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import warnings
import torch
import torch.nn as nn
try:
import flash_attn_interface
FLASH_ATTN_3_AVAILABLE = True
except ModuleNotFoundError:
FLASH_ATTN_3_AVAILABLE = False
try:
import flash_attn
FLASH_ATTN_2_AVAILABLE = True
except ModuleNotFoundError:
FLASH_ATTN_2_AVAILABLE = False
try:
major, minor = torch.cuda.get_device_capability(0)
if f"{major}.{minor}" == "8.0":
from sageattention_sm80 import sageattn
SAGE_ATTENTION_AVAILABLE = True
elif f"{major}.{minor}" == "8.6":
from sageattention_sm86 import sageattn
SAGE_ATTENTION_AVAILABLE = True
elif f"{major}.{minor}" == "8.9":
from sageattention_sm89 import sageattn
SAGE_ATTENTION_AVAILABLE = True
elif f"{major}.{minor}" == "9.0":
from sageattention_sm90 import sageattn
SAGE_ATTENTION_AVAILABLE = True
elif major > 9:
from sageattention_sm120 import sageattn
SAGE_ATTENTION_AVAILABLE = True
except Exception:
try:
from sageattention import sageattn
SAGE_ATTENTION_AVAILABLE = True
except Exception:
sageattn = None
SAGE_ATTENTION_AVAILABLE = False
from .attention_kernel import _sparse_linear_attention, get_block_map
def convert_qkv_dtype(q, k, v):
try:
"""Unify the dtype of q, k, v tensors"""
dtypes = {q.dtype, k.dtype, v.dtype}
# If any tensor is float16/bfloat16
if torch.float16 in dtypes or torch.bfloat16 in dtypes:
target_dtype = torch.bfloat16 if torch.bfloat16 in dtypes else torch.float16
# If all tensors are float32
elif dtypes == {torch.float32}:
target_dtype = torch.bfloat16 if (torch.cuda.is_available() and
torch.cuda.get_device_capability()[0] >= 8) else torch.float16
else:
return q, k, v # No conversion for other cases
return q.to(target_dtype), k.to(target_dtype), v.to(target_dtype)
except Exception:
return q, k, v
def _convert_attn_mask_to_lens(attn_mask):
"""
Convert attention mask to sequence lengths for Flash Attention.
Args:
attn_mask: Attention mask, can be:
- [B, L] with 1=valid, 0=padding
- [B, 1, L] or [B, 1, 1, L] attention bias with 0=valid, -inf/-10000=padding
- [B, H, Lq, Lk] full attention mask
Returns:
k_lens: [B] tensor of valid sequence lengths, or None if not a simple padding mask
"""
if attn_mask is None:
return None
# Squeeze to simplest form
while attn_mask.ndim > 2 and attn_mask.shape[1] == 1:
attn_mask = attn_mask.squeeze(1)
# Only handle [B, L] case (simple padding mask)
if attn_mask.ndim != 2:
return None
# Check if it's attention bias format (0 and -inf/-10000) or binary mask (0/1)
unique_vals = torch.unique(attn_mask)
if len(unique_vals) > 2:
return None # Complex mask, can't convert
# Determine which value means "valid"
max_val = unique_vals.max().item()
if max_val <= 0: # Attention bias format: 0=valid, negative=padding
valid_mask = (attn_mask >= -1.0) # 0 is valid
else: # Binary format: 1=valid, 0=padding
valid_mask = (attn_mask > 0.5)
# Check if it's a simple left-padded or right-padded mask
# For right-padding: [1,1,1,0,0] -> valid tokens are contiguous from start
k_lens = valid_mask.sum(dim=-1).to(torch.int32)
# Verify it's actually a contiguous padding mask by reconstruction
B, L = valid_mask.shape
reconstructed = torch.arange(L, device=valid_mask.device).unsqueeze(0) < k_lens.unsqueeze(1)
if not torch.all(reconstructed == valid_mask):
return None # Not a simple contiguous padding mask
return k_lens
def flash_attention_naive(
q,
k,
v,
cu_seqlens_q=None,
cu_seqlens_k=None,
max_seqlen_q=None,
max_seqlen_k=None,
):
# apply attention
if FLASH_ATTN_3_AVAILABLE:
# Note: dropout_p, window_size are not supported in FA3 now.
x = flash_attn_interface.flash_attn_varlen_func(
q=q,
k=k,
v=v,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_q,
max_seqlen_k=max_seqlen_k,
)[0]
else:
assert FLASH_ATTN_2_AVAILABLE
x = flash_attn.flash_attn_varlen_func(
q=q,
k=k,
v=v,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_q,
max_seqlen_k=max_seqlen_k,
)
# output
return x.type(q.dtype)
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, lk, out_dtype = q.size(0), q.size(1), 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 (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
# Note: dropout_p, window_size are not supported in FA3 now.
x = 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(q.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)
if isinstance(x, (tuple, list)):
x = x[0].unflatten(0, (b, lq))
else:
x = x.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(
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,
attention_type=None,
attn_mask=None,
):
attention_type = os.environ.get("VIDEOX_ATTENTION_TYPE", "FLASH_ATTENTION") if attention_type is None else attention_type
if torch.is_grad_enabled() and attention_type == "SAGE_ATTENTION":
attention_type = "FLASH_ATTENTION"
# Convert attn_mask to k_lens for Flash Attention if possible
# Note: flash_attention doesn't support variable-length query, only set k_lens
if attn_mask is not None and k_lens is None and attention_type == "FLASH_ATTENTION":
converted_lens = _convert_attn_mask_to_lens(attn_mask)
if converted_lens is not None:
k_lens = converted_lens
attn_mask = None # Successfully converted, clear the mask
else:
# Conversion failed, fallback to SDPA which supports attn_mask
attention_type = "SDPA"
if attention_type == "SAGE_ATTENTION" and SAGE_ATTENTION_AVAILABLE:
if q_lens is not None or k_lens is not None:
warnings.warn(
'Padding mask is disabled when using SAGE_ATTENTION. It can have a significant impact on performance.'
)
q, k, v = convert_qkv_dtype(q, k, v)
out = sageattn(
q, k, v, attn_mask=attn_mask, tensor_layout="NHD", is_causal=causal, dropout_p=dropout_p)
elif attention_type == "FLASH_ATTENTION" and (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.'
)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
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
class SparseLinearAttention(nn.Module):
# Modified from https://github.com/thu-ml/TurboDiffusion/blob/main/turbodiffusion/SLA/kernel.py
def __init__(self, head_dim, topk, feature_map='softmax', BLKQ=64, BLKK=64, use_bf16=True, tie_feature_map_qk=True):
R'''
Args:
head_dim: dimension of each head.
topk: ratio of keys selected for sparse attention, shared across all queries.
feature_map: feature map for linear attention, one of ['hedgehog', 'elu', 'relu', 'softmax'].
BLKQ: block size for query.
BLKK: block size for key.
use_bf16: whether to use bfloat16 (default) or float16 for computation. The conversion to bf16/fp16 is done inside the module.
tie_feature_map_qk: whether to use the same feature map for query and key.
'''
super().__init__()
self.dtype = torch.bfloat16 if use_bf16 else torch.float16
self.topk = topk
self.BLKQ = BLKQ
self.BLKK = BLKK
self.proj_l = nn.Linear(head_dim, head_dim, dtype=torch.float32)
if feature_map == 'elu':
def elu_feature_map(x):
return torch.nn.functional.elu(x) + 1
self.feature_map_q = elu_feature_map
self.feature_map_k = elu_feature_map
elif feature_map == 'relu':
self.feature_map_q = nn.ReLU()
self.feature_map_k = nn.ReLU()
elif feature_map == 'softmax':
def softmax_feature_map(x):
return torch.nn.functional.softmax(x, dim=-1)
self.feature_map_q = softmax_feature_map
self.feature_map_k = softmax_feature_map
else:
raise NotImplementedError(f'Not supported feature map {feature_map}.')
if tie_feature_map_qk:
self.feature_map_k = self.feature_map_q
self.init_weights_()
def init_weights_(self):
with torch.no_grad():
nn.init.zeros_(self.proj_l.weight)
nn.init.zeros_(self.proj_l.bias)
def forward(self, q, k, v, return_sparsity=False):
R'''
Args:
q: queries [B, L, H, D]
k: keys [B, L, H, D].
v: values [B, L, H, D].
return_sparsity: whether to return the actual sparsity.
'''
dtype = q.dtype
q = q.transpose(1, 2).contiguous()
k = k.transpose(1, 2).contiguous()
v = v.transpose(1, 2).contiguous()
sparse_map, lut, real_topk = get_block_map(q, k, topk_ratio=self.topk, BLKQ=self.BLKQ, BLKK=self.BLKK)
q = q.to(self.dtype)
k = k.to(self.dtype)
v = v.to(self.dtype)
o_s = _sparse_linear_attention.apply(q, k, v, sparse_map, lut, real_topk, self.BLKQ, self.BLKK)
q = self.feature_map_q(q).contiguous().to(self.dtype) # c_q
k = self.feature_map_k(k).contiguous().to(self.dtype) # c_k
def calc_linear(q, k, v):
kvsum = k.transpose(-1, -2) @ v
ksum = torch.sum(k, dim=-2, keepdim=True)
return (q @ kvsum) / (1e-5 + (q * ksum).sum(dim=-1, keepdim=True))
o_l = calc_linear(q, k, v)
with torch.amp.autocast('cuda', dtype=self.dtype):
o_l = self.proj_l(o_l)
o = (o_s + o_l).to(dtype).transpose(1, 2)
if return_sparsity:
return o, real_topk / sparse_map.shape[-1]
else:
return o |