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# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
from typing import Optional, Tuple
import torch
from fla.ops.simple_gla.chunk import chunk_simple_gla
@torch.compiler.disable
def chunk_retention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
scale: Optional[float] = None,
initial_state: Optional[torch.Tensor] = None,
output_final_state: bool = False,
cu_seqlens: Optional[torch.LongTensor] = None,
head_first: bool = True
) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Args:
q (torch.Tensor):
queries of shape `[B, H, T, K]` if `head_first=True` else `[B, T, H, K]`.
k (torch.Tensor):
keys of shape `[B, H, T, K]` if `head_first=True` else `[B, T, H, K]`.
v (torch.Tensor):
values of shape `[B, H, T, V]` if `head_first=True` else `[B, T, H, V]`.
scale (Optional[int]):
Scale factor for the attention scores.
If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
initial_state (Optional[torch.Tensor]):
Initial state of shape `[N, H, K, V]` for `N` input sequences.
For equal-length input sequences, `N` equals the batch size `B`.
Default: `None`.
output_final_state (Optional[bool]):
Whether to output the final state of shape `[N, H, K, V]`. 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, which is not supported for variable-length inputs.
Default: `True`.
Returns:
o (torch.Tensor):
Outputs of shape `[B, H, T, V]` if `head_first=True` else `[B, T, H, V]`.
final_state (torch.Tensor):
Final state of shape `[N, H, K, V]` if `output_final_state=True` else `None`.
"""
if head_first:
n_heads = q.shape[1]
else:
n_heads = q.shape[2]
s = (1 - q.new_tensor(2., dtype=torch.float).pow(-5. - q.new_tensor(range(n_heads), dtype=torch.float))).log()
if head_first:
g = s[None, :, None].expand(q.shape[0], q.shape[1], q.shape[2]).contiguous()
else:
g = s[None, None, :].expand(q.shape[0], q.shape[1], q.shape[2]).contiguous()
return chunk_simple_gla(
q=q,
k=k,
v=v,
scale=scale,
g=g,
initial_state=initial_state,
output_final_state=output_final_state,
head_first=head_first,
cu_seqlens=cu_seqlens
)
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