Curate inference workloads; add SGLang baselines and KDA configs

README.md

@@ -9,13 +9,13 @@ tags:
 ---
 # Inference Kernels
 
-24 [FlashInfer Trace](https://github.com/flashinfer-ai/flashinfer-bench/tree/main/docs/flashinfer-trace) operator definitions, with self-contained PyTorch references, open-source baseline solutions, and workloads captured from actual SGLang inference. Model dimensions are constants. Runtime batch sizes, sequence lengths, page sizes, and image grids remain variable.
+21 [FlashInfer Trace](https://github.com/flashinfer-ai/flashinfer-bench/tree/main/docs/flashinfer-trace) operator definitions, with self-contained PyTorch references, open-source baseline solutions, and workloads captured from actual inference: 19 through SGLang, and the 2 gated delta definitions from the [MLSys 2026 FlashInfer contest](https://huggingface.co/datasets/flashinfer-ai/mlsys26-contest/tree/5e832ce88dd1013032b22a0e942979d7d2769cc3). Model dimensions are constants. Runtime batch sizes, sequence lengths, page sizes, and image grids remain variable.
 
 Every published tensor input references a complete `safetensors` snapshot. Python scalar arguments use scalar descriptors. There are no random-input descriptors or synthetic activation generators. `captures.jsonl` joins each workload UUID to its model revision, request, engine operation, input adaptations, actual outputs, and validation results.
 
-**1,383 real workloads cover all 24 definitions and pass complete-output replay:** 257 Qwen Image, 641 Qwen Flash-Next, 278 DeepSeek, and 207 LTX. The complete dataset is published at [humanfia-lab/inference-kernels](https://huggingface.co/datasets/humanfia-lab/inference-kernels), including definitions, workloads, capture records, baseline solutions, and tensors. The 6,763 tensor paths contain 6,223 unique snapshots of inputs, complete engine outputs, and auxiliary capture tensors. Fetch them with Git LFS before running workloads.
+**1,104 real workloads cover all 21 definitions:** 133 Qwen Image, 384 Qwen Flash-Next, 226 DeepSeek, 207 LTX, and 154 Qwen3-Next from the MLSys 2026 contest. The 950 SGLang captures pass complete-output replay; the contest publishes no engine outputs, so its workloads are checked by running the reference. The complete dataset is published at [humanfia-lab/inference-kernels](https://huggingface.co/datasets/humanfia-lab/inference-kernels), including definitions, workloads, capture records, baseline solutions, and tensors. The 5,506 tensor paths contain 5,124 unique snapshots of inputs, complete engine outputs, and auxiliary capture tensors. Fetch them with Git LFS before running workloads.
 
-The dataset was regenerated from full pinned checkpoints through SGLang on NVIDIA B300 GPUs. Definitions, reference code, tolerances, and baseline solutions are unchanged.
+The dataset was regenerated from full pinned checkpoints through SGLang on NVIDIA B300 GPUs. Definitions, reference code, tolerances, and baseline solutions are unchanged, apart from the [curation](#curation) for kernels that real inference uses.
 
 ## Kernels
 
@@ -23,23 +23,23 @@ BF16, FP16, FP32, and FP64 mean 16-bit Brain Floating Point, IEEE half precision
 
 | Family | Model | Fixed dimensions | Definitions | Workloads |
 |---|---|---|---:|---:|
-| Dense matrix multiplication | Qwen Image 2.1 Turbo | `(N,K)`: `(4096,4096)`, `(12288,4096)`, `(4096,12288)`, `(4096,64)`, `(64,4096)` | 5 | 217 |
-| Grouped expert matrix multiplication | DeepSeek V4.1 Flash | 384 experts; `(N,K)`: `(2304,5120)`, `(4608,5120)`, `(5120,2304)`; FP8 × FP4 | 3 | 156 |
+| Dense matrix multiplication | Qwen Image 2.1 Turbo | `(N,K)`: `(4096,4096)`, `(12288,4096)`, `(4096,12288)`; `M >= 4096` | 3 | 93 |
+| Grouped expert matrix multiplication | DeepSeek V4.1 Flash | 384 experts; `(N,K)`: `(4608,5120)`, `(5120,2304)`; FP8 × FP4 | 2 | 104 |
 | Mixed-format latent attention | DeepSeek V4.1 Flash | 64 heads × 512 dimensions; 128 FP8 window slots and 512 FP4 compressed slots | 1 | 42 |
 | Sparse multi-head latent attention (MLA), prefill | DeepSeek V4.1 Flash | 64 query heads × 512 dimensions; 1 shared key/value head; BF16; separate 128-slot and 640-slot definitions | 2 | 30 |
-| Gated delta recurrence | Qwen 3.8 Flash-Next | 16 shared query/key heads, 48 value heads, dimensions 128; prefill and decode | 2 | 257 |
-| Noncausal dense attention | LTX-2.5 | Video: 32 heads × 128; audio: 32 heads × 64 | 2 | 162 |
+| Gated delta recurrence | Qwen3-Next (MLSys 2026 contest) | 4 query/key heads, 8 value heads (TP=4 shard), dimension 128; k-last state; prefill and decode | 2 | 154 |
+| Noncausal dense attention | LTX-2.5 | Video: 32 heads × 128; audio: 32 heads × 64; no mask | 2 | 162 |
 | Qwen sparse attention | Qwen 3.8 Flash-Next | 24 query heads, 2 key/value heads, dimension 256; 512 four-token blocks | 1 | 192 |
 | Prefix-cached block-causal attention | Qwen Image 2.1 Turbo | 32 heads × 128; empty-prefix and cached-prefix cases | 2 | 40 |
 | Sparse index scoring and selection | Qwen 3.8 Flash-Next | 4 query heads × 128; Top-512 complete four-token blocks | 1 | 192 |
 | Expert router | DeepSeek V4.1 Flash | Hidden dimension 5120, 384 experts, Top-6 selection | 1 | 50 |
 | Three-dimensional neighborhood attention | LTX-2.5 diffusion decoder | Dimension 64; `(heads, window)`: `(32,3×7×7)`, `(16,3×7×7)`, `(8,3×5×5)`, `(4,11×11×11)` | 4 | 45 |
 
-**[Shape distribution histograms for all 24 kernels](docs/shape-distributions/README.md)** show how the 1,383 workloads are distributed across exact runtime shapes. Each bar counts captured workloads with the same complete combination of variable dimensions; fixed model dimensions are listed above the plot. These counts describe this dataset, not AgentX traffic frequencies. Regenerate the figures with `python scripts/plot_shape_distributions.py` (requires Matplotlib; no GPU or tensor files needed).
+**[Shape distribution histograms for all 21 kernels](docs/shape-distributions/README.md)** show how the 1,104 workloads are distributed across exact runtime shapes. Each bar counts captured workloads with the same complete combination of variable dimensions; fixed model dimensions are listed above the plot. These counts describe this dataset, not AgentX traffic frequencies. Regenerate the figures with `python scripts/plot_shape_distributions.py` (requires Matplotlib; no GPU or tensor files needed).
 
 Definitions describe complete logical operators. Captures preserve all model heads and experts; distributed shards must be gathered before publication. Backend padding, layout changes, and page remapping are recorded explicitly. Cache snapshots retain every referenced page, including its unused tail; unreachable allocator pages are omitted. Tensor strides are normalized to contiguous storage without changing values.
 
-Gated delta prefill receives the engine's normalized BF16 Q/K, with each shared head repeated three times. Decode receives raw Q/K and gate inputs because the engine performs normalization and gating inside the kernel using FP32 arithmetic. Both interfaces use state layout `[batch, head, key_dimension, value_dimension]`.
+The gated delta definitions are the MLSys 2026 contest's: Q/K/V with the gate inputs `A_log`, `a`, `dt_bias` and `b`, and the k-last state layout `[sequence, value head, value dimension, key dimension]`. Prefill takes variable-length sequences through `cu_seqlens`. These are unchanged Qwen3-Next TP=4 contest contracts, not a layout conversion of the removed Qwen Flash-Next captures. Their references do not perform Q/K L2 normalization, and they accept per-batch states rather than the original SGLang state pool and slot indices.
 
 Quantized byte carriers use `int8` because the trace schema lacks unsigned-byte and exponent-only scale types. The byte patterns remain unchanged. Grouped matrix multiplication uses logical scale layouts with one scale per 32 K elements. Mixed attention preserves the engine's physical pages: each page stores all payload rows followed by all scale rows. Both cache formats share the softmax denominator, including the zero-value attention sink.
 
@@ -100,14 +100,14 @@ Each is submitted with 1, 2, and 4 requested outputs, at 24 frames per second. M
 
 `solutions/` contains native FlashInfer Trace Solution JSON files, with embedded source code, entry points, and pinned dependencies. Each solution accepts the definition's complete inputs and returns its complete outputs. GPU layout conversions, padding, sparse gathers, and output reordering are included in the solution call.
 
-The existing baseline implementations and B200 measurements come from the original 768-workload dataset. They are retained unchanged. The regenerated 1,383-workload dataset has passed complete engine-output replay against the independent references; baseline solutions have not been rebenchmarked on this new dataset.
+The existing baseline implementations and B200 measurements come from the original 768-workload dataset. They are retained unchanged. The regenerated dataset's SGLang captures have passed complete engine-output replay against the independent references; baseline solutions have not been rebenchmarked on this new dataset.
 
 | Kernels | Open-source implementation |
 |---|---|
 | Dense and grouped matrix multiplication | [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d); selected small dense shapes use [FlashInfer](https://github.com/flashinfer-ai/flashinfer) 0.7.2 CuTe DSL |
 | Sparse MLA prefill and mixed-format MLA decode | [FlashMLA](https://github.com/deepseek-ai/FlashMLA/tree/0f11e178da1f67becb087a324b400854931ec3e3) |
 | Dense, block-causal, and sparse grouped-query attention | [FlashAttention-4](https://github.com/Dao-AILab/flash-attention) 4.0.0b35, with runtime masks or sparse-input adapters |
-| Gated delta recurrence | FlashInfer 0.7.2 prefill; adapted [SGLang](https://github.com/sgl-project/sglang/tree/9d394dfbd755759d3add89cfd0bc86d59b31f78c) fused Triton decode |
+| Gated delta recurrence | The MLSys 2026 contest's FlashInfer prefill and decode wrappers, unchanged |
 | Sparse index scoring and selection | SGLang TileLang scoring, exact-threshold Top-512, and Triton compaction that preserves lower-ID ties |
 | Expert router | FlashInfer CUTLASS FP32-output projection and compiled PyTorch scoring/selection |
 | Three-dimensional neighborhood attention | [NATTEN](https://github.com/SHI-Labs/NATTEN/tree/39375ea1d2f3b1bb284315fa5f79f6ecbebb6382) 0.21.7 Blackwell fused neighborhood attention |
@@ -126,6 +126,33 @@ python scripts/benchmark_solutions.py \
 
 The runner uses the native Trace builder, validates every output element against the unchanged definition reference, then measures GPU time with CUDA events over CUDA Graph replays. Compilation and input loading occur before timing. `--solution NAME` selects a solution and `--validation-only` skips timing. `--compare-to capture` explicitly compares against saved engine outputs instead; two approximate implementations can differ beyond tolerance even when both pass against the independent reference.
 
+### SGLang baselines
+
+Each SGLang-captured definition also has `solutions/<family>/sglang_<definition>.json`: SGLang's own implementation at the capture commit `684fea8a`, called the way the engine calls it, so that a kernel can be timed against what serving actually runs. The module docstring cites the engine call path. Run these solutions inside the capture image `lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8`. Each module also exposes `prepare(*inputs)`, which does the host-side setup the engine does once (layouts, block masks, workspaces) and returns a zero-argument `launch`; `run(*inputs)` does both.
+
+The SGLang solutions adapt the dataset's independent operator interfaces to the pinned engine implementations. Those adapters include padding, layouts, per-rank calls, metadata and output ordering where needed; their module docstrings describe the boundary. A passing standalone workload does not establish that a candidate can replace an engine call without integration work.
+
+### KDA acceptance configs
+
+`kda/<definition>.json` is how KDA (Kernel Design Agents) judges a candidate kernel on that definition. The definition's reference is the truth, and the SGLang baseline above is timed live on every workload, with only `launch` timed. The fields are:
+- `tolerance`: `atol` and `rtol` apply per element. A non-null `required_matched_ratio` is the fraction of elements that must pass; the LTX dense attention uses 0.9999 because fused attention kernels round the probabilities to BF16 and miss the every-element bound on 1–3 of up to 10⁹ elements.
+- `dev`: at most ten representative workloads for quick iteration; final judgments use every workload.
+- `group_axis` and `timing` (`warmup`, `iters`, `trials`).
+
+## Curation
+
+This revision curates inference operator tasks for KDA (Kernel Design Agents). Per-shape definitions, workload files and solution names stay separate; model dimensions remain constant axes. Captured interface adaptations are retained explicitly.
+- **Removed `dense_gemm_bf16_nt_n64_k4096` and `dense_gemm_bf16_nt_n4096_k64`.** These are the image output and input projections: memory-bound 8–45 µs GEMMs that run once per forward.
+- **Removed `grouped_gemm_fp8_fp4_e384_n2304_k5120_g32`.** It was cut from the columns of the concatenated gate/up GEMM; no engine runs a separate gate or up GEMM.
+- **Replaced the gated delta definitions.** `gated_delta_rule_prefill_h48_dk128_dv128` pre-repeated 16 Q/K heads to 48; the old decode kept 16 raw Q/K heads and included normalization and gating. Both captures extracted states from the engine pool and transposed them to `[B, H, K, V]`. They are replaced by the [MLSys 2026 FlashInfer contest](https://huggingface.co/datasets/flashinfer-ai/mlsys26-contest/tree/5e832ce88dd1013032b22a0e942979d7d2769cc3) definitions `gdn_{decode,prefill}_qk4_v8_d128_k_last`, with their workloads and FlashInfer baselines unchanged. Each input tensor is stored in its own content-addressed file. Each receipt records the source workload, plus a check that the reference returns finite outputs of the declared shapes.
+- **The three remaining dense GEMMs keep only their `M >= 4096` rows.** The `M <= 32` rows were 11–27 µs launches.
+- **The LTX dense attention definitions drop the dense `[B, H, Nq, Nk]` visibility mask.** It was all True on every captured call (up to 839 MB). On one GPU the engine passes no mask, or an all-visible prompt mask. Replay against the captured engine outputs still passes.
+- **Receipts and tensors.** The receipts of removed workloads are dropped, along with every tensor that nothing references.
+- **Scripts.**
+  - `check_workload_inputs.py` evaluates constraints that read inputs, such as `cu_seqlens[-1]`.
+  - The replay in `capture_inputs.py` skips imported workloads, which have no engine output.
+  - `plot_shape_distributions.py` accepts axes without a description.
+
 ## Reproduce
 
 The capture engine image is `lmsysorg/sglang:nightly-dev-cu13-20261014-684fea8a`, verified as `lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8`. It contains PyTorch `2.14.1+cu130` and SGLang build `684fea8af3b11a5b51ec9966c02959a32ad3421c`. The native dataset checker was run with FlashInfer-Bench commit `15618a67e8d7b7f49ca61b5ba3b9f26a876d0bee`. The Modal runner defaults to its cached image in the current workspace; `--image` accepts another prepared Modal image ID. Install the Modal client locally and authenticate it before using the runner. Model downloads use a CPU worker and the task's persistent volume.
@@ -176,7 +203,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/capture_llm.py \
 
 To reproduce each validated LTX batch, use `--audio-sdpa-backend math --video-sdpa-backend math`, and run all three request types with `--num-outputs 1`, `2`, and `4` under distinct run names. Keep the same reference image for image editing and image-conditioned video.
 
-The published dataset keeps every passing workload from the nine canonical runs: images 101 + 78 + 78; Qwen 193 serial + 448 concurrent; DeepSeek 278 of 279; LTX 69 + 69 + 69. The optional Modal retrieval filters (`--batch-sizes` and `--context-endpoints`) were not used for this publication. DeepSeek retains 52 workloads per grouped matrix definition, 50 router calls, 42 mixed-attention calls, and 30 sparse-prefill calls. Each grouped definition covers actual row counts 6/12/18/768/2688/11904/14592/17664/20736/24192/24576. Decode captures include early, middle, and final steps and actual concurrent batches 1/2/3.
+The capture kept every passing workload from the nine canonical runs (the [curation](#curation) then removed some): images 101 + 78 + 78; Qwen 193 serial + 448 concurrent; DeepSeek 278 of 279; LTX 69 + 69 + 69. The optional Modal retrieval filters (`--batch-sizes` and `--context-endpoints`) were not used for this publication. DeepSeek retains 52 workloads per grouped matrix definition, 50 router calls, 42 mixed-attention calls, and 30 sparse-prefill calls. Each grouped definition covers actual row counts 6/12/18/768/2688/11904/14592/17664/20736/24192/24576. Decode captures include early, middle, and final steps and actual concurrent batches 1/2/3.
 
 All 768 original request/operator/step locations remain represented. New UUIDs and tensor hashes identify the regenerated values. Images, Qwen, and LTX retain every original exact runtime shape. DeepSeek mixed attention keeps the actual reachable physical pages selected by the new full-model execution: 14 calls differ in their `N4`/`N8` page counts, leaving 12 original exact storage shapes absent. These content-dependent cache footprints are not padded to imitate earlier allocations.
 
@@ -202,7 +229,7 @@ BF16 comparisons use `rtol=0.016, atol=0.005`; FP32 uses `rtol=1e-4, atol=2e-5`;
 
 One complete router workload is excluded from the 279 DeepSeek raw captures: at two rows of a 4,096-row call, FP32 ranking ties produce different expert selections. The original FP32 reference and exact selected-set requirement remain unchanged. This call was also absent from the original dataset. No rows are removed from published tensors. All nine LTX requests pass and are included.
 
-Tensor files are published directly as `blob/workloads/<definition>/<hash>.safetensors` through Git LFS. Every workload and capture record references tensors under its definition directory. Shared snapshots have identical LFS objects across directories: 6,763 paths contain 6,223 unique snapshots totaling 168.49 GB (169.51 GB when materialized at every path).
+Tensor files are published directly as `blob/workloads/<definition>/<hash>.safetensors` through Git LFS. Every workload and capture record references tensors under its definition directory. Shared snapshots have identical LFS objects across directories: 5,506 paths contain 5,124 unique snapshots totaling 146.34 GB (146.44 GB when materialized at every path).
 
 ```bash
 git clone https://huggingface.co/datasets/humanfia-lab/inference-kernels

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