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import torch
try:
import flash_attn_interface
FLASH_ATTN_3_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
FLASH_ATTN_3_AVAILABLE = False
try:
import flash_attn
FLASH_ATTN_2_AVAILABLE = hasattr(flash_attn, "flash_attn_varlen_func")
except (ImportError, ModuleNotFoundError):
FLASH_ATTN_2_AVAILABLE = False
import warnings
__all__ = [
'flash_attention',
'attention',
]
def _prepare_sdpa_inputs(q, k, v, dtype):
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
if q.device.type == 'cpu' and dtype in (torch.float16, torch.bfloat16):
q = q.float()
k = k.float()
v = v.float()
else:
q = q.to(dtype)
k = k.to(dtype)
v = v.to(dtype)
return q, k, v
def _build_length_mask(batch_size, q_len, k_len, device, q_lens, k_lens, causal, window_size):
mask = torch.ones((batch_size, q_len, k_len), dtype=torch.bool, device=device)
q_idx = torch.arange(q_len, device=device).view(1, q_len, 1)
k_idx = torch.arange(k_len, device=device).view(1, 1, k_len)
if q_lens is not None:
q_lens = q_lens.to(device=device, dtype=torch.long)
mask = mask & (q_idx < q_lens.view(batch_size, 1, 1))
if k_lens is not None:
k_lens = k_lens.to(device=device, dtype=torch.long)
mask = mask & (k_idx < k_lens.view(batch_size, 1, 1))
if causal:
mask = mask & (k_idx <= q_idx)
if window_size != (-1, -1):
left, right = window_size
if left >= 0:
mask = mask & (k_idx >= q_idx - left)
if right >= 0:
mask = mask & (k_idx <= q_idx + right)
return mask.unsqueeze(1)
def _merge_sdpa_masks(length_mask, attn_mask, dtype):
if attn_mask is None:
return length_mask
if attn_mask.dtype == torch.bool:
return length_mask & attn_mask
additive_mask = torch.zeros_like(length_mask, dtype=dtype)
additive_mask = additive_mask.masked_fill(~length_mask, float('-inf'))
return additive_mask + attn_mask.to(dtype)
def _sdpa_attention_fallback(
q,
k,
v,
q_lens=None,
k_lens=None,
dropout_p=0.,
softmax_scale=None,
q_scale=None,
causal=False,
window_size=(-1, -1),
dtype=torch.bfloat16,
attn_mask=None,
):
out_dtype = q.dtype
batch_size, q_len, k_len = q.size(0), q.size(1), k.size(1)
q, k, v = _prepare_sdpa_inputs(q, k, v, dtype)
total_scale = 1.0
if q_scale is not None:
total_scale *= q_scale
if softmax_scale is not None:
total_scale *= softmax_scale
if total_scale != 1.0:
q = q * total_scale
mask = _build_length_mask(
batch_size=batch_size,
q_len=q_len,
k_len=k_len,
device=q.device,
q_lens=q_lens,
k_lens=k_lens,
causal=causal,
window_size=window_size,
)
mask = _merge_sdpa_masks(mask, attn_mask, q.dtype)
out = torch.nn.functional.scaled_dot_product_attention(
q,
k,
v,
attn_mask=mask,
is_causal=False,
dropout_p=dropout_p,
)
if q_lens is not None:
q_valid = (
torch.arange(q_len, device=out.device).view(1, q_len, 1)
< q_lens.to(device=out.device, dtype=torch.long).view(batch_size, 1, 1)
).unsqueeze(1)
out = out.masked_fill(~q_valid, 0)
return out.transpose(1, 2).contiguous().to(out_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
if not (FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE):
warnings.warn(
'flash_attn is not installed; falling back to scaled_dot_product_attention.',
stacklevel=2,
)
return _sdpa_attention_fallback(
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,
dtype=dtype,
)
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),
max_seqlen_q=lq,
max_seqlen_k=lk,
softmax_scale=softmax_scale,
causal=causal,
deterministic=deterministic)[0].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,
attn_mask=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:
return _sdpa_attention_fallback(
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,
dtype=dtype,
attn_mask=attn_mask,
)
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