| |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from torch.nn.functional import scaled_dot_product_attention |
|
|
|
|
| class GeGluMlp(nn.Module): |
| def __init__( |
| self, |
| in_features, |
| hidden_features, |
| ): |
| super().__init__() |
| self.norm = nn.LayerNorm(in_features) |
| self.act = nn.GELU(approximate="tanh") |
| self.w0 = nn.Linear(in_features, hidden_features) |
| self.w1 = nn.Linear(in_features, hidden_features) |
| self.w2 = nn.Linear(hidden_features, in_features) |
|
|
| def forward(self, x): |
| x = self.norm(x) |
| x = self.act(self.w0(x)) * self.w1(x) |
| x = self.w2(x) |
| return x |
|
|
|
|
| class CausalAttention(nn.Module): |
| def __init__(self, in_dim, out_dim, num_heads): |
| super().__init__() |
| if in_dim > out_dim: |
| |
| self.head_dim = in_dim // num_heads |
| self.qkv = nn.Linear(in_dim, in_dim * 3, bias=False) |
| self.q_bias = nn.Parameter(torch.zeros(in_dim)) |
| self.v_bias = nn.Parameter(torch.zeros(in_dim)) |
| self.register_buffer("zero_k_bias", torch.zeros(in_dim)) |
| else: |
| |
| self.head_dim = out_dim // num_heads |
| self.qkv = nn.Linear(in_dim, out_dim * 3, bias=False) |
| self.q_bias = nn.Parameter(torch.zeros(out_dim)) |
| self.v_bias = nn.Parameter(torch.zeros(out_dim)) |
| self.register_buffer("zero_k_bias", torch.zeros(out_dim)) |
|
|
| self.in_dim = in_dim |
| self.out_dim = out_dim |
| self.num_heads = num_heads |
| self.scale = self.head_dim**-0.5 |
| self.proj = nn.Linear(out_dim, out_dim) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| B, N, C = x.shape |
| qkv = F.linear(input=x, weight=self.qkv.weight, bias=torch.cat((self.q_bias, self.zero_k_bias, self.v_bias))) |
| q, k, v = qkv.reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4).unbind(0) |
|
|
| x = scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True) |
|
|
| if self.in_dim > self.out_dim: |
| x = torch.mean(x, dim=1) |
| if self.in_dim // self.num_heads != self.out_dim: |
| x = nn.functional.adaptive_avg_pool1d(x, self.out_dim) |
| else: |
| x = x.transpose(1, 2).reshape(B, N, -1) |
| x = self.proj(x) |
| return x |
|
|
|
|
| class AttnProjection(nn.Module): |
| def __init__(self, in_dim, out_dim, num_heads, norm_layer=nn.LayerNorm, mlp_ratio=2): |
| super().__init__() |
| assert out_dim % in_dim == 0 or in_dim % out_dim == 0 |
| self.in_dim = in_dim |
| self.out_dim = out_dim |
| self.norm1 = norm_layer(in_dim) |
| self.attn = CausalAttention(in_dim, out_dim, num_heads) |
| self.proj = nn.Linear(in_dim, out_dim) |
| self.norm3 = norm_layer(in_dim) |
|
|
| self.norm2 = norm_layer(out_dim) |
| hidden_dim = int(out_dim * mlp_ratio) |
| self.mlp = GeGluMlp(in_features=out_dim, hidden_features=hidden_dim) |
| |
|
|
| def forward(self, x): |
| x = self.proj(self.norm3(x)) + self.attn(self.norm1(x)) |
| x = x + self.mlp(self.norm2(x)) |
| return x |
|
|
