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import torch
from einops import rearrange
from torch import Tensor
# Flash-Attention 2 (optional)
try:
from flash_attn.flash_attn_interface import flash_attn_func # type: ignore
_HAS_FLASH = True
except (ImportError, ModuleNotFoundError):
_HAS_FLASH = False
def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask: Tensor) -> Tensor:
q, k = apply_rope(q, k, pe)
# mask should have shape [B, H, L, D]
if _HAS_FLASH and mask is None and q.is_cuda:
x = flash_attn_func(
rearrange(q, "B H L D -> B L H D").contiguous(),
rearrange(k, "B H L D -> B L H D").contiguous(),
rearrange(v, "B H L D -> B L H D").contiguous(),
dropout_p=0.0,
softmax_scale=None,
causal=False,
)
x = rearrange(x, "B L H D -> B H L D")
else:
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
x = rearrange(x, "B H L D -> B L (H D)")
return x
def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
assert dim % 2 == 0
scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
omega = 1.0 / (theta**scale)
out = torch.einsum("...n,d->...nd", pos, omega)
out = torch.stack(
[torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1
)
out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
return out.float()
def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
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