src/models/SoulX-LiveAct/model_liveact/model_memory_sp.py
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import copy
import math
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from xfuser.core.distributed import (
get_sequence_parallel_rank,
get_sequence_parallel_world_size,
get_sp_group,
)
from einops import rearrange
from diffusers import ModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin
from xfuser.core.long_ctx_attention import xFuserLongContextAttention
from .attention import flash_attention, sdpa_attention, flex_attention
from fp8_gemm import FP8Linear
import logging
logger = logging.getLogger(__name__)
try:
from sageattention import sageattn
USE_SAGEATTN = True
logger.info("Using sageattn")
except Exception:
USE_SAGEATTN = False
from yunchang.kernels import AttnType
__all__ = ['WanModel']
_SAGE_ATTN_TYPES_BY_DEVICE: dict[int, object | None] = {}
_LOGGED_SAGE_ATTN_TYPES: set[str] = set()
def _get_cuda_device_index(device) -> int | None:
if isinstance(device, torch.device):
if device.type != "cuda":
return None
return torch.cuda.current_device() if device.index is None else device.index
if isinstance(device, str):
parsed = torch.device(device)
if parsed.type != "cuda":
return None
return torch.cuda.current_device() if parsed.index is None else parsed.index
return int(device)
def _get_sage_attn_type_for_device(device):
if not torch.cuda.is_available():
return None
device_index = _get_cuda_device_index(device)
if device_index is None:
return None
if device_index in _SAGE_ATTN_TYPES_BY_DEVICE:
return _SAGE_ATTN_TYPES_BY_DEVICE[device_index]
major, _ = torch.cuda.get_device_capability(device_index)
attn_type = None
if major == 9 and hasattr(AttnType, "SAGE_FP8_SM90"):
attn_type = AttnType.SAGE_FP8_SM90
elif major == 12:
if hasattr(AttnType, "SAGE_FP8_SM120"):
attn_type = AttnType.SAGE_FP8_SM120
elif hasattr(AttnType, "SAGE_FP8"):
attn_type = AttnType.SAGE_FP8
_SAGE_ATTN_TYPES_BY_DEVICE[device_index] = attn_type
if attn_type is not None:
attn_type_name = str(attn_type)
if attn_type_name not in _LOGGED_SAGE_ATTN_TYPES:
logger.info("Using sageattn %s", attn_type)
_LOGGED_SAGE_ATTN_TYPES.add(attn_type_name)
return attn_type
def sinusoidal_embedding_1d(dim, position):
# preprocess
assert dim % 2 == 0
half = dim // 2
position = position.type(torch.float64)
# calculation
sinusoid = torch.outer(
position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
return x
# @amp.autocast(enabled=False)
def rope_params(max_seq_len, dim, theta=10000):
assert dim % 2 == 0
freqs = torch.outer(
torch.arange(max_seq_len),
1.0 / torch.pow(theta,
torch.arange(0, dim, 2).to(torch.float64).div(dim)))
freqs = torch.polar(torch.ones_like(freqs), freqs)
return freqs
# @amp.autocast(enabled=False)
def causal_rope_apply(x, grid_sizes, freqs, sp_size, sp_rank, start_frame=0, _f=None):
s, n, c = x.size(1), x.size(2), x.size(3) // 2
freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
output = []
for i, (f, h, w) in enumerate(grid_sizes.tolist()):
f = _f if _f else f
seq_len = f * h * w
x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape(
s, n, -1, 2))
freqs_i = torch.cat([
freqs[0][start_frame:start_frame + 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)
s_per_rank = s
freqs_i = freqs_i[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
freqs_i = freqs_i.to(device=x_i.device)
x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
output.append(x_i)
return torch.stack(output) # .float()
def rope_apply(x, grid_sizes, freqs, f_list=[], rope_list=[]):
s, n, c = x.size(1), x.size(2), x.size(3) // 2
freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
output = []
for f_l, r_l in zip(f_list, rope_list):
start_f, end_f = f_l
start_r, end_r = r_l
f = end_f - start_f
_, h, w = grid_sizes.tolist()[0]
seq_len = (end_f - start_f) * h * w
x_i = torch.view_as_complex(
x[0, start_f * h * w:end_f * h * w].to(torch.float64) \
.reshape(seq_len, n, -1, 2)
)
freqs_i = torch.cat([
freqs[0][start_r:end_r].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)
freqs_i = freqs_i.to(device=x_i.device)
x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
output.append(x_i)
return torch.concat(output, dim=0).unsqueeze(0)
class WanRMSNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.dim = dim
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
r"""
Args:
x(Tensor): Shape [B, L, C]
"""
return self._norm(x.float()).to(dtype=x.dtype) * self.weight.to(dtype=x.dtype)
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
class WanLayerNorm(nn.LayerNorm):
def __init__(self, dim, eps=1e-6, elementwise_affine=False):
super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
origin_dtype = inputs.dtype
out = F.layer_norm(
inputs.float(),
self.normalized_shape,
None if self.weight is None else self.weight.float(),
None if self.bias is None else self.bias.float(),
self.eps
).to(origin_dtype)
return out
class WanSelfAttention(nn.Module):
def __init__(self,
dim,
num_heads,
window_size=(-1, -1),
qk_norm=True,
eps=1e-6):
assert dim % num_heads == 0
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.window_size = window_size
self.qk_norm = qk_norm
self.eps = eps
# layers
self.q = nn.Linear(dim, dim)
self.k = nn.Linear(dim, dim)
self.v = nn.Linear(dim, dim)
self.o = nn.Linear(dim, dim)
self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
self.attn_mask = None
self.memory_proj_k = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False)
self.memory_proj_v = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False)
def post_init(self, device):
self.memory_proj_k = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False).to(
device, dtype=torch.bfloat16)
self.memory_proj_v = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False).to(
device, dtype=torch.bfloat16)
nn.init.constant_(self.memory_proj_k.weight, 1.0 / 5.0)
nn.init.constant_(self.memory_proj_v.weight, 1.0 / 5.0)
# @torch.compiler.disable
def k_compress(self, k, n_frame=5):
B, N, H, C = k.shape
assert N % n_frame == 0
T = N // n_frame
k = k.view(B, N, H * C).transpose(1, 2)
k = self.memory_proj_k(k)
k = k.view(B, H, C, T).permute(0, 3, 1, 2)
return k
# @torch.compiler.disable
def v_compress(self, v, n_frame=5):
B, N, H, C = v.shape
assert N % n_frame == 0
T = N // n_frame
v = v.view(B, N, H * C).transpose(1, 2)
v = self.memory_proj_k(v)
v = v.view(B, H, C, T).permute(0, 3, 1, 2)
return v
def kv_mean(self, kv, n_frame=5):
B, N, H, C = kv.shape
assert N % n_frame == 0
T = N // n_frame
kv = kv.view(B, T, n_frame, H, C).mean(dim=2)
return kv
def init_kvidx(self, frame_len, world_size):
self.frame_seqlen = frame_len
self.kv_idx0 = torch.tensor(list(range(6 * frame_len // world_size)),
device=f'cuda:{int(os.getenv("RANK", 0))}')
self.kv_idx2 = torch.tensor(list(range(14 * frame_len // world_size)),
device=f'cuda:{int(os.getenv("RANK", 0))}')
def _move_kv_cache_to_device(self, kv_cache, device):
kv_cache["k"] = kv_cache["k"].to(device=device, non_blocking=True)
kv_cache["v"] = kv_cache["v"].to(device=device, non_blocking=True)
if kv_cache.get("k_scale") is not None:
kv_cache["k_scale"] = kv_cache["k_scale"].to(device=device, non_blocking=True)
if kv_cache.get("v_scale") is not None:
kv_cache["v_scale"] = kv_cache["v_scale"].to(device=device, non_blocking=True)
def _quantize_kv_tensor(self, kv):
fp8_max = torch.finfo(torch.float8_e4m3fn).max
scale = kv.detach().abs().amax(dim=-1, keepdim=True).to(torch.float32)
scale = torch.clamp(scale / fp8_max, min=1e-12)
q_kv = (kv / scale.to(dtype=kv.dtype)).to(torch.float8_e4m3fn)
return q_kv.contiguous(), scale.contiguous()
def _dequantize_kv_tensor(self, q_kv, scale, dtype):
return q_kv.to(dtype=dtype) * scale.to(device=q_kv.device, dtype=dtype)
def _load_kv_cache(self, kv_cache, device, dtype):
if kv_cache["offload_cache"]:
self._move_kv_cache_to_device(kv_cache, device)
if kv_cache.get("fp8_kv_cache", False):
k_cache = self._dequantize_kv_tensor(kv_cache["k"], kv_cache["k_scale"], dtype)
v_cache = self._dequantize_kv_tensor(kv_cache["v"], kv_cache["v_scale"], dtype)
else:
if kv_cache["k"].dtype != dtype:
kv_cache["k"] = kv_cache["k"].to(dtype=dtype)
if kv_cache["v"].dtype != dtype:
kv_cache["v"] = kv_cache["v"].to(dtype=dtype)
k_cache = kv_cache["k"]
v_cache = kv_cache["v"]
return k_cache, v_cache
def _store_kv_cache(self, kv_cache, k_cache, v_cache):
if kv_cache.get("fp8_kv_cache", False):
kv_cache["k"], kv_cache["k_scale"] = self._quantize_kv_tensor(k_cache)
kv_cache["v"], kv_cache["v_scale"] = self._quantize_kv_tensor(v_cache)
else:
kv_cache["k"] = k_cache
kv_cache["v"] = v_cache
if kv_cache["offload_cache"]:
self._move_kv_cache_to_device(kv_cache, 'cpu')
def forward(self, x, seq_lens, grid_sizes, freqs, sp_size, sp_rank, kv_cache={}, start_idx=None, end_idx=None,
update_cache=False):
b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
# 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)
k_cache, v_cache = self._load_kv_cache(kv_cache, f'cuda:{int(os.getenv("RANK", 0))}', torch.bfloat16)
# print('----q.shape, k.shape, v.shape:', q.shape, k.shape, v.shape)
frame_seqlen = self.frame_seqlen
if update_cache:
if kv_cache["mean_memory"]:
k_compress, v_compress = self.kv_mean, self.kv_mean
else:
k_compress, v_compress = self.k_compress, self.v_compress
if sp_rank == 1:
k_cache[:, : 1 * frame_seqlen].copy_(k_compress(k_cache[:, : 5 * frame_seqlen]))
v_cache[:, : 1 * frame_seqlen].copy_(v_compress(v_cache[:, : 5 * frame_seqlen]))
k_cache[:, 1 * frame_seqlen: 3 * frame_seqlen].copy_(k_cache[:, 5 * frame_seqlen: 7 * frame_seqlen])
v_cache[:, 1 * frame_seqlen: 3 * frame_seqlen].copy_(v_cache[:, 5 * frame_seqlen: 7 * frame_seqlen])
elif sp_rank == 0:
k_cache[:, 2 * frame_seqlen: 3 * frame_seqlen, ...].copy_(
k_compress(k_cache[:, 2 * frame_seqlen: 7 * frame_seqlen]))
v_cache[:, 2 * frame_seqlen: 3 * frame_seqlen, ...].copy_(
v_compress(v_cache[:, 2 * frame_seqlen: 7 * frame_seqlen]))
pass
if start_idx != 0:
k_cache[:, 3 * frame_seqlen:] = k
v_cache[:, 3 * frame_seqlen:] = v
else:
k_cache[:, : 3 * frame_seqlen] = k
v_cache[:, : 3 * frame_seqlen] = v
kv_idx = self.kv_idx0 if end_idx == 6 * frame_seqlen else \
self.kv_idx2 if end_idx == 14 * frame_seqlen else -1
rope_list = [[0 + 3 * sp_rank, 3 + 3 * sp_rank]] if end_idx == 6 * frame_seqlen else \
[[0 + 3 * sp_rank, 3 + 3 * sp_rank], [6 + 4 * sp_rank, 10 + 4 * sp_rank]]
f_list = [[0, 3]] if end_idx == 6 * frame_seqlen else \
[[0, 3], [3, 7]] if end_idx == 14 * frame_seqlen else \
[[0, 3], [3, 7]] if end_idx == 22 * frame_seqlen else -1
sage_attn_type = _get_sage_attn_type_for_device(q.device)
attn_layer = xFuserLongContextAttention(attn_type=sage_attn_type) \
if sage_attn_type is not None else xFuserLongContextAttention()
x = attn_layer(
None,
query=causal_rope_apply(q, grid_sizes, freqs, sp_size, sp_rank,
start_frame=0 if end_idx == 6 * frame_seqlen else 6).type_as(v),
key=rope_apply(kv_cache["k"][:, kv_idx], grid_sizes, freqs, f_list=f_list, rope_list=rope_list).type_as(v),
value=kv_cache["v"][:, kv_idx],
window_size=self.window_size
)
self._store_kv_cache(kv_cache, k_cache, v_cache)
# output
x = x.flatten(2)
x = self.o(x)
return x, None
class WanI2VCrossAttention(nn.Module):
def __init__(self,
dim,
num_heads,
window_size=(-1, -1),
qk_norm=True,
eps=1e-6):
assert dim % num_heads == 0
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.window_size = window_size
self.qk_norm = qk_norm
self.eps = eps
# layers
self.q = nn.Linear(dim, dim)
self.k = nn.Linear(dim, dim)
self.v = nn.Linear(dim, dim)
self.o = nn.Linear(dim, dim)
self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
self.k_img = nn.Linear(dim, dim)
self.v_img = nn.Linear(dim, dim)
self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
def forward(self, x, context, context_lens, cross_kv_cache={}):
context_img = context[:, :257]
context = context[:, 257:]
b, n, d = x.size(0), self.num_heads, self.head_dim
# compute query, key, value
q = self.norm_q(self.q(x)).view(b, -1, n, d)
k = self.norm_k(self.k(context)).view(b, -1, n, d)
v = self.v(context).view(b, -1, n, d)
k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
v_img = self.v_img(context_img).view(b, -1, n, d)
if USE_SAGEATTN:
img_x = sageattn(q, k_img, v_img, tensor_layout='NHD')
x = sageattn(q, k, v, tensor_layout='NHD')
else:
img_x = sdpa_attention(q, k_img, v_img, k_lens=None)
x = sdpa_attention(q, k, v, k_lens=context_lens)
# output
x = x.flatten(2)
img_x = img_x.flatten(2)
x = x + img_x
x = self.o(x)
return x
class SingleStreamAttention(nn.Module):
def __init__(
self,
dim: int,
encoder_hidden_states_dim: int,
num_heads: int,
qkv_bias: bool,
qk_norm: bool,
norm_layer: nn.Module,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
eps: float = 1e-6,
) -> None:
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.dim = dim
self.encoder_hidden_states_dim = encoder_hidden_states_dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.qk_norm = qk_norm
self.q_linear = nn.Linear(dim, dim, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim, eps=eps) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim, eps=eps) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.kv_linear = nn.Linear(encoder_hidden_states_dim, dim * 2, bias=qkv_bias)
self.add_q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.add_k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.q_buf = None # torch.empty((B, H, Lpad, D), device=x.device, dtype=x.dtype)
def forward(
self,
x,
encoder_hidden_states,
sp_size,
sp_rank,
shape=None,
start_f=0,
frame_seqlen=None,
) -> torch.Tensor:
encoder_hidden_states = encoder_hidden_states.squeeze(0)
if frame_seqlen is None:
if shape is None:
raise ValueError("Either frame_seqlen or shape must be provided.")
frame_seqlen = int(shape[1]) * int(shape[2])
batch_size, seq_tokens, channels = x.shape
num_frames = seq_tokens // frame_seqlen
x = x.reshape(batch_size, num_frames, frame_seqlen, channels)
x = x.reshape(batch_size * num_frames, frame_seqlen, channels)
# get q for hidden_state
B, N, C = x.shape # [f, N_h*N_w, dim]
q = self.q_linear(x)
q_shape = (B, N, self.num_heads, self.head_dim)
q = q.view(q_shape).permute((0, 2, 1, 3)) # B H N K = [f, 40, N_h*N_w, head_dim]
if self.qk_norm:
q = self.q_norm(q)
# get kv from encoder_hidden_states
B_e, N_a, _ = encoder_hidden_states.shape # [21, 32, 768]
encoder_kv = self.kv_linear(encoder_hidden_states)
encoder_kv_shape = (B_e, N_a, 2, self.num_heads, self.head_dim) # [21, 32, 2, 40, 128]
encoder_kv = encoder_kv.view(encoder_kv_shape)[start_f + sp_rank * B:start_f + (sp_rank + 1) * B].permute(
(2, 0, 3, 1, 4)) # [2, B, 40, 32, 128]
encoder_k, encoder_v = encoder_kv.unbind(0) # [B, 40, 32, 128]
if self.qk_norm:
encoder_k = self.add_k_norm(encoder_k)
if USE_SAGEATTN:
x = sageattn(q, encoder_k, encoder_v, tensor_layout='HND')
else:
x = torch.nn.functional.scaled_dot_product_attention(
q, encoder_k, encoder_v, attn_mask=None, is_causal=False, dropout_p=0.0) # [f, 40, N_h*N_w, head_dim]
# linear transform
x_output_shape = (B, N, C)
x = x.transpose(1, 2)
x = x.reshape(x_output_shape) # [f, N_h*N_w, 40*head_dim]
x = self.proj(x)
x = self.proj_drop(x)
x = x.reshape(batch_size, num_frames, frame_seqlen, C)
x = x.reshape(batch_size, num_frames * frame_seqlen, C)
return x
class WanAttentionBlock(nn.Module):
def __init__(self,
cross_attn_type,
dim,
ffn_dim,
num_heads,
window_size=(-1, -1),
qk_norm=True,
cross_attn_norm=False,
eps=1e-6,
output_dim=768,
norm_input_visual=True):
super().__init__()
self.dim = dim
self.ffn_dim = ffn_dim
self.num_heads = num_heads
self.window_size = window_size
self.qk_norm = qk_norm
self.cross_attn_norm = cross_attn_norm
self.eps = eps
# layers
self.norm1 = WanLayerNorm(dim, eps)
self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
self.norm3 = WanLayerNorm(
dim, eps,
elementwise_affine=True) if cross_attn_norm else nn.Identity()
self.cross_attn = WanI2VCrossAttention(dim,
num_heads,
(-1, -1),
qk_norm,
eps)
self.norm2 = WanLayerNorm(dim, eps)
self.ffn = nn.Sequential(
nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
nn.Linear(ffn_dim, dim))
# modulation
self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim ** 0.5)
# init audio module
self.audio_cross_attn = SingleStreamAttention(
dim=dim,
encoder_hidden_states_dim=output_dim,
num_heads=num_heads,
qk_norm=False,
qkv_bias=True,
eps=eps,
norm_layer=WanRMSNorm,
)
self.norm_x = WanLayerNorm(dim, eps, elementwise_affine=True) if norm_input_visual else nn.Identity()
def forward(
self,
x,
e,
seq_lens,
grid_sizes,
freqs,
context,
context_lens,
kv_cache={},
start_idx=None,
end_idx=None,
update_cache=False,
cross_kv_cache={},
audio_embedding=None,
ref_target_masks=None,
human_num=None,
skip_audio=False,
):
dtype = x.dtype
# assert e.dtype == torch.float32
if len(e.shape) == 3:
# with amp.autocast(dtype=torch.float32):
e = (self.modulation.to(e.device) + e).chunk(6, dim=1)
else:
# with amp.autocast(dtype=torch.float32):
e = (self.modulation.unsqueeze(-2).to(e.device) + e)[0].chunk(6, dim=0)
# assert e[0].dtype == torch.float32
sp_size = get_sequence_parallel_world_size()
sp_rank = get_sequence_parallel_rank()
# self-attention
y, x_ref_attn_map = self.self_attn(
(self.norm1(x).float() * (1 + e[1]) + e[0]).type_as(x), seq_lens, grid_sizes,
freqs, sp_size, sp_rank, kv_cache=kv_cache, start_idx=start_idx, end_idx=end_idx,
update_cache=update_cache,
)
# with amp.autocast(dtype=torch.float32):
x = x + y * e[2]
x = x.to(dtype)
# cross-attention of text
x = x + self.cross_attn(self.norm3(x), context, context_lens, cross_kv_cache=cross_kv_cache)
# cross attn of audio
if not skip_audio:
frame_seqlen = self.self_attn.frame_seqlen
start_f = start_idx // frame_seqlen
x_a = self.audio_cross_attn(self.norm_x(x), audio_embedding,
sp_size, sp_rank, frame_seqlen=frame_seqlen, start_f=start_f)
if start_f == 0 and sp_rank == 0:
x_a[:, :frame_seqlen] = 0
x = x + x_a
y = self.ffn((self.norm2(x).float() * (1 + e[4]) + e[3]).to(dtype))
# with amp.autocast(dtype=torch.float32):
x = x + y * e[5]
x = x.to(dtype)
return x
class Head(nn.Module):
def __init__(self, dim, out_dim, patch_size, eps=1e-6):
super().__init__()
self.dim = dim
self.out_dim = out_dim
self.patch_size = patch_size
self.eps = eps
# layers
out_dim = math.prod(patch_size) * out_dim
self.norm = WanLayerNorm(dim, eps)
self.head = nn.Linear(dim, out_dim)
# modulation
self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim ** 0.5)
def forward(self, x, e):
r"""
Args:
x(Tensor): Shape [B, L1, C]
e(Tensor): Shape [B, C]
"""
# assert e.dtype == torch.float32
# with amp.autocast(dtype=torch.float32):
e = (self.modulation.to(e.device) + e.unsqueeze(1)).chunk(2, dim=1)
x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
return x
class MLPProj(torch.nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.proj = torch.nn.Sequential(
torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim),
torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim),
torch.nn.LayerNorm(out_dim))
def forward(self, image_embeds):
clip_extra_context_tokens = self.proj(image_embeds)
return clip_extra_context_tokens
class AudioProjModel(ModelMixin, ConfigMixin):
def __init__(
self,
seq_len=5,
seq_len_vf=12,
blocks=12,
channels=768,
intermediate_dim=512,
output_dim=768,
context_tokens=32,
norm_output_audio=False,
):
super().__init__()
self.seq_len = seq_len
self.blocks = blocks
self.channels = channels
self.input_dim = seq_len * blocks * channels
self.input_dim_vf = seq_len_vf * blocks * channels
self.intermediate_dim = intermediate_dim
self.context_tokens = context_tokens
self.output_dim = output_dim
# define multiple linear layers
self.proj1 = nn.Linear(self.input_dim, intermediate_dim)
self.proj1_vf = nn.Linear(self.input_dim_vf, intermediate_dim)
self.proj2 = nn.Linear(intermediate_dim, intermediate_dim)
self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim)
self.norm = nn.LayerNorm(output_dim) if norm_output_audio else nn.Identity()
def forward(self, audio_embeds, audio_embeds_vf):
video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1]
B, _, _, S, C = audio_embeds.shape
# process audio of first frame
audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
batch_size, window_size, blocks, channels = audio_embeds.shape
audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)
# process audio of latter frame
audio_embeds_vf = rearrange(audio_embeds_vf, "bz f w b c -> (bz f) w b c")
batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape
audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_vf)
# first projection
audio_embeds = torch.relu(self.proj1(audio_embeds))
audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf))
audio_embeds = rearrange(audio_embeds, "(bz f) c -> bz f c", bz=B)
audio_embeds_vf = rearrange(audio_embeds_vf, "(bz f) c -> bz f c", bz=B)
audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1)
batch_size_c, N_t, C_a = audio_embeds_c.shape
audio_embeds_c = audio_embeds_c.view(batch_size_c * N_t, C_a)
# second projection
audio_embeds_c = torch.relu(self.proj2(audio_embeds_c))
context_tokens = self.proj3(audio_embeds_c).reshape(batch_size_c * N_t, self.context_tokens, self.output_dim)
# normalization and reshape
# with amp.autocast(dtype=torch.float32):
context_tokens = self.norm(context_tokens)
context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length)
return context_tokens
from torch.utils.checkpoint import checkpoint
class WanBlockOffloadManager:
def __init__(self, blocks, onload_device, offload_device='cpu'):
self.blocks = blocks
self.onload_device = torch.device(onload_device)
self.offload_device = torch.device(offload_device)
self.prefetch_stream = torch.cuda.Stream(device=self.onload_device)
self.compute_slot = 0
self.prefetch_slot = 1
self.pending_slots = set()
self.slot_block_indices = [None, None]
self.cuda_blocks = nn.ModuleList([
copy.deepcopy(self.blocks[0]).to(self.onload_device),
copy.deepcopy(self.blocks[0]).to(self.onload_device),
])
for block in self.blocks:
block.to(self.offload_device)
self._pin_module_memory(block)
def _copy_tensor(self, dst, src):
dst.copy_(src, non_blocking=True)
def _pin_tensor(self, tensor):
if tensor is None or tensor.device.type != 'cpu' or tensor.is_pinned():
return tensor
return tensor.pin_memory()
def _pin_module_memory(self, module):
for name, param in module.named_parameters(recurse=False):
if param is not None:
param.data = self._pin_tensor(param.data)
for name, buffer in module.named_buffers(recurse=False):
if buffer is not None:
module._buffers[name] = self._pin_tensor(buffer)
if isinstance(module, FP8Linear):
module._fp16_weight_cpu = self._pin_tensor(module._fp16_weight_cpu)
module._fp16_bias_cpu = self._pin_tensor(module._fp16_bias_cpu)
for child in module.children():
self._pin_module_memory(child)
def _copy_fp8_linear(self, dst_module, src_module):
if dst_module.linear is not None and src_module.linear is not None:
self._copy_module_state(dst_module.linear, src_module.linear)
if dst_module.bias is not None and src_module.bias is not None:
self._copy_tensor(dst_module.bias.data, src_module.bias.data)
dst_module._fp16_weight_cpu = src_module._fp16_weight_cpu
dst_module._fp16_bias_cpu = src_module._fp16_bias_cpu
if src_module._fp8_weight is None or src_module._fp8_weight_scale is None:
dst_module._fp8_weight = None
dst_module._fp8_weight_scale = None
dst_module._weight_cache_device = None
if dst_module._fp16_weight_cpu is not None:
dst_module.materialize_fp8_weight(self.onload_device)
else:
if dst_module._fp8_weight is None or dst_module._fp8_weight.shape != src_module._fp8_weight.shape:
dst_module._fp8_weight = src_module._fp8_weight.to(device=self.onload_device, non_blocking=True)
else:
self._copy_tensor(dst_module._fp8_weight, src_module._fp8_weight)
if dst_module._fp8_weight_scale is None or dst_module._fp8_weight_scale.shape != src_module._fp8_weight_scale.shape:
dst_module._fp8_weight_scale = src_module._fp8_weight_scale.to(device=self.onload_device,
non_blocking=True)
else:
self._copy_tensor(dst_module._fp8_weight_scale, src_module._fp8_weight_scale)
dst_module._weight_cache_device = dst_module._cached_fp8_device()
dst_module._last_weight_version = src_module._last_weight_version
def _copy_module_state(self, dst_module, src_module):
if isinstance(dst_module, FP8Linear) and isinstance(src_module, FP8Linear):
self._copy_fp8_linear(dst_module, src_module)
return
dst_params = dict(dst_module.named_parameters(recurse=False))
src_params = dict(src_module.named_parameters(recurse=False))
for name, dst_param in dst_params.items():
src_param = src_params.get(name)
if src_param is not None:
self._copy_tensor(dst_param.data, src_param.data)
dst_buffers = dict(dst_module.named_buffers(recurse=False))
src_buffers = dict(src_module.named_buffers(recurse=False))
for name, dst_buffer in dst_buffers.items():
src_buffer = src_buffers.get(name)
if src_buffer is not None:
self._copy_tensor(dst_buffer, src_buffer)
dst_children = dict(dst_module.named_children())
src_children = dict(src_module.named_children())
for name, dst_child in dst_children.items():
src_child = src_children.get(name)
if src_child is not None:
self._copy_module_state(dst_child, src_child)
def _load_slot(self, slot_idx, block_idx, async_transfer=False):
def copy_block():
self._copy_module_state(self.cuda_blocks[slot_idx], self.blocks[block_idx])
self.slot_block_indices[slot_idx] = block_idx
if async_transfer:
with torch.cuda.stream(self.prefetch_stream):
copy_block()
self.pending_slots.add(slot_idx)
else:
copy_block()
self.pending_slots.discard(slot_idx)
def _wait_slot(self, slot_idx):
if slot_idx in self.pending_slots:
torch.cuda.current_stream(device=self.onload_device).wait_stream(self.prefetch_stream)
self.pending_slots.discard(slot_idx)
def get_block(self, block_idx):
if self.slot_block_indices[self.compute_slot] == block_idx:
self._wait_slot(self.compute_slot)
elif self.slot_block_indices[self.prefetch_slot] == block_idx:
self._wait_slot(self.prefetch_slot)
self.compute_slot, self.prefetch_slot = self.prefetch_slot, self.compute_slot
else:
self._load_slot(self.compute_slot, block_idx, async_transfer=False)
next_idx = block_idx + 1
if next_idx < len(self.blocks) and self.slot_block_indices[self.prefetch_slot] != next_idx:
# We are about to overwrite self.prefetch_slot on the prefetch stream.
# Must ensure the compute stream has finished using it from previous steps.
self.prefetch_stream.wait_stream(torch.cuda.current_stream(device=self.onload_device))
self._load_slot(self.prefetch_slot, next_idx, async_transfer=True)
return self.cuda_blocks[self.compute_slot]
def unload_all(self):
torch.cuda.current_stream(device=self.onload_device).wait_stream(self.prefetch_stream)
self.pending_slots.clear()
self.slot_block_indices = [None, None]
class WanModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
r"""
Wan diffusion backbone supporting both text-to-video and image-to-video.
"""
ignore_for_config = [
'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size'
]
_no_split_modules = ['WanAttentionBlock']
@register_to_config
def __init__(self,
model_type='i2v',
patch_size=(1, 2, 2),
text_len=512,
in_dim=16,
dim=2048,
ffn_dim=8192,
freq_dim=256,
text_dim=4096,
out_dim=16,
num_heads=16,
num_layers=32,
window_size=(-1, -1),
qk_norm=True,
cross_attn_norm=True,
eps=1e-6,
# audio params
audio_window=5,
intermediate_dim=512,
output_dim=768,
context_tokens=32,
vae_scale=4,
norm_input_visual=True,
norm_output_audio=True,
weight_init=True):
super().__init__()
assert model_type == 'i2v', 'MultiTalk model requires your model_type is i2v.'
self.model_type = model_type
self.patch_size = patch_size
self.text_len = text_len
self.in_dim = in_dim
self.dim = dim
self.ffn_dim = ffn_dim
self.freq_dim = freq_dim
self.text_dim = text_dim
self.out_dim = out_dim
self.num_heads = num_heads
self.num_layers = num_layers
self.window_size = window_size
self.qk_norm = qk_norm
self.cross_attn_norm = cross_attn_norm
self.eps = eps
self.gradient_checkpointing = False
self.norm_output_audio = norm_output_audio
self.audio_window = audio_window
self.intermediate_dim = intermediate_dim
self.vae_scale = vae_scale
self.return_layers_cosine = False
self.cos_sims = []
self.skip_layer = []
self.block_offload_manager = None
self.block_offload_enabled = False
# embeddings
self.patch_embedding = nn.Conv3d(
in_dim, dim, kernel_size=patch_size, stride=patch_size)
self.text_embedding = nn.Sequential(
nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'),
nn.Linear(dim, dim))
self.time_embedding = nn.Sequential(
nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
# blocks
cross_attn_type = 'i2v_cross_attn'
self.blocks = nn.ModuleList([
WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
window_size, qk_norm, cross_attn_norm, eps,
output_dim=output_dim, norm_input_visual=norm_input_visual)
for _ in range(num_layers)
])
# head
self.head = Head(dim, out_dim, patch_size, eps)
assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
d = dim // num_heads
self.freqs = torch.cat([
rope_params(1024, d - 4 * (d // 6)),
rope_params(1024, 2 * (d // 6)),
rope_params(1024, 2 * (d // 6))
],
dim=1)
if model_type == 'i2v':
self.img_emb = MLPProj(1280, dim)
else:
raise NotImplementedError('Not supported model type.')
# init audio adapter
self.audio_proj = AudioProjModel(
seq_len=audio_window,
seq_len_vf=audio_window + vae_scale - 1,
intermediate_dim=intermediate_dim,
output_dim=output_dim,
context_tokens=context_tokens,
norm_output_audio=norm_output_audio,
)
# initialize weights
if weight_init:
self.init_weights()
def init_freqs(self):
d = self.dim // self.num_heads
self.freqs = torch.cat([
rope_params(1024, d - 4 * (d // 6)),
rope_params(1024, 2 * (d // 6)),
rope_params(1024, 2 * (d // 6))
],
dim=1)
def enable_block_offload(self, onload_device=None, offload_device='cpu'):
if onload_device is None:
onload_device = self.patch_embedding.weight.device
onload_device = torch.device(onload_device)
if onload_device.type != 'cuda':
raise ValueError("WanModel block offload requires a CUDA onload device.")
self.block_offload_manager = WanBlockOffloadManager(
self.blocks,
onload_device=onload_device,
offload_device=offload_device,
)
self.block_offload_enabled = True
torch.cuda.empty_cache()
return self
def forward(
self,
x,
t,
context,
clip_fea=None,
y=None,
audio=None,
ref_target_masks=None,
kv_cache={},
start_idx=None,
end_idx=None,
cross_kv_cache={},
update_cache=False,
skip_audio=False,
):
assert clip_fea is not None and y is not None
# params
device = self.patch_embedding.weight.device
if self.freqs.device != device:
self.freqs = self.freqs.to(device)
_, T, H, W = x[0].shape
N_t = T // self.patch_size[0]
N_h = H // self.patch_size[1]
N_w = W // self.patch_size[2]
if y is not None:
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
x[0] = x[0].to(context[0].dtype)
# 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)
x = torch.cat(x)
# time embeddings
# with amp.autocast(dtype=torch.float32):
e = self.time_embedding(
sinusoidal_embedding_1d(self.freq_dim, t).float())
e0 = self.time_projection(e).unflatten(1, (6, self.dim))
# assert e.dtype == torch.float32 and e0.dtype == torch.float32
# text embedding
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
]))
# clip embedding
if clip_fea is not None:
context_clip = self.img_emb(clip_fea)
context = torch.concat([context_clip, context], dim=1).to(x.dtype)
audio_cond = audio.to(device=x.device, dtype=x.dtype)
first_frame_audio_emb_s = audio_cond[:, :1, ...]
latter_frame_audio_emb = audio_cond[:, 1:, ...]
latter_frame_audio_emb = rearrange(latter_frame_audio_emb, "b (n_t n) w s c -> b n_t n w s c", n=self.vae_scale)
middle_index = self.audio_window // 2
latter_first_frame_audio_emb = latter_frame_audio_emb[:, :, :1, :middle_index + 1, ...]
latter_first_frame_audio_emb = rearrange(latter_first_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
latter_last_frame_audio_emb = latter_frame_audio_emb[:, :, -1:, middle_index:, ...]
latter_last_frame_audio_emb = rearrange(latter_last_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
latter_middle_frame_audio_emb = latter_frame_audio_emb[:, :, 1:-1, middle_index:middle_index + 1, ...]
latter_middle_frame_audio_emb = rearrange(latter_middle_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
latter_frame_audio_emb_s = torch.concat(
[latter_first_frame_audio_emb, latter_middle_frame_audio_emb, latter_last_frame_audio_emb], dim=2)
audio_embedding = self.audio_proj(first_frame_audio_emb_s, latter_frame_audio_emb_s)
human_num = len(audio_embedding)
audio_embedding = torch.concat(audio_embedding.split(1), dim=2).to(x.dtype)
# convert ref_target_masks to token_ref_target_masks
if ref_target_masks is not None:
ref_target_masks = ref_target_masks.unsqueeze(0) # .to(torch.float32)
token_ref_target_masks = nn.functional.interpolate(ref_target_masks, size=(N_h, N_w), mode='nearest')
token_ref_target_masks = token_ref_target_masks.squeeze(0)
token_ref_target_masks = (token_ref_target_masks > 0)
token_ref_target_masks = token_ref_target_masks.view(token_ref_target_masks.shape[0], -1)
token_ref_target_masks = token_ref_target_masks.to(x.dtype)
# Context Parallel
x = torch.chunk(
x, get_sequence_parallel_world_size(),
dim=1)[get_sequence_parallel_rank()]
# arguments
kwargs = dict(
e=e0,
seq_lens=seq_lens,
grid_sizes=grid_sizes,
freqs=self.freqs,
context=context,
context_lens=context_lens,
audio_embedding=audio_embedding,
ref_target_masks=token_ref_target_masks,
human_num=human_num,
start_idx=start_idx,
end_idx=end_idx,
update_cache=update_cache,
)
block_offload_manager = self.block_offload_manager if self.block_offload_enabled else None
if torch.is_grad_enabled() and self.gradient_checkpointing:
for block_index, block in enumerate(self.blocks):
if block_offload_manager is not None:
block = block_offload_manager.get_block(block_index)
if kv_cache.get(block_index) is None: kv_cache[block_index] = {}
if cross_kv_cache.get(block_index) is None: cross_kv_cache[block_index] = {}
x = checkpoint(
block, x, kv_cache=kv_cache[block_index], cross_kv_cache=cross_kv_cache[block_index],
skip_audio=skip_audio, use_reentrant=False, **kwargs
)
else:
for block_index, block in enumerate(self.blocks):
if block_offload_manager is not None:
block = block_offload_manager.get_block(block_index)
if kv_cache.get(block_index) is None: kv_cache[block_index] = {}
if cross_kv_cache.get(block_index) is None: cross_kv_cache[block_index] = {}
x = block(x, kv_cache=kv_cache[block_index], cross_kv_cache=cross_kv_cache[block_index],
skip_audio=skip_audio, **kwargs)
# head
x = self.head(x, e)
# Context Parallel
x = get_sp_group().all_gather(x, dim=1)
# unpatchify
x = self.unpatchify(x, grid_sizes)
return torch.stack(x) # .float()
def unpatchify(self, x, grid_sizes):
r"""
Reconstruct video tensors from patch embeddings.
Args:
x (List[Tensor]):
List of patchified features, each with shape [L, C_out * prod(patch_size)]
grid_sizes (Tensor):
Original spatial-temporal grid dimensions before patching,
shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
Returns:
List[Tensor]:
Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
"""
c = self.out_dim
out = []
for u, v in zip(x, grid_sizes.tolist()):
u = u[:math.prod(v)].view(*v, *self.patch_size, c)
u = torch.einsum('fhwpqrc->cfphqwr', u)
u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
out.append(u)
return out
def init_weights(self):
r"""
Initialize model parameters using Xavier initialization.
"""
# basic init
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
# init embeddings
nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
for m in self.text_embedding.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=.02)
for m in self.time_embedding.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=.02)
# init output layer
nn.init.zeros_(self.head.head.weight)