139 lines
5.5 KiB
Python
139 lines
5.5 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from mamba_ssm import Mamba2
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class ValueEmbedding(nn.Module):
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"""
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对每个时间步的单通道标量做线性投影到 d_model，并可选 Dropout。
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不包含 temporal embedding 和 positional embedding。
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"""
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def __init__(self, in_dim: int, d_model: int, dropout: float = 0.0, bias: bool = True):
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super().__init__()
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self.proj = nn.Linear(in_dim, d_model, bias=bias)
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self.dropout = nn.Dropout(dropout) if dropout and dropout > 0.0 else nn.Identity()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# x: [B, L, 1] -> [B, L, d_model]
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return self.dropout(self.proj(x))
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class ChannelMambaBlock(nn.Module):
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"""
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针对单个通道的两层 Mamba-2 处理块：
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- 输入: [B, L, 1]，先做投影到 d_model
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- 两层 Mamba2，且在第一层输出和第二层输出均添加残差连接
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- 每层后接 LayerNorm
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- 输出: [B, L, d_model]
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"""
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def __init__(self, d_model: int, dropout: float, m2_kwargs: dict):
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super().__init__()
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self.embed = ValueEmbedding(in_dim=1, d_model=d_model, dropout=dropout, bias=True)
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# 两层 Mamba-2
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self.mamba1 = Mamba2(d_model=d_model, **m2_kwargs)
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self.mamba2 = Mamba2(d_model=d_model, **m2_kwargs)
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# 每层后接的归一化
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self.ln1 = nn.LayerNorm(d_model)
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self.ln2 = nn.LayerNorm(d_model)
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def forward(self, x_ch: torch.Tensor) -> torch.Tensor:
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# x_ch: [B, L, 1]
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x = self.embed(x_ch) # [B, L, d_model]
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# 第一层 + 残差
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y1 = self.mamba1(x) # [B, L, d_model]
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y1 = self.ln1(x + y1) # 残差1 + LN
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# 第二层 + 残差
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y2 = self.mamba2(y1) # [B, L, d_model]
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y2 = self.ln2(y1 + y2) # 残差2 + LN
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return y2 # [B, L, d_model]
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class Model(nn.Module):
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"""
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按通道独立处理的 Mamba-2 分类模型：
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- 将输入的每个通道拆开，分别使用独立的两层 Mamba2（含两处残差）
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- 每个通道得到 [B, L, d_model] 输出
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- 取各通道最后时间步的表示拼接，接分类头
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输入:
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- x_enc: [B, L, D] 多变量时间序列
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输出:
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- logits: [B, num_class]
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"""
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def __init__(self, configs):
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super().__init__()
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self.task_name = getattr(configs, 'task_name', 'classification')
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assert self.task_name == 'classification', "当前模型仅实现 classification 任务"
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# 基本配置
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self.enc_in = configs.enc_in # 通道数 D
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self.d_model = configs.d_model # 每通道的模型维度
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self.num_class = configs.num_class
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self.dropout = getattr(configs, 'dropout', 0.1)
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# Mamba-2 超参数（按需从 configs 读取）
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# 注意：此处不再使用 e_layers 的堆叠，而是固定每通道两层以满足“在第一层和第二层输出处添加残差”的要求
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m2_kwargs = dict(
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d_state=getattr(configs, 'd_state', 64),
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d_conv=getattr(configs, 'd_conv', 4),
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expand=getattr(configs, 'expand', 2),
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headdim=getattr(configs, 'headdim', 64),
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d_ssm=getattr(configs, 'd_ssm', None),
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ngroups=getattr(configs, 'ngroups', 1),
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A_init_range=getattr(configs, 'A_init_range', (1, 16)),
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D_has_hdim=getattr(configs, 'D_has_hdim', False),
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rmsnorm=getattr(configs, 'rmsnorm', True),
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norm_before_gate=getattr(configs, 'norm_before_gate', False),
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dt_min=getattr(configs, 'dt_min', 0.001),
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dt_max=getattr(configs, 'dt_max', 0.1),
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dt_init_floor=getattr(configs, 'dt_init_floor', 1e-4),
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dt_limit=getattr(configs, 'dt_limit', (0.0, float("inf"))),
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bias=getattr(configs, 'bias', False),
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conv_bias=getattr(configs, 'conv_bias', True),
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chunk_size=getattr(configs, 'chunk_size', 256),
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use_mem_eff_path=getattr(configs, 'use_mem_eff_path', True),
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)
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# 为每个通道构建独立的两层 Mamba2 处理块
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self.channel_blocks = nn.ModuleList([
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ChannelMambaBlock(d_model=self.d_model, dropout=self.dropout, m2_kwargs=m2_kwargs)
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for _ in range(self.enc_in)
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])
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# 分类头：将各通道最后时间步的表示拼接后 -> GELU -> Dropout -> Linear
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self.act = nn.GELU()
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self.head = nn.Sequential(
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nn.Dropout(self.dropout),
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nn.Linear(self.d_model * self.enc_in, self.num_class)
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)
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def classification(self, x_enc: torch.Tensor) -> torch.Tensor:
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# x_enc: [B, L, D]
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B, L, D = x_enc.shape
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assert D == self.enc_in, f"输入通道数 {D} 与 enc_in {self.enc_in} 不一致"
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per_channel_last = []
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for c in range(D):
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# 取出单通道序列 [B, L] -> [B, L, 1]
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x_ch = x_enc[:, :, c].unsqueeze(-1)
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y_ch = self.channel_blocks[c](x_ch) # [B, L, d_model]
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per_channel_last.append(y_ch[:, -1, :]) # [B, d_model]
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# 拼接各通道最后时刻的表示 -> [B, D * d_model]
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h_last = torch.cat(per_channel_last, dim=-1)
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# 分类头
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h_last = self.act(h_last)
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logits = self.head(h_last) # [B, num_class]
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return logits
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# 与 TimesNet 的 forward 签名保持一致；忽略 x_mark_enc / x_dec / x_mark_dec / mask
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def forward(self, x_enc, x_mark_enc=None, x_dec=None, x_mark_dec=None, mask=None):
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return self.classification(x_enc)
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