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c404b2968c
Summary: After QAT is completed or given pre-tuned weight observer via tunable PTQ algorithm, it should not over-write again with a given weight, at least for static QAT never. Dynamic QAT also does not require to re-run weight observer again by design. This is a fix Test Plan: Signals Differential Revision: D57747749 Pull Request resolved: https://github.com/pytorch/pytorch/pull/127309 Approved by: https://github.com/jerryzh168
28 lines
777 B
Python
28 lines
777 B
Python
import torch
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__all__ = ['Dropout']
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class Dropout(torch.nn.Dropout):
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r"""This is the quantized equivalent of :class:`~torch.nn.Dropout`.
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And this is a placeholder to enable models where fp32 tensors
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had dropout to work with quantized tensors in train and eval mode.
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Args:
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p: probability of an element to be zeroed
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inplace: can optionally do the operation in-place. Default: ``False``
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"""
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def forward(self, input):
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return input
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def _get_name(self):
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return 'QuantizedDropout'
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@classmethod
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def from_float(cls, mod, use_precomputed_fake_quant=False):
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return cls(mod.p, mod.inplace)
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@classmethod
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def from_reference(cls, mod, scale, zero_point):
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return cls(mod.p, mod.inplace)
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