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# File: diffusion-fast-main/prepare_results.py
import argparse
import glob
import os
import sys
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from huggingface_hub import upload_file
sys.path.append('.')
from utils.benchmarking_utils import collate_csv
REPO_ID = 'sayakpaul/sample-datasets'
def prepare_plot(df, args):
columns_to_drop = ['batch_size', 'num_inference_steps', 'pipeline_cls', 'ckpt_id', 'upcast_vae', 'memory (gbs)', 'actual_gpu_memory (gbs)', 'tag']
df_filtered = df.drop(columns=columns_to_drop)
df_filtered[['quant']] = df_filtered[['do_quant']].fillna('None')
df_filtered.drop(columns=['do_quant'], inplace=True)
df_filtered['settings'] = df_filtered.apply(lambda row: ', '.join([f'{col}-{row[col]}' for col in df_filtered.columns if col != 'time (secs)']), axis=1)
df_filtered['formatted_settings'] = df_filtered['settings'].str.replace(', ', '\n', regex=False)
df_filtered.loc[0, 'formatted_settings'] = 'default'
plt.figure(figsize=(12, 10))
sns.set_style('whitegrid')
n_settings = len(df_filtered['formatted_settings'].unique())
bar_positions = range(n_settings)
palette = sns.color_palette('husl', n_settings)
bar_width = 0.25
for (i, setting) in enumerate(df_filtered['formatted_settings'].unique()):
mean_time = df_filtered[df_filtered['formatted_settings'] == setting]['time (secs)'].mean()
plt.bar(i, mean_time, width=bar_width, align='center', color=palette[i])
plt.text(i, mean_time + 0.01, f'{mean_time:.2f}', ha='center', va='bottom', fontsize=14, fontweight='bold')
plt.xticks(bar_positions, df_filtered['formatted_settings'].unique(), rotation=45, ha='right', fontsize=10)
plt.ylabel('Time in Seconds', fontsize=14, labelpad=15)
plt.xlabel('Settings', fontsize=14, labelpad=15)
plt.title(args.plot_title, fontsize=18, fontweight='bold', pad=20)
plt.grid(axis='y', linestyle='--', linewidth=0.7, alpha=0.7)
plt.tight_layout()
plt.subplots_adjust(top=0.9, bottom=0.2)
plot_path = args.plot_title.replace(' ', '_') + '.png'
plt.savefig(plot_path, bbox_inches='tight', dpi=300)
if args.push_to_hub:
upload_file(repo_id=REPO_ID, path_in_repo=plot_path, path_or_fileobj=plot_path, repo_type='dataset')
print(f'Plot successfully uploaded. Find it here: https://huggingface.co/datasets/{REPO_ID}/blob/main/{args.plot_file_path}')
plt.show()
def main(args):
all_csvs = sorted(glob.glob(f'{args.base_path}/*.csv'))
all_csvs = [os.path.join(args.base_path, x) for x in all_csvs]
is_pixart = 'PixArt-alpha' in all_csvs[0]
collate_csv(all_csvs, args.final_csv_filename, is_pixart=is_pixart)
if args.push_to_hub:
upload_file(repo_id=REPO_ID, path_in_repo=args.final_csv_filename, path_or_fileobj=args.final_csv_filename, repo_type='dataset')
print(f'CSV successfully uploaded. Find it here: https://huggingface.co/datasets/{REPO_ID}/blob/main/{args.final_csv_filename}')
if args.plot_title is not None:
df = pd.read_csv(args.final_csv_filename)
prepare_plot(df, args)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--base_path', type=str, default='.')
parser.add_argument('--final_csv_filename', type=str, default='collated_results.csv')
parser.add_argument('--plot_title', type=str, default=None)
parser.add_argument('--push_to_hub', action='store_true')
args = parser.parse_args()
main(args)
# File: diffusion-fast-main/run_benchmark.py
import torch
torch.set_float32_matmul_precision('high')
import sys
sys.path.append('.')
from utils.benchmarking_utils import benchmark_fn, create_parser, generate_csv_dict, write_to_csv
from utils.pipeline_utils import load_pipeline
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)
run_inference(pipeline, args)
run_inference(pipeline, args)
run_inference(pipeline, args)
time = benchmark_fn(run_inference, pipeline, args)
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)
# File: diffusion-fast-main/run_benchmark_pixart.py
import torch
torch.set_float32_matmul_precision('high')
import sys
sys.path.append('.')
from utils.benchmarking_utils import benchmark_fn, create_parser, generate_csv_dict, write_to_csv
from utils.pipeline_utils_pixart import load_pipeline
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_transformer=args.compile_transformer, compile_vae=args.compile_vae, no_sdpa=args.no_sdpa, no_bf16=args.no_bf16, 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)
run_inference(pipeline, args)
run_inference(pipeline, args)
run_inference(pipeline, args)
time = benchmark_fn(run_inference, pipeline, args)
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(is_pixart=True)
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@NA-steps@{args.num_inference_steps}-transformer@{args.compile_transformer}-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}.jpeg')
write_to_csv(name, data_dict, is_pixart=True)
# File: diffusion-fast-main/run_profile.py
import torch
torch.set_float32_matmul_precision('high')
from torch._inductor import config as inductorconfig
inductorconfig.triton.unique_kernel_names = True
import functools
import sys
sys.path.append('.')
from utils.benchmarking_utils import create_parser
from utils.pipeline_utils import load_pipeline
def profiler_runner(path, fn, *args, **kwargs):
with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA], record_shapes=True) as prof:
result = fn(*args, **kwargs)
prof.export_chrome_trace(path)
return result
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)
run_inference(pipeline, args)
run_inference(pipeline, args)
trace_path = 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}-device@{args.device}.json'
runner = functools.partial(profiler_runner, trace_path)
with torch.autograd.profiler.record_function('sdxl-brrr'):
runner(run_inference, pipeline, args)
return trace_path
if __name__ == '__main__':
parser = create_parser()
args = parser.parse_args()
if not args.compile_unet:
args.compile_mode = 'NA'
trace_path = main(args)
print(f'Trace generated at: {trace_path}')
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