-diffusion-3-medium-diffusers": "sd3",
 "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS": "pixart",
 "fal/AuraFlow": "auraflow",
}

def flush():
 """Wipes off memory."""
 gc.collect()
 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()

def load_pipeline(ckpt_id, torch_dtype, qtype=None, exclude_layers=None, qte=False, fuse=False):
 pipe = DiffusionPipeline.from_pretrained(ckpt_id, torch_dtype=torch_dtype).to("cuda")
 if fuse:
 pipe.transformer.fuse_qkv_projections()

 if qtype:
 quantize(pipe.transformer, weights=qtype, exclude=exclude_layers)
 freeze(pipe.transformer)

 if qte:
 quantize(pipe.text_encoder, weights=qtype)
 freeze(pipe.text_encoder)
 if hasattr(pipe, "text_encoder_2"):
 quantize(pipe.text_encoder_2, weights=qtype)
 freeze(pipe.text_encoder_2)
 if hasattr(pipe, "text_encoder_3"):
 quantize(pipe.text_encoder_3, weights=qtype)
 freeze(pipe.text_encoder_3)

 pipe.set_progress_bar_config(disable=True)
 return pipe


def run_inference(pipe, batch_size=1):
 _ = pipe(
 prompt=PROMPT,
 num_images_per_prompt=batch_size,
 generator=torch.manual_seed(0),
 )


def benchmark_fn(f, *args, **kwargs):
 t0 = benchmark.Timer(stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f})
 return f"{(t0.blocked_autorange().mean):.3f}"


def bytes_to_giga_bytes(bytes):
 return f"{(bytes / 1024 / 1024 / 1024):.3f}"


if __name__ == "__main__":
 parser = argparse.ArgumentParser()
 parser.add_argument(
 "--ckpt_id",
 type=str,
 default="stabilityai/stable-diffusion-3-medium-diffusers",
 choices=list(PREFIXES.keys()),
 )
 parser.add_argument("--batch_size", type=int, default=1)
 parser.add_argument("--torch_dtype", type=str, default="fp16", choices=list(TORCH_DTYPES.keys()))
 parser.add_argument("--qtype", type=str, default="none", choices=list(QTYPES.keys()))
 parser.add_argument("--qte", type=int, default=0, help="Quantize text encoder")
 parser.add_argument("--fuse", type=int, default=0)
 parser.add_argument("--exclude_layers", metavar="N", type=str, nargs="*", default=None)
 args = parser.parse_args()

 flush()

 print(
 f"Running with ckpt_id: {args.ckpt_id}, batch_size: {args.batch_size}, torch_dtype: {args.torch_dtype}, qtype: {args.qtype}, qte: {bool(args.qte)}, {args.exclude_layers=}, {args.fuse=}"
 )
 pipeline = load_pipeline(
 ckpt_id=args.ckpt_id,
 torch_dtype=TORCH_DTYPES[args.torch_dtype],
 qtype=QTYPES[args.qtype],
 exclude_layers=args.exclude_layers,
 qte=args.qte,
 fuse=bool(args.fuse),
 )

 for _ in range(WARM_UP_ITERS):
 run_inference(pipeline, args.batch_size)

 time = benchmark_fn(run_inference, pipeline, args.batch_size)
 torch.cuda.empty_cache()
 memory = bytes_to_giga_bytes(torch.cuda.memory_allocated()) # in GBs.
 print(
 f"ckpt: {args.ckpt_id} batch_size: {args.batch_size}, qte: {args.qte}, {args.exclude_layers=} "
 f"torch_dtype: {args.torch_dtype}, qtype: {args.qtype} in {time} seconds and {memory} GBs."
 )

 ckpt_id = PREFIXES[args.ckpt_id]
 img_name = f"ckpt@{ckpt_id}-bs@{args.batch_size}-dtype@{args.torch_dtype}-qtype@{args.qtype}-qte@{args.qte}-fuse@{args.fuse}"
 if args.exclude_layers:
 exclude_layers = "_".join(args.exclude_layers)
 img_name += f"-exclude@{exclude_layers}"
 image = pipeline(
 prompt=PROMPT,
 num_images_per_prompt=args.batch_size,
 generator=torch.manual_seed(0),
 ).images[0]
 image.save(f"{img_name}.png")

 info = dict(
 batch_size=args.batch_size,
 memory=memory,
 time=time,
 dtype=args.torch_dtype,
 qtype=args.qtype,
 qte=args.qte,
 exclude_layers=args.exclude_layers,
 fuse=args.fuse,
 )
 info_file = f"{img_name}_info.json"
 with open(info_file, "w") as f:
 json.dump(info, f)