| import torch |
|
|
| from diffusers.models.attention_dispatch import AttentionBackendName |
|
|
|
|
| _BF16_REQUIRED_BACKENDS = { |
| AttentionBackendName._NATIVE_CUDNN, |
| AttentionBackendName.FLASH_HUB, |
| AttentionBackendName.FLASH_VARLEN_HUB, |
| AttentionBackendName._FLASH_3_HUB, |
| } |
|
|
|
|
| def _maybe_cast_to_bf16(backend, model, inputs_dict): |
| """Cast model and floating-point inputs to bfloat16 when the backend requires it.""" |
| if not backend or backend not in _BF16_REQUIRED_BACKENDS: |
| return model, inputs_dict |
| if getattr(model, "_keep_in_fp32_modules", None): |
| raise NotImplementedError("Do not know how to define casting for models with `_keep_in_fp32_modules`.") |
| model = model.to(dtype=torch.bfloat16) |
| inputs_dict = { |
| k: v.to(dtype=torch.bfloat16) if isinstance(v, torch.Tensor) and v.is_floating_point() else v |
| for k, v in inputs_dict.items() |
| } |
| return model, inputs_dict |
|
|
