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from functools import partial
import torch
from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
from diffusers import BitsAndBytesConfig, FluxTransformer2DModel
from diffusers.utils.testing_utils import torch_device
CKPT_ID = "black-forest-labs/FLUX.1-dev"
RESULT_FILENAME = "flux.csv"
def get_input_dict(**device_dtype_kwargs):
# resolution: 1024x1024
# maximum sequence length 512
hidden_states = torch.randn(1, 4096, 64, **device_dtype_kwargs)
encoder_hidden_states = torch.randn(1, 512, 4096, **device_dtype_kwargs)
pooled_prompt_embeds = torch.randn(1, 768, **device_dtype_kwargs)
image_ids = torch.ones(512, 3, **device_dtype_kwargs)
text_ids = torch.ones(4096, 3, **device_dtype_kwargs)
timestep = torch.tensor([1.0], **device_dtype_kwargs)
guidance = torch.tensor([1.0], **device_dtype_kwargs)
return {
"hidden_states": hidden_states,
"encoder_hidden_states": encoder_hidden_states,
"img_ids": image_ids,
"txt_ids": text_ids,
"pooled_projections": pooled_prompt_embeds,
"timestep": timestep,
"guidance": guidance,
}
if __name__ == "__main__":
scenarios = [
BenchmarkScenario(
name=f"{CKPT_ID}-bf16",
model_cls=FluxTransformer2DModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "transformer",
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=model_init_fn,
compile_kwargs={"fullgraph": True},
),
BenchmarkScenario(
name=f"{CKPT_ID}-bnb-nf4",
model_cls=FluxTransformer2DModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "transformer",
"quantization_config": BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4"
),
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=model_init_fn,
),
BenchmarkScenario(
name=f"{CKPT_ID}-layerwise-upcasting",
model_cls=FluxTransformer2DModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "transformer",
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
),
BenchmarkScenario(
name=f"{CKPT_ID}-group-offload-leaf",
model_cls=FluxTransformer2DModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "transformer",
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=partial(
model_init_fn,
group_offload_kwargs={
"onload_device": torch_device,
"offload_device": torch.device("cpu"),
"offload_type": "leaf_level",
"use_stream": True,
"non_blocking": True,
},
),
),
]
runner = BenchmarkMixin()
runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)
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