Mx FP Quantization About Subnorm

[Jzz24]

When quantizing Mx fp, the quantization scales of subnormal and normal values ​​should be different. Why does L394 clip to min_exp? I understand that it should clip to 1.

Looking forward to your reply

if exp_bits != 0:
    private_exp = torch.floor(torch.log2(torch.abs(A) + (A == 0).type(A.dtype)))

    # #The minimum representable exponent for 8 exp bits is -126
    # min_exp = -(2 ** (exp_bits - 1)) + 2
    # private_exp = private_exp.clip(min=min_exp)

    # subnorm and norm part has different scale
    # private_exp >= 1, norm scale
    # private_exp < 1, subnorm scale
    private_exp = private_exp.clip(min=1.0)
else:
    private_exp = None

https://github.com/intel/neural-compressor/blob/master/neural_compressor/torch/algorithms/mx_quant/utils.py#L394
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