File size: 2,986 Bytes
751adfb |
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 |
from functools import partial
import torch
from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
from diffusers import LTXVideoTransformer3DModel
from diffusers.utils.testing_utils import torch_device
CKPT_ID = "Lightricks/LTX-Video-0.9.7-dev"
RESULT_FILENAME = "ltx.csv"
def get_input_dict(**device_dtype_kwargs):
# 512x704 (161 frames)
# `max_sequence_length`: 256
hidden_states = torch.randn(1, 7392, 128, **device_dtype_kwargs)
encoder_hidden_states = torch.randn(1, 256, 4096, **device_dtype_kwargs)
encoder_attention_mask = torch.ones(1, 256, **device_dtype_kwargs)
timestep = torch.tensor([1.0], **device_dtype_kwargs)
video_coords = torch.randn(1, 3, 7392, **device_dtype_kwargs)
return {
"hidden_states": hidden_states,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
"timestep": timestep,
"video_coords": video_coords,
}
if __name__ == "__main__":
scenarios = [
BenchmarkScenario(
name=f"{CKPT_ID}-bf16",
model_cls=LTXVideoTransformer3DModel,
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=LTXVideoTransformer3DModel,
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=LTXVideoTransformer3DModel,
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)
|