File size: 2,603 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 71 72 73 74 75 76 77 | # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
import warnings
import torch
from fla.ops.generalized_delta_rule import chunk_dplr_delta_rule
def chunk_rwkv7(
r: torch.Tensor,
w: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
a: torch.Tensor,
b: torch.Tensor,
scale: float = 1.0,
initial_state: torch.Tensor = None,
output_final_state: bool = True,
cu_seqlens: torch.LongTensor | None = None,
head_first: bool = False,
):
"""
Args:
r (torch.Tensor):
r of shape `[B, T, H, K]`.
w (torch.Tensor):
log decay of shape `[B, T, H, K]`.
k (torch.Tensor):
k of shape `[B, T, H, K]`.
v (torch.Tensor):
v of shape `[B, T, H, V]`.
a (torch.Tensor):
a of shape `[B, T, H, K]`.
b (torch.Tensor):
b of shape `[B, T, H, K]`.
scale (float):
scale of the attention.
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. Default: `False`.
This argument has been deprecated.
"""
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 r.shape[1] < r.shape[2]:
warnings.warn(
f"Input tensor shape suggests potential format mismatch: seq_len ({r.shape[1]}) < num_heads ({r.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, ...].",
)
return chunk_dplr_delta_rule(
q=r,
k=k,
v=v,
a=a,
b=b,
gk=w,
scale=scale,
initial_state=initial_state,
output_final_state=output_final_state,
cu_seqlens=cu_seqlens,
head_first=head_first,
)
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