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Part of #109802 Pull Request resolved: https://github.com/pytorch/pytorch/pull/111377 Approved by: https://github.com/albanD, https://github.com/aaronenyeshi
28 lines
1.1 KiB
ReStructuredText
28 lines
1.1 KiB
ReStructuredText
torch.utils.deterministic
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=========================
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.. py:module:: torch.utils.deterministic
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.. currentmodule:: torch.utils.deterministic
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.. attribute:: fill_uninitialized_memory
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A :class:`bool` that, if True, causes uninitialized memory to be filled with
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a known value when :meth:`torch.use_deterministic_algorithms()` is set to
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``True``. Floating point and complex values are set to NaN, and integer
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values are set to the maximum value.
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Default: ``True``
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Filling uninitialized memory is detrimental to performance. So if your
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program is valid and does not use uninitialized memory as the input to an
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operation, then this setting can be turned off for better performance and
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still be deterministic.
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The following operations will fill uninitialized memory when this setting is
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turned on:
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* :func:`torch.Tensor.resize_` when called with a tensor that is not
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quantized
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* :func:`torch.empty`
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* :func:`torch.empty_strided`
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* :func:`torch.empty_permuted`
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* :func:`torch.empty_like` |