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