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FlashKDA

FlashKDA: Flash Kimi Delta Attention — high-performance KDA kernels built on CUTLASS

News

• 2026-04-22 — Deep-Dive Blog: the design decisions behind FlashKDA v1, read it here.

Requirements

• SM90 and above
• CUDA 12.9 and above
• PyTorch 2.4 and above

Installation

git clone https://github.com/0xwilliamortiz/FlashKDA.git flash-kda
cd flash-kda
python setup.py
git submodule update --init --recursive
pip install -v --no-build-isolation .

By default, the build detects the current CUDA device and compiles for that architecture. For wheel or CI builds, compile all supported architectures explicitly:

FLASH_KDA_CUDA_ARCHS=all pip install -v --no-build-isolation .

Supported values are auto (default), all, or a comma-separated arch list such as 90a,100a.

Using FlashKDA as an FLA backend

Once installed, FlashKDA is auto-dispatched from flash-linear-attention’s chunk_kda. See fla-org/flash-linear-attention#852 for integration details.

Requirements

1. Install flash-linear-attention >= 0.5.0:

   pip install -U flash-linear-attention

2. Call chunk_kda under torch.inference_mode()

   import torch
   from fla.ops.kda import chunk_kda
   
   with torch.inference_mode():
       out, final_state = chunk_kda(
           q=q, k=k, v=v, g=g, beta=beta,
           scale=scale,
           initial_state=h0,
           output_final_state=True,
           use_gate_in_kernel=True,
           use_qk_l2norm_in_kernel=True,
           use_beta_sigmoid_in_kernel=True,
           safe_gate=True,
           A_log=A_log, dt_bias=dt_bias,
           lower_bound=lower_bound,
           transpose_state_layout=True,
           cu_seqlens=cu_seqlens,
       )

Opt out: set FLA_FLASH_KDA=0 to fall back to the Triton path.

Debug dispatch: add logging.basicConfig(level=logging.INFO) to see [FLA Backend] kda.chunk_kda -> flashkda on hit, or ... rejected: on miss.

Performance

See BENCHMARK_H20.md.

Tests

bash tests/test.sh

• tests/test_fwd.py — correctness tests (exact match against the torch reference; compared with flash-linear-attention)

Kernel API

flash_kda.fwd

flash_kda.fwd(q, k, v, g, beta, scale, out, A_log, dt_bias, lower_bound,
              initial_state=None, final_state=None, cu_seqlens=None)

Parameters:

Parameter	Dtype	Shape	Description
q	bf16	[B, T, H, K]	Query
k	bf16	[B, T, H, K]	Key
v	bf16	[B, T, H, V]	Value
g	bf16	[B, T, H, K]	Gate before activation
beta	bf16	[B, T, H]	Beta logits (pre-activation; sigmoid applied internally)
scale	float	scalar	scaling factor
out	bf16	[B, T, H, V]	Output tensor
A_log	fp32	[H]	Log-gate parameter
dt_bias	fp32	[H, K]	Gate bias
lower_bound	float	scalar	Gate lower bound (range from -5.0 to 0)
initial_state	bf16/fp32/None	[B, H, V, K] or [N, H, V, K]	(optional) Initial recurrent state
final_state	bf16/fp32/None	[B, H, V, K] or [N, H, V, K]	(optional, output) Final recurrent state
cu_seqlens	int64	[N+1]	(optional) Cumulative sequence lengths for variable-length batching

• Currently requires K = V = 128.
• initial_state / final_state accept None (stateless), bf16, or fp32 tensors. When both are provided, their dtypes must match.
• When cu_seqlens is provided, B must be 1, T is the total length across all sequences, and initial_state / final_state have shape [N, H, V, K].
• When cu_seqlens is None, each batch element is treated as an independent sequence, and the state shape is [B, H, V, K].

Development

To set up IntelliSense (clangd) for the CUDA/C++ sources, run:

bash setup_clangd.sh

This generates a .clangd file with the correct repository paths and installs the global clangd config.yaml to ~/.config/clangd/.

Citation

@misc{flashkda2026,
      title={FlashKDA: Flash Kimi Delta Attention},
      author={Yutian Chen, Zhiyuan Li, Yucheng Wang, Ming Wei},
      year={2026},
      publisher = {GitHub},
      howpublished = {\url{https://github.com/MoonshotAI/FlashKDA}},
}