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import torch
from torch import nn, Tensor
from typing import List
import math
class MLP(nn.Module):
def __init__(self,dim):
super().__init__()
self.proj_1 = nn.Linear(dim,dim,bias=False)
self.proj_2 = nn.Linear(dim,dim,bias=False)
self.gelu = nn.GELU()
def forward(self, x):
x = self.proj_1(x)
x = self.gelu(x)
x = self.proj_2(x)
return x
class LocalMappingUnit(nn.Module):
def __init__(self,dim):
super().__init__()
self.mapping = MLP(dim)
self.norm = nn.LayerNorm(dim,elementwise_affine=False)
def forward(self, x):
x = self.norm(x)
x = self.mapping(x)
return x
class GlobalMappingUnit(nn.Module):
def __init__(self, dim,heads):
super().__init__()
self.num_heads = heads
self.hidden_dim = dim
self.head_dim = dim // self.num_heads
self.norm = nn.LayerNorm(dim,elementwise_affine=False)
assert self.head_dim * self.num_heads == self.hidden_dim
def forward(self, x):
batch_size, seq_len, _ = x.size()
x = self.norm(x)
P,S = x,x
P = P.view(batch_size, seq_len, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
S = S.view(batch_size, seq_len, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
attention_scores = P @ S.transpose(-1, -2) / math.sqrt(self.head_dim)
attention_weights = torch.softmax(attention_scores, dim=-1)
context = attention_weights @ S
context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, self.hidden_dim)
return context
class SmallFormerBlock(nn.Module):
def __init__(self, d_model,heads):
super().__init__()
self.local_mapping = LocalMappingUnit(d_model)
self.global_mapping = GlobalMappingUnit(d_model,heads)
def forward(self, x):
residual = x
x = self.global_mapping(x)
x = x + residual
residual = x
x = self.local_mapping(x)
out = x + residual
return out
class SmallFormer(nn.Module):
def __init__(self, d_model,heads, num_layers):
super().__init__()
self.model = nn.Sequential(
*[SmallFormerBlock(d_model,heads) for _ in range(num_layers)]
)
def forward(self, x):
return self.model(x)
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