File size: 8,021 Bytes
10a1462 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | """
Profile diffusers pipelines with torch.profiler.
Usage:
python profiling/profiling_pipelines.py --pipeline flux --mode eager
python profiling/profiling_pipelines.py --pipeline flux --mode compile
python profiling/profiling_pipelines.py --pipeline flux --mode both
python profiling/profiling_pipelines.py --pipeline all --mode eager
python profiling/profiling_pipelines.py --pipeline wan --mode eager --full_decode
python profiling/profiling_pipelines.py --pipeline flux --mode compile --num_steps 4
Benchmarking (wall-clock time, no profiler overhead):
python profiling/profiling_pipelines.py --pipeline flux --mode compile --benchmark
python profiling/profiling_pipelines.py --pipeline flux --mode both --benchmark --num_runs 10 --num_warmups 3
"""
import argparse
import copy
import logging
import torch
from profiling_utils import PipelineProfiler, PipelineProfilingConfig
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
logger = logging.getLogger(__name__)
PROMPT = "A cat holding a sign that says hello world"
def build_registry():
"""Build the pipeline config registry. Imports are deferred to avoid loading all pipelines upfront."""
from diffusers import Flux2KleinPipeline, FluxPipeline, LTX2Pipeline, QwenImagePipeline, WanPipeline
return {
"flux": PipelineProfilingConfig(
name="flux",
pipeline_cls=FluxPipeline,
pipeline_init_kwargs={
"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-dev",
"torch_dtype": torch.bfloat16,
},
pipeline_call_kwargs={
"prompt": PROMPT,
"height": 1024,
"width": 1024,
"num_inference_steps": 4,
"guidance_scale": 3.5,
"output_type": "latent",
},
),
"flux2": PipelineProfilingConfig(
name="flux2",
pipeline_cls=Flux2KleinPipeline,
pipeline_init_kwargs={
"pretrained_model_name_or_path": "black-forest-labs/FLUX.2-klein-base-9B",
"torch_dtype": torch.bfloat16,
},
pipeline_call_kwargs={
"prompt": PROMPT,
"height": 1024,
"width": 1024,
"num_inference_steps": 4,
"guidance_scale": 3.5,
"output_type": "latent",
},
),
"wan": PipelineProfilingConfig(
name="wan",
pipeline_cls=WanPipeline,
pipeline_init_kwargs={
"pretrained_model_name_or_path": "Wan-AI/Wan2.1-T2V-14B-Diffusers",
"torch_dtype": torch.bfloat16,
},
pipeline_call_kwargs={
"prompt": PROMPT,
"negative_prompt": "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards",
"height": 480,
"width": 832,
"num_frames": 81,
"num_inference_steps": 4,
"output_type": "latent",
},
),
"ltx2": PipelineProfilingConfig(
name="ltx2",
pipeline_cls=LTX2Pipeline,
pipeline_init_kwargs={
"pretrained_model_name_or_path": "Lightricks/LTX-2",
"torch_dtype": torch.bfloat16,
},
pipeline_call_kwargs={
"prompt": PROMPT,
"negative_prompt": "worst quality, inconsistent motion, blurry, jittery, distorted",
"height": 512,
"width": 768,
"num_frames": 121,
"num_inference_steps": 4,
"guidance_scale": 4.0,
"output_type": "latent",
},
),
"qwenimage": PipelineProfilingConfig(
name="qwenimage",
pipeline_cls=QwenImagePipeline,
pipeline_init_kwargs={
"pretrained_model_name_or_path": "Qwen/Qwen-Image",
"torch_dtype": torch.bfloat16,
},
pipeline_call_kwargs={
"prompt": PROMPT,
"negative_prompt": " ",
"height": 1024,
"width": 1024,
"num_inference_steps": 4,
"true_cfg_scale": 4.0,
"output_type": "latent",
},
),
}
def main():
parser = argparse.ArgumentParser(description="Profile diffusers pipelines with torch.profiler")
parser.add_argument(
"--pipeline",
choices=["flux", "flux2", "wan", "ltx2", "qwenimage", "all"],
required=True,
help="Which pipeline to profile",
)
parser.add_argument(
"--mode",
choices=["eager", "compile", "both"],
default="eager",
help="Run in eager mode, compile mode, or both",
)
parser.add_argument("--output_dir", default="profiling_results", help="Directory for trace output")
parser.add_argument("--num_steps", type=int, default=None, help="Override num_inference_steps")
parser.add_argument("--full_decode", action="store_true", help="Profile including VAE decode (output_type='pil')")
parser.add_argument(
"--compile_mode",
default="default",
choices=["default", "reduce-overhead", "max-autotune"],
help="torch.compile mode",
)
parser.add_argument("--compile_fullgraph", action="store_true", help="Use fullgraph=True for torch.compile")
parser.add_argument(
"--compile_regional",
action="store_true",
help="Use compile_repeated_blocks() instead of full model compile",
)
parser.add_argument(
"--benchmark",
action="store_true",
help="Benchmark wall-clock time instead of profiling. Uses CUDA events, no profiler overhead.",
)
parser.add_argument("--num_runs", type=int, default=5, help="Number of timed runs for benchmarking")
parser.add_argument("--num_warmups", type=int, default=2, help="Number of warmup runs for benchmarking")
args = parser.parse_args()
registry = build_registry()
pipeline_names = list(registry.keys()) if args.pipeline == "all" else [args.pipeline]
modes = ["eager", "compile"] if args.mode == "both" else [args.mode]
for pipeline_name in pipeline_names:
for mode in modes:
config = copy.deepcopy(registry[pipeline_name])
# Apply overrides
if args.num_steps is not None:
config.pipeline_call_kwargs["num_inference_steps"] = args.num_steps
if args.full_decode:
config.pipeline_call_kwargs["output_type"] = "pil"
if mode == "compile":
config.compile_kwargs = {
"fullgraph": args.compile_fullgraph,
"mode": args.compile_mode,
}
config.compile_regional = args.compile_regional
profiler = PipelineProfiler(config, args.output_dir)
try:
if args.benchmark:
logger.info(f"Benchmarking {pipeline_name} in {mode} mode...")
profiler.benchmark(num_runs=args.num_runs, num_warmups=args.num_warmups)
else:
logger.info(f"Profiling {pipeline_name} in {mode} mode...")
trace_file = profiler.run()
logger.info(f"Done: {trace_file}")
except Exception as e:
logger.error(f"Failed to {'benchmark' if args.benchmark else 'profile'} {pipeline_name} ({mode}): {e}")
if __name__ == "__main__":
main()
|