ShopTRAINING/server/models/slstm_model.py

import torch
import torch.nn as nn

# 定义sLSTM单元
class sLSTMCell(nn.Module):
    def __init__(self, input_size: int, hidden_size: int, bias: bool = True) -> None:
        super().__init__()

        # 存储输入和隐藏层大小
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.bias = bias

        # 组合权重和循环权重到单个矩阵
        self.W = nn.Parameter(
            nn.init.xavier_uniform_(
                torch.randn(self.input_size + self.hidden_size, 4 * self.hidden_size)
            ),
            requires_grad=True,
        )
        # 组合偏置到单个矩阵
        if self.bias:
            self.B = nn.Parameter(
                (torch.zeros(4 * self.hidden_size)), requires_grad=True
            )

    def forward(
        self,
        x: torch.Tensor,
        internal_state: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
    ) -> tuple[
        torch.Tensor, tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
    ]:
        # 解包内部状态
        h, c, n, m = internal_state  # (batch_size, hidden_size)

        # 组合权重和输入
        combined = torch.cat((x, h), dim=1)  # (batch_size, input_size + hidden_size)
        # 计算线性变换
        gates = torch.matmul(combined, self.W)  # (batch_size, 4 * hidden_size)

        # 如果包括偏置，则添加
        if self.bias:
            gates += self.B

        # 将门分为输入、遗忘、输出和稳定化门
        z_tilda, i_tilda, f_tilda, o_tilda = torch.split(gates, self.hidden_size, dim=1)

        # 计算状态的激活
        z_t = torch.tanh(z_tilda)  # (batch_size, hidden_size)
        # 输入门的指数激活
        i_t = torch.exp(i_tilda)  # (batch_size, hidden_size)
        # 遗忘门的sigmoid激活
        f_t = torch.sigmoid(f_tilda)  # (batch_size, hidden_size)

        # 输出门的sigmoid激活
        o_t = torch.sigmoid(o_tilda)  # (batch_size, input_size)
        # 计算稳定化状态
        m_t = torch.max(torch.log(f_t) + m, torch.log(i_t))  # (batch_size, hidden_size)
        # 计算输入稳定化状态
        i_prime = torch.exp(i_tilda - m_t)  # (batch_size, hidden_size)

        # 计算新的内部状态
        c_t = f_t * c + i_prime * z_t  # (batch_size, hidden_size)
        n_t = f_t * n + i_prime  # (batch_size, hidden_size)

        # 计算稳定化隐藏状态
        h_tilda = c_t / n_t  # (batch_size, hidden_size)

        # 计算新的隐藏状态
        h_t = o_t * h_tilda  # (batch_size, hidden_size)
        return h_t, (
            h_t,
            c_t,
            n_t,
            m_t,
        )

    def init_hidden(
        self, batch_size: int, **kwargs
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        return (
            torch.zeros(batch_size, self.hidden_size, **kwargs),
            torch.zeros(batch_size, self.hidden_size, **kwargs),
            torch.zeros(batch_size, self.hidden_size, **kwargs),
            torch.zeros(batch_size, self.hidden_size, **kwargs),
        )

# 定义sLSTM层
class sLSTM(nn.Module):
    def __init__(
        self,
        input_size: int,
        hidden_size: int,
        num_layers: int,
        bias: bool = True,
        batch_first: bool = False,
    ) -> None:
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bias = bias
        self.batch_first = batch_first

        self.cells = nn.ModuleList(
            [
                sLSTMCell(input_size if layer == 0 else hidden_size, hidden_size, bias)
                for layer in range(num_layers)
            ]
        )

    def forward(
        self,
        x: torch.Tensor,
        hidden_states=None,
    ):
        # 如果batch_first为True，置换输入张量
        if self.batch_first:
            x = x.permute(1, 0, 2)

        # 如果未提供隐藏状态，则初始化
        if hidden_states is None:
            hidden_states = self.init_hidden(x.size(1), device=x.device, dtype=x.dtype)
        else:
            # 检查隐藏状态是否正确长度
            if len(hidden_states) != self.num_layers:
                raise ValueError(
                    f"Expected hidden states of length {self.num_layers}, but got {len(hidden_states)}"
                )
            if any(state[0].size(0) != x.size(1) for state in hidden_states):
                raise ValueError(
                    f"Expected hidden states of batch size {x.size(1)}, but got {hidden_states[0][0].size(0)}"
                )

        H, C, N, M = [], [], [], []

        # 通过每一层处理序列
        outputs = []
        for t in range(x.size(0)):
            x_t = x[t]
            for layer in range(self.num_layers):
                if layer == 0:
                    h_t, state = self.cells[layer](x_t, hidden_states[layer])
                else:
                    h_t, state = self.cells[layer](h_t, hidden_states[layer])
                hidden_states[layer] = state
            outputs.append(h_t)

        # 将输出堆叠成序列
        outputs = torch.stack(outputs)

        # 如果batch_first为True，置换输出张量
        if self.batch_first:
            outputs = outputs.permute(1, 0, 2)

        return outputs, hidden_states

    def init_hidden(
        self, batch_size: int, **kwargs
    ):
        return [cell.init_hidden(batch_size, **kwargs) for cell in self.cells]