linear

class torch.ao.nn.quantized.functional.linear(input, weight, bias=None, scale=None, zero_point=None)

Applies a linear transformation to the incoming quantized data: \(y = xA^T + b\). See Linear

Note

Current implementation packs weights on every call, which has penalty on performance. If you want to avoid the overhead, use Linear.

Parameters:

• input (Tensor) – Quantized input of type torch.quint8

• weight (Tensor) – Quantized weight of type torch.qint8

• bias (Tensor) – None or fp32 bias of type torch.float

• scale (double) – output scale. If None, derived from the input scale

• zero_point (python:long) – output zero point. If None, derived from the input zero_point

Return type:

Tensor

Shape:

• Input: \((N, *, in\_features)\) where * means any number of additional dimensions

• Weight: \((out\_features, in\_features)\)

• Bias: \((out\_features)\)

• Output: \((N, *, out\_features)\)