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

import torch
from import activation_is_memoryless

[docs]class Linear(torch.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 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 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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