agent/references.md

Reference implementations — read for technique

These are the open-source kernels that demonstrate each technique best. In a graded KBench container they are deliberately absent (offline, restricted toolchain), so this is a map of ideas to reproduce, not dependencies to import. Knowing how FlashAttention-3 overlaps softmax with the next MMA is what transfers; the import statement is worthless.

Attention

repo	what to take from it
Dao-AILab/flash-attention	online softmax with running max/sum; the FA2 split over the query axis so each block keeps its accumulator in registers; FA3's warp specialization (producer warps issue TMA, consumer warps do MMA) and the ping-pong schedule that hides softmax under the next GEMM
flashinfer-ai/flashinfer	paged KV cache attention; JIT specialization per (head_dim, page_size, causal) instead of one general kernel; cascade attention sharing a common prefix across requests; prefill/decode split
vLLM csrc/attention	the paged-attention v1/v2 split; partitioning long contexts across blocks and reducing partial softmax states
Colfax research posts	the clearest written derivation of Hopper WGMMA + TMA attention

GEMM and quantization

repo	what to take from it
NVIDIA/cutlass	CuTe layout algebra — the thing to internalize is that a layout is a function from coordinate to offset, and swizzles are layout composition; collective mainloop; TMA multicast; epilogue fusion
deepseek-ai/DeepGEMM	FP8 GEMM with fine-grained (block/tile) scaling; persistent warp-specialized scheduling; grouped GEMM for MoE where each expert has a different M
Marlin / Machete (vLLM)	W4A16: weight layout repacking done once offline so the inner loop dequantizes with minimal ALU work; how to interleave nibbles so unpacking is a few shifts
pytorch/ao	reference semantics for int8/int4/fp8 scaling granularities — per-tensor vs per-row vs per-block

Fusion and training kernels

repo	what to take from it
linkedin/Liger-Kernel	chunked fused linear+cross-entropy: never materialize the full [B*T, vocab] logits — the single biggest memory win in LLM training; fused RMSNorm/SwiGLU/RoPE with recompute in backward
triton-lang tutorials	fused softmax, matmul with autotuning, persistent matmul — the idiomatic Triton patterns
HazyResearch/ThunderKittens	tile abstraction over registers and shared memory; shows how little code a Hopper kernel needs when tiles are the primitive

Sequence models

repo	what to take from it
fla-org/flash-linear-attention	chunked linear attention; the delta rule and gated variants (KDA, GatedDeltaNet) expressed as a chunk-parallel recurrence — sequential scan becomes a sequence of GEMMs via a WY-like representation
state-spaces/mamba, causal-conv1d	selective scan as a work-efficient parallel scan kept entirely in SRAM

Distributed

repo	what to take from it
NVIDIA/TransformerEngine	fp8 recipes and amax history; comm/compute overlap for TP
NVSHMEM-based MoE dispatch (DeepEP)	all-to-all expert dispatch/combine overlapped with expert compute

How to read a kernel you have never seen

1. Find the inner loop and ask what stays in registers across iterations.
2. Find where data is staged (global → shared → register) and what overlaps that staging.
3. Find the layout/swizzle — usually where the cleverness is.
4. Only then read the setup and epilogue.