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