samout 结构再优化 收敛速度再加快

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)self.layer_nor=torch.nn.LayerNorm(hidden_size)self.down=torch.nn.ModuleList([torch.nn.Linear(2*hidden_size,hidden_size) for _ in range(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(self.down[i](torch.concat([torch.zeros([x.shape[0],1,1]).to("cuda")+pos , x],-1)), 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]))

解释

这段代码定义了一个基于PyTorch的神经网络模型，该模型包含自定义的解码器层和输出层，用于处理序列数据。下面是代码的逐行解析：

import torch
import numpy as np

• 导入PyTorch库和NumPy库。

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

• 定义一个名为MaxState的类，继承自torch.nn.Module。这是自定义的一个模块，用于处理状态的最大化。

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

• 断言检查，确保隐藏层的维度可以被注意力头的数量整除。

        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)

• 初始化类成员变量，包括线性层、注意力头数量、窗口大小、掩码和层归一化。

    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.win

• 前向传播方法，获取输入数据的形状参数。

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

• 创建一个窗口张量，并将其移动到CUDA设备。

        out = self.head(input_data)

• 应用线性层到输入数据。

        out = out.unsqueeze(-1) @ window

• 扩展维度并进行矩阵乘法。

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

• 调整输出的维度顺序。

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

• 初始化状态张量，如果状态为None，则创建一个初始状态。

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

• 循环处理每个窗口大小的数据块。

        return out, state

• 返回处理后的输出和状态。
  接下来是FeedForward、DecoderLayer和SamOut类的定义，这些类分别实现了前馈网络、解码器层和整个模型的输出部分。代码结构与MaxState类类似，包含了初始化和前向传播方法。

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

• 如果当前脚本作为主程序运行，创建一个SamOut模型实例，并使用随机整数张量作为输入进行测试。