first commit

models/MambaSimple.py

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+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat, einsum
+
+from layers.Embed import DataEmbedding
+
+
+class Model(nn.Module):
+    """
+    Mamba, linear-time sequence modeling with selective state spaces O(L)
+    Paper link: https://arxiv.org/abs/2312.00752
+    Implementation refernce: https://github.com/johnma2006/mamba-minimal/
+    """
+
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.task_name = configs.task_name
+        self.pred_len = configs.pred_len
+
+        self.d_inner = configs.d_model * configs.expand
+        self.dt_rank = math.ceil(configs.d_model / 16)
+
+        self.embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq, configs.dropout)
+
+        self.layers = nn.ModuleList([ResidualBlock(configs, self.d_inner, self.dt_rank) for _ in range(configs.e_layers)])
+        self.norm = RMSNorm(configs.d_model)
+
+        self.out_layer = nn.Linear(configs.d_model, configs.c_out, bias=False)
+
+    def forecast(self, x_enc, x_mark_enc):
+        mean_enc = x_enc.mean(1, keepdim=True).detach()
+        x_enc = x_enc - mean_enc
+        std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach()
+        x_enc = x_enc / std_enc
+
+        x = self.embedding(x_enc, x_mark_enc)
+        for layer in self.layers:
+            x = layer(x)
+
+        x = self.norm(x)
+        x_out = self.out_layer(x)
+
+        x_out = x_out * std_enc + mean_enc
+        return x_out
+
+    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        if self.task_name in ['short_term_forecast', 'long_term_forecast']:
+            x_out = self.forecast(x_enc, x_mark_enc)
+            return x_out[:, -self.pred_len:, :]
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, configs, d_inner, dt_rank):
+        super(ResidualBlock, self).__init__()
+        
+        self.mixer = MambaBlock(configs, d_inner, dt_rank)
+        self.norm = RMSNorm(configs.d_model)
+
+    def forward(self, x):
+        output = self.mixer(self.norm(x)) + x
+        return output
+
+class MambaBlock(nn.Module):
+    def __init__(self, configs, d_inner, dt_rank):
+        super(MambaBlock, self).__init__()
+        self.d_inner = d_inner
+        self.dt_rank = dt_rank
+
+        self.in_proj = nn.Linear(configs.d_model, self.d_inner * 2, bias=False)
+        
+        self.conv1d = nn.Conv1d(
+            in_channels = self.d_inner,
+            out_channels = self.d_inner,
+            bias = True,
+            kernel_size = configs.d_conv,
+            padding = configs.d_conv - 1,
+            groups = self.d_inner
+        )
+
+        # takes in x and outputs the input-specific delta, B, C
+        self.x_proj = nn.Linear(self.d_inner, self.dt_rank + configs.d_ff * 2, bias=False)
+
+        # projects delta
+        self.dt_proj = nn.Linear(self.dt_rank, self.d_inner, bias=True)
+
+        A = repeat(torch.arange(1, configs.d_ff + 1), "n -> d n", d=self.d_inner).float()
+        self.A_log = nn.Parameter(torch.log(A))
+        self.D = nn.Parameter(torch.ones(self.d_inner))
+
+        self.out_proj = nn.Linear(self.d_inner, configs.d_model, bias=False)
+
+    def forward(self, x):
+        """
+        Figure 3 in Section 3.4 in the paper
+        """
+        (b, l, d) = x.shape
+
+        x_and_res = self.in_proj(x) # [B, L, 2 * d_inner]
+        (x, res) = x_and_res.split(split_size=[self.d_inner, self.d_inner], dim=-1)
+
+        x = rearrange(x, "b l d -> b d l")
+        x = self.conv1d(x)[:, :, :l]
+        x = rearrange(x, "b d l -> b l d")
+
+        x = F.silu(x)
+
+        y = self.ssm(x)
+        y = y * F.silu(res)
+
+        output = self.out_proj(y)
+        return output
+
+
+    def ssm(self, x):
+        """
+        Algorithm 2 in Section 3.2 in the paper
+        """
+        
+        (d_in, n) = self.A_log.shape
+
+        A = -torch.exp(self.A_log.float()) # [d_in, n]
+        D = self.D.float() # [d_in]
+
+        x_dbl = self.x_proj(x) # [B, L, d_rank + 2 * d_ff]
+        (delta, B, C) = x_dbl.split(split_size=[self.dt_rank, n, n], dim=-1) # delta: [B, L, d_rank]; B, C: [B, L, n]
+        delta = F.softplus(self.dt_proj(delta)) # [B, L, d_in]
+        y = self.selective_scan(x, delta, A, B, C, D)
+
+        return y
+
+    def selective_scan(self, u, delta, A, B, C, D):
+        (b, l, d_in) = u.shape
+        n = A.shape[1]
+
+        deltaA = torch.exp(einsum(delta, A, "b l d, d n -> b l d n")) # A is discretized using zero-order hold (ZOH) discretization
+        deltaB_u = einsum(delta, B, u, "b l d, b l n, b l d -> b l d n") # B is discretized using a simplified Euler discretization instead of ZOH. From a discussion with authors: "A is the more important term and the performance doesn't change much with the simplification on B"
+
+        # selective scan, sequential instead of parallel
+        x = torch.zeros((b, d_in, n), device=deltaA.device)
+        ys = []
+        for i in range(l):
+            x = deltaA[:, i] * x + deltaB_u[:, i]
+            y = einsum(x, C[:, i, :], "b d n, b n -> b d")
+            ys.append(y)
+
+        y = torch.stack(ys, dim=1) # [B, L, d_in]
+        y = y + u * D
+
+        return y
+
+class RMSNorm(nn.Module):
+    def __init__(self, d_model, eps=1e-5):
+        super(RMSNorm, self).__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(d_model))
+
+    def forward(self, x):
+        output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
+        return output