<filename>Fastformer.py
import einops
from einops import rearrange
import torch
import torch.nn as nn

class Fastformer(nn.Module):
    def __init__(self, dim = 3, decode_dim = 16):
        super(Fastformer, self).__init__()
        # Generate weight for Wquery、Wkey and Wvalue
        self.to_qkv = nn.Linear(dim, decode_dim * 3, bias = False)
        self.weight_q = nn.Linear(dim, decode_dim, bias = False)
        self.weight_k = nn.Linear(dim, decode_dim, bias = False)
        self.weight_v = nn.Linear(dim, decode_dim, bias = False)
        self.weight_r = nn.Linear(decode_dim, decode_dim, bias = False)
        self.weight_alpha = nn.Parameter(torch.randn(decode_dim))
        self.weight_beta = nn.Parameter(torch.randn(decode_dim))
        self.scale_factor = decode_dim ** -0.5

    def forward(self, x, mask = None):
        query = self.weight_q(x)
        key = self.weight_k(x)
        value = self.weight_v(x)
        b, n, d = query.shape

        mask_value = -torch.finfo(x.dtype).max
        mask = rearrange(mask, 'b n -> b () n')

        # Caculate the global query
        alpha_weight = (torch.mul(query, self.weight_alpha) * self.scale_factor).masked_fill(~mask, mask_value)
        alpha_weight = torch.softmax(alpha_weight, dim = -1)
        global_query = query * alpha_weight
        global_query = torch.einsum('b n d -> b d', global_query)

        # Model the interaction between global query vector and the key vector
        repeat_global_query = einops.repeat(global_query, 'b d -> b copy d', copy = n)
        p = repeat_global_query * key
        beta_weight = (torch.mul(p, self.weight_beta) * self.scale_factor).masked_fill(~mask, mask_value)
        beta_weight = torch.softmax(beta_weight, dim = -1)
        global_key = p * beta_weight
        global_key = torch.einsum('b n d -> b d', global_key)

        # key-value
        key_value_interaction = torch.einsum('b j, b n j -> b n j', global_key, value)
        key_value_interaction_out = self.weight_r(key_value_interaction)
        result = key_value_interaction_out + query
        return result

if __name__ == '__main__':
    model = Fastformer(dim = 3, decode_dim = 8)
    x = torch.randn(4, 6, 3)
    mask = torch.ones(1, 8).bool()
    result = model(x, mask)
    print(result[0])
<filename>Fastformer.py
import einops
from einops import rearrange
import torch
import torch.nn as nn

class Fastformer(nn.Module):
    def __init__(self, dim = 3, decode_dim = 16):
        super(Fastformer, self).__init__()
        # Generate weight for Wquery、Wkey and Wvalue
        self.to_qkv = nn.Linear(dim, decode_dim * 3, bias = False)
        self.weight_q = nn.Linear(dim, decode_dim, bias = False)
        self.weight_k = nn.Linear(dim, decode_dim, bias = False)
        self.weight_v = nn.Linear(dim, decode_dim, bias = False)
        self.weight_r = nn.Linear(decode_dim, decode_dim, bias = False)
        self.weight_alpha = nn.Parameter(torch.randn(decode_dim))
        self.weight_beta = nn.Parameter(torch.randn(decode_dim))
        self.scale_factor = decode_dim ** -0.5

    def forward(self, x, mask = None):
        query = self.weight_q(x)
        key = self.weight_k(x)
        value = self.weight_v(x)
        b, n, d = query.shape

        mask_value = -torch.finfo(x.dtype).max
        mask = rearrange(mask, 'b n -> b () n')

        # Caculate the global query
        alpha_weight = (torch.mul(query, self.weight_alpha) * self.scale_factor).masked_fill(~mask, mask_value)
        alpha_weight = torch.softmax(alpha_weight, dim = -1)
        global_query = query * alpha_weight
        global_query = torch.einsum('b n d -> b d', global_query)

        # Model the interaction between global query vector and the key vector
        repeat_global_query = einops.repeat(global_query, 'b d -> b copy d', copy = n)
        p = repeat_global_query * key
        beta_weight = (torch.mul(p, self.weight_beta) * self.scale_factor).masked_fill(~mask, mask_value)
        beta_weight = torch.softmax(beta_weight, dim = -1)
        global_key = p * beta_weight
        global_key = torch.einsum('b n d -> b d', global_key)

        # key-value
        key_value_interaction = torch.einsum('b j, b n j -> b n j', global_key, value)
        key_value_interaction_out = self.weight_r(key_value_interaction)
        result = key_value_interaction_out + query
        return result

if __name__ == '__main__':
    model = Fastformer(dim = 3, decode_dim = 8)
    x = torch.randn(4, 6, 3)
    mask = torch.ones(1, 8).bool()
    result = model(x, mask)
    print(result[0])
# Generate weight for Wquery、Wkey and Wvalue # Caculate the global query # Model the interaction between global query vector and the key vector # key-value