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from typing import Optional
from collections import namedtuple
import torch
from torch import Tensor, einsum, nn
from einops import rearrange
import torch.nn.functional as F
Config = namedtuple(
"FlashAttentionConfig", ["enable_flash", "enable_math", "enable_mem_efficient"]
)
class Attend(nn.Module):
def __init__(self, use_flash: bool) -> None:
super().__init__()
self.use_flash = use_flash
self.cpu_cfg = Config(True, True, True)
self.cuda_cfg = None
if not torch.cuda.is_available():
return
device_properties = torch.cuda.get_device_properties(torch.device("cuda"))
if device_properties.major == 8 and device_properties.minor == 0:
self.cuda_cfg = Config(True, False, False)
else:
self.cuda_cfg = Config(False, True, True)
def flash_attn(
self,
q: Tensor,
k: Tensor,
v: Tensor,
mask: Optional[Tensor],
causal: bool,
) -> Tensor:
cfg = self.cuda_cfg if q.is_cuda else self.cpu_cfg
if causal:
i, j, device = q.shape[-2], k.shape[-2], q.device
causal_mask = create_causal_mask(i, j, device)
mask = mask & ~causal_mask
causal = False
with torch.backends.cuda.sdp_kernel(**cfg._asdict()):
out = F.scaled_dot_product_attention(
q, k, v, attn_mask=mask, is_causal=causal
)
return out
def attn(
self,
q: Tensor,
k: Tensor,
v: Tensor,
mask: Optional[Tensor],
causal: bool,
) -> Tensor:
q.shape[0]
weight = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
mask_value = -torch.finfo(weight.dtype).max
if mask is not None:
weight = weight.masked_fill(~mask, mask_value)
if causal:
i, j, device = weight.shape[-2:], weight.device
causal_mask = create_causal_mask(i, j, device)
weight = weight.masked_fill(causal_mask, mask_value)
weight = F.softmax(weight, dim=-1)
weight = self.dropout(weight)
return einsum("b h i j, b h j d -> b h i d", weight, v)
def forward(
self,
q: Tensor,
k: Tensor,
v: Tensor,
causal: bool,
mask: Optional[Tensor] = None,
) -> Tensor:
if mask is not None:
mask = rearrange(mask, "b j -> b 1 1 j")
if self.use_flash:
return self.flash_attn(q, k, v, mask, causal)
else:
return self.attn(q, k, v, mask, causal)
def create_causal_mask(
i: int,
j: int,
device: torch.device,
) -> Tensor:
"""Applies a causal mask to the weight tensor."""
return torch.ones((i, j), device=device, dtype=torch.bool).triu(j - i + 1)
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