torch.fake_quantize_per_tensor_affine — PyTorch 2.7 documentation (original) (raw)

torch.fake_quantize_per_tensor_affine(input, scale, zero_point, quant_min, quant_max) → Tensor¶

Returns a new tensor with the data in input fake quantized using scale,zero_point, quant_min and quant_max.

output=(min(quant_max,max(quant_min,std::nearby_int(input/scale)+zero_point))−zero_point)×scale\text{output} = ( min( \text{quant\_max}, max( \text{quant\_min}, \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} ) ) - \text{zero\_point} ) \times \text{scale}

Parameters

• input (Tensor) – the input value(s), torch.float32 tensor
• scale (double scalar or float32 Tensor) – quantization scale
• zero_point (int64 scalar or int32 Tensor) – quantization zero_point
• quant_min (int64) – lower bound of the quantized domain
• quant_max (int64) – upper bound of the quantized domain

Returns

A newly fake_quantized torch.float32 tensor

Return type

Tensor

Example:

x = torch.randn(4) x tensor([ 0.0552, 0.9730, 0.3973, -1.0780]) torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255) tensor([0.1000, 1.0000, 0.4000, 0.0000]) torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255) tensor([0.1000, 1.0000, 0.4000, 0.0000])