| 103 | |
| 104 | |
| 105 | class WanSelfAttention(nn.Module): |
| 106 | |
| 107 | def __init__(self, |
| 108 | dim, |
| 109 | num_heads, |
| 110 | window_size=(-1, -1), |
| 111 | qk_norm=True, |
| 112 | eps=1e-6): |
| 113 | assert dim % num_heads == 0 |
| 114 | super().__init__() |
| 115 | self.dim = dim |
| 116 | self.num_heads = num_heads |
| 117 | self.head_dim = dim // num_heads |
| 118 | self.window_size = window_size |
| 119 | self.qk_norm = qk_norm |
| 120 | self.eps = eps |
| 121 | |
| 122 | # layers |
| 123 | self.q = nn.Linear(dim, dim) |
| 124 | self.k = nn.Linear(dim, dim) |
| 125 | self.v = nn.Linear(dim, dim) |
| 126 | self.o = nn.Linear(dim, dim) |
| 127 | self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() |
| 128 | self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() |
| 129 | |
| 130 | def forward(self, x, seq_lens, grid_sizes, freqs): |
| 131 | r""" |
| 132 | Args: |
| 133 | x(Tensor): Shape [B, L, num_heads, C / num_heads] |
| 134 | seq_lens(Tensor): Shape [B] |
| 135 | grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) |
| 136 | freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] |
| 137 | """ |
| 138 | b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim |
| 139 | |
| 140 | # query, key, value function |
| 141 | def qkv_fn(x): |
| 142 | q = self.norm_q(self.q(x)).view(b, s, n, d) |
| 143 | k = self.norm_k(self.k(x)).view(b, s, n, d) |
| 144 | v = self.v(x).view(b, s, n, d) |
| 145 | return q, k, v |
| 146 | |
| 147 | q, k, v = qkv_fn(x) |
| 148 | |
| 149 | x = flash_attention( |
| 150 | q=rope_apply(q, grid_sizes, freqs), |
| 151 | k=rope_apply(k, grid_sizes, freqs), |
| 152 | v=v, |
| 153 | k_lens=seq_lens, |
| 154 | window_size=self.window_size) |
| 155 | |
| 156 | # output |
| 157 | x = x.flatten(2) |
| 158 | x = self.o(x) |
| 159 | return x |
| 160 | |
| 161 | |
| 162 | class WanT2VCrossAttention(WanSelfAttention): |