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from functools import partial
import torch
from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
from diffusers import WanTransformer3DModel
from diffusers.utils.testing_utils import torch_device
CKPT_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
RESULT_FILENAME = "wan.csv"
def get_input_dict(**device_dtype_kwargs):
# height: 480
# width: 832
# num_frames: 81
# max_sequence_length: 512
hidden_states = torch.randn(1, 16, 21, 60, 104, **device_dtype_kwargs)
encoder_hidden_states = torch.randn(1, 512, 4096, **device_dtype_kwargs)
timestep = torch.tensor([1.0], **device_dtype_kwargs)
return {"hidden_states": hidden_states, "encoder_hidden_states": encoder_hidden_states, "timestep": timestep}
if __name__ == "__main__":
scenarios = [
BenchmarkScenario(
name=f"{CKPT_ID}-bf16",
model_cls=WanTransformer3DModel,
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}-layerwise-upcasting",
model_cls=WanTransformer3DModel,
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=WanTransformer3DModel,
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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