---
library_name: kernels
tags:
- cuda
- pytorch
- flashrt
- gated-delta
- linear-attention
- qwen3
- transformer
---

# Gated Delta Attention

BF16 Gated DeltaNet recurrent/chunk/WY kernels from FlashRT, packaged for
Hugging Face Kernel Hub. The v2 public profile covers Qwen3.6-style
linear-attention decode recurrence and prefill WY building blocks.

## Available functions

- `gated_delta_recurrent_bf16`
- `gated_delta_recurrent_inout_bf16`
- `gated_delta_recurrent_f32state_bf16io`
- `gated_delta_chunk_bf16`
- `gated_delta_chunk_smem_bf16`
- `lin_split_qkv_broadcast_bf16`
- `lin_split_qkv_gqa_bf16`
- `split_q_gate_bf16`
- `gdn_gating_bf16`
- `gdn_gating_strided_bf16`
- `gdn_chunk_from_conv_smem_bf16`
- `gdn_wy_norm_cumsum_pack_qk_bf16`
- `gdn_wy_kkt_b64_bf16`
- `gdn_wy_solve_tril_b64_f32`
- `gdn_wy_recompute_wu_b64_bf16`
- `gdn_wy_chunk_h_b64_bf16`
- `gdn_wy_output_o_b64_bf16`

## Usage

```python
from kernels import get_kernel

gdn = get_kernel("flashrt/gated-delta-attention", version=2, trust_remote_code=True)
out = gdn.gated_delta_recurrent_bf16(q, k, v, g, beta, state)
```

The WY helpers use the Qwen3.6 profile: `conv_out=(S,10240)`,
Q/K heads `16`, value heads `48`, head dimension `128`, and 64-token WY
blocks.