0c60922fb0614132433779ad45ab8f30783db2ae,test/quantization/test_workflow_module.py,,_fake_quantize_learnable_per_channel_affine_grad_reference,#Any#Any#Any#Any#Any#Any#Any#Any#,146

Before Change

        X_i = X_flattened[i]
        dY_i = dY_flattened[i]

        Xq_i = _quantize_per_tensor(
            X_i, scale_i, zero_point_i, quant_min, quant_max).to(device)
        Xfq_i = (Xq_i - zero_point_i) * scale_i

        indicate_small_scale_i = (Xq_i == quant_min).float().to(device)
        indicate_big_scale_i = (Xq_i == quant_max).float().to(device)

After Change

        X_i = X_flattened[i]
        dY_i = dY_flattened[i]

        Xq_i = ((X_i / scale_i) + zero_point_i).round()
        Xfq_i = (Xq_i - zero_point_i) * scale_i

        indicate_small_scale_i = (Xq_i < quant_min).float().to(device)
        indicate_big_scale_i = (Xq_i > quant_max).float().to(device)
        indicate_middle_scale_i = torch.ones(indicate_small_scale_i.shape).to(device) - \
            indicate_small_scale_i - indicate_big_scale_i

        indicate_saturate_zp_i = ((Xq_i < quant_min).float() +
                                  (Xq_i > quant_max).float()).to(device)
        indicate_unsaturate_zp_i = torch.ones(indicate_saturate_zp_i.shape).to(device) - \
            indicate_saturate_zp_i

        Xq_i = Xq_i.clamp(quant_min, quant_max)
        Xfq_i = (Xq_i - zero_point_i) * scale_i

        grad_small_scale_i = quant_min - zero_point_i
        grad_big_scale_i = quant_max - zero_point_i

Instances