mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 14:16:57 +00:00
203 lines
7.4 KiB
Python
203 lines
7.4 KiB
Python
from typing import Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from einops.layers.torch import Rearrange
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from ..ext.rotary_embeddings import apply_rope
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from ..model.low_level import MLP, ChannelLastConv1d, ConvMLP
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def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
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return x * (1 + scale) + shift
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def attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
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# training will crash without these contiguous calls and the CUDNN limitation
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# I believe this is related to https://github.com/pytorch/pytorch/issues/133974
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# unresolved at the time of writing
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q = q.contiguous()
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k = k.contiguous()
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v = v.contiguous()
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out = F.scaled_dot_product_attention(q, k, v)
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out = rearrange(out, 'b h n d -> b n (h d)').contiguous()
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return out
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class SelfAttention(nn.Module):
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def __init__(self, dim: int, nheads: int):
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super().__init__()
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self.dim = dim
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self.nheads = nheads
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self.qkv = nn.Linear(dim, dim * 3, bias=True)
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self.q_norm = nn.RMSNorm(dim // nheads)
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self.k_norm = nn.RMSNorm(dim // nheads)
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self.split_into_heads = Rearrange('b n (h d j) -> b h n d j',
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h=nheads,
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d=dim // nheads,
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j=3)
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def pre_attention(
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self, x: torch.Tensor,
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rot: Optional[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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# x: batch_size * n_tokens * n_channels
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qkv = self.qkv(x)
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q, k, v = self.split_into_heads(qkv).chunk(3, dim=-1)
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q = q.squeeze(-1)
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k = k.squeeze(-1)
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v = v.squeeze(-1)
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q = self.q_norm(q)
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k = self.k_norm(k)
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if rot is not None:
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q = apply_rope(q, rot)
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k = apply_rope(k, rot)
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return q, k, v
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def forward(
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self,
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x: torch.Tensor, # batch_size * n_tokens * n_channels
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) -> torch.Tensor:
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q, v, k = self.pre_attention(x)
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out = attention(q, k, v)
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return out
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class MMDitSingleBlock(nn.Module):
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def __init__(self,
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dim: int,
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nhead: int,
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mlp_ratio: float = 4.0,
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pre_only: bool = False,
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kernel_size: int = 7,
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padding: int = 3):
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super().__init__()
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self.norm1 = nn.LayerNorm(dim, elementwise_affine=False)
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self.attn = SelfAttention(dim, nhead)
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self.pre_only = pre_only
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if pre_only:
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self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 2 * dim, bias=True))
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else:
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if kernel_size == 1:
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self.linear1 = nn.Linear(dim, dim)
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else:
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self.linear1 = ChannelLastConv1d(dim, dim, kernel_size=kernel_size, padding=padding)
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self.norm2 = nn.LayerNorm(dim, elementwise_affine=False)
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if kernel_size == 1:
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self.ffn = MLP(dim, int(dim * mlp_ratio))
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else:
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self.ffn = ConvMLP(dim,
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int(dim * mlp_ratio),
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kernel_size=kernel_size,
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padding=padding)
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self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 6 * dim, bias=True))
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def pre_attention(self, x: torch.Tensor, c: torch.Tensor, rot: Optional[torch.Tensor]):
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# x: BS * N * D
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# cond: BS * D
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modulation = self.adaLN_modulation(c)
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if self.pre_only:
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(shift_msa, scale_msa) = modulation.chunk(2, dim=-1)
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gate_msa = shift_mlp = scale_mlp = gate_mlp = None
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else:
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(shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp,
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gate_mlp) = modulation.chunk(6, dim=-1)
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x = modulate(self.norm1(x), shift_msa, scale_msa)
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q, k, v = self.attn.pre_attention(x, rot)
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return (q, k, v), (gate_msa, shift_mlp, scale_mlp, gate_mlp)
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def post_attention(self, x: torch.Tensor, attn_out: torch.Tensor, c: tuple[torch.Tensor]):
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if self.pre_only:
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return x
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(gate_msa, shift_mlp, scale_mlp, gate_mlp) = c
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x = x + self.linear1(attn_out) * gate_msa
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r = modulate(self.norm2(x), shift_mlp, scale_mlp)
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x = x + self.ffn(r) * gate_mlp
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return x
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def forward(self, x: torch.Tensor, cond: torch.Tensor,
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rot: Optional[torch.Tensor]) -> torch.Tensor:
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# x: BS * N * D
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# cond: BS * D
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x_qkv, x_conditions = self.pre_attention(x, cond, rot)
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attn_out = attention(*x_qkv)
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x = self.post_attention(x, attn_out, x_conditions)
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return x
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class JointBlock(nn.Module):
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def __init__(self, dim: int, nhead: int, mlp_ratio: float = 4.0, pre_only: bool = False):
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super().__init__()
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self.pre_only = pre_only
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self.latent_block = MMDitSingleBlock(dim,
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nhead,
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mlp_ratio,
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pre_only=False,
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kernel_size=3,
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padding=1)
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self.clip_block = MMDitSingleBlock(dim,
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nhead,
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mlp_ratio,
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pre_only=pre_only,
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kernel_size=3,
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padding=1)
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self.text_block = MMDitSingleBlock(dim, nhead, mlp_ratio, pre_only=pre_only, kernel_size=1)
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def forward(self, latent: torch.Tensor, clip_f: torch.Tensor, text_f: torch.Tensor,
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global_c: torch.Tensor, extended_c: torch.Tensor, latent_rot: torch.Tensor,
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clip_rot: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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# latent: BS * N1 * D
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# clip_f: BS * N2 * D
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# c: BS * (1/N) * D
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x_qkv, x_mod = self.latent_block.pre_attention(latent, extended_c, latent_rot)
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c_qkv, c_mod = self.clip_block.pre_attention(clip_f, global_c, clip_rot)
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t_qkv, t_mod = self.text_block.pre_attention(text_f, global_c, rot=None)
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latent_len = latent.shape[1]
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clip_len = clip_f.shape[1]
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text_len = text_f.shape[1]
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joint_qkv = [torch.cat([x_qkv[i], c_qkv[i], t_qkv[i]], dim=2) for i in range(3)]
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attn_out = attention(*joint_qkv)
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x_attn_out = attn_out[:, :latent_len]
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c_attn_out = attn_out[:, latent_len:latent_len + clip_len]
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t_attn_out = attn_out[:, latent_len + clip_len:]
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latent = self.latent_block.post_attention(latent, x_attn_out, x_mod)
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if not self.pre_only:
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clip_f = self.clip_block.post_attention(clip_f, c_attn_out, c_mod)
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text_f = self.text_block.post_attention(text_f, t_attn_out, t_mod)
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return latent, clip_f, text_f
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class FinalBlock(nn.Module):
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def __init__(self, dim, out_dim):
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super().__init__()
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self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 2 * dim, bias=True))
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self.norm = nn.LayerNorm(dim, elementwise_affine=False)
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self.conv = ChannelLastConv1d(dim, out_dim, kernel_size=7, padding=3)
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def forward(self, latent, c):
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shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
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latent = modulate(self.norm(latent), shift, scale)
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latent = self.conv(latent)
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return latent
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