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import torch
import torch.nn as nn
from einops import rearrange
from shared.attention import pay_attention
from .rope_3d import RotaryPositionalEmbedding
from ..blocks import RMSNorm_FP32, _take_tensor
from ...audio_process.torch_utils import get_attn_map_with_target
from .rope_3d import RotaryPositionalEmbedding1D
def _run_attention(x_list, out_dtype, **attn_kwargs):
q, k, v = x_list
if out_dtype in (torch.float16, torch.bfloat16):
attn_dtype = out_dtype
else:
attn_dtype = torch.bfloat16
if q.dtype != attn_dtype:
q = q.to(attn_dtype)
k = k.to(attn_dtype)
v = v.to(attn_dtype)
x_list[:] = [q, k, v]
del q, k, v
attn_kwargs.setdefault("recycle_q", True)
x = pay_attention(x_list, **attn_kwargs)
if x.dtype != out_dtype:
x = x.to(out_dtype)
return x
def _run_sparse_attention(x_list, out_dtype, shape, bsa_params, **attn_kwargs):
raise NotImplementedError("LongCat sparse/BSA attention is not wired to WanGP shared attention.")
def normalize_and_scale(column, source_range, target_range, epsilon=1e-8):
source_min, source_max = source_range
new_min, new_max = target_range
normalized = (column - source_min) / (source_max - source_min + epsilon)
scaled = normalized * (new_max - new_min) + new_min
return scaled
class Attention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
enable_flashattn3: bool = False,
enable_flashattn2: bool = False,
enable_xformers: bool = False,
enable_bsa: bool = False,
bsa_params: dict = None,
cp_split_hw: Optional[List[int]] = None
) -> None:
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.enable_flashattn3 = enable_flashattn3
self.enable_flashattn2 = enable_flashattn2
self.enable_xformers = enable_xformers
self.enable_bsa = enable_bsa
self.bsa_params = bsa_params
self.cp_split_hw = cp_split_hw
self.qkv = nn.Linear(dim, dim * 3, bias=True)
self.q_norm = RMSNorm_FP32(self.head_dim, eps=1e-6)
self.k_norm = RMSNorm_FP32(self.head_dim, eps=1e-6)
self.proj = nn.Linear(dim, dim)
self.rope_3d = RotaryPositionalEmbedding(
self.head_dim,
cp_split_hw=cp_split_hw
)
def _process_attn(self, q, k, v, shape, out_dtype):
"""
function wrapper to do attention with q, k, v
"""
if self.enable_bsa:
return _run_sparse_attention([q, k, v], out_dtype, shape, self.bsa_params)
return _run_attention([q, k, v], out_dtype)
def forward(self, x: torch.Tensor, shape=None, num_cond_latents=None, return_kv=False, num_ref_latents=None, ref_img_index=None, mask_frame_range=None, ref_target_masks=None) -> torch.Tensor:
"""
"""
x = _take_tensor(x)
B, N, C = x.shape
out_dtype = x.dtype
qkv = self.qkv(x)
x = None
if qkv.dtype != out_dtype:
qkv = qkv.to(out_dtype)
qkv_shape = (B, N, 3, self.num_heads, self.head_dim)
qkv = qkv.view(qkv_shape)
q, k, v = qkv.unbind(2)
q, k = self.q_norm(q), self.k_norm(k)
v = v.contiguous()
del qkv
if return_kv:
k_cache, v_cache = k.clone(), v.clone()
q, k = self.rope_3d(q, k, shape, ref_img_index, num_ref_latents)
N_t, N_h, N_w = shape
# cond mode
if num_cond_latents is not None and num_cond_latents == 1:
# image to video
num_cond_latents_thw = num_cond_latents * (N // N_t)
# process the condition tokens
q_cond = q[:, :num_cond_latents_thw].contiguous()
k_cond = k[:, :num_cond_latents_thw].contiguous()
v_cond = v[:, :num_cond_latents_thw].contiguous()
x_cond = self._process_attn(q_cond, k_cond, v_cond, shape, out_dtype)
# process the noise tokens
q_noise = q[:, num_cond_latents_thw:].contiguous()
x_noise = self._process_attn(q_noise, k, v, shape, out_dtype)
# merge x_cond and x_noise
x = x_cond.new_empty(B, N, self.num_heads, self.head_dim)
x[:, :num_cond_latents_thw].copy_(x_cond)
x[:, num_cond_latents_thw:].copy_(x_noise)
del x_cond, x_noise
elif num_cond_latents is not None and num_cond_latents > 1:
# video continuation
assert num_ref_latents is not None and ref_img_index is not None, f"No specified insertion position for reference frame"
num_ref_latents_thw = (N // N_t)
num_cond_latents_thw = num_cond_latents * (N // N_t)
# process the condition tokens
q_ref = q[:, :num_ref_latents_thw].contiguous()
k_ref = k[:, :num_ref_latents_thw].contiguous()
v_ref = v[:, :num_ref_latents_thw].contiguous()
q_cond = q[:, num_ref_latents_thw:num_cond_latents_thw].contiguous()
k_cond = k[:, num_ref_latents_thw:num_cond_latents_thw].contiguous()
v_cond = v[:, num_ref_latents_thw:num_cond_latents_thw].contiguous()
x_ref = self._process_attn(q_ref, k_ref, v_ref, shape, out_dtype)
x_cond = self._process_attn(q_cond, k_cond, v_cond, shape, out_dtype)
if num_cond_latents == N_t:
x = x_ref.new_empty(B, num_cond_latents_thw, self.num_heads, self.head_dim)
x[:, :num_ref_latents_thw].copy_(x_ref)
x[:, num_ref_latents_thw:num_cond_latents_thw].copy_(x_cond)
del x_ref, x_cond
else:
# process the noise tokens
q_noise = q[:, num_cond_latents_thw:].contiguous()
start_noise, end_noise, num_noisy_frames = 0, 0, N_t - num_cond_latents
if mask_frame_range is not None and mask_frame_range > 0:
start_noise = ref_img_index - mask_frame_range - num_cond_latents + num_ref_latents
end_noise = ref_img_index + mask_frame_range - num_cond_latents + num_ref_latents + 1
if start_noise >= 0 and end_noise > start_noise and end_noise <= num_noisy_frames:
# remove attention with the reference image in the target range, preventing repeated actions.
start_pos = start_noise * (N // N_t)
end_pos = end_noise * (N // N_t)
q_noise_front = q_noise[:, :start_pos].contiguous()
q_noise_maskref = q_noise[:, start_pos:end_pos].contiguous()
q_noise_back = q_noise[:, end_pos:].contiguous()
k_non_ref = k[:, num_ref_latents_thw:].contiguous()
v_non_ref = v[:, num_ref_latents_thw:].contiguous()
x_noise_front = self._process_attn(q_noise_front, k, v, shape, out_dtype) # q_front has attention with ref + cond + noisy
x_noise_back = self._process_attn(q_noise_back, k, v, shape, out_dtype) # q_back has attention with ref + cond + noisy
x_noise_maskref = self._process_attn(q_noise_maskref, k_non_ref, v_non_ref, shape, out_dtype) # q_mask has attention with cond+noisy
x_noise = x_noise_front.new_empty(B, q_noise.shape[1], self.num_heads, self.head_dim)
x_noise[:, :start_pos].copy_(x_noise_front)
x_noise[:, start_pos:end_pos].copy_(x_noise_maskref)
x_noise[:, end_pos:].copy_(x_noise_back)
del x_noise_front, x_noise_maskref, x_noise_back
else:
x_noise = self._process_attn(q_noise, k, v, shape, out_dtype)
# merge x_cond and x_noise
x = x_ref.new_empty(B, N, self.num_heads, self.head_dim)
x[:, :num_ref_latents_thw].copy_(x_ref)
x[:, num_ref_latents_thw:num_cond_latents_thw].copy_(x_cond)
x[:, num_cond_latents_thw:].copy_(x_noise)
del x_ref, x_cond, x_noise
else:
# text to video
x = self._process_attn(q, k, v, shape, out_dtype)
x_output_shape = (B, N, C)
x = x.reshape(x_output_shape)
x = self.proj(x)
# calculate attention mask for the given area in reference image
x_ref_attn_map = None
if ref_target_masks is not None:
assert num_cond_latents is not None and num_cond_latents > 0, f"currently, multitalk only supports image to video or video continuation"
x_ref_attn_map = get_attn_map_with_target(
q[:, num_cond_latents_thw:].type_as(x),
k.type_as(x),
shape,
ref_target_masks=ref_target_masks,
cp_split_hw=self.cp_split_hw,
)
q = k = v = None
if return_kv:
return x, (k_cache, v_cache), x_ref_attn_map
else:
return x, x_ref_attn_map
def forward_with_kv_cache(self, x: torch.Tensor, shape=None, num_cond_latents=None, kv_cache=None, num_ref_latents=None, ref_img_index=None, mask_frame_range=None, ref_target_masks=None) -> torch.Tensor:
"""
"""
x = _take_tensor(x)
B, N, C = x.shape
out_dtype = x.dtype
qkv = self.qkv(x)
x = None
if qkv.dtype != out_dtype:
qkv = qkv.to(out_dtype)
qkv_shape = (B, N, 3, self.num_heads, self.head_dim)
qkv = qkv.view(qkv_shape)
q, k, v = qkv.unbind(2)
q, k = self.q_norm(q), self.k_norm(k)
v = v.contiguous()
del qkv
N_t, N_h, N_w = shape
k_cache, v_cache = kv_cache
if k_cache.shape[0] == 1:
k_cache = k_cache.repeat(B, 1, 1, 1)
v_cache = v_cache.repeat(B, 1, 1, 1)
if num_cond_latents is not None and num_cond_latents > 0:
k_full = torch.cat([k_cache, k], dim=1).contiguous()
v_full = torch.cat([v_cache, v], dim=1).contiguous()
q_padding = torch.cat([torch.empty_like(k_cache), q], dim=1).contiguous()
q_padding, k_full = self.rope_3d(q_padding, k_full, (N_t + num_cond_latents, N_h, N_w), ref_img_index, num_ref_latents)
q = q_padding[:, -N:].contiguous()
del q_padding
else:
k_full = k
v_full = v
start_noise, end_noise, num_noisy_frames = 0, 0, N_t
if mask_frame_range is not None and mask_frame_range > 0:
start_noise = ref_img_index - mask_frame_range - num_cond_latents + num_ref_latents
end_noise = ref_img_index + mask_frame_range - num_cond_latents + num_ref_latents + 1
if start_noise >= 0 and end_noise > start_noise and end_noise <= num_noisy_frames:
# remove attention with the reference image in the target range, preventing repeated actions.
num_ref_latents_thw = (N // N_t)
start_pos = start_noise * (N // N_t)
end_pos = end_noise * (N // N_t)
q_noise_front = q[:, :start_pos].contiguous()
q_noise_maskref = q[:, start_pos:end_pos].contiguous()
q_noise_back = q[:, end_pos:].contiguous()
k_non_ref = k_full[:, num_ref_latents_thw:].contiguous()
v_non_ref = v_full[:, num_ref_latents_thw:].contiguous()
x_noise_front = self._process_attn(q_noise_front, k_full, v_full, shape, out_dtype) # q_front --> ref+cond+noisy
x_noise_back = self._process_attn(q_noise_back, k_full, v_full, shape, out_dtype) # q_back --> ref+cond+noisy
x_noise_maskref = self._process_attn(q_noise_maskref, k_non_ref, v_non_ref, shape, out_dtype) # q_mask --> cond+noisy
x = x_noise_front.new_empty(B, N, self.num_heads, self.head_dim)
x[:, :start_pos].copy_(x_noise_front)
x[:, start_pos:end_pos].copy_(x_noise_maskref)
x[:, end_pos:].copy_(x_noise_back)
del x_noise_front, x_noise_maskref, x_noise_back
else:
x = self._process_attn(q, k_full, v_full, shape, out_dtype)
x_output_shape = (B, N, C)
x = x.reshape(x_output_shape)
x = self.proj(x)
# calculate attention mask for the given area in reference image
x_ref_attn_map = None
if ref_target_masks is not None:
assert num_cond_latents is not None and num_cond_latents > 0, f"currently, multitalk only supports image to video or video continuation"
x_ref_attn_map = get_attn_map_with_target(
q.type_as(x),
k_full.type_as(x),
shape,
ref_target_masks=ref_target_masks,
cp_split_hw=self.cp_split_hw,
)
q = k = v = k_full = v_full = None
return x, x_ref_attn_map
class SingleStreamAttention(nn.Module):
def __init__(
self,
dim: int,
encoder_hidden_states_dim: int,
num_heads: int,
qkv_bias: bool,
qk_norm: bool,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
eps: float = 1e-6,
class_range: int = 24,
class_interval: int = 4,
cp_split_hw: Optional[List[int]] = None,
enable_flashattn3: bool = False,
enable_flashattn2: bool = False,
enable_xformers: bool = False,
) -> 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.cp_split_hw = cp_split_hw
self.enable_flashattn3 = enable_flashattn3
self.enable_flashattn2 = enable_flashattn2
self.enable_xformers = enable_xformers
self.q_linear = nn.Linear(dim, dim, bias=qkv_bias)
self.q_norm = RMSNorm_FP32(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.k_norm = RMSNorm_FP32(self.head_dim, eps=eps) if qk_norm else nn.Identity()
# multitalk related params
self.class_interval = class_interval
self.class_range = class_range
self.rope_h1 = (0, self.class_interval)
self.rope_h2 = (self.class_range - self.class_interval, self.class_range)
self.rope_bak = int(self.class_range // 2)
self.rope_1d = RotaryPositionalEmbedding1D(self.head_dim)
def _process_cross_attn(self, x, cond, frames_num=None, x_ref_attn_map=None, human_num=None, speaker_token_masks=None):
x = _take_tensor(x)
cond = _take_tensor(cond)
N_t = frames_num
out_dtype = x.dtype
x = rearrange(x, "B (N_t S) C -> (B N_t) S C", N_t=N_t)
# get q for hidden_state
B, N, C = x.shape
q = self.q_linear(x).view(B, N, self.num_heads, self.head_dim)
x = None
if q.dtype != out_dtype:
q = q.to(out_dtype)
q = self.q_norm(q)
# multitalk with rope1d pe
if x_ref_attn_map is not None:
max_values = x_ref_attn_map.max(1).values[:, None, None]
min_values = x_ref_attn_map.min(1).values[:, None, None]
max_min_values = torch.cat([max_values, min_values], dim=2)
human1_max_value, human1_min_value = max_min_values[0, :, 0].max(), max_min_values[0, :, 1].min()
human2_max_value, human2_min_value = max_min_values[1, :, 0].max(), max_min_values[1, :, 1].min()
human1 = normalize_and_scale(x_ref_attn_map[0], (human1_min_value, human1_max_value), (self.rope_h1[0], self.rope_h1[1]))
human2 = normalize_and_scale(x_ref_attn_map[1], (human2_min_value, human2_max_value), (self.rope_h2[0], self.rope_h2[1]))
background_pos = self.rope_bak if x_ref_attn_map.shape[0] <= 3 else 100
back = torch.full((x_ref_attn_map.size(1),), background_pos, dtype=human1.dtype).to(human1.device)
max_indices = x_ref_attn_map.argmax(dim=0).clamp(max=2)
normalized_map = torch.stack([human1, human2, back], dim=1)
normalized_pos = normalized_map[range(x_ref_attn_map.size(1)), max_indices]
q = rearrange(q, "(B N_t) S H C -> B (N_t S) H C", N_t=N_t)
q = self.rope_1d(q, normalized_pos)
q = rearrange(q, "B (N_t S) H C -> (B N_t) S H C", N_t=N_t)
# get kv from encoder_hidden_states
_, N_a, _ = cond.shape
encoder_kv = self.kv_linear(cond).view(B, N_a, 2, self.num_heads, self.head_dim)
cond = None
if encoder_kv.dtype != out_dtype:
encoder_kv = encoder_kv.to(out_dtype)
encoder_k, encoder_v = encoder_kv.unbind(2)
encoder_v = encoder_v.contiguous()
del encoder_kv
encoder_k = self.k_norm(encoder_k)
# multitalk with rope1d pe
if x_ref_attn_map is not None:
per_frame = torch.zeros(N_a, dtype=encoder_k.dtype).to(encoder_k.device)
human1_pos = (self.rope_h1[0] + self.rope_h1[1]) / 2
human2_pos = (self.rope_h2[0] + self.rope_h2[1]) / 2
if human_num is not None and human_num > 2:
background_pos = self.rope_bak if x_ref_attn_map.shape[0] <= 3 else 100
tokens_per_human = per_frame.size(0) // human_num
per_frame[:tokens_per_human] = human1_pos
per_frame[tokens_per_human:2*tokens_per_human] = human2_pos
per_frame[2*tokens_per_human:] = background_pos
else:
per_frame[:per_frame.size(0)//2] = human1_pos
per_frame[per_frame.size(0)//2:] = human2_pos
encoder_pos = torch.concat([per_frame] * N_t, dim=0)
encoder_k = rearrange(encoder_k, "(B N_t) S H C -> B (N_t S) H C", N_t=N_t)
encoder_k = self.rope_1d(encoder_k, encoder_pos)
encoder_k = rearrange(encoder_k, "B (N_t S) H C -> (B N_t) S H C", N_t=N_t)
attention_mask = None
if speaker_token_masks is not None and human_num == 2:
token_owner = speaker_token_masks.argmax(dim=0).clamp(max=2).unsqueeze(0).expand(N_t, -1)
if B != N_t:
token_owner = token_owner.repeat(B // N_t, 1)
split = N_a // human_num
attention_mask = torch.empty((B, N, 1, N_a), device=q.device, dtype=torch.bool)
attention_mask[..., :split] = token_owner.ne(1).unsqueeze(-1).unsqueeze(-1)
attention_mask[..., split:] = token_owner.ne(0).unsqueeze(-1).unsqueeze(-1)
qkv_list = [q, encoder_k, encoder_v]
del q, encoder_k, encoder_v
x = _run_attention(qkv_list, out_dtype, attention_mask=attention_mask)
# linear transform
x_output_shape = (B, N, C)
x = x.reshape(x_output_shape)
x = self.proj(x)
x = self.proj_drop(x)
# reshape x to origin shape
x = rearrange(x, "(B N_t) S C -> B (N_t S) C", N_t=N_t)
return x.type(out_dtype)
def forward(self, x, cond, shape=None, num_cond_latents=None, x_ref_attn_map=None, human_num=None, speaker_token_masks=None):
x = _take_tensor(x)
cond = _take_tensor(cond)
B, N, C = x.shape
if (num_cond_latents is None or num_cond_latents == 0):
# text to video
x_list = [x]
cond_list = [cond]
x = cond = None
output = self._process_cross_attn(x_list, cond_list, shape[0], x_ref_attn_map, human_num=human_num, speaker_token_masks=speaker_token_masks)
return None, output
elif num_cond_latents is not None and num_cond_latents > 0:
# image to video or video continuation
assert shape is not None, "SHOULD pass in the shape"
num_cond_latents_thw = num_cond_latents * (N // shape[0])
x_noise = x[:, num_cond_latents_thw:]
x = None
cond = rearrange(cond, "(B N_t) M C -> B N_t M C", B=B)
cond = cond[:, num_cond_latents:]
cond = rearrange(cond, "B N_t M C -> (B N_t) M C")
frames_num = shape[0] - num_cond_latents
if human_num is not None and human_num == 2:
# multitalk mode
x_noise_list = [x_noise]
cond_list = [cond]
x_noise = cond = None
output_noise = self._process_cross_attn(x_noise_list, cond_list, frames_num, x_ref_attn_map, human_num=human_num, speaker_token_masks=speaker_token_masks)
elif human_num is not None and human_num > 2:
# multitalk mode with background silent audio
x_noise_list = [x_noise]
cond_list = [cond]
x_noise = cond = None
output_noise = self._process_cross_attn(x_noise_list, cond_list, frames_num, x_ref_attn_map, human_num=human_num, speaker_token_masks=speaker_token_masks)
else:
# singletalk mode
x_noise_list = [x_noise]
cond_list = [cond]
x_noise = cond = None
output_noise = self._process_cross_attn(x_noise_list, cond_list, frames_num)
return num_cond_latents_thw, output_noise
else:
raise NotImplementedError
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