Linear¶
- class torch.ao.nn.quantized.Linear(in_features, out_features, bias_=True, dtype=torch.qint8)[source]¶
A quantized linear module with quantized tensor as inputs and outputs. 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
Linear
, attributes will be randomly initialized at module creation time and will be overwritten later- Variables
weight (Tensor) – the non-learnable quantized weights of the module of shape .
bias (Tensor) – the non-learnable bias of the module of shape . If
bias
isTrue
, the values are initialized to zero.scale – scale parameter of output Quantized Tensor, type: double
zero_point – zero_point parameter for output Quantized Tensor, type: long
Examples:
>>> m = nn.quantized.Linear(20, 30) >>> input = torch.randn(128, 20) >>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30])
- classmethod from_float(mod)[source]¶
Create a quantized module from an observed float module
- Parameters
mod (Module) – a float module, either produced by torch.ao.quantization utilities or provided by the user