关于GELU函数的实现

pytorch中Gelu的实现：

近似计算1：快，但不精确

近似计算2：精确，但是不快

i = torch.randn(20)
c1 = nn.GELU()
c2 = nn.Sigmoid()
c3 = nn.Tanh()
print(c1(i))
print(i*c2(1.702*i))
print(0.5 * i * (1 + c3(i * 0.7978845608 * (1 + 0.044715 * i * i))))
print(i * 0.5 * (1.0 + torch.erf(i / math.sqrt(2.0))))
tensor([-0.1466,  0.2441,  1.7839,  1.9687,  0.4893, -0.1635, -0.1200, -0.0208,
        -0.0149, -0.1698, -0.0576, -0.1675,  0.1781,  1.6642,  0.1036, -0.1044,
         1.0159,  0.2654,  0.9210,  0.1539])
tensor([-0.1425,  0.2472,  1.7674,  1.9497,  0.4954, -0.1582, -0.1176, -0.0207,
        -0.0342, -0.1635, -0.0745, -0.1615,  0.1801,  1.6499,  0.1045, -0.1116,
         1.0172,  0.2689,  0.9241,  0.1555])
tensor([-0.1466,  0.2441,  1.7839,  1.9688,  0.4892, -0.1636, -0.1200, -0.0208,
        -0.0144, -0.1699, -0.0576, -0.1676,  0.1781,  1.6641,  0.1036, -0.1046,
         1.0157,  0.2654,  0.9208,  0.1539])
tensor([-0.1466,  0.2441,  1.7839,  1.9687,  0.4893, -0.1635, -0.1200, -0.0208,
        -0.0149, -0.1698, -0.0576, -0.1675,  0.1781,  1.6642,  0.1036, -0.1044,
         1.0159,  0.2654,  0.9210,  0.1539])