class torch.nn.quantized.dynamic.Linear(in_features, out_features, bias_=True, dtype=torch.qint8)[source]

A dynamic quantized linear module with floating point tensor as inputs and outputs. We adopt the same interface as torch.nn.Linear, please see for documentation.

Similar to torch.nn.Linear, attributes will be randomly initialized at module creation time and will be overwritten later

  • ~Linear.weight (Tensor) – the non-learnable quantized weights of the module which are of shape (out_features,in_features)(\text{out\_features}, \text{in\_features}).

  • ~Linear.bias (Tensor) – the non-learnable floating point bias of the module of shape (out_features)(\text{out\_features}). If bias is True, the values are initialized to zero.


>>> m = nn.quantized.dynamic.Linear(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
classmethod from_float(mod)[source]

Create a dynamic quantized module from a float module or qparams_dict


mod (Module) – a float module, either produced by utilities or provided by the user

classmethod from_reference(ref_qlinear)[source]

Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized module :param ref_qlinear: a reference quantized module, either produced by :type ref_qlinear: Module :param functions or provided by the user:


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