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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import torch
import torch.cuda.amp as amp
from ..modules.model import sinusoidal_embedding_1d
from .ulysses import distributed_attention
from .util import gather_forward, get_rank, get_world_size
def pad_freqs(original_tensor, target_len):
seq_len, s1, s2 = original_tensor.shape
pad_size = target_len - seq_len
padding_tensor = torch.ones(
pad_size,
s1,
s2,
dtype=original_tensor.dtype,
device=original_tensor.device)
padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
return padded_tensor
@torch.amp.autocast('cuda', enabled=False)
def rope_apply(x, grid_sizes, freqs):
"""
x: [B, L, N, C].
grid_sizes: [B, 3].
freqs: [M, C // 2].
"""
s, n, c = x.size(1), x.size(2), x.size(3) // 2
# split freqs
freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
# loop over samples
output = []
for i, (f, h, w) in enumerate(grid_sizes.tolist()):
seq_len = f * h * w
# precompute multipliers
x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape(
s, n, -1, 2))
freqs_i = torch.cat([
freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
],
dim=-1).reshape(seq_len, 1, -1)
# apply rotary embedding
sp_size = get_world_size()
sp_rank = get_rank()
freqs_i = pad_freqs(freqs_i, s * sp_size)
s_per_rank = s
freqs_i_rank = freqs_i[(sp_rank * s_per_rank):((sp_rank + 1) *
s_per_rank), :, :]
x_i = torch.view_as_real(x_i * freqs_i_rank).flatten(2)
x_i = torch.cat([x_i, x[i, s:]])
# append to collection
output.append(x_i)
return torch.stack(output).float()
def sp_dit_forward(
self,
x,
t,
context,
seq_len,
y=None,
):
"""
x: A list of videos each with shape [C, T, H, W].
t: [B].
context: A list of text embeddings each with shape [L, C].
"""
if self.model_type == 'i2v':
assert y is not None
# params
device = self.patch_embedding.weight.device
if self.freqs.device != device:
self.freqs = self.freqs.to(device)
if y is not None:
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
# embeddings
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
grid_sizes = torch.stack(
[torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
x = [u.flatten(2).transpose(1, 2) for u in x]
seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
assert seq_lens.max() <= seq_len
x = torch.cat([
torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1)
for u in x
])
# time embeddings
if t.dim() == 1:
t = t.expand(t.size(0), seq_len)
with torch.amp.autocast('cuda', dtype=torch.float32):
bt = t.size(0)
t = t.flatten()
e = self.time_embedding(
sinusoidal_embedding_1d(self.freq_dim,
t).unflatten(0, (bt, seq_len)).float())
e0 = self.time_projection(e).unflatten(2, (6, self.dim))
assert e.dtype == torch.float32 and e0.dtype == torch.float32
# context
context_lens = None
context = self.text_embedding(
torch.stack([
torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
for u in context
]))
# Context Parallel
x = torch.chunk(x, get_world_size(), dim=1)[get_rank()]
e = torch.chunk(e, get_world_size(), dim=1)[get_rank()]
e0 = torch.chunk(e0, get_world_size(), dim=1)[get_rank()]
# arguments
kwargs = dict(
e=e0,
seq_lens=seq_lens,
grid_sizes=grid_sizes,
freqs=self.freqs,
context=context,
context_lens=context_lens)
for block in self.blocks:
x = block(x, **kwargs)
# head
x = self.head(x, e)
# Context Parallel
x = gather_forward(x, dim=1)
# unpatchify
x = self.unpatchify(x, grid_sizes)
return [u.float() for u in x]
def sp_attn_forward(self, x, seq_lens, grid_sizes, freqs, dtype=torch.bfloat16):
b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
half_dtypes = (torch.float16, torch.bfloat16)
def half(x):
return x if x.dtype in half_dtypes else x.to(dtype)
# query, key, value function
def qkv_fn(x):
q = self.norm_q(self.q(x)).view(b, s, n, d)
k = self.norm_k(self.k(x)).view(b, s, n, d)
v = self.v(x).view(b, s, n, d)
return q, k, v
q, k, v = qkv_fn(x)
q = rope_apply(q, grid_sizes, freqs)
k = rope_apply(k, grid_sizes, freqs)
x = distributed_attention(
half(q),
half(k),
half(v),
seq_lens,
window_size=self.window_size,
)
# output
x = x.flatten(2)
x = self.o(x)
return x
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