time_count.py

import json
import os.path as osp
import time

from diffusers import DiffusionPipeline
import migraphx_diffusers
from migraphx_diffusers import AutoTimer
import torch


def parse_args():
    date_str = time.strftime("%Y%m%d-%H%M%S", time.localtime())

    from argparse import ArgumentParser
    parser = ArgumentParser(description="SDXL inference with migraphx backend")

    #=========================== mdoel load and compile ========================
    parser.add_argument(
        "-m", 
        "--model-dir",
        type=str,
        required=True,
        help="Path to local model directory.",
    )
    parser.add_argument(
        "--force-compile",
        action="store_true",
        default=False,
        help="Ignore existing .mxr files and override them",
    )
    parser.add_argument(
        "--img-size",
        type=int,
        default=None,
        help="output image size",
    )
    parser.add_argument(
        "--num-images-per-prompt",
        type=int,
        default=1,
        help="The number of images to generate per prompt."
    )
    # --------------------------------------------------------------------------

    # =============================== generation ===============================
    parser.add_argument(
        "-t",
        "--num-inference-steps",
        type=int,
        default=50,
        help="Number of iteration steps",
    )
    parser.add_argument(
        "--out-csv-file",
        type=str,
        default=f"./perf-{date_str}.csv",
        help="Prefix of path for saving results",
    )
    # --------------------------------------------------------------------------

    # =============================== time count ===============================
    parser.add_argument(
        "--num-warmup-loops",
        type=int,
        default=1,
        help="warmup loops",
    )
    parser.add_argument(
        "--num-count-loops",
        type=int,
        default=100,
        help="time count loops",
    )
    # --------------------------------------------------------------------------

    args = parser.parse_args()
    return args


def get_name_and_migraphx_config(model_dir):
    model_index_json = osp.join(model_dir, "model_index.json")
    with open(model_index_json, "r") as f:
        pipe_cfg = json.load(f)
    
    if pipe_cfg["_class_name"] == "StableDiffusionXLPipeline":
        return 'sdxl', migraphx_diffusers.DEFAULT_ARGS['sdxl']
    elif pipe_cfg["_class_name"] == "StableDiffusionPipeline":
        return 'sd2.1', migraphx_diffusers.DEFAULT_ARGS['sd2.1']
    else:
        raise NotImplementedError(
            f"{pipe_cfg['_class_name']} has not been adapted yet")


def main():
    args = parse_args()
    pipe_name, migraphx_config = get_name_and_migraphx_config(args.model_dir)
    assert pipe_name in ['sdxl', 'sd2.1'], "Only support SDXL or SD2.1!"

    if args.img_size is not None:
        migraphx_config['common_args']['img_size'] = args.img_size
    migraphx_config['common_args'].update(dict(
        batch=args.num_images_per_prompt,
        force_compile=args.force_compile,
    ))

    pipe = DiffusionPipeline.from_pretrained(
        args.model_dir,
        torch_dtype=torch.float16, 
        migraphx_config=migraphx_config
    )
    pipe.to("cuda")

    t = AutoTimer()
    t.add_targets([
        (pipe, "end2end"), 
        (pipe.text_encoder, "text_encoder"), 
        (pipe.unet, "unet"),
        (pipe.vae.decode, "vae_decoder")
    ])
    if hasattr(pipe, "text_encoder_2"):
        t.add_target(pipe.text_encoder_2, key="text_encoder_2")
    
    for i in range(args.num_warmup_loops + args.num_count_loops):
        if i == args.num_warmup_loops:
            t.start_work()
        pipe(prompt="the ocean in dream", 
             negative_prompt=None, 
             num_inference_steps=args.num_inference_steps)

    table = t.summary(batchsize=migraphx_config['common_args']['batch'])
    t.clear()

    with open(args.out_csv_file, 'w') as f:
        f.write(table.get_csv_string())


if __name__ == "__main__":
    main()