新的EM 压缩效果更好

class EmAdd(paddle.nn.Layer):
def __init__(self, voc_size=9999, hidden_size=256):
super(EmAdd, self).__init__()
self.hidden_size = hidden_size
p = 0
while True:
voc_size //= hidden_size
if voc_size == 0:
break
else:
p += 1
self.em = paddle.nn.LayerList([paddle.nn.Embedding(hidden_size, hidden_size) for _ in range(p + 1)])

def forward(self, em_add_x):

add=0
for i, em in enumerate(self.em):
i += 1
x0=em(em_add_x % self.hidden_size ** i // self.hidden_size ** (i - 1))
x0= paddle.sin(x0+add)
add=x0
return x0