B = rearrange(B, "(b l) dstate -> b dstate l", l=L).contiguous() else: B = rearrange(B, "(b l) (dstate two) -> b dstate (l two)", l=L, two=2).contiguous() if C is None: # variable B C = x_dbl[:, -d_state:] # (bl d) if C_proj_bias is not None: ...
mamba的主要结构是融合了H4 block+Gateed MLP,通过引入Selecitve State-Space Model (SSM)来代替transformer中的Attention Block,通过SSM来解决attention 中O(N2)的复杂度,比避免了存储完整的上下文(KV Cache)。 SSM的结构可以用如下的迭代公式表达ht=A¯ht−1+B¯utyt=Cht 在后文中,用D=d_model, N=d...
self.D_has_hdim = D_has_hdim AssertionError: However, this error is not present when running normal Mamba. Seems to work withdim=1024for me.Mamba2( d_model=1024, d_state=64, d_conv=4, expand=2) Note from Tri Dao inanother thread: -- Mamba2 has only been tested with dim_model...
比较模型包括基于CNN的方法,ABCNet [11],MANet [42]和CMTFNet [12],基于Transformer的方法,FTUNetFormer [43],混合CNN-Transformer模型,UNetFormer [43],HST_UNet [44]和TransUNet [14],以及其他基于Mamba的方法,RS3Mamba [21]。 Iv-D1 Performance comparison on ISPRS Vaihingen 表1显示,相比其 Baseline 模...
# 残差连接self.D = nn.Linear(d_model,2*d_model, device=device) # 设置偏差属性self.out_proj.bias._no_weight_decay =True # 初始化偏差nn.init.constant_(self.out_proj.bias,1.0)# 初始化S6模块self.S6 = S6(seq_len,2*d_model, ...
self.A=nn.Parameter(F.normalize(torch.ones(d_model,state_size,device=device),p=2,dim=-1))nn.init.xavier_uniform_(self.A)self.B=torch.zeros(batch_size,self.seq_len,self.state_size,device=device)self.C=torch.zeros(batch_size,self.seq_len,self.state_size,device=device)self.delta=torch...
selective_state_update import selective_state_update 17 + except ImportError: 18 + selective_state_update = None 19 + from einops import repeat 24 20 25 21 class Mixer(nn.Module): 26 22 def __init__( 27 23 self, 28 24 d_model, 29 25 d_state=64, 30 - nheads=32...
self.delta = torch.zeros(batch_size, self.seq_len, self.d_model, device=device)self.dA = torch.zeros(batch_size, self.seq_len, self.d_model, self.state_size, device=device)self.dB = torch.zeros(batch_size, self.seq_len, self.d_...
这里极端一点:如果要让每个D都有一个类似transformer的head,state space model的矩阵形状该怎么改造了?如下:都改成D*N形状的矩阵呗! 每个Dimension都有L个数值用来做representation,这里举个更加形象通俗的例子:比如一张图片有RGB三个通道,也就是3个dimension,如果生成3*64的矩阵,那么每个dimension都有64个数字来做...
State Space Model(SSM):状态空间模型,用来刻画上一个状态对当前状态的影响,以及当前状态对输出的影响;State Space Model中假设上一个状态和当前时刻的输入会影响下一个状态,并且当前的观测结果是由当前状态决定的。SSM可以表示为如下形式,矩阵A、B、C、D为超参数; ...