kestrel-kernels 0.1.2

CUDA kernel library for Kestrel

kestrel-kernels

Precompiled CUDA kernels for Kestrel, a high-performance inference engine for Moondream, the world's most efficient vision-language model.

License: These kernels are provided for use with Kestrel only. Other use is not permitted.

These kernels are optimized for NVIDIA Ada/Hopper GPUs (SM89/SM90) and distributed as precompiled shared libraries for fast installation without CUDA compilation.

Kernel Library

CUDA Kernels (compiled via CMake)

These kernels are implemented in CUDA C++ and compiled during wheel build.

activation - GELU Residual Activation

Computes GELU(h) * (g + 1) fused gated activation used in MoE expert layers. The input tensor is split in half: h passes through GELU, g acts as a gate with +1 bias.

Tokens	CUDA	PyTorch (eager)	Compile	vs PyTorch
1	3.8 us	64 us	63 us	17x
64	2.9 us	49 us	69 us	17x
740	3.5 us	49 us	68 us	14x
1024	3.9 us	49 us	68 us	13x
2048	5.1 us	49 us	68 us	10x

PyTorch eager launches separate kernels for slice, erf, multiply, and add, with intermediate tensors hitting global memory. Our kernel fuses everything into a single pass. torch.compile is slower than eager here, likely because the dynamic x[:, :hidden] slicing prevents effective fusion.

fused_linear_residual - Linear + Bias + Residual

Fused out = x @ W.T + bias + residual using cuBLASLt epilogues.

Crops	Tokens	CUDA	PyTorch (eager)	vs PyTorch
1	729	9.0 us	24 us	2.7x
2	1458	12 us	24 us	2.0x
4	2916	16 us	29 us	1.8x
8	5832	46 us	50 us	1.1x
13	9477	44 us	77 us	1.7x

cuBLASLt epilogues fuse bias addition and residual into the matmul, avoiding extra kernel launches and memory traffic.

fused_mlp - Fused MLP with cuBLASLt

Fused out = residual + gelu(x @ W1.T + b1) @ W2.T + b2 using cuBLASLt epilogues.

Crops	Tokens	CUDA	PyTorch (eager)	vs PyTorch
1	729	43 us	56 us	1.3x
2	1458	72 us	89 us	1.2x
4	2916	97 us	124 us	1.3x
8	5832	214 us	259 us	1.2x
13	9477	283 us	379 us	1.3x

MLP is matmul-dominated so the speedup is modest. The gain comes from fusing GELU and residual add into cuBLASLt epilogues.

kv_cache_write - KV Cache Write with FP8 Quantization

Writes BF16 key/value tensors to FP8 paged KV cache with quantization.

Tokens	Kestrel	vLLM	PyTorch (eager)	vs vLLM	vs PyTorch
1	3.7 us	4.9 us	67 us	1.3x	18x
8	3.5 us	4.8 us	35 us	1.4x	10x
64	3.7 us	4.8 us	35 us	1.3x	9x
256	4.1 us	4.8 us	36 us	1.2x	9x
1024	8.6 us	9.7 us	51 us	1.1x	6x
4096	31 us	46 us	124 us	1.5x	4x

Fused K/V processing and optimized vectorization provide 1.1-1.5x speedup over vLLM's implementation.

layernorm_cuda - Fast LayerNorm Forward

Optimized LayerNorm forward pass for common hidden dimensions.

Vision Encoder (N=1152):

Crops	Tokens	CUDA	PyTorch (eager)	vs PyTorch
1	729	3.9 us	8.4 us	2.2x
2	1458	4.2 us	8.4 us	2.0x
4	2916	5.5 us	10 us	1.8x
8	5832	8.3 us	18 us	2.1x
13	9477	18 us	28 us	1.6x

Text Decoder (N=2048):

Context	Tokens	CUDA	PyTorch (eager)	vs PyTorch
decode	1	4.2 us	8.4 us	2.0x
prefill	740	3.7 us	8.4 us	2.3x

Specialized kernels for N=1152 and N=2048 use 4 rows/block with warp-only reductions, avoiding shared memory overhead. Two epilogue strategies trade register pressure vs memory bandwidth.

moe_sum - MoE Output Summation

Sums the weighted outputs from top-k MoE experts back into a single hidden state per token. Computes out[t] = sum(expert_outputs[t, 0:k]) where each token selects k=8 experts.

Context	Tokens	CUDA	PyTorch (eager)	vs PyTorch
decode	1	3.0 us	5.6 us	1.9x
batch 4	4	3.0 us	5.4 us	1.8x
batch 16	16	2.9 us	5.3 us	1.8x
prefill	740	5.5 us	10 us	1.9x
long	1024	10 us	15 us	1.5x

Vectorized 16-byte loads (8 bf16 at once), fully unrolled k=8 reduction. FP32 accumulation provides better numerical stability than bf16 accumulation. Note: vLLM has a similar kernel, but only supports topk=2,3,4 and falls back to PyTorch for topk=8.

rotary_embedding - Rotary Position Embedding

Applies rotary position embedding to query and key tensors (n_heads=32, head_dim=64).

Context	Tokens	Kestrel	vLLM	PyTorch (eager)	vs vLLM	vs PyTorch
decode	1	3.3 us	4.9 us	118 us	1.5x	36x
batch 4	4	3.1 us	4.5 us	117 us	1.5x	38x
batch 16	16	3.1 us	4.7 us	117 us	1.5x	38x
prefill	740	5.0 us	8.0 us	119 us	1.6x	24x

Vectorized bfloat162 pair processing, shared memory caching of cos/sin values, FP32 math for numerical stability. Split-head kernel for decode increases SM utilization on small batch sizes.

fp8_quant - FP8 Quantization

Converts BF16 tensors to FP8 (e4m3fn) with per-row dynamic scale computation. Used for quantizing MoE activations before FP8 GEMM.

Context	Rows	CUDA	PyTorch (eager)	vs PyTorch
decode	8	3.1 us	53 us	17x
batch 4	32	3.1 us	52 us	17x
batch 16	128	3.1 us	52 us	17x
prefill	5920	6.6 us	67 us	10x

Two kernel variants: warp-per-row for large batches (better SM utilization), block-per-row for small batches. Vectorized 16-byte loads/stores, fused absmax reduction.

tau_tail - TAU Attention Scaling

Applies per-head TAU scaling to Q and V in packed QKV. Computes scale = tanh(tok_linear) + tau_pos_table[position] then scales each head: Q *= scale_q, V *= scale_v.

Context	Tokens	CUDA	PyTorch (eager)	vs PyTorch
decode	1	4.6 us	45 us	10x
batch 4	4	4.4 us	46 us	10x
batch 16	16	9.0 us	88 us	10x
prefill	740	6.5 us	63 us	10x

---

CuTe DSL Kernels (precompiled for wheel distribution)

These kernels are written in NVIDIA CuTe DSL (Python) and precompiled to .so files during wheel build. The kernel source templates are excluded from wheel distribution.

topk - Bitonic Top-K Selection

GPU top-k selection using bitonic sort network with optional fused softmax.

Context	Tokens	Kestrel	Quack	PyTorch (eager)	vs Quack	vs PyTorch
decode	1	23 us	29 us	17 us	1.3x	0.8x
batch 16	16	22 us	27 us	17 us	1.2x	0.8x
prefill	740	22 us	28 us	17 us	1.2x	0.7x

Note: Currently slower than PyTorch for N=64, k=8. PyTorch uses radix-based QuickSelect which is more efficient for small N. Algorithm should be revisited.

Python API:

from kestrel_kernels.topk import topk_fwd

values, indices = topk_fwd(scores, k=8, softmax=True)

cute_moe - MoE Matrix Multiplications

Grouped GEMM kernels for Mixture-of-Experts layers, written in CuTe DSL for H100 (SM90). Supports BF16 and FP8 (W8A8) precision with both warp-level and WGMMA variants, automatically selected based on batch size.

FP8 W8A8 Full MoE Layer (up + activation + down + sum, E=64, k=8, with CUDA Graphs):

Context	Tokens	Kestrel	vLLM (Triton)	vs vLLM
decode	1	29 us	51 us	1.72x
batch 4	4	79 us	103 us	1.30x
batch 16	16	146 us	169 us	1.16x
prefill	740	245 us	481 us	1.96x

Python API:

from kestrel_kernels import (
    invoke_cute_moe_up,
    invoke_cute_moe_down,
    invoke_cute_moe_up_fp8,
    invoke_cute_moe_down_fp8,
)

# BF16 up projection
out_up = invoke_cute_moe_up(
    hidden_states, w1, w2,
    topk_weights, topk_ids,
    sorted_token_ids, expert_ids, num_tokens_post_pad,
)

# BF16 down projection
out_down = invoke_cute_moe_down(
    moe_out, w3,
    topk_weights, topk_ids,
    sorted_token_ids, expert_ids, num_tokens_post_pad,
)

moe_align - MoE Token Alignment

Prepares sorted token indices for block-sparse MoE operations. Given topk_ids, outputs sorted token IDs grouped by expert for block-sparse matmul.

Context	Tokens	Kestrel	vLLM	vs vLLM
decode	1	6.7 us	9.8 us	1.5x
batch 4	4	6.5 us	9.8 us	1.5x
batch 16	16	7.0 us	10 us	1.4x
prefill	740	12 us	9.2 us	0.8x
long	1024	12 us	9.5 us	0.8x

Uses optimized single-CTA shared-memory histogram for decode (numel < 1024). Prefill path needs optimization.

Python API:

from kestrel_kernels.moe_align import moe_align_block_size

moe_align_block_size(
    topk_ids, num_experts, block_size,
    sorted_token_ids, expert_ids, num_tokens_post_pad,
    expert_map,  # optional for expert parallelism
)

gelu_residual - GELU Residual Activation (CuTe DSL)

CuTe DSL implementation of GELU residual activation for BF16. Computes GELU(h) * (g + 1) fused gated activation used in MoE expert layers. Uses vectorized memory access and streaming stores.

Context	Rows	CuTe	CUDA	PyTorch	vs CUDA	vs PyTorch
decode	8	2.3 us	2.5 us	7.5 us	1.10x	3.3x
batch 4	32	2.4 us	3.0 us	8.6 us	1.24x	3.6x
batch 16	128	2.6 us	2.9 us	8.9 us	1.09x	3.4x
prefill	5920	9.9 us	11.2 us	55.9 us	1.14x	5.6x

fp8_quant_cute - FP8 Quantization (CuTe DSL)

CuTe DSL implementation of FP8 row-wise quantization. Converts BF16 tensors to FP8 (e4m3fn) with per-row dynamic scaling.

hidden=1024 (MoE down projection input):

Context	Rows	CuTe	CUDA	vs CUDA
decode	8	2.5 us	2.7 us	1.09x
batch 4	32	2.8 us	3.0 us	1.07x
batch 16	128	2.8 us	3.0 us	1.08x
prefill	5920	5.3 us	6.6 us	1.23x

hidden=2048 (MoE up projection input):

Context	Rows	CuTe	CUDA	vs CUDA
decode	8	2.6 us	2.7 us	1.02x
batch 4	32	2.9 us	3.0 us	1.04x
batch 16	128	2.9 us	3.0 us	1.04x
prefill	5920	8.2 us	10.7 us	1.31x

flash_attn - Flash Attention (Prefill & Decode)

Flash Attention kernels written in CuTe DSL, with a dedicated decode path optimized for paged FP8 KV cache. 1.3-2.5x faster than FlashInfer on typical Moondream workloads.

• FP8 KV cache with per-tensor scaling
• Paged KV (page_size=1) for fine-grained memory management
• CUDA graph compatible
• Causal and prefix-LM masking, variable-length sequences, GQA/MQA

FP8 KV Paged Decode (with CUDA Graphs):

Batch	KV Len	Kestrel	FlashInfer	vs FlashInfer
1	740	9.6 us	12.9 us	1.34x
1	1024	8.7 us	13.1 us	1.50x
4	740	17.1 us	23.9 us	1.40x
8	512	10.0 us	25.2 us	2.51x
16	256	9.6 us	17.6 us	1.83x
32	128	11.8 us	26.5 us	2.24x

FP8 KV Paged Prefill:

Seq Len	Kestrel	FlashInfer	vs FlashInfer
740	19.9 us	47.6 us	2.40x
1024	27.3 us	58.9 us	2.16x

Python API:

from kestrel_kernels.flash_attn.cute import flash_attn_func, flash_attn_varlen_func

# Fixed-length attention
out = flash_attn_func(q, k, v, causal=True)

# Variable-length attention
out = flash_attn_varlen_func(
    q, k, v,
    cu_seqlens_q, cu_seqlens_k,
    max_seqlen_q, max_seqlen_k,
    causal=True,
)