File size: 3,317 Bytes
5d9b308 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # SPDX-License-Identifier: Apache-2.0
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:
# assert in_dim // num_heads == 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:
# assert out_dim // num_heads == in_dim
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)
# self.mlp = FeedForward(out_dim, out_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
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