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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import torch
try:
import flash_attn_interface
FLASH_ATTN_3_AVAILABLE = True
print(f'FLASH_ATTN_3_AVAILABLE:{FLASH_ATTN_3_AVAILABLE}')
except ModuleNotFoundError:
print(f'faield FLASH_ATTN_3_AVAILABLE:{FLASH_ATTN_3_AVAILABLE}')
FLASH_ATTN_3_AVAILABLE = False
try:
import flash_attn
FLASH_ATTN_2_AVAILABLE = True
except ModuleNotFoundError:
FLASH_ATTN_2_AVAILABLE = False
import warnings
__all__ = [
'flash_attention',
'attention',
'attention_with_weights',
]
def flash_attention(
q,
k,
v,
q_lens=None,
k_lens=None,
dropout_p=0.,
softmax_scale=None,
q_scale=None,
causal=False,
window_size=(-1, -1),
deterministic=False,
dtype=torch.bfloat16,
version=None
):
"""
q: [B, Lq, Nq, C1].
k: [B, Lk, Nk, C1].
v: [B, Lk, Nk, C2]. Nq must be divisible by Nk.
q_lens: [B].
k_lens: [B].
dropout_p: float. Dropout probability.
softmax_scale: float. The scaling of QK^T before applying softmax.
causal: bool. Whether to apply causal attention mask.
window_size: (left right). If not (-1, -1), apply sliding window local attention.
deterministic: bool. If True, slightly slower and uses more memory.
dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16.
"""
half_dtypes = (torch.float16, torch.bfloat16)
assert dtype in half_dtypes
assert q.device.type == 'cuda' and q.size(-1) <= 256
# params
b, lq, nheads, lk, out_dtype = q.size(0), q.size(1), q.size(2), k.size(1), q.dtype
def half(x):
return x if x.dtype in half_dtypes else x.to(dtype)
# preprocess query
if q_lens is None:
q = half(q.flatten(0, 1))
q_lens = torch.tensor(
[lq] * b, dtype=torch.int32).to(
device=q.device, non_blocking=True)
else:
q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)]))
# preprocess key, value
if k_lens is None:
k = half(k.flatten(0, 1))
v = half(v.flatten(0, 1))
k_lens = torch.tensor(
[lk] * b, dtype=torch.int32).to(
device=k.device, non_blocking=True)
else:
k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))
q = q.to(v.dtype)
k = k.to(v.dtype)
if q_scale is not None:
q = q * q_scale
if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
warnings.warn(
'Flash attention 3 is not available, use flash attention 2 instead.'
)
# apply attention
if FLASH_ATTN_3_AVAILABLE:
ret = flash_attn_interface.flash_attn_varlen_func(
q=q,
k=k,
v=v,
cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
0, dtype=torch.int32).to(q.device, non_blocking=True),
cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
0, dtype=torch.int32).to(k.device, non_blocking=True),
seqused_q=None,
seqused_k=None,
max_seqlen_q=lq,
max_seqlen_k=lk,
softmax_scale=softmax_scale,
causal=causal,
deterministic=deterministic
)
# Some FA3 wheels return (out, softmax_lse); some return just out.
out0 = ret[0] if isinstance(ret, (tuple, list)) else ret
# Normalize FA3 output layout to (total_q, nheads, headdim)
total_q = b * lq
if out0.dim() == 3:
if out0.shape[0] == total_q:
pass # (total_q, nheads, headdim) -> good
elif out0.shape[0] == nheads and out0.shape[1] == total_q:
# heads-first -> transpose to (total_q, nheads, headdim)
out0 = out0.transpose(0, 1).contiguous()
else:
raise RuntimeError(
f"Unexpected FA3 output shape {tuple(out0.shape)}; "
f"expected (total_q, nheads, headdim) or (nheads, total_q, headdim)"
)
else:
raise RuntimeError(
f"Unexpected FA3 output rank {out0.dim()} with shape {tuple(out0.shape)}; "
f"expected a 3D tensor."
)
x = out0.unflatten(0, (b, lq))
else:
assert FLASH_ATTN_2_AVAILABLE
x = flash_attn.flash_attn_varlen_func(
q=q,
k=k,
v=v,
cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
0, dtype=torch.int32).to(q.device, non_blocking=True),
cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
0, dtype=torch.int32).to(q.device, non_blocking=True),
max_seqlen_q=lq,
max_seqlen_k=lk,
dropout_p=dropout_p,
softmax_scale=softmax_scale,
causal=causal,
window_size=window_size,
deterministic=deterministic).unflatten(0, (b, lq))
# output
return x.type(out_dtype)
def attention_with_weights(
q,
k,
v,
q_lens=None,
k_lens=None,
softmax_scale=None,
q_scale=None,
causal=False,
average_for_q=False,
total_video_latent_frames = 21
):
"""
Compute attention with explicit attention weights for visualization.
Returns both output and attention weights.
"""
out_dtype = q.dtype
# Handle sequence lengths
b, lq, lk = q.size(0), q.size(1), k.size(1)
if q_lens is None:
q_lens = torch.tensor([lq] * b, dtype=torch.int32, device=q.device)
else:
# Ensure q_lens is on the same device as q
q_lens = q_lens.to(q.device)
if k_lens is None:
k_lens = torch.tensor([lk] * b, dtype=torch.int32, device=k.device)
else:
# Ensure k_lens is on the same device as k
k_lens = k_lens.to(k.device)
# Apply q_scale if provided
if q_scale is not None:
q = q * q_scale
# Compute attention weights manually
# q: [B, Lq, Nq, C], k: [B, Lk, Nk, C]
scale = softmax_scale if softmax_scale is not None else (q.size(-1) ** -0.5)
# Compute scores: [B, Nq, Lq, Lk]
scores = torch.einsum('blhd,bshd->bhls', q, k) * scale
# Apply causal mask if needed
if causal:
mask = torch.triu(torch.ones(lq, lk, device=q.device, dtype=torch.bool), diagonal=1)
scores.masked_fill_(mask.unsqueeze(0).unsqueeze(0), float('-inf'))
# Mask for k_lens (columns)
k_mask = torch.arange(lk, device=k.device).unsqueeze(0) >= k_lens.unsqueeze(1) # [B, Lk]
scores.masked_fill_(k_mask.unsqueeze(1).unsqueeze(2), float('-inf')) # [B, 1, 1, Lk]
# Mask for q_lens (rows)
q_mask = torch.arange(lq, device=q.device).unsqueeze(0) >= q_lens.unsqueeze(1) # [B, Lq]
scores.masked_fill_(q_mask.unsqueeze(1).unsqueeze(3), float('-inf')) # [B, 1, Lq, 1]
# Compute attention weights
attn_weights = torch.softmax(scores, dim=-1) # [B, Nq, Lq, Lk]
assert attn_weights.shape[0] == 1, "Batch size > 1 not supported for attention visualization."
# Average attention weights to reduce memory usage before returning
# Average across batch dimension (should be 1) and query heads and query sequence length
# This gives us attention weight per video token: [Lk]
if average_for_q:
#avg_attn_weights = torch.mean(attn_weights, dim=(0, 1, 3)) # [Lq]
avg_attn_weights = torch.max(attn_weights, dim=3)[0].mean(dim=(0, 1)) # [Lq]
else:
if 0:
avg_attn_weights = torch.mean(attn_weights, dim=(0, 1, 2)) # [Lk]
elif 1:
B, H, Lq, Lk = attn_weights.shape # [1, H, Lq, Lk]
per_frame_seq_len = Lk // total_video_latent_frames
per_frame_aud_len = Lq // total_video_latent_frames
avg_attn_weights = torch.zeros((Lk,), device=attn_weights.device, dtype=attn_weights.dtype)
eps = 1e-8 # numerical stability
for i in range(total_video_latent_frames):
start_idx_v = i * per_frame_seq_len
end_idx_v = (i + 1) * per_frame_seq_len
start_idx_a = i * per_frame_aud_len
end_idx_a = (i + 1) * per_frame_aud_len
# attn_chunk: [H, La, Lv]
attn_chunk = attn_weights[0, :, start_idx_a:end_idx_a, start_idx_v:end_idx_v]
# ---- Head informativeness via (low) entropy over Lv ----
# Normalize within the Lv slice per (head, query) to make a proper distribution
p = attn_chunk / (attn_chunk.sum(dim=-1, keepdim=True) + eps) # [H, La, Lv]
entropy = -(p * (p + eps).log()).sum(dim=-1).mean(dim=1) # [H]
# Convert to positive head weights (lower entropy -> larger weight)
saliency = 1.0 / (entropy + 1e-6) # [H]
head_w = saliency / (saliency.sum() + eps) # [H], sum=1
# Reduce across audio queries first (pick strong responses), then weight heads
per_head = torch.amax(attn_chunk, dim=1) # [H, Lv]
weighted = (per_head * head_w[:, None]).sum(dim=0) # [Lv]
avg_attn_weights[start_idx_v:end_idx_v] = weighted
else:
avg_attn_weights = torch.mean(attn_weights, dim=(0, 2)).max(dim=(0))[0] # [Lk]
# Compute output: [B, Lq, Nq, C]
out = torch.einsum('bhls,bshd->blhd', attn_weights, v)
return out.to(out_dtype), avg_attn_weights.to(out_dtype)
def attention(
q,
k,
v,
q_lens=None,
k_lens=None,
dropout_p=0.,
softmax_scale=None,
q_scale=None,
causal=False,
window_size=(-1, -1),
deterministic=False,
dtype=torch.bfloat16,
fa_version=None,
):
if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE:
return flash_attention(
q=q,
k=k,
v=v,
q_lens=q_lens,
k_lens=k_lens,
dropout_p=dropout_p,
softmax_scale=softmax_scale,
q_scale=q_scale,
causal=causal,
window_size=window_size,
deterministic=deterministic,
dtype=dtype,
version=fa_version,
)
else:
if q_lens is not None or k_lens is not None:
warnings.warn(
'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.'
)
attn_mask = None
q = q.transpose(1, 2).to(dtype)
k = k.transpose(1, 2).to(dtype)
v = v.transpose(1, 2).to(dtype)
out = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
out = out.transpose(1, 2).contiguous()
return out
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