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import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class GELU(nn.Module):
def forward(self, input):
return F.gelu(input)
class Attend(nn.Module):
def __init__(self, dim=None):
super().__init__()
self.dim = dim
def forward(self, input):
return F.softmax(input, dim=self.dim, dtype=input.dtype)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout=0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.attend = Attend(dim=-1)
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self, x):
b, n, _, h = *x.shape, self.heads
qkv = self.to_qkv(x).chunk(3, dim=-1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), qkv)
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
attn = self.attend(dots)
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class Conv(nn.Module):
def __init__(self, dim, dropout=0.):
super().__init__()
self.dim = dim
self.net = nn.Sequential(
nn.Conv1d(dim, dim, kernel_size=3, stride=1, padding=0),
nn.Dropout(dropout)
)
def forward(self, x):
x = x.transpose(1, 2)
x = torch.cat([x[..., -1:], x, x[..., :1]], dim=-1)
x = self.net(x)
return x.transpose(1, 2)
class ConvTransformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)),
PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)),
PreNorm(dim, Conv(dim, dropout=dropout))
]))
def forward(self, x):
for attn, ff, cov in self.layers:
x = attn(x) + x
x = ff(x) + x
x = cov(x) + x
return x
if __name__ == '__main__':
token_dim = 1024
toke_len = 256
transformer = ConvTransformer(dim=token_dim,
depth=6,
heads=16,
dim_head=64,
mlp_dim=2048,
dropout=0.1)
total = sum(p.numel() for p in transformer.parameters())
trainable = sum(p.numel() for p in transformer.parameters() if p.requires_grad)
print('parameter total:{:,}, trainable:{:,}'.format(total, trainable))
input = torch.randn(1, toke_len, token_dim)
output = transformer(input)
print(output.shape)
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