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Source code for torch.ao.nn.qat.dynamic.modules.linear

# mypy: allow-untyped-defs
import torch

__all__ = ["Linear"]

[docs]class Linear(torch.ao.nn.qat.Linear): r""" A linear module attached with FakeQuantize modules for weight, used for dynamic quantization aware training. We adopt the same interface as `torch.nn.Linear`, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation. Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to default. """ def __init__(self, in_features, out_features, bias=True, qconfig=None, device=None, dtype=None) -> None: super().__init__(in_features, out_features, bias, qconfig, device, dtype) if not torch.ao.quantization.qconfig._activation_is_memoryless(qconfig): raise ValueError( "Dynamic QAT requires a memoryless observer." + "This means a MovingAverage observer with averaging constant equal to 1" )

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