a = numpy.array([[1, 2]])
b = numpy.array([[2], [4]])
pa = paddle.to_tensor(a, dtype=paddle.float64)
pb = paddle.to_tensor(b, dtype=paddle.float64)
ta = torch.tensor(a, dtype=torch.float64)
tb = torch.tensor(b, dtype=torch.float64)
pr = paddle.nn.functional.cosine_similarity(pa, pb)
tr = torch.nn.functional.cosine_similarity(ta, tb)
print("paddle: ", pr)
print("torch: ", tr)

paddle:  Tensor(shape=[2], dtype=float64, place=Place(gpu:0), stop_gradient=True,
       [1.34164079, 1.34164079])
torch:  tensor([0.9487, 0.9487], dtype=torch.float64)

w12 = sum(paddle.multiply(x1, x2), axis=axis)
w1 = sum(paddle.multiply(x1, x1), axis=axis)
w2 = sum(paddle.multiply(x2, x2), axis=axis)
n12 = sqrt(clip(w1 * w2, min=eps * eps))
cos_sim = w12 / n12
return cos_sim