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cbdedbaf03
* add attention backend tests. * remove existing tests/others/test_attention_backends.py file * modify generate_model_tests.py * remove native. * account for _keep_in_fp32_modules * don't skip when exception is raised. * use is_kernels_available() * mark with compile. * move rtol and atol to methods as defaults. * Apply suggestions from code review Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> * up * up
26 lines
921 B
Python
26 lines
921 B
Python
import torch
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from diffusers.models.attention_dispatch import AttentionBackendName
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_BF16_REQUIRED_BACKENDS = {
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AttentionBackendName._NATIVE_CUDNN,
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AttentionBackendName.FLASH_HUB,
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AttentionBackendName.FLASH_VARLEN_HUB,
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AttentionBackendName._FLASH_3_HUB,
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}
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def _maybe_cast_to_bf16(backend, model, inputs_dict):
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"""Cast model and floating-point inputs to bfloat16 when the backend requires it."""
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if not backend or backend not in _BF16_REQUIRED_BACKENDS:
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return model, inputs_dict
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if getattr(model, "_keep_in_fp32_modules", None):
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raise NotImplementedError("Do not know how to define casting for models with `_keep_in_fp32_modules`.")
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model = model.to(dtype=torch.bfloat16)
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inputs_dict = {
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k: v.to(dtype=torch.bfloat16) if isinstance(v, torch.Tensor) and v.is_floating_point() else v
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for k, v in inputs_dict.items()
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}
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return model, inputs_dict
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