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import torch
from torch import nn
class MemoryUnit(nn.Module):
def __init__(self,dim):
super().__init__()
self.norm_token = nn.LayerNorm(dim)
self.proj_1 = nn.Linear(dim,dim)
self.proj_2 = nn.Linear(dim,dim)
self.proj_3 = nn.Linear(dim,dim)
def forward(self, x):
x = self.norm_token(x)
u, v = x, x
u = self.proj_1(u)
u = self.norm_token(u)
v = self.proj_2(v)
g = u * v
x = self.proj_3(g)
x = self.norm_token(x)
return x
class InteractionUnit(nn.Module):
def __init__(self,dim,score_dim):
super().__init__()
self.norm_token = nn.LayerNorm(dim)
self.norm_score = nn.LayerNorm(score_dim)
def forward(self, x):
x = self.norm_token(x)
q,k,v = x,x,x
score = torch.matmul(q, k.transpose(-1, -2))
interaction = self.norm_score(score)
x = torch.matmul(interaction,v)
x = self.norm_token(x)
return x
class InteractorBlock(nn.Module):
def __init__(self, d_model, num_tokens):
super().__init__()
self.memory = MemoryUnit(d_model)
self.interaction = InteractionUnit(d_model,num_tokens)
def forward(self, x):
residual = x
x = self.interaction(x)
x = x + residual
residual = x
x = self.memory(x)
out = x + residual
return out
class Interactor(nn.Module):
def __init__(self, d_model,num_tokens, num_layers):
super().__init__()
self.model = nn.Sequential(
*[InteractorBlock(d_model,num_tokens) for _ in range(num_layers)]
)
def forward(self, x):
return self.model(x)
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