Gated Delta Attention

BF16 Gated DeltaNet recurrent/chunk/WY kernels from FlashRT, packaged for Hugging Face Kernel Hub. The v6 API extends the model-neutral parameterized Hv/Hk producer and fused recurrent prefill path. Validated profiles include Hv/Hk/D=48/16/128 and 32/16/128.

Native CUDA artifacts cover SM110 (Jetson AGX Thor) and SM120 Blackwell. The package contains the complete H32/H16 WY chain; it does not rely on a Python or host fallback for that profile.

Available functions

• gated_delta_recurrent_bf16
• gated_delta_recurrent_inout_bf16
• gdn_recurrent_inout_stream_bf16
• gated_delta_recurrent_f32state_bf16io
• gated_delta_chunk_bf16
• gated_delta_chunk_smem_bf16
• lin_split_qkv_broadcast_bf16
• lin_split_qkv_broadcast_h_bf16
• lin_split_qkv_gqa_bf16
• split_q_gate_bf16
• gdn_gating_bf16
• gdn_gating_h_bf16
• gdn_gating_strided_bf16
• gdn_gating_strided_h_bf16
• gdn_chunk_from_conv_smem_bf16
• gdn_chunk_from_conv_smem_h_bf16
• gdn_chunk_from_conv_smem_stash_bf16
• gdn_wy_norm_cumsum_pack_qk_bf16
• gdn_wy_norm_cumsum_pack_qk_v2_bf16
• batched_unit_ltri_inv64_f32
• gdn_wy_kkt_b64_bf16
• gdn_wy_kkt_b64_mma_bf16
• gdn_wy_solve_tril_b64_f32
• gdn_wy_cast_ai_f32_to_bf16
• gdn_wy_recompute_wu_b64_bf16
• gdn_wy_chunk_h_b64_bf16
• gdn_wy_output_o_b64_bf16
• gdn_wy_recompute_wu_b64_mma_fla_bf16
• gdn_wy_chunk_h_b64_mma_fla_bf16
• gdn_wy_output_o_b64_mma_fla_bf16
• gdn_wy_output_o_b64_mma_fla_rawk_bf16
• gdn_wy_norm_cumsum_pack_qk_h_bf16
• gdn_wy_kkt_b64_h_bf16
• gdn_wy_solve_tril_b64_h_f32
• gdn_wy_cast_ai_h_f32_to_bf16
• gdn_wy_recompute_wu_b64_h_bf16
• gdn_wy_chunk_h_b64_h_bf16
• gdn_wy_output_o_b64_h_bf16
• gdn_wy_recompute_wu_b64_mma_fla_h_bf16
• gdn_wy_chunk_h_b64_mma_fla_h_bf16
• gdn_wy_output_o_b64_mma_fla_h_bf16
• gdn_wy_output_o_b64_mma_fla_rawk_h_bf16

Usage

from kernels import get_kernel

gdn = get_kernel("flashrt/gated-delta-attention", version=6, 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.

The generic H32 producer profile uses conv_out=(S,8192), Q/K heads 16, value heads 32, and head dimension 128. Q/K are broadcast 16 -> 32. Per-head neg_exp_A_log and dt_bias must remain FP32. Version 6 also supports the complete 64-token WY prefill chain for this H32/H16 profile; all _h_bf16 functions take explicit num_v_heads and num_k_heads.

S, Hv, Hk, D = 64, 32, 16, 128
q, k, v = gdn.lin_split_qkv_broadcast_h_bf16(conv_out, Hv, Hk)
g, beta = gdn.gdn_gating_h_bf16(a, b, neg_exp_A_log, dt_bias)
out = gdn.gdn_chunk_from_conv_smem_h_bf16(
    conv_out, a, b, neg_exp_A_log, dt_bias, state,
    num_v_heads=Hv, num_k_heads=Hk,
)

Speculative verification can retain every carried state in caller-owned storage. Row s is the state after consuming rows 0..s:

stash = torch.empty(S, Hv, D, D, device="cuda", dtype=torch.bfloat16)
out = gdn.gdn_chunk_from_conv_smem_stash_bf16(
    conv_out, a, b, neg_exp_A_log, dt_bias, state, stash,
    num_v_heads=Hv, num_k_heads=Hk,
)
accepted_state = stash[accepted_length - 1]

The FLA-style path keeps the hot prefill chain in CUDA kernels:

q16_l2, k16_l2, q_pack, _, g_cumsum = gdn.gdn_wy_norm_cumsum_pack_qk_bf16(q16, k16, g)
A = gdn.gdn_wy_kkt_b64_mma_bf16(k16_l2, beta, g_cumsum)
Ai = gdn.gdn_wy_solve_tril_b64_f32(A, S)
Ai_pack = gdn.gdn_wy_cast_ai_f32_to_bf16(Ai, S)
w_pack, u_pack = gdn.gdn_wy_recompute_wu_b64_mma_fla_bf16(k16_l2, v48, beta, g_cumsum, Ai_pack)
h0, _, v_pack, k_pack = gdn.gdn_wy_chunk_h_b64_mma_fla_bf16(k16_l2, w_pack, u_pack, g_cumsum, state)
out = gdn.gdn_wy_output_o_b64_mma_fla_bf16(q_pack, k_pack, v_pack, h0, g_cumsum)