File size: 2,553 Bytes
b66f552 | 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 68 69 70 | # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
import warnings
import torch
from fla.ops.simple_gla.parallel import parallel_simple_gla
def parallel_retention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
scale: float | None = None,
output_attentions: bool = False,
cu_seqlens: torch.LongTensor | None = None,
head_first: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
r"""
Args:
q (torch.Tensor):
queries of shape `[B, T, H, K]`.
k (torch.Tensor):
keys of shape `[B, T, H, K]`.
v (torch.Tensor):
values of shape `[B, T, H, V]`.
scale (Optional[float]):
Scale factor for attention scores.
If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
output_attentions (bool):
Whether to output the materialized attention scores of shape [B, H, T, T]. Default: `False`.
cu_seqlens (torch.LongTensor):
Cumulative sequence lengths of shape `[N+1]` used for variable-length training,
consistent with the FlashAttention API.
head_first (Optional[bool]):
Whether the inputs are in the head-first format. Default: `False`.
This argument has been deprecated.
Returns:
o (torch.Tensor):
Outputs of shape `[B, T, H, V]`.
attn (torch.Tensor):
Attention scores of shape `[B, H, T, T]` if `output_attentions=True` else `None`
"""
if head_first:
raise DeprecationWarning(
"head_first is deprecated and will be removed in a future version. "
"Please use head_first=False for now instead.",
)
if not head_first and q.shape[1] < q.shape[2]:
warnings.warn(
f"Input tensor shape suggests potential format mismatch: seq_len ({q.shape[1]}) < num_heads ({q.shape[2]}). "
"This may indicate the inputs were passed in head-first format [B, H, T, ...] "
"when head_first=False was specified. "
"Please verify your input tensor format matches the expected shape [B, T, H, ...].",
)
s = (1 - q.new_tensor(2., dtype=torch.float).pow(-5. - q.new_tensor(range(q.shape[2]), dtype=torch.float))).log()
g = s[None, None, :].expand(q.shape[0], q.shape[1], q.shape[2])
o, attn = parallel_simple_gla(
q=q,
k=k,
v=v,
scale=scale,
g=g,
output_attentions=output_attentions,
cu_seqlens=cu_seqlens,
)
return o, attn
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