import torch
import numpy as np
class MaxState(torch.nn.Module):
def __init__(self, hidden_dim, heads, win):
super(MaxState, self).__init__()
assert hidden_dim % heads == 0, "Hidden size must be divisible by the number of heads."
self.head_size = hidden_dim // heads
self.head0 = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
self.head1 = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
self.head2 = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
# self.h_linear=torch.nn.Parameter(torch.empty(5, 1))
# torch.nn.init.xavier_uniform_(self.h_linear,0.5)
self.layer_nor=torch.nn.LayerNorm(hidden_dim)
self.head_num = heads
self.hidden = hidden_dim
def forward(self, input_data, state=None):
# self.head.to(device)
b, s, k, h = input_data.shape[0], input_data.shape[1], self.head_num, self.head_size
out = self.head0(input_data)
out1 = self.head1(input_data)
out2 = self.head2(input_data)
#
out = out.reshape([b, s, k, h]).permute([0, 2, 1, 3])
out1 = out1.reshape([b, s, k, h]).permute([0, 2, 1, 3])
# out1 = self.head1(input_data).reshape([b, s, k, h]).permute([0, 2, 1, 3])
out = torch.cummax((out + out1) / h ** 0.5, 2)[0]
out = out.permute([0, 2, 1, 3])
out1 = out1.permute([0, 2, 1, 3])
out = out.reshape([b, s, -1])
out1 = out1.reshape([b, s, -1])
# out = self.layer_nor(out)
out = out2 * out-out1
return out, state
class KAttention(torch.nn.Module):
def __init__(self, hidden_dim, heads):
super(KAttention, self).__init__()
assert hidden_dim % heads == 0, "Hidden size must be divisible by the number of heads."
self.head_size = hidden_dim // heads
self.q = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
self.k = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
self.v = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
# self.state = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
self.head_num = heads
def forward(self, x, state=None):
b, s, h, d = x.shape[0], x.shape[1], self.head_num, self.head_size
q = self.q(x).reshape([b, s, h, d]).permute([0, 2, 1, 3])
k = self.k(x).reshape([b, s, h, d]).permute([0, 2, 1, 3])
v = self.v(x).reshape([b, s, h, d]).permute([0, 2, 1, 3])
qk = (q @ k.permute([0, 1, 3, 2])) / d ** 0.5
mask = torch.triu(torch.ones(s, s).to(device))
qk = torch.where(mask.T == 1, qk, torch.Tensor([-float('inf')]).to(device))
qkv = torch.nn.functional.softmax(qk, -1) @ v
# v + torch.arange(1, 3 * s, 3).reshape([1, 1, -1, 1]).to(device) / s / 3)
qkv = qkv.permute([0, 2, 1, 3]).reshape([b, s, -1])
#
return qkv, state
class FeedForward(torch.nn.Module):
def __init__(self, hidden_size):
super(FeedForward, self).__init__()
self.ffn1 = torch.nn.Linear(hidden_size, hidden_size * 2)
self.ffn2 = torch.nn.Linear(hidden_size * 2, hidden_size)
self.gate = torch.nn.Linear(hidden_size, hidden_size * 2)
self.relu = torch.nn.ReLU()
def forward(self, x):
x1 = self.ffn1(x)
x2 = self.relu(self.gate(x))
x = x1 * x2
x = self.ffn2(x)
return x
class DecoderLayer(torch.nn.Module):
def __init__(self, hidden_size, num_heads):
super(DecoderLayer, self).__init__()
# self.self_attention = MaskMultiHeadAttention(hidden_size, num_heads)
self.self_attention = MaxState(hidden_size, num_heads, 8)
# self.self_attention = KAttention(hidden_size, num_heads)
self.ffn = FeedForward(hidden_size)
self.layer_norm = torch.nn.LayerNorm(hidden_size)
def forward(self, x, state=None, seq_len=None):
x1, state = self.self_attention(x, state)
x = self.layer_norm(self.ffn(x1) + x)
return x, state
class SamOut(torch.nn.Module):
def __init__(self, voc_size, hidden_size, num_heads, num_layers):
super(SamOut, self).__init__()
self.em = torch.nn.Embedding(voc_size, hidden_size, padding_idx=3)
self.pos = torch.nn.Embedding(1024, hidden_size)
self.decoder_layers = torch.nn.ModuleList([DecoderLayer(hidden_size, num_heads) for _ in range(num_layers)])
self.head = torch.nn.Linear(hidden_size, voc_size, False)
# self.head_state = torch.nn.Linear(hidden_size, num_layers, False)
self.down = torch.nn.ModuleList(
[torch.nn.Linear(2 * hidden_size, hidden_size, False) for _ in range(num_layers)])
def state_forward(self, state, pos, x):
if state is None:
state = [None] * len(self.decoder_layers)
i = 0
for ii, decoder_layer in enumerate(self.decoder_layers):
x = self.down[i](torch.concat([torch.zeros([x.shape[0], 1, 1]).to(device) + pos, x], -1))
x1, state[i] = decoder_layer(x, state[i])
x = x1 + x
i += 1
return x, state
def pos_forward(self, x):
if x.shape[1] >= 1024:
pos = self.pos(torch.arange(0, x.shape[1]).long().to(device) // 1024).unsqueeze(0)
pos = self.pos(torch.arange(0, x.shape[1]).long().to(device) % 1024).unsqueeze(0) + pos
else:
pos = self.pos(torch.arange(0, x.shape[1]).long().to(device)).unsqueeze(0)
return pos
def forward(self, x0):
x0, _ = self.one_forward(x0, state=None)
return x0, _
def one_forward(self, x, state=None, seq_len=None):
x = self.em(x)
pos = self.pos_forward(x)
x, state = self.state_forward(state, pos, x)
return self.head(x), state
device = "cuda"
if __name__ == '__main__':
net = SamOut(235, 256, 16, 4)
net.to(device)
net(torch.randint(0, 200, [2, 8 * 13]).to(device))
#
