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sglang/python/sglang/srt/layers/attention/linear/kernels/kda_triton.py
T
2026-02-25 23:02:44 +08:00

74 lines
1.9 KiB
Python

import torch
from sglang.srt.layers.attention.linear.kernels.kernel_backend import (
LinearAttnKernelBase,
)
from sglang.srt.utils import is_cpu
if not is_cpu():
from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import (
fused_sigmoid_gating_delta_rule_update,
)
from sglang.srt.layers.attention.fla.kda import chunk_kda
class TritonKDAKernel(LinearAttnKernelBase):
"""Triton-based kernel for KDA (Kimi Delta Attention) linear attention."""
def decode(
self,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
a: torch.Tensor,
b: torch.Tensor,
*,
A_log: torch.Tensor,
dt_bias: torch.Tensor,
ssm_states: torch.Tensor,
cache_indices: torch.Tensor,
query_start_loc: torch.Tensor,
**kwargs,
) -> torch.Tensor:
return fused_sigmoid_gating_delta_rule_update(
A_log=A_log,
dt_bias=dt_bias,
q=q,
k=k,
v=v,
a=a,
b=b,
initial_state_source=ssm_states,
initial_state_indices=cache_indices,
cu_seqlens=query_start_loc,
use_qk_l2norm_in_kernel=True,
softplus_beta=1.0,
softplus_threshold=20.0,
is_kda=True,
)
def extend(
self,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
*,
ssm_states: torch.Tensor,
cache_indices: torch.Tensor,
query_start_loc: torch.Tensor,
**kwargs,
) -> torch.Tensor:
return chunk_kda(
q=q,
k=k,
v=v,
g=g,
beta=beta,
initial_state=ssm_states,
initial_state_indices=cache_indices,
use_qk_l2norm_in_kernel=True,
cu_seqlens=query_start_loc,
)