mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 22:26:36 +00:00
329 lines
12 KiB
Python
329 lines
12 KiB
Python
import math
|
|
from dataclasses import dataclass
|
|
|
|
import torch
|
|
from einops import rearrange
|
|
from torch import Tensor, nn
|
|
|
|
from flux.math import attention, rope
|
|
|
|
def get_linear_split_map():
|
|
hidden_size = 3072
|
|
split_linear_modules_map = {
|
|
"qkv" : {"mapped_modules" : ["q", "k", "v"] , "split_sizes": [hidden_size, hidden_size, hidden_size]},
|
|
"linear1" : {"mapped_modules" : ["linear1_attn_q", "linear1_attn_k", "linear1_attn_v", "linear1_mlp"] , "split_sizes": [hidden_size, hidden_size, hidden_size, 7*hidden_size- 3*hidden_size]}
|
|
}
|
|
return split_linear_modules_map
|
|
|
|
|
|
class EmbedND(nn.Module):
|
|
def __init__(self, dim: int, theta: int, axes_dim: list[int]):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.theta = theta
|
|
self.axes_dim = axes_dim
|
|
|
|
def forward(self, ids: Tensor) -> Tensor:
|
|
n_axes = ids.shape[-1]
|
|
emb = torch.cat(
|
|
[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
|
|
dim=-3,
|
|
)
|
|
|
|
return emb.unsqueeze(1)
|
|
|
|
|
|
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
|
|
"""
|
|
Create sinusoidal timestep embeddings.
|
|
:param t: a 1-D Tensor of N indices, one per batch element.
|
|
These may be fractional.
|
|
:param dim: the dimension of the output.
|
|
:param max_period: controls the minimum frequency of the embeddings.
|
|
:return: an (N, D) Tensor of positional embeddings.
|
|
"""
|
|
t = time_factor * t
|
|
half = dim // 2
|
|
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
|
|
t.device
|
|
)
|
|
|
|
args = t[:, None].float() * freqs[None]
|
|
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
|
if dim % 2:
|
|
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
|
if torch.is_floating_point(t):
|
|
embedding = embedding.to(t)
|
|
return embedding
|
|
|
|
|
|
class MLPEmbedder(nn.Module):
|
|
def __init__(self, in_dim: int, hidden_dim: int):
|
|
super().__init__()
|
|
self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
|
|
self.silu = nn.SiLU()
|
|
self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return self.out_layer(self.silu(self.in_layer(x)))
|
|
|
|
|
|
class RMSNorm(torch.nn.Module):
|
|
def __init__(self, dim: int):
|
|
super().__init__()
|
|
self.scale = nn.Parameter(torch.ones(dim))
|
|
|
|
def forward(self, x: Tensor):
|
|
x_dtype = x.dtype
|
|
x = x.float()
|
|
rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
|
|
return (x * rrms).to(dtype=x_dtype) * self.scale
|
|
|
|
|
|
|
|
class QKNorm(torch.nn.Module):
|
|
def __init__(self, dim: int):
|
|
super().__init__()
|
|
self.query_norm = RMSNorm(dim)
|
|
self.key_norm = RMSNorm(dim)
|
|
|
|
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
|
|
if k != None:
|
|
return self.key_norm(k).to(v)
|
|
else:
|
|
return self.query_norm(q).to(v)
|
|
# q = self.query_norm(q)
|
|
# k = self.key_norm(k)
|
|
# return q.to(v), k.to(v)
|
|
|
|
|
|
class SelfAttention(nn.Module):
|
|
def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
|
self.norm = QKNorm(head_dim)
|
|
self.proj = nn.Linear(dim, dim)
|
|
|
|
def forward(self, x: Tensor, pe: Tensor) -> Tensor:
|
|
raise Exception("not implemented")
|
|
|
|
@dataclass
|
|
class ModulationOut:
|
|
shift: Tensor
|
|
scale: Tensor
|
|
gate: Tensor
|
|
|
|
|
|
def split_mlp(mlp, x, divide = 4):
|
|
x_shape = x.shape
|
|
x = x.view(-1, x.shape[-1])
|
|
chunk_size = int(x_shape[1]/divide)
|
|
x_chunks = torch.split(x, chunk_size)
|
|
for i, x_chunk in enumerate(x_chunks):
|
|
mlp_chunk = mlp[0](x_chunk)
|
|
mlp_chunk = mlp[1](mlp_chunk)
|
|
x_chunk[...] = mlp[2](mlp_chunk)
|
|
return x.reshape(x_shape)
|
|
|
|
class Modulation(nn.Module):
|
|
def __init__(self, dim: int, double: bool):
|
|
super().__init__()
|
|
self.is_double = double
|
|
self.multiplier = 6 if double else 3
|
|
self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
|
|
|
|
def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
|
|
out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
|
|
|
|
return (
|
|
ModulationOut(*out[:3]),
|
|
ModulationOut(*out[3:]) if self.is_double else None,
|
|
)
|
|
|
|
|
|
class DoubleStreamBlock(nn.Module):
|
|
def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
|
|
super().__init__()
|
|
|
|
mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
self.num_heads = num_heads
|
|
self.hidden_size = hidden_size
|
|
self.img_mod = Modulation(hidden_size, double=True)
|
|
self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
|
|
|
|
self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
|
|
self.txt_mod = Modulation(hidden_size, double=True)
|
|
self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
|
|
|
|
self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
|
|
def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
|
|
img_mod1, img_mod2 = self.img_mod(vec)
|
|
txt_mod1, txt_mod2 = self.txt_mod(vec)
|
|
|
|
# prepare image for attention
|
|
img_modulated = self.img_norm1(img)
|
|
img_modulated.mul_(1 + img_mod1.scale)
|
|
img_modulated.add_(img_mod1.shift)
|
|
|
|
shape = (*img_modulated.shape[:2], self.num_heads, int(img_modulated.shape[-1] / self.num_heads) )
|
|
img_q = self.img_attn.q(img_modulated).view(*shape).transpose(1,2)
|
|
img_k = self.img_attn.k(img_modulated).view(*shape).transpose(1,2)
|
|
img_v = self.img_attn.v(img_modulated).view(*shape).transpose(1,2)
|
|
del img_modulated
|
|
|
|
|
|
img_q= self.img_attn.norm(img_q, None, img_v)
|
|
img_k = self.img_attn.norm(None, img_k, img_v)
|
|
|
|
# prepare txt for attention
|
|
txt_modulated = self.txt_norm1(txt)
|
|
txt_modulated.mul_(1 + txt_mod1.scale)
|
|
txt_modulated.add_(txt_mod1.shift)
|
|
|
|
shape = (*txt_modulated.shape[:2], self.num_heads, int(txt_modulated.shape[-1] / self.num_heads) )
|
|
txt_q = self.txt_attn.q(txt_modulated).view(*shape).transpose(1,2)
|
|
txt_k = self.txt_attn.k(txt_modulated).view(*shape).transpose(1,2)
|
|
txt_v = self.txt_attn.v(txt_modulated).view(*shape).transpose(1,2)
|
|
del txt_modulated
|
|
|
|
|
|
txt_q = self.txt_attn.norm(txt_q, None, txt_v)
|
|
txt_k = self.txt_attn.norm(None, txt_k, txt_v)
|
|
|
|
# run actual attention
|
|
q = torch.cat((txt_q, img_q), dim=2)
|
|
del txt_q, img_q
|
|
k = torch.cat((txt_k, img_k), dim=2)
|
|
del txt_k, img_k
|
|
v = torch.cat((txt_v, img_v), dim=2)
|
|
del txt_v, img_v
|
|
|
|
qkv_list = [q, k, v]
|
|
del q, k, v
|
|
attn = attention(qkv_list, pe=pe)
|
|
|
|
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
|
|
|
|
# calculate the img blocks
|
|
img.addcmul_(self.img_attn.proj(img_attn), img_mod1.gate)
|
|
mod_img = self.img_norm2(img)
|
|
mod_img.mul_(1 + img_mod2.scale)
|
|
mod_img.add_(img_mod2.shift)
|
|
mod_img = split_mlp(self.img_mlp, mod_img)
|
|
# mod_img = self.img_mlp(mod_img)
|
|
img.addcmul_( mod_img, img_mod2.gate)
|
|
mod_img = None
|
|
|
|
# calculate the txt blocks
|
|
txt.addcmul_(self.txt_attn.proj(txt_attn), txt_mod1.gate)
|
|
txt.addcmul_(self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift), txt_mod2.gate)
|
|
return img, txt
|
|
|
|
|
|
class SingleStreamBlock(nn.Module):
|
|
"""
|
|
A DiT block with parallel linear layers as described in
|
|
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
mlp_ratio: float = 4.0,
|
|
qk_scale: float | None = None,
|
|
):
|
|
super().__init__()
|
|
self.hidden_dim = hidden_size
|
|
self.num_heads = num_heads
|
|
head_dim = hidden_size // num_heads
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
|
|
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
# qkv and mlp_in
|
|
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
|
|
# proj and mlp_out
|
|
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
|
|
|
|
self.norm = QKNorm(head_dim)
|
|
|
|
self.hidden_size = hidden_size
|
|
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
|
|
self.mlp_act = nn.GELU(approximate="tanh")
|
|
self.modulation = Modulation(hidden_size, double=False)
|
|
|
|
def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
|
|
mod, _ = self.modulation(vec)
|
|
x_mod = self.pre_norm(x)
|
|
x_mod.mul_(1 + mod.scale)
|
|
x_mod.add_(mod.shift)
|
|
|
|
##### More spagheti VRAM optimizations done by DeepBeepMeep !
|
|
# I am sure you are a nice person and as you copy this code, you will give me proper credits:
|
|
# Please link to https://github.com/deepbeepmeep/Wan2GP and @deepbeepmeep on twitter
|
|
|
|
# x_mod = (1 + mod.scale) * x + mod.shift
|
|
|
|
shape = (*x_mod.shape[:2], self.num_heads, int(x_mod.shape[-1] / self.num_heads) )
|
|
q = self.linear1_attn_q(x_mod).view(*shape).transpose(1,2)
|
|
k = self.linear1_attn_k(x_mod).view(*shape).transpose(1,2)
|
|
v = self.linear1_attn_v(x_mod).view(*shape).transpose(1,2)
|
|
|
|
q = self.norm(q, None, v)
|
|
k = self.norm(None, k, v)
|
|
|
|
# compute attention
|
|
qkv_list = [q, k, v]
|
|
del q, k, v
|
|
attn = attention(qkv_list, pe=pe)
|
|
# compute activation in mlp stream, cat again and run second linear layer
|
|
|
|
x_mod_shape = x_mod.shape
|
|
x_mod = x_mod.view(-1, x_mod.shape[-1])
|
|
chunk_size = int(x_mod_shape[1]/6)
|
|
x_chunks = torch.split(x_mod, chunk_size)
|
|
attn = attn.view(-1, attn.shape[-1])
|
|
attn_chunks =torch.split(attn, chunk_size)
|
|
for x_chunk, attn_chunk in zip(x_chunks, attn_chunks):
|
|
mlp_chunk = self.linear1_mlp(x_chunk)
|
|
mlp_chunk = self.mlp_act(mlp_chunk)
|
|
attn_mlp_chunk = torch.cat((attn_chunk, mlp_chunk), -1)
|
|
del attn_chunk, mlp_chunk
|
|
x_chunk[...] = self.linear2(attn_mlp_chunk)
|
|
del attn_mlp_chunk
|
|
x_mod = x_mod.view(x_mod_shape)
|
|
x.addcmul_(x_mod, mod.gate)
|
|
return x
|
|
|
|
|
|
class LastLayer(nn.Module):
|
|
def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
|
|
super().__init__()
|
|
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
|
|
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
|
|
|
|
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
|
|
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
|
|
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
|
|
x = self.linear(x)
|
|
return x
|