代码

import torch
import numpy as npclass 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 // headsself.head = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)self.state = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)self.head_num = headsself.win = winself.hidden = hidden_dimself.mask = torch.triu(torch.ones([win, win])).to("cuda")self.layer_nor = torch.nn.LayerNorm(hidden_dim)def forward(self, input_data, state=None):# self.head.to("cuda")b, s, k, h, w = input_data.shape[0], input_data.shape[1], self.head_num, self.head_size, self.winwindow = torch.ones([1, w]).to("cuda")out = self.head(input_data)out = out.unsqueeze(-1) @ windowout = out.permute([0, 2, 1, 3])one_list = []if state is None:state = torch.ones([out.shape[0], out.shape[1], 1, 1]) * float("-inf")state = state.to("cuda")for i in range(0, s, w):state.reshape([state.shape[0], -1])j = w + ione = out[:, :, i:j]_, _, r, c = one.shapeif r != self.win:one = torch.where(self.mask[:r, :] == 1, one, torch.Tensor([-float('inf')]).to("cuda"))else:one = torch.where(self.mask == 1, one, torch.Tensor([-float('inf')]).to("cuda"))if i == 0:one = torch.concat([one, state @ window], axis=2)state, _ = torch.max(one, axis=2, keepdim=True)else:state1, _ = torch.max(one, axis=2, keepdim=True)# state = torch.sin(self.state(state1.reshape([state1.shape[0], -1]))*state.reshape([state.shape[0], -1]))state1 = self.state(state1.permute([0, 3, 1, 2]).reshape([state1.shape[0], -1, state1.shape[1]]))state = state1.permute([0, 2, 1]).unsqueeze(-2) + state# state = state.reshape(state1.shape)one = torch.concat([one, state], axis=2)state, _ = torch.max(one, axis=2, keepdim=True)one = state.reshape([b, k, h, w])state = state[..., -1:]if r != self.win:one = one[..., :r]one = one.permute([0, 3, 1, 2])one_list.append(one)out = torch.concat(one_list, 1)out = out.reshape([b, s, -1])return out, stateclass 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 * x2x = self.ffn2(x)return xclass 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.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)  # Feed-Forward with residual connectionreturn x, stateclass 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)self.head_state = torch.nn.Linear(hidden_size, num_layers)def forward(self, x, state=None, seq_len=None):x = self.em(x)if x.shape[1] >= 1024:pos = self.pos(torch.range(0, x.shape[1] - 1).long() // 1024).unsqueeze(0)pos = self.pos(torch.range(0, x.shape[1] - 1).long() % 1024).unsqueeze(0) + poselse:pos = self.pos(torch.range(0, x.shape[1] - 1).long().to("cuda")).unsqueeze(0)if state is None:state = [None] * len(self.decoder_layers)i = 0for decoder_layer in self.decoder_layers:x1, state[i] = decoder_layer(pos + x, state[i])x = x1 + xi += 1state_data = self.head_state((torch.concat(state, -1).squeeze(-2)).permute([0, 2, 1]))return self.head(x), state, state_dataif __name__ == '__main__':net = SamOut(235, 256, 16, 4)net(torch.randint(0, 200, [2, 3000]))

解析

import torch
import numpy as np

这两行代码导入了PyTorch库和NumPy库，它们分别用于深度学习和数值计算。

class MaxState(torch.nn.Module):def __init__(self, hidden_dim, heads, win):super(MaxState, self).__init__()

这里定义了一个名为MaxState的PyTorch模块。它继承自torch.nn.Module，这是所有自定义模型的基类。

    assert hidden_dim % heads == 0, "Hidden size must be divisible by the number of heads."

这里使用assert语句确保hidden_dim能够被heads整除，这是多头注意力机制的一个要求。

    self.head_size = hidden_dim // headsself.head = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)self.state = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)self.head_num = headsself.win = winself.hidden = hidden_dimself.mask = torch.triu(torch.ones([win, win])).to("cuda")self.layer_nor = torch.nn.LayerNorm(hidden_dim)

这里初始化了一些类的属性，包括线性层、头数、窗口大小、隐藏层大小、上三角矩阵掩码以及层归一化。

    def forward(self, input_data, state=None):

定义了forward方法，这是模型的前向传播过程。

        b, s, k, h, w = input_data.shape[0], input_data.shape[1], self.head_num, self.head_size, self.win

从输入数据中提取批次大小、序列长度、头数、头大小和窗口大小。

        window = torch.ones([1, w]).to("cuda")

创建一个窗口大小的一维张量，并将其移动到GPU上。

        out = self.head(input_data)

对输入数据进行线性变换。

        out = out.unsqueeze(-1) @ window

将输出数据与窗口张量进行矩阵乘法。

        out = out.permute([0, 2, 1, 3])

调整输出数据的维度顺序。

        one_list = []if state is None:state = torch.ones([out.shape[0], out.shape[1], 1, 1]) * float("-inf")state = state.to("cuda")

如果状态为空，则初始化状态张量，并将其移动到GPU上。

        for i in range(0, s, w):# ... (省略中间代码)

对序列进行迭代，每次迭代处理一个窗口大小的数据。

        return out, state

返回最终输出和状态。
接下来是FeedForward、DecoderLayer和SamOut类的定义，它们的结构和MaxState类类似，都是自定义的PyTorch模块。

if __name__ == '__main__':net = SamOut(235, 256, 16, 4)net(torch.randint(0, 200, [2, 3000]))

最后，这段代码实例化了SamOut类，并使用随机生成的输入数据进行了一次前向传播。