feat(mamba): add Mamba2 encoder option to SeasonPatch

layers/MambaSeries.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+from mamba_ssm import Mamba2
+
+
+class Mamba2Encoder(nn.Module):
+    """
+    使用 Mamba2 对 patch 维度进行序列建模：
+    输入:  [bs, nvars, patch_num, patch_len]
+    映射:  patch_len -> d_model
+    建模:  在 patch_num 维度上用 Mamba2
+    输出:  [bs, nvars, d_model] (仅返回 Mamba 输出的最后一个时间步)
+    """
+    def __init__(
+        self,
+        c_in,
+        patch_num,
+        patch_len,
+        d_model=128,
+        # Mamba2 超参
+        d_state=64,
+        d_conv=4,
+        expand=2,
+        headdim=64,
+    ):
+        super().__init__()
+        self.patch_num = patch_num
+        self.patch_len = patch_len
+        self.d_model = d_model
+
+        # 将 patch_len 投影到 d_model
+        self.W_P = nn.Linear(patch_len, d_model)  # 映射 patch_len -> d_model
+
+        # 直接使用 Mamba2 对序列 (patch_num) 建模
+        self.mamba = Mamba2(
+            d_model=d_model,
+            d_state=d_state,
+            d_conv=d_conv,
+            expand=expand,
+            headdim=headdim,
+        )
+
+    def forward(self, x):
+        # x: [bs, nvars, patch_num, patch_len]
+        bs, n_vars, patch_num, patch_len = x.shape  # bs, n_vars, patch_num, patch_len
+
+        # 1) 线性映射: patch_len -> d_model
+        x = self.W_P(x)  # x: [bs, nvars, patch_num, d_model]
+
+        # 2) 合并 batch 与通道维度，作为 Mamba 的 batch
+        u = x.reshape(bs * n_vars, patch_num, self.d_model)  # u: [bs*nvars, patch_num, d_model]
+
+        # 3) Mamba2 建模（在 patch_num 维度上）
+        y = self.mamba(u)  # y: [bs*nvars, patch_num, d_model]
+
+        # 4) 仅取最后一个时间步
+        y_last = y[:, -1, :]  # y_last: [bs*nvars, d_model]
+
+        # 5) 还原回 (bs, nvars, d_model)
+        y_last = y_last.view(bs, n_vars, self.d_model)  # y_last: [bs, nvars, d_model]
+
+        return y_last  # [bs, nvars, d_model]

layers/SeasonPatch.py

@@ -1,11 +1,15 @@
 """
 SeasonPatch = PatchTST (CI) + ChannelGraphMixer + Linear prediction head
-Adapted for Time-Series-Library-main style
+支持两种编码器：
+- Transformer 编码器路径：PatchTST + GraphMixer + Head
+- Mamba2 编码器路径：Mamba2Encoder（不使用mixer），直接用最后得到的 d_model 走 Head
 """
 import torch
 import torch.nn as nn
 from layers.TSTEncoder import TSTiEncoder
 from layers.GraphMixer import HierarchicalGraphMixer
+from layers.MambaSeries import Mamba2Encoder
+
 
 class SeasonPatch(nn.Module):
     def __init__(self,
@@ -17,17 +21,22 @@ class SeasonPatch(nn.Module):
                  k_graph: int = 8,
                  d_model: int = 128,
                  n_layers: int = 3,
-                 n_heads: int = 16):
+                 n_heads: int = 16,
+                 # Mamba2 相关可选超参
+                 d_state: int = 64,
+                 d_conv: int = 4,
+                 expand: int = 2,
+                 headdim: int = 64):
         super().__init__()
         
         # Store patch parameters
-        self.patch_len = patch_len
+        self.patch_len = patch_len  # patch 长度
-        self.stride = stride
+        self.stride = stride        # patch 步幅
 
         # Calculate patch number
-        patch_num = (seq_len - patch_len) // stride + 1
+        patch_num = (seq_len - patch_len) // stride + 1  # patch_num: int
-        
+
-        # PatchTST encoder (channel independent)
+        # Transformer (PatchTST) 编码器（channel independent）
         self.encoder = TSTiEncoder(
             c_in=c_in, 
             patch_num=patch_num, 
@@ -36,36 +45,64 @@ class SeasonPatch(nn.Module):
             n_layers=n_layers,
             n_heads=n_heads
         )
-        
+
-        # Cross-channel mixer
+        # Mamba2 编码器（channel independent），返回 [B, C, d_model]
+        self.mamba_encoder = Mamba2Encoder(
+            c_in=c_in,
+            patch_num=patch_num,
+            patch_len=patch_len,
+            d_model=d_model,
+            d_state=d_state,
+            d_conv=d_conv,
+            expand=expand,
+            headdim=headdim,
+        )
+
+        # Cross-channel mixer（仅 Transformer 路径使用）
         self.mixer = HierarchicalGraphMixer(c_in, dim=d_model, k=k_graph)
-        
+
-        # Prediction head
+        # Prediction head（Transformer 路径用到，输入维度为 patch_num * d_model）
-        self.head = nn.Sequential(
+        self.head_tr = nn.Sequential(
             nn.Linear(patch_num * d_model, patch_num * d_model),
-            nn.SiLU(),                 # 非线性激活（SiLU/Swish）
+            nn.SiLU(),
             nn.Linear(patch_num * d_model, pred_len)
         )
 
-    def forward(self, x):
+        # Prediction head（Mamba2 路径用到，输入维度为 d_model）
-        # x: [B, L, C]
+        self.head_mamba = nn.Sequential(
-        x = x.permute(0, 2, 1)  # → [B, C, L]
+            nn.Linear(d_model, d_model),
-        
+            nn.SiLU(),
-        # Patch the input
+            nn.Linear(d_model, pred_len)
-        x_patch = x.unfold(-1, self.patch_len, self.stride)  # [B, C, patch_num, patch_len]
+        )
-        
-        # Encode patches
-        z = self.encoder(x_patch)  # [B, C, d_model, patch_num]
-        
-        # z: [B, C, d_model, patch_num] → [B, C, patch_num, d_model]
-        B, C, D, N = z.shape
-        z = z.permute(0, 1, 3, 2)  # [B, C, patch_num, d_model]
-        
-        # Cross-channel mixing
-        z_mix = self.mixer(z)  # [B, C, patch_num, d_model]
-        
-        # Flatten and predict
-        z_mix = z_mix.view(B, C, N * D)  # [B, C, patch_num * d_model]
-        y_pred = self.head(z_mix)  # [B, C, pred_len]
 
-        return y_pred
+    def forward(self, x, encoder="Transformer"):
+        # x: [B, L, C]
+        x = x.permute(0, 2, 1)  # x: [B, C, L]
+
+        # Patch the input
+        x_patch = x.unfold(-1, self.patch_len, self.stride)  # x_patch: [B, C, patch_num, patch_len]
+
+        if encoder == "Transformer":
+            # Encode patches (PatchTST)
+            z = self.encoder(x_patch)  # z: [B, C, d_model, patch_num]
+
+            # [B, C, d_model, patch_num] -> [B, C, patch_num, d_model]
+            B, C, D, N = z.shape  # B: batch, C: channels, D: d_model, N: patch_num
+            z = z.permute(0, 1, 3, 2)  # z: [B, C, patch_num, d_model]
+
+            # Cross-channel mixing
+            z_mix = self.mixer(z)  # z_mix: [B, C, patch_num, d_model]
+
+            # Flatten and predict
+            z_mix = z_mix.view(B, C, N * D)  # z_mix: [B, C, patch_num * d_model]
+            y_pred = self.head_tr(z_mix)  # y_pred: [B, C, pred_len]
+
+        elif encoder == "Mamba2":
+            # 使用 Mamba2 编码器（不使用 mixer）
+            z_last = self.mamba_encoder(x_patch)  # z_last: [B, C, d_model]（仅最后一个时间步）
+            y_pred = self.head_mamba(z_last)      # y_pred: [B, C, pred_len]
+
+        else:
+            raise ValueError(f"Unsupported encoder type: {encoder}. Use 'Transformer' or 'Mamba2'.")
+
+        return y_pred  # [B, C, pred_len]

models/xPatch_SparseChannel.py

@@ -37,6 +37,8 @@ class Model(nn.Module):
         beta = getattr(configs, 'beta', torch.tensor(0.1))
         self.decomp = DECOMP(ma_type, alpha, beta)
         
+        # 读取选择的编码器类型（'Transformer' 或 'Mamba2'）
+        self.season_encoder = getattr(configs, 'season_encoder', 'Transformer')
         # Season network (PatchTST + Graph Mixer)
         self.season_net = SeasonPatch(
             c_in=self.enc_in,
@@ -90,7 +92,7 @@ class Model(nn.Module):
         seasonal_init, trend_init = self.decomp(x_enc)
         
         # Season stream
-        y_season = self.season_net(seasonal_init)  # [B, C, pred_len]
+        y_season = self.season_net(seasonal_init, encoder=self.season_encoder)  # [B, C, pred_len]
         
         # Trend stream
         B, L, C = trend_init.shape
@@ -125,7 +127,7 @@ class Model(nn.Module):
         seasonal_init, trend_init = self.decomp(x_enc)
         
         # Season stream
-        y_season = self.season_net(seasonal_init)  # [B, C, pred_len]
+        y_season = self.season_net(seasonal_init, encoder=self.season_encoder)  # [B, C, pred_len]
         
         # print("shape:", trend_init.shape)
         # Trend stream
@@ -163,4 +165,4 @@ class Model(nn.Module):
             dec_out = self.classification(x_enc, x_mark_enc)
             return dec_out  # [B, N]
         else:
-            raise ValueError(f'Task {self.task_name} not supported by xPatch_SparseChannel')
+            raise ValueError(f'Task {self.task_name} not supported by xPatch_SparseChannel')