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3e936b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import torch
try:
import flash_attn_interface
def is_hopper_gpu():
if torch.cuda.is_available():
major, _ = torch.cuda.get_device_capability()
return major >= 9
return False
FLASH_ATTN_3_AVAILABLE = is_hopper_gpu()
except ModuleNotFoundError:
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']
def flash_attention(q, k, v, q_lens=None, k_lens=None, dropout_p=0.0, softmax_scale=None, q_scale=None, causal=False, window_size=(-1, -1), deterministic=False, dtype=torch.bfloat16, version=None):
half_dtypes = (torch.float16, torch.bfloat16)
assert dtype in half_dtypes
assert q.device.type == 'cuda' and q.size(-1) <= 256
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)
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)]))
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.')
if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
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).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))
return x.type(out_dtype)
def attention(q, k, v, q_lens=None, k_lens=None, dropout_p=0.0, 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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