diff --git a/README.md b/README.md index fa4d7fd8bc043fbfeb22caebd3ed45020961dc60..c1830fa177c588ef3df75240d4c5e9b4194df560 100644 --- a/README.md +++ b/README.md @@ -9,11 +9,11 @@ tags: --- # Inference Kernels -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. +13 [FlashInfer Trace](https://github.com/flashinfer-ai/flashinfer-bench/tree/main/docs/flashinfer-trace) operator definitions, one per operator except for the empty-prefix case of block-causal attention, with self-contained PyTorch references, open-source baseline solutions, and workloads captured from actual inference: 11 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, except where one operator serves several model shapes (weight shapes, head dimensions, windows, slot counts); those are runtime axes, like batch sizes, sequence lengths, page sizes, and image grids. 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. -**1,104 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. +**1,104 real workloads cover all 13 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 5,455 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, apart from the [curation](#curation) for kernels that real inference uses. @@ -23,19 +23,19 @@ 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)`; `M >= 4096` | 3 | 93 | -| Grouped expert matrix multiplication | DeepSeek V4.1 Flash | 384 experts; `(N,K)`: `(4608,5120)`, `(5120,2304)`; FP8 × FP4 | 2 | 104 | +| Dense matrix multiplication | Qwen Image 2.1 Turbo | `(N,K)`: `(4096,4096)`, `(12288,4096)`, `(4096,12288)`; `M >= 4096` | 1 | 93 | +| Grouped expert matrix multiplication | DeepSeek V4.1 Flash | 384 experts; `(N,K)`: `(4608,5120)`, `(5120,2304)`; FP8 × FP4 | 1 | 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 | +| Sparse multi-head latent attention (MLA), prefill | DeepSeek V4.1 Flash | 64 query heads × 512 dimensions; 1 shared key/value head; BF16; 128 or 640 slots | 1 | 30 | | 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 | +| Noncausal dense attention | LTX-2.5 | 32 heads; dimension 128 (video) or 64 (audio); no mask | 1 | 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 | +| 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)`; the window is an input | 1 | 45 | -**[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). +**[Shape distribution histograms for all 13 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. @@ -104,7 +104,7 @@ The existing baseline implementations and B200 measurements come from the origin | 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 | +| Dense and grouped matrix multiplication | [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d) | | 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 | The MLSys 2026 contest's FlashInfer prefill and decode wrappers, unchanged | @@ -145,6 +145,10 @@ This revision keeps only kernels that real inference uses, as KDA (Kernel Design - **Replaced the gated delta definitions.** `gated_delta_rule_{decode,prefill}_h48_dk128_dv128` gave Q/K pre-repeated to 48 heads and a `[B, H, K, V]` state, which is not an engine's interface. 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. +- **One definition per operator.** The per-shape definitions of an operator are merged into one, in which the shape constants are runtime axes: dense GEMM `N`/`K`, grouped GEMM `N`/`K` (with `K_packed`, `K_groups`), dense attention `D`, neighborhood `H`, and sparse MLA `TopK`. Block-causal attention keeps its empty-prefix definition (`Np = 0`), because FlashInfer Trace requires positive runtime axes; KDA judges the two as one task. The references were identical across shapes. The neighborhood window becomes three scalar inputs (`Kt`, `Ky`, `Kx`), because no tensor shape carries it. + - Tensors move to the merged definition's directory; identical snapshots collapse into one path each. + - Receipts take the merged definition and axes, and keep the definition they were captured under as `captured_definition`. + - The per-shape open-source solutions merge too: one DeepGEMM BF16 solution for every `(N, K)` (the CuTe DSL branch served only the removed `M <= 32` rows); one DeepGEMM grouped solution; FlashAttention 4 without a mask (FP16 for `D = 64`, as before); NATTEN with the window from the inputs; and one FlashMLA solution. - **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]`. @@ -227,7 +231,7 @@ 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//.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). +Tensor files are published directly as `blob/workloads//.safetensors` through Git LFS. Every workload and capture record references tensors under its definition directory. Shared snapshots have identical LFS objects across directories: 5,455 paths contain 5,124 unique snapshots totaling 146.34 GB (146.41 GB when materialized at every path). ```bash git clone https://huggingface.co/datasets/humanfia-lab/inference-kernels diff --git a/blob/workloads/dense_gemm_bf16_nt_n12288_k4096/0011fcf356b915718dc22610388ef6bb0a06e952e61cb610954122ea6d9b569d.safetensors b/blob/workloads/dense_gemm_bf16_nt/0011fcf356b915718dc22610388ef6bb0a06e952e61cb610954122ea6d9b569d.safetensors similarity index 100% rename from blob/workloads/dense_gemm_bf16_nt_n12288_k4096/0011fcf356b915718dc22610388ef6bb0a06e952e61cb610954122ea6d9b569d.safetensors rename to blob/workloads/dense_gemm_bf16_nt/0011fcf356b915718dc22610388ef6bb0a06e952e61cb610954122ea6d9b569d.safetensors diff --git a/blob/workloads/dense_gemm_bf16_nt_n4096_k4096/00b199614f81d11ba2763b59714d61372a4c2add5d193d6e896b191f280974e2.safetensors 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Self attention uses Nq=Nk; cross attention allows unequal lengths. No dropout. Projection, normalization, rotary embeddings and output gates are outside the definition. No mask: every captured call's expanded visibility mask was all True (on one GPU the engine passes no mask, or an all-visible prompt mask), so KDA drops the dataset's mask input.", "tags": [ @@ -25,8 +25,8 @@ "value": 32 }, "D": { - "type": "const", - "value": 128 + "type": "var", + "description": "Head dimension: 64 for audio, 128 for video." } }, "inputs": { @@ -60,7 +60,7 @@ "sm_scale": { "shape": null, "dtype": "float32", - "description": "Model default 1/sqrt(128)." + "description": "Model default 1/sqrt(D)." } }, "outputs": { diff --git a/definitions/dense_attention/noncausal_dense_attention_h32_d64.json b/definitions/dense_attention/noncausal_dense_attention_h32_d64.json deleted file mode 100644 index 40bf10ce999a71a4a471f5c80b764c70afadc778..0000000000000000000000000000000000000000 --- a/definitions/dense_attention/noncausal_dense_attention_h32_d64.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "name": "noncausal_dense_attention_h32_d64", - "op_type": "dense_attention", - "description": "Noncausal self/cross attention with FP32 accumulation and BF16 output. Self attention uses Nq=Nk; cross attention allows unequal lengths. No dropout. Projection, normalization, rotary embeddings and output gates are outside the definition. No mask: every captured call's expanded visibility mask was all True (on one GPU the engine passes no mask, or an all-visible prompt mask), so KDA drops the dataset's mask input.", - "tags": [ - "model:ltx-2.5", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "B": { - "type": "var", - "description": "Runtime batch size." - }, - "Nq": { - "type": "var", - "description": "Runtime query length." - }, - "Nk": { - "type": "var", - "description": "Runtime key/value length." - }, - "H": { - "type": "const", - "value": 32 - }, - "D": { - "type": "const", - "value": 64 - } - }, - "inputs": { - "Q": { - "shape": [ - "B", - "Nq", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "K": { - "shape": [ - "B", - "Nk", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "V": { - "shape": [ - "B", - "Nk", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "sm_scale": { - "shape": null, - "dtype": "float32", - "description": "Model default 1/sqrt(64)." - } - }, - "outputs": { - "O": { - "shape": [ - "B", - "Nq", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "B >= 1", - "Nq >= 1", - "Nk >= 1" - ], - "reference": "import math\nimport torch\n\n@torch.no_grad()\ndef run(Q, K, V, sm_scale):\n \"\"\"Noncausal attention without a mask.\"\"\"\n scores = torch.einsum(\"bqhd,bkhd->bhqk\", Q.float(), K.float())\n scores = scores * float(sm_scale)\n probabilities = torch.softmax(scores, dim=-1)\n return torch.einsum(\"bhqk,bkhd->bqhd\", probabilities, V.float()).to(torch.bfloat16)\n" -} diff --git a/definitions/gemm/dense_gemm_bf16_nt_n4096_k4096.json b/definitions/gemm/dense_gemm_bf16_nt.json similarity index 66% rename from definitions/gemm/dense_gemm_bf16_nt_n4096_k4096.json rename to definitions/gemm/dense_gemm_bf16_nt.json index 5ffb01ebe4b7a6548b9d5ee26de0fa2356986789..abf338a3cc6f197264218552b19be32ab024ae83 100644 --- a/definitions/gemm/dense_gemm_bf16_nt_n4096_k4096.json +++ b/definitions/gemm/dense_gemm_bf16_nt.json @@ -1,7 +1,7 @@ { - "name": "dense_gemm_bf16_nt_n4096_k4096", + "name": "dense_gemm_bf16_nt", "op_type": "gemm", - "description": "Independent A @ W.T with FP32 accumulation and BF16 output; Q/K/V/output and context projections. Contiguous row-major inputs. No bias, activation, residual or quantization.", + "description": "Independent A @ W.T with FP32 accumulation and BF16 output; the Qwen Image 2.1 Turbo transformer's (N, K) = (4096, 4096) Q/K/V, output and context projections, (12288, 4096) feed-forward gate/up and packed Q/K/V, and (4096, 12288) feed-forward down projections. Contiguous row-major inputs. No bias, activation, residual or quantization.", "tags": [ "model:qwen-image-2.1-turbo", "status:draft", @@ -13,12 +13,12 @@ "description": "Runtime number of token rows." }, "N": { - "type": "const", - "value": 4096 + "type": "var", + "description": "Output features." }, "K": { - "type": "const", - "value": 4096 + "type": "var", + "description": "Reduction dimension." } }, "inputs": { diff --git a/definitions/gemm/dense_gemm_bf16_nt_n12288_k4096.json b/definitions/gemm/dense_gemm_bf16_nt_n12288_k4096.json deleted file mode 100644 index b5dcbe03b82859dbbe32bad295ea562aab061e41..0000000000000000000000000000000000000000 --- a/definitions/gemm/dense_gemm_bf16_nt_n12288_k4096.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "name": "dense_gemm_bf16_nt_n12288_k4096", - "op_type": "gemm", - "description": "Independent A @ W.T with FP32 accumulation and BF16 output; feed-forward gate/up projections. Contiguous row-major inputs. No bias, activation, residual or quantization.", - "tags": [ - "model:qwen-image-2.1-turbo", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "M": { - "type": "var", - "description": "Runtime number of token rows." - }, - "N": { - "type": "const", - "value": 12288 - }, - "K": { - "type": "const", - "value": 4096 - } - }, - "inputs": { - "A": { - "shape": [ - "M", - "K" - ], - "dtype": "bfloat16" - }, - "W": { - "shape": [ - "N", - "K" - ], - "dtype": "bfloat16" - } - }, - "outputs": { - "C": { - "shape": [ - "M", - "N" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "M >= 1" - ], - "reference": "import math\nimport torch\n\n@torch.no_grad()\ndef run(A, W):\n \"\"\"BF16 inputs, FP32 accumulation, one BF16 output rounding.\"\"\"\n return (A.float() @ W.float().T).to(torch.bfloat16)\n" -} diff --git a/definitions/gemm/dense_gemm_bf16_nt_n4096_k12288.json b/definitions/gemm/dense_gemm_bf16_nt_n4096_k12288.json deleted file mode 100644 index dda4e9d88a93f832403fbef619566ecd37649d51..0000000000000000000000000000000000000000 --- a/definitions/gemm/dense_gemm_bf16_nt_n4096_k12288.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "name": "dense_gemm_bf16_nt_n4096_k12288", - "op_type": "gemm", - "description": "Independent A @ W.T with FP32 accumulation and BF16 output; feed-forward down projection. Contiguous row-major inputs. No bias, activation, residual or quantization.", - "tags": [ - "model:qwen-image-2.1-turbo", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "M": { - "type": "var", - "description": "Runtime number of token rows." - }, - "N": { - "type": "const", - "value": 4096 - }, - "K": { - "type": "const", - "value": 12288 - } - }, - "inputs": { - "A": { - "shape": [ - "M", - "K" - ], - "dtype": "bfloat16" - }, - "W": { - "shape": [ - "N", - "K" - ], - "dtype": "bfloat16" - } - }, - "outputs": { - "C": { - "shape": [ - "M", - "N" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "M >= 1" - ], - "reference": "import math\nimport torch\n\n@torch.no_grad()\ndef run(A, W):\n \"\"\"BF16 inputs, FP32 accumulation, one BF16 output rounding.\"\"\"\n return (A.float() @ W.float().T).to(torch.bfloat16)\n" -} diff --git a/definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json b/definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_g32.json similarity index 84% rename from definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json rename to definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_g32.json index 772116e7413248013e4b897d7eab05bdc44082c8..e36c59d73d5d0d4c33ac2afad7cbfaaccc668790 100644 --- a/definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json +++ b/definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_g32.json @@ -1,7 +1,7 @@ { - "name": "grouped_gemm_fp8_fp4_e384_n4608_k5120_g32", + "name": "grouped_gemm_fp8_fp4_e384_g32", "op_type": "grouped_gemm", - "description": "Grouped E4M3 activation x packed E2M1 weight GEMM, concatenated gate/up projection. Scale groups cover 32 consecutive K values. Logical unpacked scale layout, no backend swizzle. Input rows are already routed/permuted; no activation, routing weights or scatter.", + "description": "Grouped E4M3 activation x packed E2M1 weight GEMM: the concatenated gate/up projection (N=4608, K=5120) and the down projection (N=5120, K=2304). Scale groups cover 32 consecutive K values. Logical unpacked scale layout, no backend swizzle. Input rows are already routed/permuted; no activation, routing weights or scatter.", "tags": [ "model:deepseek-v4.1-flash", "status:draft", @@ -17,20 +17,20 @@ "value": 384 }, "N": { - "type": "const", - "value": 4608 + "type": "var", + "description": "Output features." }, "K": { - "type": "const", - "value": 5120 + "type": "var", + "description": "Reduction dimension." }, "K_packed": { - "type": "const", - "value": 2560 + "type": "var", + "description": "K / 2 bytes of packed E2M1 weights." }, "K_groups": { - "type": "const", - "value": 160 + "type": "var", + "description": "K / 32 scale groups." }, "group_size": { "type": "const", diff --git a/definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json b/definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json deleted file mode 100644 index 37e7deef0afa0b22c9692b21dd5177d90e521cab..0000000000000000000000000000000000000000 --- a/definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json +++ /dev/null @@ -1,97 +0,0 @@ -{ - "name": "grouped_gemm_fp8_fp4_e384_n5120_k2304_g32", - "op_type": "grouped_gemm", - "description": "Grouped E4M3 activation x packed E2M1 weight GEMM, down projection. Scale groups cover 32 consecutive K values. Logical unpacked scale layout, no backend swizzle. Input rows are already routed/permuted; no activation, routing weights or scatter.", - "tags": [ - "model:deepseek-v4.1-flash", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "M": { - "type": "var", - "description": "Runtime number of token rows." - }, - "E": { - "type": "const", - "value": 384 - }, - "N": { - "type": "const", - "value": 5120 - }, - "K": { - "type": "const", - "value": 2304 - }, - "K_packed": { - "type": "const", - "value": 1152 - }, - "K_groups": { - "type": "const", - "value": 72 - }, - "group_size": { - "type": "const", - "value": 32 - } - }, - "inputs": { - "Aq": { - "shape": [ - "M", - "K" - ], - "dtype": "float8_e4m3fn" - }, - "Wq_packed": { - "shape": [ - "E", - "N", - "K_packed" - ], - "dtype": "int8", - "description": "Raw FP4 byte carrier; even K is the low nibble." - }, - "scale_A": { - "shape": [ - "M", - "K_groups" - ], - "dtype": "int8", - "description": "Raw E8M0 exponent byte carrier, not signed numeric scales." - }, - "scale_W": { - "shape": [ - "E", - "N", - "K_groups" - ], - "dtype": "int8", - "description": "Raw E8M0 exponent byte carrier, not signed numeric scales." - }, - "expert_id": { - "shape": [ - "M" - ], - "dtype": "int32", - "description": "Nondecreasing expert IDs in [0,384); empty experts are allowed." - } - }, - "outputs": { - "Y": { - "shape": [ - "M", - "N" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "M >= 1", - "K == 2 * K_packed", - "K == group_size * K_groups" - ], - "reference": "import math\nimport torch\n\ndef unpack_e2m1(packed):\n \"\"\"Decode low-nibble-first FP4 bytes without interpreting int8 numerically.\"\"\"\n raw = packed.to(torch.int16) & 255\n codes = torch.stack((raw & 15, raw >> 4), dim=-1).flatten(-2)\n table = torch.tensor(\n [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0],\n dtype=torch.float32, device=packed.device,\n )\n return table[(codes & 7).long()] * torch.where((codes & 8) == 0, 1.0, -1.0)\n\n\ndef decode_e8m0(encoded):\n \"\"\"E8M0 raw exponent bytes: 0..254 mean 2**(byte-127); 255 is invalid.\"\"\"\n raw = encoded.to(torch.int32) & 255\n assert bool((raw < 255).all()), \"E8M0 NaN scale is outside the contract\"\n return torch.ldexp(torch.ones_like(raw, dtype=torch.float32), raw - 127)\n\n\n@torch.no_grad()\ndef run(Aq, Wq_packed, scale_A, scale_W, expert_id):\n \"\"\"Rows are sorted by expert; routing and quantization are outside this op.\"\"\"\n m, k = Aq.shape\n experts, n, packed_k = Wq_packed.shape\n assert k == 2 * packed_k and k % 32 == 0\n assert bool(((expert_id >= 0) & (expert_id < experts)).all())\n assert bool((expert_id[1:] >= expert_id[:-1]).all())\n a = Aq.float() * decode_e8m0(scale_A).repeat_interleave(32, dim=-1)\n result = torch.empty((m, n), dtype=torch.bfloat16, device=Aq.device)\n for expert in torch.unique(expert_id).tolist():\n rows = torch.nonzero(expert_id == expert, as_tuple=True)[0]\n w = unpack_e2m1(Wq_packed[expert])\n w = w * decode_e8m0(scale_W[expert]).repeat_interleave(32, dim=-1)\n result[rows] = (a[rows] @ w.T).to(torch.bfloat16)\n return result\n" -} diff --git a/definitions/neighborhood_attention/neighborhood_attention_3d_d64.json b/definitions/neighborhood_attention/neighborhood_attention_3d_d64.json new file mode 100644 index 0000000000000000000000000000000000000000..2aed61c097a5632476eed926bfb397937811af9c --- /dev/null +++ b/definitions/neighborhood_attention/neighborhood_attention_3d_d64.json @@ -0,0 +1,106 @@ +{ + "name": "neighborhood_attention_3d_d64", + "op_type": "neighborhood_attention", + "description": "3D neighborhood attention core for the LTX-2.5 diffusion decoder. The head count and window vary by stage: (H, Kt x Ky x Kx) = (32, 3x7x7) in stage 1, (16, 3x7x7) in stage 2, (8, 3x5x5) in stages 3/4 and (4, 11x11x11) in stage 5. Q/K are normalized and rotary-embedded; Q already includes 1/sqrt(64), so no additional attention scaling occurs. Window starts clamp(p-floor(k/2),0,L-k), keeping the full window by shifting inward at boundaries. Noncausal in time. Grid dimensions must be at least the window dimensions. No auxiliary keyframe stream or enclosing block fusion.", + "tags": [ + "model:ltx-2.5", + "status:draft", + "parallelism:unsharded" + ], + "axes": { + "B": { + "type": "var", + "description": "Runtime batch size." + }, + "T": { + "type": "var", + "description": "Runtime frame count." + }, + "Y": { + "type": "var", + "description": "Runtime grid height." + }, + "X": { + "type": "var", + "description": "Runtime grid width." + }, + "H": { + "type": "var", + "description": "Attention heads of the decoder stage." + }, + "D": { + "type": "const", + "value": 64 + } + }, + "inputs": { + "Q": { + "shape": [ + "B", + "T", + "Y", + "X", + "H", + "D" + ], + "dtype": "bfloat16" + }, + "K": { + "shape": [ + "B", + "T", + "Y", + "X", + "H", + "D" + ], + "dtype": "bfloat16" + }, + "V": { + "shape": [ + "B", + "T", + "Y", + "X", + "H", + "D" + ], + "dtype": "bfloat16" + }, + "Kt": { + "shape": null, + "dtype": "int32", + "description": "Window extent (t)." + }, + "Ky": { + "shape": null, + "dtype": "int32", + "description": "Window extent (y)." + }, + "Kx": { + "shape": null, + "dtype": "int32", + "description": "Window extent (x)." + } + }, + "outputs": { + "O": { + "shape": [ + "B", + "T", + "Y", + "X", + "H", + "D" + ], + "dtype": "bfloat16" + } + }, + "constraints": [ + "B >= 1", + "T >= Kt", + "Y >= Ky", + "X >= Kx" + ], + "reference": "import math\nimport torch\n\n@torch.no_grad()\ndef run(Q, K, V, Kt, Ky, Kx):\n \"\"\"Q is pre-scaled; shift complete fixed-size windows inward at borders.\"\"\"\n kt, ky, kx = int(Kt), int(Ky), int(Kx)\n bsz, frames, height, width, heads, dim = Q.shape\n assert frames >= kt and height >= ky and width >= kx\n qflat, kflat, vflat = (x.flatten(1, 3) for x in (Q, K, V))\n out = torch.empty_like(qflat)\n offsets = (\n torch.arange(kt, device=Q.device)[:, None, None] * height * width\n + torch.arange(ky, device=Q.device)[None, :, None] * width\n + torch.arange(kx, device=Q.device)[None, None, :]\n ).flatten()\n # Bound gathered-neighborhood memory independently of the full video volume.\n for first in range(0, qflat.shape[1], 32):\n rows = torch.arange(first, min(first + 32, qflat.shape[1]), device=Q.device)\n t, y, x = rows // (height * width), (rows // width) % height, rows % width\n start = ((t - kt // 2).clamp(0, frames - kt) * height * width\n + (y - ky // 2).clamp(0, height - ky) * width\n + (x - kx // 2).clamp(0, width - kx))\n neighbors = start[:, None] + offsets[None, :]\n keys, values = kflat[:, neighbors].float(), vflat[:, neighbors].float()\n scores = torch.einsum(\"bqhd,bqkhd->bqhk\", qflat[:, rows].float(), keys)\n out[:, rows] = torch.einsum(\n \"bqhk,bqkhd->bqhd\", scores.softmax(-1), values\n ).to(torch.bfloat16)\n return out.reshape_as(Q)\n" +} diff --git a/definitions/neighborhood_attention/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json b/definitions/neighborhood_attention/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json deleted file mode 100644 index b8f83ee869040de251842b61c269e219e883a8fc..0000000000000000000000000000000000000000 --- a/definitions/neighborhood_attention/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "name": "neighborhood_attention_3d_h16_d64_kt3_ky7_kx7", - "op_type": "neighborhood_attention", - "description": "3D neighborhood attention core for LTX-2.5 diffusion decoder stages 2. Q/K are normalized and rotary-embedded; Q already includes 1/sqrt(64), so no additional attention scaling occurs. Window starts clamp(p-floor(k/2),0,L-k), keeping the full window by shifting inward at boundaries. Noncausal in time. Grid dimensions must be at least the window dimensions. No auxiliary keyframe stream or enclosing block fusion.", - "tags": [ - "model:ltx-2.5", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "B": { - "type": "var", - "description": "Runtime batch size." - }, - "T": { - "type": "var", - "description": "Runtime frame count." - }, - "Y": { - "type": "var", - "description": "Runtime grid height." - }, - "X": { - "type": "var", - "description": "Runtime grid width." - }, - "H": { - "type": "const", - "value": 16 - }, - "D": { - "type": "const", - "value": 64 - }, - "Kt": { - "type": "const", - "value": 3 - }, - "Ky": { - "type": "const", - "value": 7 - }, - "Kx": { - "type": "const", - "value": 7 - } - }, - "inputs": { - "Q": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "K": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "V": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "outputs": { - "O": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "B >= 1", - "T >= Kt", - "Y >= Ky", - "X >= Kx" - ], - "reference": "import math\nimport torch\n\nWINDOW = (3, 7, 7)\n\n@torch.no_grad()\ndef run(Q, K, V):\n \"\"\"Q is pre-scaled; shift complete fixed-size windows inward at borders.\"\"\"\n kt, ky, kx = WINDOW\n bsz, frames, height, width, heads, dim = Q.shape\n assert frames >= kt and height >= ky and width >= kx\n qflat, kflat, vflat = (x.flatten(1, 3) for x in (Q, K, V))\n out = torch.empty_like(qflat)\n offsets = (\n torch.arange(kt, device=Q.device)[:, None, None] * height * width\n + torch.arange(ky, device=Q.device)[None, :, None] * width\n + torch.arange(kx, device=Q.device)[None, None, :]\n ).flatten()\n # Bound gathered-neighborhood memory independently of the full video volume.\n for first in range(0, qflat.shape[1], 32):\n rows = torch.arange(first, min(first + 32, qflat.shape[1]), device=Q.device)\n t, y, x = rows // (height * width), (rows // width) % height, rows % width\n start = ((t - kt // 2).clamp(0, frames - kt) * height * width\n + (y - ky // 2).clamp(0, height - ky) * width\n + (x - kx // 2).clamp(0, width - kx))\n neighbors = start[:, None] + offsets[None, :]\n keys, values = kflat[:, neighbors].float(), vflat[:, neighbors].float()\n scores = torch.einsum(\"bqhd,bqkhd->bqhk\", qflat[:, rows].float(), keys)\n out[:, rows] = torch.einsum(\n \"bqhk,bqkhd->bqhd\", scores.softmax(-1), values\n ).to(torch.bfloat16)\n return out.reshape_as(Q)\n" -} diff --git a/definitions/neighborhood_attention/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json b/definitions/neighborhood_attention/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json deleted file mode 100644 index 7a9330586857b96517b11f8efca7307e6c5d148d..0000000000000000000000000000000000000000 --- a/definitions/neighborhood_attention/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "name": "neighborhood_attention_3d_h32_d64_kt3_ky7_kx7", - "op_type": "neighborhood_attention", - "description": "3D neighborhood attention core for LTX-2.5 diffusion decoder stages 1. Q/K are normalized and rotary-embedded; Q already includes 1/sqrt(64), so no additional attention scaling occurs. Window starts clamp(p-floor(k/2),0,L-k), keeping the full window by shifting inward at boundaries. Noncausal in time. Grid dimensions must be at least the window dimensions. No auxiliary keyframe stream or enclosing block fusion.", - "tags": [ - "model:ltx-2.5", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "B": { - "type": "var", - "description": "Runtime batch size." - }, - "T": { - "type": "var", - "description": "Runtime frame count." - }, - "Y": { - "type": "var", - "description": "Runtime grid height." - }, - "X": { - "type": "var", - "description": "Runtime grid width." - }, - "H": { - "type": "const", - "value": 32 - }, - "D": { - "type": "const", - "value": 64 - }, - "Kt": { - "type": "const", - "value": 3 - }, - "Ky": { - "type": "const", - "value": 7 - }, - "Kx": { - "type": "const", - "value": 7 - } - }, - "inputs": { - "Q": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "K": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "V": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "outputs": { - "O": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "B >= 1", - "T >= Kt", - "Y >= Ky", - "X >= Kx" - ], - "reference": "import math\nimport torch\n\nWINDOW = (3, 7, 7)\n\n@torch.no_grad()\ndef run(Q, K, V):\n \"\"\"Q is pre-scaled; shift complete fixed-size windows inward at borders.\"\"\"\n kt, ky, kx = WINDOW\n bsz, frames, height, width, heads, dim = Q.shape\n assert frames >= kt and height >= ky and width >= kx\n qflat, kflat, vflat = (x.flatten(1, 3) for x in (Q, K, V))\n out = torch.empty_like(qflat)\n offsets = (\n torch.arange(kt, device=Q.device)[:, None, None] * height * width\n + torch.arange(ky, device=Q.device)[None, :, None] * width\n + torch.arange(kx, device=Q.device)[None, None, :]\n ).flatten()\n # Bound gathered-neighborhood memory independently of the full video volume.\n for first in range(0, qflat.shape[1], 32):\n rows = torch.arange(first, min(first + 32, qflat.shape[1]), device=Q.device)\n t, y, x = rows // (height * width), (rows // width) % height, rows % width\n start = ((t - kt // 2).clamp(0, frames - kt) * height * width\n + (y - ky // 2).clamp(0, height - ky) * width\n + (x - kx // 2).clamp(0, width - kx))\n neighbors = start[:, None] + offsets[None, :]\n keys, values = kflat[:, neighbors].float(), vflat[:, neighbors].float()\n scores = torch.einsum(\"bqhd,bqkhd->bqhk\", qflat[:, rows].float(), keys)\n out[:, rows] = torch.einsum(\n \"bqhk,bqkhd->bqhd\", scores.softmax(-1), values\n ).to(torch.bfloat16)\n return out.reshape_as(Q)\n" -} diff --git a/definitions/neighborhood_attention/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json b/definitions/neighborhood_attention/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json deleted file mode 100644 index 5716a90ff00bff0b0f2b6e52022b608278de99e8..0000000000000000000000000000000000000000 --- a/definitions/neighborhood_attention/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "name": "neighborhood_attention_3d_h4_d64_kt11_ky11_kx11", - "op_type": "neighborhood_attention", - "description": "3D neighborhood attention core for LTX-2.5 diffusion decoder stages 5. Q/K are normalized and rotary-embedded; Q already includes 1/sqrt(64), so no additional attention scaling occurs. Window starts clamp(p-floor(k/2),0,L-k), keeping the full window by shifting inward at boundaries. Noncausal in time. Grid dimensions must be at least the window dimensions. No auxiliary keyframe stream or enclosing block fusion.", - "tags": [ - "model:ltx-2.5", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "B": { - "type": "var", - "description": "Runtime batch size." - }, - "T": { - "type": "var", - "description": "Runtime frame count." - }, - "Y": { - "type": "var", - "description": "Runtime grid height." - }, - "X": { - "type": "var", - "description": "Runtime grid width." - }, - "H": { - "type": "const", - "value": 4 - }, - "D": { - "type": "const", - "value": 64 - }, - "Kt": { - "type": "const", - "value": 11 - }, - "Ky": { - "type": "const", - "value": 11 - }, - "Kx": { - "type": "const", - "value": 11 - } - }, - "inputs": { - "Q": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "K": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "V": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "outputs": { - "O": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "B >= 1", - "T >= Kt", - "Y >= Ky", - "X >= Kx" - ], - "reference": "import math\nimport torch\n\nWINDOW = (11, 11, 11)\n\n@torch.no_grad()\ndef run(Q, K, V):\n \"\"\"Q is pre-scaled; shift complete fixed-size windows inward at borders.\"\"\"\n kt, ky, kx = WINDOW\n bsz, frames, height, width, heads, dim = Q.shape\n assert frames >= kt and height >= ky and width >= kx\n qflat, kflat, vflat = (x.flatten(1, 3) for x in (Q, K, V))\n out = torch.empty_like(qflat)\n offsets = (\n torch.arange(kt, device=Q.device)[:, None, None] * height * width\n + torch.arange(ky, device=Q.device)[None, :, None] * width\n + torch.arange(kx, device=Q.device)[None, None, :]\n ).flatten()\n # Bound gathered-neighborhood memory independently of the full video volume.\n for first in range(0, qflat.shape[1], 32):\n rows = torch.arange(first, min(first + 32, qflat.shape[1]), device=Q.device)\n t, y, x = rows // (height * width), (rows // width) % height, rows % width\n start = ((t - kt // 2).clamp(0, frames - kt) * height * width\n + (y - ky // 2).clamp(0, height - ky) * width\n + (x - kx // 2).clamp(0, width - kx))\n neighbors = start[:, None] + offsets[None, :]\n keys, values = kflat[:, neighbors].float(), vflat[:, neighbors].float()\n scores = torch.einsum(\"bqhd,bqkhd->bqhk\", qflat[:, rows].float(), keys)\n out[:, rows] = torch.einsum(\n \"bqhk,bqkhd->bqhd\", scores.softmax(-1), values\n ).to(torch.bfloat16)\n return out.reshape_as(Q)\n" -} diff --git a/definitions/neighborhood_attention/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json b/definitions/neighborhood_attention/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json deleted file mode 100644 index 20bde0c98793ba08b0cdb135f736030f6dbc42ba..0000000000000000000000000000000000000000 --- a/definitions/neighborhood_attention/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "name": "neighborhood_attention_3d_h8_d64_kt3_ky5_kx5", - "op_type": "neighborhood_attention", - "description": "3D neighborhood attention core for LTX-2.5 diffusion decoder stages 3/4. Q/K are normalized and rotary-embedded; Q already includes 1/sqrt(64), so no additional attention scaling occurs. Window starts clamp(p-floor(k/2),0,L-k), keeping the full window by shifting inward at boundaries. Noncausal in time. Grid dimensions must be at least the window dimensions. No auxiliary keyframe stream or enclosing block fusion.", - "tags": [ - "model:ltx-2.5", - "status:draft", - "parallelism:unsharded" - ], - "axes": { - "B": { - "type": "var", - "description": "Runtime batch size." - }, - "T": { - "type": "var", - "description": "Runtime frame count." - }, - "Y": { - "type": "var", - "description": "Runtime grid height." - }, - "X": { - "type": "var", - "description": "Runtime grid width." - }, - "H": { - "type": "const", - "value": 8 - }, - "D": { - "type": "const", - "value": 64 - }, - "Kt": { - "type": "const", - "value": 3 - }, - "Ky": { - "type": "const", - "value": 5 - }, - "Kx": { - "type": "const", - "value": 5 - } - }, - "inputs": { - "Q": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "K": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "V": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "outputs": { - "O": { - "shape": [ - "B", - "T", - "Y", - "X", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "B >= 1", - "T >= Kt", - "Y >= Ky", - "X >= Kx" - ], - "reference": "import math\nimport torch\n\nWINDOW = (3, 5, 5)\n\n@torch.no_grad()\ndef run(Q, K, V):\n \"\"\"Q is pre-scaled; shift complete fixed-size windows inward at borders.\"\"\"\n kt, ky, kx = WINDOW\n bsz, frames, height, width, heads, dim = Q.shape\n assert frames >= kt and height >= ky and width >= kx\n qflat, kflat, vflat = (x.flatten(1, 3) for x in (Q, K, V))\n out = torch.empty_like(qflat)\n offsets = (\n torch.arange(kt, device=Q.device)[:, None, None] * height * width\n + torch.arange(ky, device=Q.device)[None, :, None] * width\n + torch.arange(kx, device=Q.device)[None, None, :]\n ).flatten()\n # Bound gathered-neighborhood memory independently of the full video volume.\n for first in range(0, qflat.shape[1], 32):\n rows = torch.arange(first, min(first + 32, qflat.shape[1]), device=Q.device)\n t, y, x = rows // (height * width), (rows // width) % height, rows % width\n start = ((t - kt // 2).clamp(0, frames - kt) * height * width\n + (y - ky // 2).clamp(0, height - ky) * width\n + (x - kx // 2).clamp(0, width - kx))\n neighbors = start[:, None] + offsets[None, :]\n keys, values = kflat[:, neighbors].float(), vflat[:, neighbors].float()\n scores = torch.einsum(\"bqhd,bqkhd->bqhk\", qflat[:, rows].float(), keys)\n out[:, rows] = torch.einsum(\n \"bqhk,bqkhd->bqhd\", scores.softmax(-1), values\n ).to(torch.bfloat16)\n return out.reshape_as(Q)\n" -} diff --git a/definitions/sparse_mla/sparse_mla_prefill_h64_d512_k128.json b/definitions/sparse_mla/sparse_mla_prefill_h64_d512.json similarity index 96% rename from definitions/sparse_mla/sparse_mla_prefill_h64_d512_k128.json rename to definitions/sparse_mla/sparse_mla_prefill_h64_d512.json index f4ac089f1f189fe94377f70c5d65d558f5ddc7ca..9ce351336f88b10e7714bd21ec99cbf10cc8c1a7 100644 --- a/definitions/sparse_mla/sparse_mla_prefill_h64_d512_k128.json +++ b/definitions/sparse_mla/sparse_mla_prefill_h64_d512.json @@ -1,5 +1,5 @@ { - "name": "sparse_mla_prefill_h64_d512_k128", + "name": "sparse_mla_prefill_h64_d512", "op_type": "sparse_mla", "description": "Sparse Multi-head Latent Attention for prefill, with BF16 Q and a BF16 latent workspace. K and V are the same latent vector. Each query has its own sparse index list and length; visibility is fully specified by these inputs, with no additional causal mask. One softmax includes all valid selected slots and a per-head zero-value sink. Queries with no selected keys return zero. Cache dequantization, index selection, Q normalization/rotary embedding and output projection are outside this operator.", "tags": [ @@ -30,8 +30,8 @@ "value": 1 }, "TopK": { - "type": "const", - "value": 128 + "type": "var", + "description": "Selected slots per query: 128 or 640." } }, "inputs": { diff --git a/definitions/sparse_mla/sparse_mla_prefill_h64_d512_k640.json b/definitions/sparse_mla/sparse_mla_prefill_h64_d512_k640.json deleted file mode 100644 index 4614d82bd0554ce966ceb30d7b8cec0585c3f538..0000000000000000000000000000000000000000 --- a/definitions/sparse_mla/sparse_mla_prefill_h64_d512_k640.json +++ /dev/null @@ -1,98 +0,0 @@ -{ - "name": "sparse_mla_prefill_h64_d512_k640", - "op_type": "sparse_mla", - "description": "Sparse Multi-head Latent Attention for prefill, with BF16 Q and a BF16 latent workspace. K and V are the same latent vector. Each query has its own sparse index list and length; visibility is fully specified by these inputs, with no additional causal mask. One softmax includes all valid selected slots and a per-head zero-value sink. Queries with no selected keys return zero. Cache dequantization, index selection, Q normalization/rotary embedding and output projection are outside this operator.", - "tags": [ - "model:deepseek-v4.1-flash", - "status:draft", - "parallelism:unsharded", - "stage:prefill" - ], - "axes": { - "M": { - "type": "var", - "description": "Runtime number of token rows." - }, - "Nkv": { - "type": "var", - "description": "Number of latent vectors in the engine workspace." - }, - "H": { - "type": "const", - "value": 64 - }, - "D": { - "type": "const", - "value": 512 - }, - "KVH": { - "type": "const", - "value": 1 - }, - "TopK": { - "type": "const", - "value": 640 - } - }, - "inputs": { - "Q": { - "shape": [ - "M", - "H", - "D" - ], - "dtype": "bfloat16" - }, - "KV": { - "shape": [ - "Nkv", - "KVH", - "D" - ], - "dtype": "bfloat16" - }, - "indices": { - "shape": [ - "M", - "KVH", - "TopK" - ], - "dtype": "int32", - "description": "Physical workspace indices; -1 or indices >= Nkv are ignored." - }, - "topk_length": { - "shape": [ - "M" - ], - "dtype": "int32", - "description": "Only slots before this per-query length are considered." - }, - "attn_sink": { - "shape": [ - "H" - ], - "dtype": "float32", - "description": "Per-head zero-value sink logit; -inf disables, +inf forces zero output." - }, - "sm_scale": { - "shape": null, - "dtype": "float32", - "description": "Model default 1/sqrt(512)." - } - }, - "outputs": { - "O": { - "shape": [ - "M", - "H", - "D" - ], - "dtype": "bfloat16" - } - }, - "constraints": [ - "M >= 1", - "Nkv >= 1" - ], - "reference": "import math\nimport torch\n\n@torch.no_grad()\ndef run(Q, KV, indices, topk_length, attn_sink, sm_scale):\n \"\"\"Per-query sparse BF16 latent attention, including the zero-value sink.\"\"\"\n result = torch.empty_like(Q)\n slots = torch.arange(indices.shape[-1], device=Q.device)\n sink = attn_sink.float()[None, :, None]\n for start in range(0, Q.shape[0], 16):\n end = min(start + 16, Q.shape[0])\n ids = indices[start:end, 0].long()\n valid = ((ids >= 0) & (ids < KV.shape[0]) &\n (slots[None] < topk_length[start:end, None]))\n values = KV[ids.clamp(0, KV.shape[0] - 1), 0].float()\n values = values.masked_fill(~valid[..., None], 0)\n logits = torch.bmm(Q[start:end].float(), values.transpose(1, 2)) * float(sm_scale)\n logits.masked_fill_(~valid[:, None], -torch.inf)\n # Empty rows return zero even when the sink is disabled with -inf.\n empty = ~valid.any(-1)\n row_sink = sink.expand(end - start, -1, -1)\n row_sink = torch.where(empty[:, None, None], 0.0, row_sink)\n probabilities = torch.softmax(torch.cat((logits, row_sink), -1), -1)[..., :-1]\n probabilities = torch.where(torch.isposinf(sink), 0.0, probabilities)\n result[start:end] = torch.bmm(probabilities, values).to(Q.dtype)\n return result\n" -} diff --git a/docs/shape-distributions/README.md b/docs/shape-distributions/README.md index 3bfd477a20ea5f2ee48ac3792b21e6753fa78d16..0e640434a40b4ebf648bc385455324d3bf789dea 100644 --- a/docs/shape-distributions/README.md +++ b/docs/shape-distributions/README.md @@ -1,6 +1,6 @@ # Kernel shape distributions -**21 kernels, 1104 captured workloads.** Each histogram counts the workload records in one kernel definition's JSONL file. Every bar is one exact combination of its runtime dimensions; identical shapes are combined. The labels retain all runtime dimensions, including dimensions that happen to have one observed value. Model constants appear above each plot. +**13 kernels, 1104 captured workloads.** Each histogram counts the workload records in one kernel definition's JSONL file. Every bar is one exact combination of its runtime dimensions; identical shapes are combined. The labels retain all runtime dimensions, including dimensions that happen to have one observed value. Model constants appear above each plot. Read bar height (or length in horizontal plots) as the number of captured workloads with that shape. Bins are equally spaced categories, sorted by the dimension values, not continuous numeric intervals. A bar of 1 means one published example; a bar of 6 means six. These are capture counts, not request probabilities, runtime invocation frequencies, or AgentX traffic weights. @@ -18,25 +18,17 @@ Only definition and workload metadata are needed; tensor snapshots and a GPU are |---|---:|---:| | [prefix_cached_block_causal_h32_d128](#prefix_cached_block_causal_h32_d128) | 28 | 4 | | [prefix_cached_block_causal_h32_d128_prefix0](#prefix_cached_block_causal_h32_d128_prefix0) | 12 | 4 | -| [noncausal_dense_attention_h32_d128](#noncausal_dense_attention_h32_d128) | 54 | 18 | -| [noncausal_dense_attention_h32_d64](#noncausal_dense_attention_h32_d64) | 108 | 30 | +| [noncausal_dense_attention_h32](#noncausal_dense_attention_h32) | 162 | 48 | | [gdn_decode_qk4_v8_d128_k_last](#gdn_decode_qk4_v8_d128_k_last) | 54 | 7 | | [gdn_prefill_qk4_v8_d128_k_last](#gdn_prefill_qk4_v8_d128_k_last) | 100 | 77 | -| [dense_gemm_bf16_nt_n12288_k4096](#dense_gemm_bf16_nt_n12288_k4096) | 31 | 7 | -| [dense_gemm_bf16_nt_n4096_k12288](#dense_gemm_bf16_nt_n4096_k12288) | 31 | 7 | -| [dense_gemm_bf16_nt_n4096_k4096](#dense_gemm_bf16_nt_n4096_k4096) | 31 | 7 | -| [grouped_gemm_fp8_fp4_e384_n4608_k5120_g32](#grouped_gemm_fp8_fp4_e384_n4608_k5120_g32) | 52 | 11 | -| [grouped_gemm_fp8_fp4_e384_n5120_k2304_g32](#grouped_gemm_fp8_fp4_e384_n5120_k2304_g32) | 52 | 11 | +| [dense_gemm_bf16_nt](#dense_gemm_bf16_nt) | 93 | 21 | +| [grouped_gemm_fp8_fp4_e384_g32](#grouped_gemm_fp8_fp4_e384_g32) | 104 | 22 | | [mixed_mla_decode_deepseek41_h64_d512_k8128_k4512](#mixed_mla_decode_deepseek41_h64_d512_k8128_k4512) | 42 | 30 | | [moe_router_gemm_topk_deepseek41_d5120_e384_topk6](#moe_router_gemm_topk_deepseek41_d5120_e384_topk6) | 50 | 11 | -| [neighborhood_attention_3d_h16_d64_kt3_ky7_kx7](#neighborhood_attention_3d_h16_d64_kt3_ky7_kx7) | 9 | 9 | -| [neighborhood_attention_3d_h32_d64_kt3_ky7_kx7](#neighborhood_attention_3d_h32_d64_kt3_ky7_kx7) | 9 | 9 | -| [neighborhood_attention_3d_h4_d64_kt11_ky11_kx11](#neighborhood_attention_3d_h4_d64_kt11_ky11_kx11) | 9 | 9 | -| [neighborhood_attention_3d_h8_d64_kt3_ky5_kx5](#neighborhood_attention_3d_h8_d64_kt3_ky5_kx5) | 18 | 18 | +| [neighborhood_attention_3d_d64](#neighborhood_attention_3d_d64) | 45 | 45 | | [qsa_index_topk_decode_qwen38_h4_d128_topk512_c4](#qsa_index_topk_decode_qwen38_h4_d128_topk512_c4) | 192 | 77 | | [qsa_sparse_gqa_decode_qwen38_h24_kv2_d256_topk512_c4](#qsa_sparse_gqa_decode_qwen38_h24_kv2_d256_topk512_c4) | 192 | 64 | -| [sparse_mla_prefill_h64_d512_k128](#sparse_mla_prefill_h64_d512_k128) | 10 | 9 | -| [sparse_mla_prefill_h64_d512_k640](#sparse_mla_prefill_h64_d512_k640) | 20 | 20 | +| [sparse_mla_prefill_h64_d512](#sparse_mla_prefill_h64_d512) | 30 | 29 | ## prefix_cached_block_causal_h32_d128 @@ -54,21 +46,13 @@ Only definition and workload metadata are needed; tensor snapshots and a GPU are **Runtime dimensions:** `B`: Runtime batch size. `Nq`: Runtime current query/token count. `Nall`: Total prefix and current length. -## noncausal_dense_attention_h32_d128 +## noncausal_dense_attention_h32 -[Definition](../../definitions/dense_attention/noncausal_dense_attention_h32_d128.json) · [Workloads](../../workloads/dense_attention/noncausal_dense_attention_h32_d128.jsonl) · [SVG](noncausal_dense_attention_h32_d128.svg) +[Definition](../../definitions/dense_attention/noncausal_dense_attention_h32.json) · [Workloads](../../workloads/dense_attention/noncausal_dense_attention_h32.jsonl) · [SVG](noncausal_dense_attention_h32.svg) -![Shape distribution for noncausal_dense_attention_h32_d128: 54 workloads across 18 shapes](noncausal_dense_attention_h32_d128.svg) +![Shape distribution for noncausal_dense_attention_h32: 162 workloads across 48 shapes](noncausal_dense_attention_h32.svg) -**Runtime dimensions:** `B`: Runtime batch size. `Nq`: Runtime query length. `Nk`: Runtime key/value length. - -## noncausal_dense_attention_h32_d64 - -[Definition](../../definitions/dense_attention/noncausal_dense_attention_h32_d64.json) · [Workloads](../../workloads/dense_attention/noncausal_dense_attention_h32_d64.jsonl) · [SVG](noncausal_dense_attention_h32_d64.svg) - -![Shape distribution for noncausal_dense_attention_h32_d64: 108 workloads across 30 shapes](noncausal_dense_attention_h32_d64.svg) - -**Runtime dimensions:** `B`: Runtime batch size. `Nq`: Runtime query length. `Nk`: Runtime key/value length. +**Runtime dimensions:** `B`: Runtime batch size. `Nq`: Runtime query length. `Nk`: Runtime key/value length. `D`: Head dimension: 64 for audio, 128 for video. ## gdn_decode_qk4_v8_d128_k_last @@ -86,45 +70,21 @@ Only definition and workload metadata are needed; tensor snapshots and a GPU are **Runtime dimensions:** `total_seq_len`: total_seq_len `num_seqs`: num_seqs `len_cu_seqlens`: Length of cu_seqlens array (num_seqs + 1). -## dense_gemm_bf16_nt_n12288_k4096 - -[Definition](../../definitions/gemm/dense_gemm_bf16_nt_n12288_k4096.json) · [Workloads](../../workloads/gemm/dense_gemm_bf16_nt_n12288_k4096.jsonl) · [SVG](dense_gemm_bf16_nt_n12288_k4096.svg) +## dense_gemm_bf16_nt -![Shape distribution for dense_gemm_bf16_nt_n12288_k4096: 31 workloads across 7 shapes](dense_gemm_bf16_nt_n12288_k4096.svg) +[Definition](../../definitions/gemm/dense_gemm_bf16_nt.json) · [Workloads](../../workloads/gemm/dense_gemm_bf16_nt.jsonl) · [SVG](dense_gemm_bf16_nt.svg) -**Runtime dimensions:** `M`: Runtime number of token rows. - -## dense_gemm_bf16_nt_n4096_k12288 - -[Definition](../../definitions/gemm/dense_gemm_bf16_nt_n4096_k12288.json) · [Workloads](../../workloads/gemm/dense_gemm_bf16_nt_n4096_k12288.jsonl) · [SVG](dense_gemm_bf16_nt_n4096_k12288.svg) +![Shape distribution for dense_gemm_bf16_nt: 93 workloads across 21 shapes](dense_gemm_bf16_nt.svg) -![Shape distribution for dense_gemm_bf16_nt_n4096_k12288: 31 workloads across 7 shapes](dense_gemm_bf16_nt_n4096_k12288.svg) - -**Runtime dimensions:** `M`: Runtime number of token rows. +**Runtime dimensions:** `M`: Runtime number of token rows. `N`: Output features. `K`: Reduction dimension. -## dense_gemm_bf16_nt_n4096_k4096 - -[Definition](../../definitions/gemm/dense_gemm_bf16_nt_n4096_k4096.json) · [Workloads](../../workloads/gemm/dense_gemm_bf16_nt_n4096_k4096.jsonl) · [SVG](dense_gemm_bf16_nt_n4096_k4096.svg) - -![Shape distribution for dense_gemm_bf16_nt_n4096_k4096: 31 workloads across 7 shapes](dense_gemm_bf16_nt_n4096_k4096.svg) - -**Runtime dimensions:** `M`: Runtime number of token rows. - -## grouped_gemm_fp8_fp4_e384_n4608_k5120_g32 - -[Definition](../../definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json) · [Workloads](../../workloads/grouped_gemm/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.jsonl) · [SVG](grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.svg) - -![Shape distribution for grouped_gemm_fp8_fp4_e384_n4608_k5120_g32: 52 workloads across 11 shapes](grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.svg) - -**Runtime dimensions:** `M`: Runtime number of token rows. +## grouped_gemm_fp8_fp4_e384_g32 -## grouped_gemm_fp8_fp4_e384_n5120_k2304_g32 +[Definition](../../definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_g32.json) · [Workloads](../../workloads/grouped_gemm/grouped_gemm_fp8_fp4_e384_g32.jsonl) · [SVG](grouped_gemm_fp8_fp4_e384_g32.svg) -[Definition](../../definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json) · [Workloads](../../workloads/grouped_gemm/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.jsonl) · [SVG](grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.svg) +![Shape distribution for grouped_gemm_fp8_fp4_e384_g32: 104 workloads across 22 shapes](grouped_gemm_fp8_fp4_e384_g32.svg) -![Shape distribution for grouped_gemm_fp8_fp4_e384_n5120_k2304_g32: 52 workloads across 11 shapes](grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.svg) - -**Runtime dimensions:** `M`: Runtime number of token rows. +**Runtime dimensions:** `M`: Runtime number of token rows. `N`: Output features. `K`: Reduction dimension. `K_packed`: K / 2 bytes of packed E2M1 weights. `K_groups`: K / 32 scale groups. ## mixed_mla_decode_deepseek41_h64_d512_k8128_k4512 @@ -142,37 +102,13 @@ Only definition and workload metadata are needed; tensor snapshots and a GPU are **Runtime dimensions:** `M`: Runtime number of token rows. -## neighborhood_attention_3d_h16_d64_kt3_ky7_kx7 - -[Definition](../../definitions/neighborhood_attention/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json) · [Workloads](../../workloads/neighborhood_attention/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.jsonl) · [SVG](neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.svg) - -![Shape distribution for neighborhood_attention_3d_h16_d64_kt3_ky7_kx7: 9 workloads across 9 shapes](neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.svg) - -**Runtime dimensions:** `B`: Runtime batch size. `T`: Runtime frame count. `Y`: Runtime grid height. `X`: Runtime grid width. - -## neighborhood_attention_3d_h32_d64_kt3_ky7_kx7 - -[Definition](../../definitions/neighborhood_attention/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json) · [Workloads](../../workloads/neighborhood_attention/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.jsonl) · [SVG](neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.svg) - -![Shape distribution for neighborhood_attention_3d_h32_d64_kt3_ky7_kx7: 9 workloads across 9 shapes](neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.svg) - -**Runtime dimensions:** `B`: Runtime batch size. `T`: Runtime frame count. `Y`: Runtime grid height. `X`: Runtime grid width. +## neighborhood_attention_3d_d64 -## neighborhood_attention_3d_h4_d64_kt11_ky11_kx11 +[Definition](../../definitions/neighborhood_attention/neighborhood_attention_3d_d64.json) · [Workloads](../../workloads/neighborhood_attention/neighborhood_attention_3d_d64.jsonl) · [SVG](neighborhood_attention_3d_d64.svg) -[Definition](../../definitions/neighborhood_attention/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json) · [Workloads](../../workloads/neighborhood_attention/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.jsonl) · [SVG](neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.svg) +![Shape distribution for neighborhood_attention_3d_d64: 45 workloads across 45 shapes](neighborhood_attention_3d_d64.svg) -![Shape distribution for neighborhood_attention_3d_h4_d64_kt11_ky11_kx11: 9 workloads across 9 shapes](neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.svg) - -**Runtime dimensions:** `B`: Runtime batch size. `T`: Runtime frame count. `Y`: Runtime grid height. `X`: Runtime grid width. - -## neighborhood_attention_3d_h8_d64_kt3_ky5_kx5 - -[Definition](../../definitions/neighborhood_attention/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json) · [Workloads](../../workloads/neighborhood_attention/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.jsonl) · [SVG](neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.svg) - -![Shape distribution for neighborhood_attention_3d_h8_d64_kt3_ky5_kx5: 18 workloads across 18 shapes](neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.svg) - -**Runtime dimensions:** `B`: Runtime batch size. `T`: Runtime frame count. `Y`: Runtime grid height. `X`: Runtime grid width. +**Runtime dimensions:** `B`: Runtime batch size. `T`: Runtime frame count. `Y`: Runtime grid height. `X`: Runtime grid width. `H`: Attention heads of the decoder stage. ## qsa_index_topk_decode_qwen38_h4_d128_topk512_c4 @@ -190,18 +126,10 @@ Only definition and workload metadata are needed; tensor snapshots and a GPU are **Runtime dimensions:** `B`: Runtime batch size. `NP`: Allocated cache page count. `P`: Backend token-cache page size. `MP`: Page-table capacity per sequence. -## sparse_mla_prefill_h64_d512_k128 - -[Definition](../../definitions/sparse_mla/sparse_mla_prefill_h64_d512_k128.json) · [Workloads](../../workloads/sparse_mla/sparse_mla_prefill_h64_d512_k128.jsonl) · [SVG](sparse_mla_prefill_h64_d512_k128.svg) - -![Shape distribution for sparse_mla_prefill_h64_d512_k128: 10 workloads across 9 shapes](sparse_mla_prefill_h64_d512_k128.svg) - -**Runtime dimensions:** `M`: Runtime number of token rows. `Nkv`: Number of latent vectors in the engine workspace. - -## sparse_mla_prefill_h64_d512_k640 +## sparse_mla_prefill_h64_d512 -[Definition](../../definitions/sparse_mla/sparse_mla_prefill_h64_d512_k640.json) · [Workloads](../../workloads/sparse_mla/sparse_mla_prefill_h64_d512_k640.jsonl) · [SVG](sparse_mla_prefill_h64_d512_k640.svg) +[Definition](../../definitions/sparse_mla/sparse_mla_prefill_h64_d512.json) · [Workloads](../../workloads/sparse_mla/sparse_mla_prefill_h64_d512.jsonl) · [SVG](sparse_mla_prefill_h64_d512.svg) -![Shape distribution for sparse_mla_prefill_h64_d512_k640: 20 workloads across 20 shapes](sparse_mla_prefill_h64_d512_k640.svg) +![Shape distribution for sparse_mla_prefill_h64_d512: 30 workloads across 29 shapes](sparse_mla_prefill_h64_d512.svg) -**Runtime dimensions:** `M`: Runtime number of token rows. `Nkv`: Number of latent vectors in the engine workspace. +**Runtime dimensions:** `M`: Runtime number of token rows. `Nkv`: Number of latent vectors in the engine workspace. `TopK`: Selected slots per query: 128 or 640. diff --git a/docs/shape-distributions/sparse_mla_prefill_h64_d512_k640.svg b/docs/shape-distributions/dense_gemm_bf16_nt.svg similarity index 51% rename from docs/shape-distributions/sparse_mla_prefill_h64_d512_k640.svg rename to docs/shape-distributions/dense_gemm_bf16_nt.svg index b8c77e7d51d50a759aec15a33472cb2af6d6deb6..5212f26ddb5bd88fbde8a1ea40d338aa4167ac7e 100644 --- a/docs/shape-distributions/sparse_mla_prefill_h64_d512_k640.svg +++ b/docs/shape-distributions/dense_gemm_bf16_nt.svg @@ -1,13 +1,13 @@ - - Shape distribution: sparse_mla_prefill_h64_d512_k640 + + Shape distribution: dense_gemm_bf16_nt - Shape distribution: sparse_mla_prefill_h64_d512_k640 + Shape distribution: dense_gemm_bf16_nt image/svg+xml @@ -22,8 +22,8 @@ - - - +" clip-path="url(#p383a4d5cb5)" style="fill: none; stroke: #e3e9ef; stroke-width: 0.8; stroke-linecap: square"/> - 0 + 0 - + - 1 + 2 - - Captured workloads (count) + + + + + + + 4 + + + + + + + + + 6 + + + + + + + + + 8 + + + + + + + + + 10 + + + + Captured workloads (count) - - - (128, 192) + + + (4096, 4096, 4096) - - - (128, 256) + + + (4096, 4096, 12288) - - - (448, 71583) + + + (4096, 12288, 4096) - - - (448, 142591) + + + (4125, 4096, 4096) - - - (1984, 37662) + + + (4125, 4096, 12288) - - - (1984, 73086) + + + (4125, 12288, 4096) - - - (2432, 497471) + + + (6144, 4096, 4096) - - - (2432, 992383) + + + (6144, 4096, 12288) - - - (2944, 4416) + + + (6144, 12288, 4096) - - - (2944, 5888) + + + (8192, 4096, 4096) - - - (3456, 278335) + + + (8192, 4096, 12288) - - - (3456, 553087) + + + (8192, 12288, 4096) - - - (4032, 39070) + + + (12288, 4096, 4096) - - - (4032, 73854) + + + (12288, 4096, 12288) - - - (4096, 6144) + + + (12288, 12288, 4096) - - - (4096, 8192) + + + (16384, 4096, 4096) - - - (4096, 8319) + + + (16384, 4096, 12288) - - - (4096, 12415) + + + (16384, 12288, 4096) - - - (4096, 277247) + + + (24576, 4096, 4096) - - - (4096, 550271) + + + (24576, 4096, 12288) + + + + + + (24576, 12288, 4096) - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - +" clip-path="url(#p383a4d5cb5)" style="fill: #2563a6"/> - + - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 + + - 1 + 9 - 1 + 9 - 1 + 9 - 1 + 3 - 1 + 3 - 1 + 3 - 1 + 3 - 1 + 3 - 1 + 3 - 1 + 6 - 1 + 6 - 1 + 6 - 1 + 2 - 1 + 2 - 1 + 2 + + + 6 + + + 6 + + + 6 + + + 2 + + + 2 + + + 2 - + Shape distribution - - sparse_mla_prefill_h64_d512_k640 + + dense_gemm_bf16_nt - - 20 captured workloads | 20 distinct shapes + + 93 captured workloads | 21 distinct shapes - - Fixed dimensions: H=64, D=512, KVH=1, TopK=640 + + Fixed dimensions: - - Runtime shape: (M, Nkv) + + Runtime shape: (M, N, K) - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. + + One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - + + diff --git a/docs/shape-distributions/dense_gemm_bf16_nt_n12288_k4096.svg b/docs/shape-distributions/dense_gemm_bf16_nt_n12288_k4096.svg deleted file mode 100644 index 2d4491ecb0190dc2162e1b94ba2a3442e1a0029e..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/dense_gemm_bf16_nt_n12288_k4096.svg +++ /dev/null @@ -1,268 +0,0 @@ - - - - Shape distribution: dense_gemm_bf16_nt_n12288_k4096 - - - - - Shape distribution: dense_gemm_bf16_nt_n12288_k4096 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - 4096 - - - - - - 4125 - - - - - - 6144 - - - - - - 8192 - - - - - - 12288 - - - - - - 16384 - - - - - - 24576 - - - - M — Runtime number of token rows - - - - - - - - - - 0 - - - - - - - - - 2 - - - - - - - - - 4 - - - - - - - - - 6 - - - - - - - - - 8 - - - - - - - - - 10 - - - - Captured workloads (count) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 9 - - - 3 - - - 3 - - - 6 - - - 2 - - - 6 - - - 2 - - - - Shape distribution - - - dense_gemm_bf16_nt_n12288_k4096 - - - 31 captured workloads | 7 distinct shapes - - - Fixed dimensions: N=12288, K=4096 - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/dense_gemm_bf16_nt_n4096_k12288.svg b/docs/shape-distributions/dense_gemm_bf16_nt_n4096_k12288.svg deleted file mode 100644 index 26c8e61c96f66f3cf95899fe22e2f58154554d9c..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/dense_gemm_bf16_nt_n4096_k12288.svg +++ /dev/null @@ -1,268 +0,0 @@ - - - - Shape distribution: dense_gemm_bf16_nt_n4096_k12288 - - - - - Shape distribution: dense_gemm_bf16_nt_n4096_k12288 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - 4096 - - - - - - 4125 - - - - - - 6144 - - - - - - 8192 - - - - - - 12288 - - - - - - 16384 - - - - - - 24576 - - - - M — Runtime number of token rows - - - - - - - - - - 0 - - - - - - - - - 2 - - - - - - - - - 4 - - - - - - - - - 6 - - - - - - - - - 8 - - - - - - - - - 10 - - - - Captured workloads (count) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 9 - - - 3 - - - 3 - - - 6 - - - 2 - - - 6 - - - 2 - - - - Shape distribution - - - dense_gemm_bf16_nt_n4096_k12288 - - - 31 captured workloads | 7 distinct shapes - - - Fixed dimensions: N=4096, K=12288 - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/dense_gemm_bf16_nt_n4096_k4096.svg b/docs/shape-distributions/dense_gemm_bf16_nt_n4096_k4096.svg deleted file mode 100644 index 6b083b1e13b06331dd9218eb14093076f2583a0f..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/dense_gemm_bf16_nt_n4096_k4096.svg +++ /dev/null @@ -1,268 +0,0 @@ - - - - Shape distribution: dense_gemm_bf16_nt_n4096_k4096 - - - - - Shape distribution: dense_gemm_bf16_nt_n4096_k4096 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - 4096 - - - - - - 4125 - - - - - - 6144 - - - - - - 8192 - - - - - - 12288 - - - - - - 16384 - - - - - - 24576 - - - - M — Runtime number of token rows - - - - - - - - - - 0 - - - - - - - - - 2 - - - - - - - - - 4 - - - - - - - - - 6 - - - - - - - - - 8 - - - - - - - - - 10 - - - - Captured workloads (count) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 9 - - - 3 - - - 3 - - - 6 - - - 2 - - - 6 - - - 2 - - - - Shape distribution - - - dense_gemm_bf16_nt_n4096_k4096 - - - 31 captured workloads | 7 distinct shapes - - - Fixed dimensions: N=4096, K=4096 - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_g32.svg b/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_g32.svg new file mode 100644 index 0000000000000000000000000000000000000000..b0e4b09f22db15cf633562bd12ed0b4caaf19779 --- /dev/null +++ b/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_g32.svg @@ -0,0 +1,545 @@ + + + + Shape distribution: grouped_gemm_fp8_fp4_e384_g32 + + + + + Shape distribution: grouped_gemm_fp8_fp4_e384_g32 + image/svg+xml + + + Matplotlib v3.11.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + 5 + + + + + + + + + 10 + + + + + + + + + 15 + + + + + + + + + 20 + + + + + + + + + 25 + + + + + + + + + 30 + + + + + + + + + 35 + + + + Captured workloads (count) + + + + + + + (6, 4608, 5120, 2560, 160) + + + + + + (6, 5120, 2304, 1152, 72) + + + + + + (12, 4608, 5120, 2560, 160) + + + + + + (12, 5120, 2304, 1152, 72) + + + + + + (18, 4608, 5120, 2560, 160) + + + + + + (18, 5120, 2304, 1152, 72) + + + + + + (768, 4608, 5120, 2560, 160) + + + + + + (768, 5120, 2304, 1152, 72) + + + + + + (2688, 4608, 5120, 2560, 160) + + + + + + (2688, 5120, 2304, 1152, 72) + + + + + + (11904, 4608, 5120, 2560, 160) + + + + + + (11904, 5120, 2304, 1152, 72) + + + + + + (14592, 4608, 5120, 2560, 160) + + + + + + (14592, 5120, 2304, 1152, 72) + + + + + + (17664, 4608, 5120, 2560, 160) + + + + + + (17664, 5120, 2304, 1152, 72) + + + + + + (20736, 4608, 5120, 2560, 160) + + + + + + (20736, 5120, 2304, 1152, 72) + + + + + + (24192, 4608, 5120, 2560, 160) + + + + + + (24192, 5120, 2304, 1152, 72) + + + + + + (24576, 4608, 5120, 2560, 160) + + + + + + (24576, 5120, 2304, 1152, 72) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 34 + + + 34 + + + 4 + + + 4 + + + 4 + + + 4 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 3 + + + 3 + + + + Shape distribution + + + grouped_gemm_fp8_fp4_e384_g32 + + + 104 captured workloads | 22 distinct shapes + + + Fixed dimensions: E=384, group_size=32 + + + Runtime shape: (M, N, K, K_packed, K_groups) + + + One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. + + + + + + + + diff --git a/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.svg b/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.svg deleted file mode 100644 index 6200fb4821719c02302d20145cd9c93f010125e7..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.svg +++ /dev/null @@ -1,347 +0,0 @@ - - - - Shape distribution: grouped_gemm_fp8_fp4_e384_n4608_k5120_g32 - - - - - Shape distribution: grouped_gemm_fp8_fp4_e384_n4608_k5120_g32 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - 6 - - - - - - 12 - - - - - - 18 - - - - - - 768 - - - - - - 2688 - - - - - - 11904 - - - - - - 14592 - - - - - - 17664 - - - - - - 20736 - - - - - - 24192 - - - - - - 24576 - - - - M — Runtime number of token rows - - - - - - - - - - 0 - - - - - - - - - 6 - - - - - - - - - 12 - - - - - - - - - 18 - - - - - - - - - 24 - - - - - - - - - 30 - - - - - - - - - 36 - - - - Captured workloads (count) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 34 - - - 4 - - - 4 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 3 - - - - Shape distribution - - - grouped_gemm_fp8_fp4_e384_n4608_k5120_g32 - - - 52 captured workloads | 11 distinct shapes - - - Fixed dimensions: E=384, N=4608, K=5120, K_packed=2560, K_groups=160, group_size=32 - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.svg b/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.svg deleted file mode 100644 index 58dd18613a525704f5838103563be5cdf674c890..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.svg +++ /dev/null @@ -1,347 +0,0 @@ - - - - Shape distribution: grouped_gemm_fp8_fp4_e384_n5120_k2304_g32 - - - - - Shape distribution: grouped_gemm_fp8_fp4_e384_n5120_k2304_g32 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - 6 - - - - - - 12 - - - - - - 18 - - - - - - 768 - - - - - - 2688 - - - - - - 11904 - - - - - - 14592 - - - - - - 17664 - - - - - - 20736 - - - - - - 24192 - - - - - - 24576 - - - - M — Runtime number of token rows - - - - - - - - - - 0 - - - - - - - - - 6 - - - - - - - - - 12 - - - - - - - - - 18 - - - - - - - - - 24 - - - - - - - - - 30 - - - - - - - - - 36 - - - - Captured workloads (count) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 34 - - - 4 - - - 4 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 3 - - - - Shape distribution - - - grouped_gemm_fp8_fp4_e384_n5120_k2304_g32 - - - 52 captured workloads | 11 distinct shapes - - - Fixed dimensions: E=384, N=5120, K=2304, K_packed=1152, K_groups=72, group_size=32 - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/neighborhood_attention_3d_d64.svg b/docs/shape-distributions/neighborhood_attention_3d_d64.svg new file mode 100644 index 0000000000000000000000000000000000000000..1d1f628d4d41500cd80ec5c0080632a5fef67dc1 --- /dev/null +++ b/docs/shape-distributions/neighborhood_attention_3d_d64.svg @@ -0,0 +1,870 @@ + + + + Shape distribution: neighborhood_attention_3d_d64 + + + + + Shape distribution: neighborhood_attention_3d_d64 + image/svg+xml + + + Matplotlib v3.11.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + 1 + + + + Captured workloads (count) + + + + + + + (1, 9, 10, 16, 32) + + + + + + (1, 9, 14, 24, 32) + + + + + + (1, 9, 20, 32, 16) + + + + + + (1, 9, 28, 48, 16) + + + + + + (1, 17, 20, 32, 8) + + + + + + (1, 17, 28, 48, 8) + + + + + + (1, 18, 10, 16, 32) + + + + + + (1, 18, 20, 32, 16) + + + + + + (1, 33, 40, 64, 8) + + + + + + (1, 33, 56, 96, 8) + + + + + + (1, 35, 20, 32, 8) + + + + + + (1, 49, 80, 128, 4) + + + + + + (1, 49, 112, 192, 4) + + + + + + (1, 69, 40, 64, 8) + + + + + + (1, 121, 80, 128, 4) + + + + + + (2, 9, 10, 16, 32) + + + + + + (2, 9, 14, 24, 32) + + + + + + (2, 9, 20, 32, 16) + + + + + + (2, 9, 28, 48, 16) + + + + + + (2, 17, 20, 32, 8) + + + + + + (2, 17, 28, 48, 8) + + + + + + (2, 18, 10, 16, 32) + + + + + + (2, 18, 20, 32, 16) + + + + + + (2, 33, 40, 64, 8) + + + + + + (2, 33, 56, 96, 8) + + + + + + (2, 35, 20, 32, 8) + + + + + + (2, 49, 80, 128, 4) + + + + + + (2, 49, 112, 192, 4) + + + + + + (2, 69, 40, 64, 8) + + + + + + (2, 121, 80, 128, 4) + + + + + + (4, 9, 10, 16, 32) + + + + + + (4, 9, 14, 24, 32) + + + + + + (4, 9, 20, 32, 16) + + + + + + (4, 9, 28, 48, 16) + + + + + + (4, 17, 20, 32, 8) + + + + + + (4, 17, 28, 48, 8) + + + + + + (4, 18, 10, 16, 32) + + + + + + (4, 18, 20, 32, 16) + + + + + + (4, 33, 40, 64, 8) + + + + + + (4, 33, 56, 96, 8) + + + + + + (4, 35, 20, 32, 8) + + + + + + (4, 49, 80, 128, 4) + + + + + + (4, 49, 112, 192, 4) + + + + + + (4, 69, 40, 64, 8) + + + + + + (4, 121, 80, 128, 4) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + + + + Shape distribution + + + neighborhood_attention_3d_d64 + + + 45 captured workloads | 45 distinct shapes + + + Fixed dimensions: D=64 + + + Runtime shape: (B, T, Y, X, H) + + + One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. + + + + + + + + diff --git a/docs/shape-distributions/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.svg b/docs/shape-distributions/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.svg deleted file mode 100644 index 5301b3dee323bee33f830bb3b301d4def8f8c451..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.svg +++ /dev/null @@ -1,258 +0,0 @@ - - - - Shape distribution: neighborhood_attention_3d_h16_d64_kt3_ky7_kx7 - - - - - Shape distribution: neighborhood_attention_3d_h16_d64_kt3_ky7_kx7 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - 0 - - - - - - - - - 1 - - - - Captured workloads (count) - - - - - - - (1, 9, 20, 32) - - - - - - (1, 9, 28, 48) - - - - - - (1, 18, 20, 32) - - - - - - (2, 9, 20, 32) - - - - - - (2, 9, 28, 48) - - - - - - (2, 18, 20, 32) - - - - - - (4, 9, 20, 32) - - - - - - (4, 9, 28, 48) - - - - - - (4, 18, 20, 32) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - - Shape distribution - - - neighborhood_attention_3d_h16_d64_kt3_ky7_kx7 - - - 9 captured workloads | 9 distinct shapes - - - Fixed dimensions: H=16, D=64, Kt=3, Ky=7, Kx=7 - - - Runtime shape: (B, T, Y, X) - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.svg b/docs/shape-distributions/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.svg deleted file mode 100644 index c563b30286f8174c1109024f73e562bae25336eb..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.svg +++ /dev/null @@ -1,258 +0,0 @@ - - - - Shape distribution: neighborhood_attention_3d_h32_d64_kt3_ky7_kx7 - - - - - Shape distribution: neighborhood_attention_3d_h32_d64_kt3_ky7_kx7 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - 0 - - - - - - - - - 1 - - - - Captured workloads (count) - - - - - - - (1, 9, 10, 16) - - - - - - (1, 9, 14, 24) - - - - - - (1, 18, 10, 16) - - - - - - (2, 9, 10, 16) - - - - - - (2, 9, 14, 24) - - - - - - (2, 18, 10, 16) - - - - - - (4, 9, 10, 16) - - - - - - (4, 9, 14, 24) - - - - - - (4, 18, 10, 16) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - - Shape distribution - - - neighborhood_attention_3d_h32_d64_kt3_ky7_kx7 - - - 9 captured workloads | 9 distinct shapes - - - Fixed dimensions: H=32, D=64, Kt=3, Ky=7, Kx=7 - - - Runtime shape: (B, T, Y, X) - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.svg b/docs/shape-distributions/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.svg deleted file mode 100644 index 7edf62cb59b9d027c47a7e77f4730bf96a27c654..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.svg +++ /dev/null @@ -1,258 +0,0 @@ - - - - Shape distribution: neighborhood_attention_3d_h4_d64_kt11_ky11_kx11 - - - - - Shape distribution: neighborhood_attention_3d_h4_d64_kt11_ky11_kx11 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - 0 - - - - - - - - - 1 - - - - Captured workloads (count) - - - - - - - (1, 49, 80, 128) - - - - - - (1, 49, 112, 192) - - - - - - (1, 121, 80, 128) - - - - - - (2, 49, 80, 128) - - - - - - (2, 49, 112, 192) - - - - - - (2, 121, 80, 128) - - - - - - (4, 49, 80, 128) - - - - - - (4, 49, 112, 192) - - - - - - (4, 121, 80, 128) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - - Shape distribution - - - neighborhood_attention_3d_h4_d64_kt11_ky11_kx11 - - - 9 captured workloads | 9 distinct shapes - - - Fixed dimensions: H=4, D=64, Kt=11, Ky=11, Kx=11 - - - Runtime shape: (B, T, Y, X) - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.svg b/docs/shape-distributions/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.svg deleted file mode 100644 index 716b645067e2123477bfdba8c5a3c3ec7d6115a6..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.svg +++ /dev/null @@ -1,411 +0,0 @@ - - - - Shape distribution: neighborhood_attention_3d_h8_d64_kt3_ky5_kx5 - - - - - Shape distribution: neighborhood_attention_3d_h8_d64_kt3_ky5_kx5 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - 0 - - - - - - - - - 1 - - - - Captured workloads (count) - - - - - - - (1, 17, 20, 32) - - - - - - (1, 17, 28, 48) - - - - - - (1, 33, 40, 64) - - - - - - (1, 33, 56, 96) - - - - - - (1, 35, 20, 32) - - - - - - (1, 69, 40, 64) - - - - - - (2, 17, 20, 32) - - - - - - (2, 17, 28, 48) - - - - - - (2, 33, 40, 64) - - - - - - (2, 33, 56, 96) - - - - - - (2, 35, 20, 32) - - - - - - (2, 69, 40, 64) - - - - - - (4, 17, 20, 32) - - - - - - (4, 17, 28, 48) - - - - - - (4, 33, 40, 64) - - - - - - (4, 33, 56, 96) - - - - - - (4, 35, 20, 32) - - - - - - (4, 69, 40, 64) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - - Shape distribution - - - neighborhood_attention_3d_h8_d64_kt3_ky5_kx5 - - - 18 captured workloads | 18 distinct shapes - - - Fixed dimensions: H=8, D=64, Kt=3, Ky=5, Kx=5 - - - Runtime shape: (B, T, Y, X) - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/noncausal_dense_attention_h32.svg b/docs/shape-distributions/noncausal_dense_attention_h32.svg new file mode 100644 index 0000000000000000000000000000000000000000..4156571d4e2702af3f6e34a40cb9c59eb6ecb8d8 --- /dev/null +++ b/docs/shape-distributions/noncausal_dense_attention_h32.svg @@ -0,0 +1,976 @@ + + + + Shape distribution: noncausal_dense_attention_h32 + + + + + Shape distribution: noncausal_dense_attention_h32 + image/svg+xml + + + Matplotlib v3.11.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + 1 + + + + + + + + + 2 + + + + + + + + + 3 + + + + + + + + + 4 + + + + + + + + + 5 + + + + + + + + + 6 + + + + Captured workloads (count) + + + + + + + (1, 51, 51, 64) + + + + + + (1, 51, 1024, 64) + + + + + + (1, 51, 1120, 64) + + + + + + (1, 51, 2352, 64) + + + + + + (1, 126, 126, 64) + + + + + + (1, 126, 1024, 64) + + + + + + (1, 126, 2560, 64) + + + + + + (1, 1120, 51, 64) + + + + + + (1, 1120, 1024, 128) + + + + + + (1, 1120, 1120, 128) + + + + + + (1, 2352, 51, 64) + + + + + + (1, 2352, 1024, 128) + + + + + + (1, 2352, 2352, 128) + + + + + + (1, 2560, 126, 64) + + + + + + (1, 2560, 1024, 128) + + + + + + (1, 2560, 2560, 128) + + + + + + (2, 51, 51, 64) + + + + + + (2, 51, 1024, 64) + + + + + + (2, 51, 1120, 64) + + + + + + (2, 51, 2352, 64) + + + + + + (2, 126, 126, 64) + + + + + + (2, 126, 1024, 64) + + + + + + (2, 126, 2560, 64) + + + + + + (2, 1120, 51, 64) + + + + + + (2, 1120, 1024, 128) + + + + + + (2, 1120, 1120, 128) + + + + + + (2, 2352, 51, 64) + + + + + + (2, 2352, 1024, 128) + + + + + + (2, 2352, 2352, 128) + + + + + + (2, 2560, 126, 64) + + + + + + (2, 2560, 1024, 128) + + + + + + (2, 2560, 2560, 128) + + + + + + (4, 51, 51, 64) + + + + + + (4, 51, 1024, 64) + + + + + + (4, 51, 1120, 64) + + + + + + (4, 51, 2352, 64) + + + + + + (4, 126, 126, 64) + + + + + + (4, 126, 1024, 64) + + + + + + (4, 126, 2560, 64) + + + + + + (4, 1120, 51, 64) + + + + + + (4, 1120, 1024, 128) + + + + + + (4, 1120, 1120, 128) + + + + + + (4, 2352, 51, 64) + + + + + + (4, 2352, 1024, 128) + + + + + + (4, 2352, 2352, 128) + + + + + + (4, 2560, 126, 64) + + + + + + (4, 2560, 1024, 128) + + + + + + (4, 2560, 2560, 128) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 6 + + + 6 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 6 + + + 6 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 6 + + + 6 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + 3 + + + + Shape distribution + + + noncausal_dense_attention_h32 + + + 162 captured workloads | 48 distinct shapes + + + Fixed dimensions: H=32 + + + Runtime shape: (B, Nq, Nk, D) + + + One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. + + + + + + + + diff --git a/docs/shape-distributions/noncausal_dense_attention_h32_d128.svg b/docs/shape-distributions/noncausal_dense_attention_h32_d128.svg deleted file mode 100644 index cd1b4ff9b781b30230da158c34664f44db1c5719..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/noncausal_dense_attention_h32_d128.svg +++ /dev/null @@ -1,433 +0,0 @@ - - - - Shape distribution: noncausal_dense_attention_h32_d128 - - - - - Shape distribution: noncausal_dense_attention_h32_d128 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - 0 - - - - - - - - - 1 - - - - - - - - - 2 - - - - - - - - - 3 - - - - Captured workloads (count) - - - - - - - (1, 1120, 1024) - - - - - - (1, 1120, 1120) - - - - - - (1, 2352, 1024) - - - - - - (1, 2352, 2352) - - - - - - (1, 2560, 1024) - - - - - - (1, 2560, 2560) - - - - - - (2, 1120, 1024) - - - - - - (2, 1120, 1120) - - - - - - (2, 2352, 1024) - - - - - - (2, 2352, 2352) - - - - - - (2, 2560, 1024) - - - - - - (2, 2560, 2560) - - - - - - (4, 1120, 1024) - - - - - - (4, 1120, 1120) - - - - - - (4, 2352, 1024) - - - - - - (4, 2352, 2352) - - - - - - (4, 2560, 1024) - - - - - - (4, 2560, 2560) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - - Shape distribution - - - noncausal_dense_attention_h32_d128 - - - 54 captured workloads | 18 distinct shapes - - - Fixed dimensions: H=32, D=128 - - - Runtime shape: (B, Nq, Nk) - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/docs/shape-distributions/noncausal_dense_attention_h32_d64.svg b/docs/shape-distributions/sparse_mla_prefill_h64_d512.svg similarity index 52% rename from docs/shape-distributions/noncausal_dense_attention_h32_d64.svg rename to docs/shape-distributions/sparse_mla_prefill_h64_d512.svg index 17366e7d1757305cd5bd6828c2443f727ac74a4b..c1805f92c509d3cd91db513af570e2cdcd590de8 100644 --- a/docs/shape-distributions/noncausal_dense_attention_h32_d64.svg +++ b/docs/shape-distributions/sparse_mla_prefill_h64_d512.svg @@ -1,13 +1,13 @@ - - Shape distribution: noncausal_dense_attention_h32_d64 + + Shape distribution: sparse_mla_prefill_h64_d512 - Shape distribution: noncausal_dense_attention_h32_d64 + Shape distribution: sparse_mla_prefill_h64_d512 image/svg+xml @@ -22,8 +22,8 @@ - - - +" clip-path="url(#p7160ae0ea8)" style="fill: none; stroke: #e3e9ef; stroke-width: 0.8; stroke-linecap: square"/> - 0 + 0 - + - 1 + 1 - + - 2 - - - - - - - - - 3 - - - - - - - - - 4 + 2 - - - - - - - 5 - - - - - - - - - 6 - - - - Captured workloads (count) + + Captured workloads (count) - - - (1, 51, 51) + + + (128, 128, 128) - - - (1, 51, 1024) + + + (128, 192, 640) - - - (1, 51, 1120) + + + (128, 256, 640) - - - (1, 51, 2352) + + + (448, 575, 128) - - - (1, 126, 126) + + + (448, 71583, 640) - - - (1, 126, 1024) + + + (448, 142591, 640) - - - (1, 126, 2560) + + + (1984, 2238, 128) - - - (1, 1120, 51) + + + (1984, 37662, 640) - - - (1, 2352, 51) + + + (1984, 73086, 640) - - - (1, 2560, 126) + + + (2432, 2559, 128) - - - (2, 51, 51) + + + (2432, 497471, 640) - - - (2, 51, 1024) + + + (2432, 992383, 640) - - - (2, 51, 1120) + + + (2944, 2944, 128) - - - (2, 51, 2352) + + + (2944, 4416, 640) - - - (2, 126, 126) + + + (2944, 5888, 640) - - - (2, 126, 1024) + + + (3456, 3583, 128) - - - (2, 126, 2560) + + + (3456, 278335, 640) - - - (2, 1120, 51) + + + (3456, 553087, 640) - - - (2, 2352, 51) + + + (4032, 4286, 128) - - - (2, 2560, 126) + + + (4032, 39070, 640) - - - (4, 51, 51) + + + (4032, 73854, 640) - - - (4, 51, 1024) + + + (4096, 4096, 128) - - - (4, 51, 1120) + + + (4096, 4223, 128) - - - (4, 51, 2352) + + + (4096, 6144, 640) - - - (4, 126, 126) + + + (4096, 8192, 640) - - - (4, 126, 1024) + + + (4096, 8319, 640) - - - (4, 126, 2560) + + + (4096, 12415, 640) - - - (4, 1120, 51) + + + (4096, 277247, 640) - - - (4, 2352, 51) - - - - - - (4, 2560, 126) + + + (4096, 550271, 640) - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - +" clip-path="url(#p7160ae0ea8)" style="fill: #2563a6"/> - - - - - - + + + 1 + + + 1 + + + 1 + + + 1 + + + 1 + - 6 + 1 - 6 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 6 + 1 - 6 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 1 - 3 + 2 - 3 + 1 - 3 + 1 - 6 + 1 - 6 + 1 - 3 + 1 - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 - - - 3 + 1 - + Shape distribution - - noncausal_dense_attention_h32_d64 + + sparse_mla_prefill_h64_d512 - - 108 captured workloads | 30 distinct shapes + + 30 captured workloads | 29 distinct shapes - - Fixed dimensions: H=32, D=64 + + Fixed dimensions: H=64, D=512, KVH=1 - - Runtime shape: (B, Nq, Nk) + + Runtime shape: (M, Nkv, TopK) - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. + + One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - + + diff --git a/docs/shape-distributions/sparse_mla_prefill_h64_d512_k128.svg b/docs/shape-distributions/sparse_mla_prefill_h64_d512_k128.svg deleted file mode 100644 index 902233d77b8e0dcc26b2729d871001d43e38f8bc..0000000000000000000000000000000000000000 --- a/docs/shape-distributions/sparse_mla_prefill_h64_d512_k128.svg +++ /dev/null @@ -1,269 +0,0 @@ - - - - Shape distribution: sparse_mla_prefill_h64_d512_k128 - - - - - Shape distribution: sparse_mla_prefill_h64_d512_k128 - image/svg+xml - - - Matplotlib v3.11.2, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - 0 - - - - - - - - - 1 - - - - - - - - - 2 - - - - Captured workloads (count) - - - - - - - (128, 128) - - - - - - (448, 575) - - - - - - (1984, 2238) - - - - - - (2432, 2559) - - - - - - (2944, 2944) - - - - - - (3456, 3583) - - - - - - (4032, 4286) - - - - - - (4096, 4096) - - - - - - (4096, 4223) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 1 - - - 2 - - - - Shape distribution - - - sparse_mla_prefill_h64_d512_k128 - - - 10 captured workloads | 9 distinct shapes - - - Fixed dimensions: H=64, D=512, KVH=1, TopK=128 - - - Runtime shape: (M, Nkv) - - - One bin per exact runtime shape; equal spacing. Counts describe this dataset, not production traffic. - - - - - - - - diff --git a/kda/dense_gemm_bf16_nt.json b/kda/dense_gemm_bf16_nt.json new file mode 100644 index 0000000000000000000000000000000000000000..0c8febe8359a45f5221d88d6c4c8eb060fd0c6db --- /dev/null +++ b/kda/dense_gemm_bf16_nt.json @@ -0,0 +1,30 @@ +{ + "definition": "definitions/gemm/dense_gemm_bf16_nt.json", + "workloads": [ + "workloads/gemm/dense_gemm_bf16_nt.jsonl" + ], + "baseline": "solutions/gemm/sglang_dense_gemm_bf16_nt.json", + "tolerance": { + "atol": 0.005, + "rtol": 0.016, + "required_matched_ratio": null + }, + "group_axis": "M", + "dev": [ + "59e21fcd-7080-5d70-aa46-c4500984dddc", + "ff62c8f2-f87c-586b-b295-7d130dd1a037", + "1ef85b69-b938-5554-b019-1cd333110897", + "bc334c45-f866-5289-891d-4e12672c7e97", + "8bc7ebff-12d1-5ac5-99bb-d18a9cbdb505", + "17f61747-5e4c-5e99-a7c4-f096b147fb60", + "8a5941ee-6c6f-5312-a860-d0b26263e260", + "717bab44-772a-580e-bd31-8f449703fca5", + "f49b336e-43c9-5d0e-a40d-277dc443e7e1", + "90021c8c-9d0c-55ea-857e-e12ad4fe7979" + ], + "timing": { + "warmup": 5, + "iters": 20, + "trials": 2 + } +} diff --git a/kda/dense_gemm_bf16_nt_n12288_k4096.json b/kda/dense_gemm_bf16_nt_n12288_k4096.json deleted file mode 100644 index 74daa47b6e7826c57eab27062ba9e6dae8f198a3..0000000000000000000000000000000000000000 --- a/kda/dense_gemm_bf16_nt_n12288_k4096.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "definition": "definitions/gemm/dense_gemm_bf16_nt_n12288_k4096.json", - "workloads": [ - "workloads/gemm/dense_gemm_bf16_nt_n12288_k4096.jsonl" - ], - "baseline": "solutions/gemm/sglang_dense_gemm_bf16_nt_n12288_k4096.json", - "tolerance": { - "atol": 0.005, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "M", - "dev": [ - "ff62c8f2-f87c-586b-b295-7d130dd1a037", - "8bc7ebff-12d1-5ac5-99bb-d18a9cbdb505", - "717bab44-772a-580e-bd31-8f449703fca5", - "b571dcbc-df9a-515c-90ac-93ba5e03a0dd", - "d89ba855-bc0f-52a5-9825-2f44455164ea", - "6c24a9cf-df14-538e-9876-3431874e3701", - "8c811c7c-db79-50a4-a48d-c1e483e9418b" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 2 - } -} diff --git a/kda/dense_gemm_bf16_nt_n4096_k12288.json b/kda/dense_gemm_bf16_nt_n4096_k12288.json deleted file mode 100644 index 7f6c83a14580b1c43214d3811d0bdfab0d4c8217..0000000000000000000000000000000000000000 --- a/kda/dense_gemm_bf16_nt_n4096_k12288.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "definition": "definitions/gemm/dense_gemm_bf16_nt_n4096_k12288.json", - "workloads": [ - "workloads/gemm/dense_gemm_bf16_nt_n4096_k12288.jsonl" - ], - "baseline": "solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k12288.json", - "tolerance": { - "atol": 0.005, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "M", - "dev": [ - "1ef85b69-b938-5554-b019-1cd333110897", - "17f61747-5e4c-5e99-a7c4-f096b147fb60", - "f49b336e-43c9-5d0e-a40d-277dc443e7e1", - "d26eaeed-136a-5399-999a-baf0a72a8818", - "f53807e9-dd75-5cca-9c00-8cc6f48f3a3a", - "f567a1a4-8780-53bc-bd48-6b7d561d8eb8", - "89b32f3e-3660-5591-8885-48dffb786922" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 2 - } -} diff --git a/kda/dense_gemm_bf16_nt_n4096_k4096.json b/kda/dense_gemm_bf16_nt_n4096_k4096.json deleted file mode 100644 index 05426a55372305c119fa298f5b47f26be0a586f0..0000000000000000000000000000000000000000 --- a/kda/dense_gemm_bf16_nt_n4096_k4096.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "definition": "definitions/gemm/dense_gemm_bf16_nt_n4096_k4096.json", - "workloads": [ - "workloads/gemm/dense_gemm_bf16_nt_n4096_k4096.jsonl" - ], - "baseline": "solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k4096.json", - "tolerance": { - "atol": 0.005, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "M", - "dev": [ - "59e21fcd-7080-5d70-aa46-c4500984dddc", - "bc334c45-f866-5289-891d-4e12672c7e97", - "8a5941ee-6c6f-5312-a860-d0b26263e260", - "90021c8c-9d0c-55ea-857e-e12ad4fe7979", - "4995e0dc-1c17-5f42-9530-b660dce2f09d", - "54ea04a7-b4b5-5279-bed3-34f4ae1cf45f" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 3 - } -} diff --git a/kda/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json b/kda/grouped_gemm_fp8_fp4_e384_g32.json similarity index 59% rename from kda/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json rename to kda/grouped_gemm_fp8_fp4_e384_g32.json index f1178b5badfcba22767d1f754554627701926211..ae006e9a5f2211fd0af6c88e9f022c352a03f85f 100644 --- a/kda/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json +++ b/kda/grouped_gemm_fp8_fp4_e384_g32.json @@ -1,26 +1,26 @@ { - "definition": "definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json", + "definition": "definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_g32.json", "workloads": [ - "workloads/grouped_gemm/grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.jsonl" + "workloads/grouped_gemm/grouped_gemm_fp8_fp4_e384_g32.jsonl" ], - "baseline": "solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json", + "baseline": "solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_g32.json", "tolerance": { "atol": 0.005, "rtol": 0.016, "required_matched_ratio": null }, - "group_axis": "M", + "group_axis": "N", "dev": [ "998dd71e-de7d-52ce-b64b-fd65c93b2c8d", + "3e554d41-bfbe-56cc-a05b-179acb8a60f4", "54cc9aa4-83ff-56e0-830b-a9634ca9a54c", + "9f6a3944-09d7-52ca-abb8-4b01960f3577", "d4b15562-fea6-5caf-ba58-a6ede4195c3f", + "22791a4a-6151-5445-806d-c3793c972208", "edde6f2e-b9cf-5ae4-b99c-4e313bf6199f", + "59c73c1c-f9e6-54f7-b15f-f6384e355503", "669309b6-8328-5a7b-a2ff-dcae81dfd4f8", - "7e7da669-03fe-5ee3-a1cf-b496e7614a56", - "2718713b-be58-55b3-aeb5-e3a38cce6f96", - "630f85b0-9ace-54b6-88a0-08bb930237f1", - "c24c30ba-f971-5480-9756-4906df92201e", - "75452890-0209-5264-bb51-e75d6c5b75c0" + "9972c26f-b34d-56a2-a043-a50684289467" ], "timing": { "warmup": 5, diff --git a/kda/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json b/kda/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json deleted file mode 100644 index f0e40ca46082706f5797dc722047366be8a6e4ca..0000000000000000000000000000000000000000 --- a/kda/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "definition": "definitions/grouped_gemm/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json", - "workloads": [ - "workloads/grouped_gemm/grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.jsonl" - ], - "baseline": "solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json", - "tolerance": { - "atol": 0.005, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "M", - "dev": [ - "3e554d41-bfbe-56cc-a05b-179acb8a60f4", - "9f6a3944-09d7-52ca-abb8-4b01960f3577", - "22791a4a-6151-5445-806d-c3793c972208", - "59c73c1c-f9e6-54f7-b15f-f6384e355503", - "9972c26f-b34d-56a2-a043-a50684289467", - "7db1bb57-9034-57c0-95b3-2a45bafb1409", - "852cde60-0529-5e42-83ce-0ebfe07a7c0e", - "acae606c-4112-548b-b870-cec7314a3f82", - "a51a750b-1fee-5798-ac8c-6254b2d4d4f8", - "b1fb4669-1248-56ff-978d-f610aa47ea2a" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 1 - } -} diff --git a/kda/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json b/kda/neighborhood_attention_3d_d64.json similarity index 53% rename from kda/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json rename to kda/neighborhood_attention_3d_d64.json index 1c3338d98effe8508893a0255ab8064ee3b2e954..0a43676775e8be2e954f727bfaef5cfc09d8e639 100644 --- a/kda/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json +++ b/kda/neighborhood_attention_3d_d64.json @@ -1,26 +1,30 @@ { - "definition": "definitions/neighborhood_attention/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json", + "definition": "definitions/neighborhood_attention/neighborhood_attention_3d_d64.json", "workloads": [ - "workloads/neighborhood_attention/neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.jsonl" + "workloads/neighborhood_attention/neighborhood_attention_3d_d64.jsonl" ], - "baseline": "solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json", + "baseline": "solutions/neighborhood_attention/sglang_neighborhood_attention_3d_d64.json", "tolerance": { - "atol": 0.005, + "atol": 0.006, "rtol": 0.016, "required_matched_ratio": null }, - "group_axis": "B", + "group_axis": "H", "dev": [ "503c803e-a524-562c-a980-daf2bad42368", + "ba0a26ae-0688-5564-a415-b40944dc1d2c", + "95130464-5fd0-59c3-81b3-5759675a89f0", + "4bc37940-bacd-56bd-b86c-51279edc24de", "d30833f7-16da-581b-84f4-66007ed77ae3", + "572aaf07-1f53-5279-b4d3-b8cc9ae232c8", + "3eaae3b6-58d1-5ad4-94ba-ecf658aa5687", + "bb8a6ae2-035a-51c1-b6a2-2aff689b88f1", "d9ce70f4-0314-530a-a4d3-cb359744d16b", - "01fc8439-9178-5ae6-b4a9-8a81d737d807", - "867a9c5a-089e-5005-bb09-db1354c3e298", - "2882e24d-2dc3-5644-8e69-8c22c2b13dda" + "5b35db05-d838-5b18-9415-11dcf35d9cff" ], "timing": { "warmup": 5, "iters": 20, - "trials": 3 + "trials": 1 } } diff --git a/kda/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json b/kda/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json deleted file mode 100644 index 1d5272fd60027d4b3f61b2b3492364746d22ac3d..0000000000000000000000000000000000000000 --- a/kda/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "definition": "definitions/neighborhood_attention/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json", - "workloads": [ - "workloads/neighborhood_attention/neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.jsonl" - ], - "baseline": "solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json", - "tolerance": { - "atol": 0.005, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "B", - "dev": [ - "ba0a26ae-0688-5564-a415-b40944dc1d2c", - "572aaf07-1f53-5279-b4d3-b8cc9ae232c8", - "5b35db05-d838-5b18-9415-11dcf35d9cff", - "d6c3cd98-bcbb-5014-96a6-e228b4157aab", - "e8f46b73-bd40-55e5-96d0-32dc0199b4f1", - "7a7c903b-01cd-5e52-a7ad-610af7f4b4d4" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 3 - } -} diff --git a/kda/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json b/kda/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json deleted file mode 100644 index 256d5c19696826bd42f05b64959edb7f4bcec977..0000000000000000000000000000000000000000 --- a/kda/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "definition": "definitions/neighborhood_attention/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json", - "workloads": [ - "workloads/neighborhood_attention/neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.jsonl" - ], - "baseline": "solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json", - "tolerance": { - "atol": 0.006, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "B", - "dev": [ - "4bc37940-bacd-56bd-b86c-51279edc24de", - "bb8a6ae2-035a-51c1-b6a2-2aff689b88f1", - "f0fe22fd-bde6-5d95-b486-a1233f5342de", - "3aacba35-3f6d-5c90-8c63-a78396ea8d44", - "b188ec47-6dff-5a21-a6fb-1f0d4431ee7a" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 1 - } -} diff --git a/kda/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json b/kda/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json deleted file mode 100644 index b5852776041636a679d3c710d37bae4420e2e52c..0000000000000000000000000000000000000000 --- a/kda/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "definition": "definitions/neighborhood_attention/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json", - "workloads": [ - "workloads/neighborhood_attention/neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.jsonl" - ], - "baseline": "solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json", - "tolerance": { - "atol": 0.005, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "B", - "dev": [ - "95130464-5fd0-59c3-81b3-5759675a89f0", - "3eaae3b6-58d1-5ad4-94ba-ecf658aa5687", - "65bd58a7-ae16-5b36-82e1-7c801c494fc3", - "d25c33ca-dcd8-58c5-b326-9a5e77c4d857", - "fdcc196f-3464-5db0-b64f-f2e29fe569fa", - "b573c4cd-8e41-5e92-af6a-362e7b16f0a1", - "1a60d4e5-bf8d-509a-aa67-96eb03bd8e7e", - "6ed37cda-b6d1-57c6-bbd2-5ef073b679e1", - "9866c54d-a157-5fdd-89d1-84af0fb714e6", - "0253ef4d-943e-5839-9cb0-8d384be40c67" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 1 - } -} diff --git a/kda/noncausal_dense_attention_h32_d64.json b/kda/noncausal_dense_attention_h32.json similarity index 60% rename from kda/noncausal_dense_attention_h32_d64.json rename to kda/noncausal_dense_attention_h32.json index 7fddf7d26348443ff91f2f938be5f0c56846513e..822256f1212a9c0bfaaac81da6ddde08f9655912 100644 --- a/kda/noncausal_dense_attention_h32_d64.json +++ b/kda/noncausal_dense_attention_h32.json @@ -1,26 +1,26 @@ { - "definition": "definitions/dense_attention/noncausal_dense_attention_h32_d64.json", + "definition": "definitions/dense_attention/noncausal_dense_attention_h32.json", "workloads": [ - "workloads/dense_attention/noncausal_dense_attention_h32_d64.jsonl" + "workloads/dense_attention/noncausal_dense_attention_h32.jsonl" ], - "baseline": "solutions/dense_attention/sglang_noncausal_dense_attention_h32_d64.json", + "baseline": "solutions/dense_attention/sglang_noncausal_dense_attention_h32.json", "tolerance": { "atol": 0.005, "rtol": 0.016, "required_matched_ratio": 0.9999 }, - "group_axis": "Nq", + "group_axis": "D", "dev": [ "8867a364-4a86-5118-aeeb-e8adc8419445", + "43bb2e12-3ee6-5e23-9192-040a8405a7ae", "fe5368a5-d8b1-53b5-a6c7-24893c1a525a", + "b76a8861-ae59-5f3b-92b4-c02019ca4941", "b19d6db5-9600-536d-8f4c-543dba8b95d0", + "82872221-aef0-5c8a-83c1-8c116c94a80b", "2f9ae066-bf34-53c8-bb42-abe64c739e10", + "a34594b2-2c8d-58cd-b443-630151fec624", "12c50ef4-b8c1-5f90-88e3-8721a5aa5c70", - "9182253a-2949-52dd-8da2-d2ebea4c91fc", - "365db76e-b098-5d94-84a6-2e0791f4ea86", - "67d71bcc-ecf0-57a2-8af2-2de9f591c6e0", - "475965cf-cbad-5baa-b685-479860697719", - "efa942e5-4b8c-5cb5-9554-232fa3b7b889" + "27af7d20-3b72-5881-ab32-4a3ab8d2cdd0" ], "timing": { "warmup": 5, diff --git a/kda/noncausal_dense_attention_h32_d128.json b/kda/noncausal_dense_attention_h32_d128.json deleted file mode 100644 index 600befaacc4e12440176ff546850ecbc121fc401..0000000000000000000000000000000000000000 --- a/kda/noncausal_dense_attention_h32_d128.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "definition": "definitions/dense_attention/noncausal_dense_attention_h32_d128.json", - "workloads": [ - "workloads/dense_attention/noncausal_dense_attention_h32_d128.jsonl" - ], - "baseline": "solutions/dense_attention/sglang_noncausal_dense_attention_h32_d128.json", - "tolerance": { - "atol": 0.005, - "rtol": 0.016, - "required_matched_ratio": 0.9999 - }, - "group_axis": "Nq", - "dev": [ - "43bb2e12-3ee6-5e23-9192-040a8405a7ae", - "b76a8861-ae59-5f3b-92b4-c02019ca4941", - "82872221-aef0-5c8a-83c1-8c116c94a80b", - "a34594b2-2c8d-58cd-b443-630151fec624", - "27af7d20-3b72-5881-ab32-4a3ab8d2cdd0", - "06131807-accf-5812-a37b-8f9ae948e0f5", - "6e7f3c9e-ea8c-519d-b268-8c1b5c19a937", - "080b6a8e-f168-5131-9f1f-a4eeaf37cbbf", - "6ca03b08-4582-5408-8c48-6f4b30225045", - "919246a0-b394-5215-b9c0-afda319ecefa" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 1 - } -} diff --git a/kda/sparse_mla_prefill_h64_d512_k128.json b/kda/sparse_mla_prefill_h64_d512.json similarity index 60% rename from kda/sparse_mla_prefill_h64_d512_k128.json rename to kda/sparse_mla_prefill_h64_d512.json index 2388f2c5e359b9d457c88c2aecdb0bbebe74632c..81791993930f735cc02230a684e484a40038ac16 100644 --- a/kda/sparse_mla_prefill_h64_d512_k128.json +++ b/kda/sparse_mla_prefill_h64_d512.json @@ -1,22 +1,26 @@ { - "definition": "definitions/sparse_mla/sparse_mla_prefill_h64_d512_k128.json", + "definition": "definitions/sparse_mla/sparse_mla_prefill_h64_d512.json", "workloads": [ - "workloads/sparse_mla/sparse_mla_prefill_h64_d512_k128.jsonl" + "workloads/sparse_mla/sparse_mla_prefill_h64_d512.jsonl" ], - "baseline": "solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k128.json", + "baseline": "solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512.json", "tolerance": { - "atol": 0.005, + "atol": 0.006, "rtol": 0.016, "required_matched_ratio": null }, - "group_axis": "M", + "group_axis": "TopK", "dev": [ "63072e3a-2bf9-5eed-bc07-c48f323b15d4", + "d9165058-a73b-5e77-8e61-5f92a8458b70", "d1602ca9-fae0-56ef-9bb2-a408fe19a22b", + "546bcf0e-32bd-5dd8-9ea9-14c58e6a52c9", "aa1f4b6e-c84d-5723-8007-090ffa76004f", + "495000a6-c8b0-5057-99f4-88ed4a96a4a1", "ec18845f-e1bb-5eba-9649-3d90d470ce9b", + "0b988a7a-2b52-5f49-8c99-eaa373baf797", "c6c6e0c9-61e2-505e-831a-fef983c9dad9", - "d1c89357-aa68-53ac-b13f-62d900007022" + "52560299-af35-5351-a4f0-cdcf97141e87" ], "timing": { "warmup": 5, diff --git a/kda/sparse_mla_prefill_h64_d512_k640.json b/kda/sparse_mla_prefill_h64_d512_k640.json deleted file mode 100644 index 04c38942f635906eed269ec55ce4e2591505ed75..0000000000000000000000000000000000000000 --- a/kda/sparse_mla_prefill_h64_d512_k640.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "definition": "definitions/sparse_mla/sparse_mla_prefill_h64_d512_k640.json", - "workloads": [ - "workloads/sparse_mla/sparse_mla_prefill_h64_d512_k640.jsonl" - ], - "baseline": "solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k640.json", - "tolerance": { - "atol": 0.006, - "rtol": 0.016, - "required_matched_ratio": null - }, - "group_axis": "M", - "dev": [ - "d9165058-a73b-5e77-8e61-5f92a8458b70", - "546bcf0e-32bd-5dd8-9ea9-14c58e6a52c9", - "495000a6-c8b0-5057-99f4-88ed4a96a4a1", - "0b988a7a-2b52-5f49-8c99-eaa373baf797", - "52560299-af35-5351-a4f0-cdcf97141e87", - "42f3b7ee-5af7-5dd1-bf58-71493057d3f5", - "c0332d0e-dddb-5656-a8c7-0ced515dbbfa", - "6385d1ff-f172-5c14-b73d-1c2b5943f045", - "7fd83f55-ee1c-5bec-b886-047096621576", - "b1ee31d7-328c-59af-a60c-189c90e2d248" - ], - "timing": { - "warmup": 5, - "iters": 20, - "trials": 3 - } -} diff --git a/solutions/dense_attention/fa4_noncausal_h32.json b/solutions/dense_attention/fa4_noncausal_h32.json new file mode 100644 index 0000000000000000000000000000000000000000..6943b05f221062bacc44fccceb558294fa38172d --- /dev/null +++ b/solutions/dense_attention/fa4_noncausal_h32.json @@ -0,0 +1,26 @@ +{ + "name": "fa4_noncausal_h32", + "definition": "noncausal_dense_attention_h32", + "author": "inference-kernels", + "description": "FlashAttention 4 CuTe DSL without a mask. For the dimension-64 audio inputs it computes in FP16 to reduce probability-rounding error and returns BF16; all conversions are included in the call. Merged by KDA from the per-shape solutions when the per-shape definitions were merged. Upstream: https://github.com/Dao-AILab/flash-attention (BSD-3-Clause).", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B200" + ], + "entry_point": "main.py::run", + "dependencies": [ + "torch>=2.11", + "flash-attn-4==4.0.0b35", + "nvidia-cutlass-dsl[cu13]==4.7.1", + "quack-kernels==0.6.5" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "import torch\nfrom flash_attn.cute.interface import flash_attn_func\n\ndef run(Q, K, V, sm_scale):\n if Q.shape[-1] == 64:\n output = flash_attn_func(Q.to(torch.float16), K.to(torch.float16), V.to(torch.float16), softmax_scale=sm_scale)[0]\n return output.to(Q.dtype)\n return flash_attn_func(Q, K, V, softmax_scale=sm_scale)[0]\n" + } + ] +} diff --git a/solutions/dense_attention/fa4_noncausal_h32_d128.json b/solutions/dense_attention/fa4_noncausal_h32_d128.json deleted file mode 100644 index daf5737df99af46f9dcce0919d098c34f100dbe0..0000000000000000000000000000000000000000 --- a/solutions/dense_attention/fa4_noncausal_h32_d128.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "fa4_noncausal_h32_d128", - "definition": "noncausal_dense_attention_h32_d128", - "author": "inference-kernels", - "description": "FlashAttention 4 CuTe DSL with a runtime Boolean visibility mask. No assumption about mask contents. Upstream: https://github.com/Dao-AILab/flash-attention (BSD-3-Clause).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "flash-attn-4==4.0.0b35", "nvidia-cutlass-dsl[cu13]==4.7.1", "quack-kernels==0.6.5"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "import cutlass\nimport cutlass.cute as cute\nfrom flash_attn.cute import utils\nfrom flash_attn.cute.interface import flash_attn_func\n\n@cute.jit\ndef visible(batch, head, m_idx, n_idx, seqlen_info, aux_tensors):\n m = cutlass.min(m_idx[0], seqlen_info.seqlen_q - 1)\n n = cutlass.min(n_idx[0], seqlen_info.seqlen_k - 1)\n return utils.scalar_to_ssa(aux_tensors[0][batch[0], head[0], m, n], cutlass.Boolean)\n\ndef run(Q, K, V, mask, sm_scale):\n return flash_attn_func(Q, K, V, softmax_scale=sm_scale, mask_mod=visible, aux_tensors=[mask])[0]\n"}] -} diff --git a/solutions/dense_attention/fa4_noncausal_h32_d64.json b/solutions/dense_attention/fa4_noncausal_h32_d64.json deleted file mode 100644 index f36898e5453880438bad659234c6ced78c651367..0000000000000000000000000000000000000000 --- a/solutions/dense_attention/fa4_noncausal_h32_d64.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "fa4_noncausal_h32_d64", - "definition": "noncausal_dense_attention_h32_d64", - "author": "inference-kernels", - "description": "FlashAttention 4 CuTe DSL with a runtime Boolean visibility mask. Converts the captured normalized audio-attention inputs to FP16 to reduce probability-rounding error, and returns BF16. All conversions are included in the call. Upstream: https://github.com/Dao-AILab/flash-attention (BSD-3-Clause).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "flash-attn-4==4.0.0b35", "nvidia-cutlass-dsl[cu13]==4.7.1", "quack-kernels==0.6.5"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "import torch\nimport cutlass\nimport cutlass.cute as cute\nfrom flash_attn.cute import utils\nfrom flash_attn.cute.interface import flash_attn_func\n\n@cute.jit\ndef visible(batch, head, m_idx, n_idx, seqlen_info, aux_tensors):\n m = cutlass.min(m_idx[0], seqlen_info.seqlen_q - 1)\n n = cutlass.min(n_idx[0], seqlen_info.seqlen_k - 1)\n return utils.scalar_to_ssa(aux_tensors[0][batch[0], head[0], m, n], cutlass.Boolean)\n\ndef run(Q, K, V, mask, sm_scale):\n output = flash_attn_func(Q.to(torch.float16), K.to(torch.float16), V.to(torch.float16), softmax_scale=sm_scale, mask_mod=visible, aux_tensors=[mask])[0]\n return output.to(Q.dtype)\n"}] -} diff --git a/solutions/dense_attention/sglang_noncausal_dense_attention_h32.json b/solutions/dense_attention/sglang_noncausal_dense_attention_h32.json new file mode 100644 index 0000000000000000000000000000000000000000..fe9e6d1778d82a67df625bc93c9d9df0390aba20 --- /dev/null +++ b/solutions/dense_attention/sglang_noncausal_dense_attention_h32.json @@ -0,0 +1,23 @@ +{ + "name": "sglang_noncausal_dense_attention_h32", + "definition": "noncausal_dense_attention_h32", + "author": "kda", + "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B300" + ], + "entry_point": "main.py::run", + "dependencies": [ + "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for noncausal_dense_attention_h32: LTX-2.5 audio (D=64) and\nvideo (D=128) attention.\n\nSGLang diffusion runtime at commit 684fea8a, paths under\npython/sglang/multimodal_gen/runtime/: LTX2Attention.forward\n(models/dits/ltx_2.py:997-1006) calls ``self.attn(q, k, v, attn_mask=mask)``\non [B, N, H, D] Q/K/V for the video ``attn1`` (USPAttention, :857-871) and\n``attn2`` (LocalAttention, :841-855) and the audio ``audio_attn1``,\n``audio_to_video_attn`` and ``video_to_audio_attn`` (USPAttention or\nLocalAttention) and ``audio_attn2`` (LocalAttention); every layer has\nsoftmax_scale=None -> head_size**-0.5 and allow_cudnn_sdp=True. On one GPU\nthe mask is None, except for the prompt cross attentions' all-ones mask,\nwhose additive mask is exactly zero; the definition has no mask.\n\nUSPAttention.forward (layers/attention/layer.py:1466-1468) or\nLocalAttention.forward (:799) -> SDPAImpl.forward\n(layers/attention/backends/sdpa.py:76-114): one SDPA call under\n``sdpa_kernel(_PYTORCH_DEFAULT_CUDA_SDP_BACKENDS, set_priority=True)``, which\ntries cuDNN, Flash and memory-efficient attention before math.\n\nSDPAImpl returns a [B, Nq, H, D] transpose of the SDPA output;\n``.contiguous().detach()`` hands it over dense, without a copy when the kernel\nwrote it sequence-major.\n\"\"\"\n\nfrom sglang.multimodal_gen.runtime.layers.attention.backends.sdpa import SDPAImpl\n\n\ndef prepare(Q, K, V, sm_scale):\n impl = SDPAImpl(\n num_heads=Q.shape[2],\n head_size=Q.shape[3],\n causal=False,\n softmax_scale=sm_scale,\n num_kv_heads=K.shape[2],\n allow_cudnn_sdp=True,\n )\n\n def launch():\n return impl.forward(Q, K, V, None).contiguous().detach()\n\n return launch\n\n\ndef run(Q, K, V, sm_scale):\n return prepare(Q, K, V, sm_scale)()\n" + } + ] +} diff --git a/solutions/dense_attention/sglang_noncausal_dense_attention_h32_d128.json b/solutions/dense_attention/sglang_noncausal_dense_attention_h32_d128.json deleted file mode 100644 index c52c16e69ee6de0ed92ba5e9813477f0c1be79df..0000000000000000000000000000000000000000 --- a/solutions/dense_attention/sglang_noncausal_dense_attention_h32_d128.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_noncausal_dense_attention_h32_d128", - "definition": "noncausal_dense_attention_h32_d128", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for noncausal_dense_attention_h32_d128: LTX-2.5 video attention.\n\nSGLang diffusion runtime at commit 684fea8a, paths under\npython/sglang/multimodal_gen/runtime/: LTX2Attention.forward\n(models/dits/ltx_2.py:997-1006) calls ``self.attn(q, k, v, attn_mask=mask)``\non [B, N, H, D] Q/K/V for ``attn1`` (USPAttention, :857-871) and ``attn2``\n(LocalAttention, :841-855); every layer has\nsoftmax_scale=None -> head_size**-0.5 and allow_cudnn_sdp=True. On one GPU the\nmask is None, except for ``attn2``'s all-ones prompt mask, whose additive\nmask is exactly zero; the definition has no mask.\n\nUSPAttention.forward (layers/attention/layer.py:1466-1468) or\nLocalAttention.forward (:799) -> SDPAImpl.forward\n(layers/attention/backends/sdpa.py:76-114): one SDPA call under\n``sdpa_kernel(_PYTORCH_DEFAULT_CUDA_SDP_BACKENDS, set_priority=True)``, which\ntries cuDNN, Flash and memory-efficient attention before math.\n\nSDPAImpl returns a [B, Nq, H, D] transpose of the SDPA output;\n``.contiguous().detach()`` hands it over dense, without a copy when the kernel\nwrote it sequence-major.\n\"\"\"\n\nfrom sglang.multimodal_gen.runtime.layers.attention.backends.sdpa import SDPAImpl\n\n\ndef prepare(Q, K, V, sm_scale):\n impl = SDPAImpl(\n num_heads=Q.shape[2],\n head_size=Q.shape[3],\n causal=False,\n softmax_scale=sm_scale,\n num_kv_heads=K.shape[2],\n allow_cudnn_sdp=True,\n )\n\n def launch():\n return impl.forward(Q, K, V, None).contiguous().detach()\n\n return launch\n\n\ndef run(Q, K, V, sm_scale):\n return prepare(Q, K, V, sm_scale)()\n" - } - ] -} diff --git a/solutions/dense_attention/sglang_noncausal_dense_attention_h32_d64.json b/solutions/dense_attention/sglang_noncausal_dense_attention_h32_d64.json deleted file mode 100644 index e8da2b944acabada715ac87e0b14d36327f890ed..0000000000000000000000000000000000000000 --- a/solutions/dense_attention/sglang_noncausal_dense_attention_h32_d64.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_noncausal_dense_attention_h32_d64", - "definition": "noncausal_dense_attention_h32_d64", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for noncausal_dense_attention_h32_d64: LTX-2.5 audio attention.\n\nSGLang diffusion runtime at commit 684fea8a, paths under\npython/sglang/multimodal_gen/runtime/: LTX2Attention.forward\n(models/dits/ltx_2.py:997-1006) calls ``self.attn(q, k, v, attn_mask=mask)``\non [B, N, H, D] Q/K/V for ``audio_attn1``, ``audio_to_video_attn`` and\n``video_to_audio_attn`` (USPAttention, :857-871, or LocalAttention) and\n``audio_attn2`` (LocalAttention, :841-855); every layer has\nsoftmax_scale=None -> head_size**-0.5 and allow_cudnn_sdp=True. On one GPU the\nmask is None, except for ``audio_attn2``'s all-ones prompt mask, whose additive\nmask is exactly zero; the definition has no mask.\n\nUSPAttention.forward (layers/attention/layer.py:1466-1468) or\nLocalAttention.forward (:799) -> SDPAImpl.forward\n(layers/attention/backends/sdpa.py:76-114): one SDPA call under\n``sdpa_kernel(_PYTORCH_DEFAULT_CUDA_SDP_BACKENDS, set_priority=True)``, which\ntries cuDNN, Flash and memory-efficient attention before math.\n\nSDPAImpl returns a [B, Nq, H, D] transpose of the SDPA output;\n``.contiguous().detach()`` hands it over dense, without a copy when the kernel\nwrote it sequence-major.\n\"\"\"\n\nfrom sglang.multimodal_gen.runtime.layers.attention.backends.sdpa import SDPAImpl\n\n\ndef prepare(Q, K, V, sm_scale):\n impl = SDPAImpl(\n num_heads=Q.shape[2],\n head_size=Q.shape[3],\n causal=False,\n softmax_scale=sm_scale,\n num_kv_heads=K.shape[2],\n allow_cudnn_sdp=True,\n )\n\n def launch():\n return impl.forward(Q, K, V, None).contiguous().detach()\n\n return launch\n\n\ndef run(Q, K, V, sm_scale):\n return prepare(Q, K, V, sm_scale)()\n" - } - ] -} diff --git a/solutions/gemm/deepgemm_bf16.json b/solutions/gemm/deepgemm_bf16.json new file mode 100644 index 0000000000000000000000000000000000000000..6b29ac0f65f42f8c9664e16b4741e8df6cc59c07 --- /dev/null +++ b/solutions/gemm/deepgemm_bf16.json @@ -0,0 +1,24 @@ +{ + "name": "deepgemm_bf16", + "definition": "dense_gemm_bf16_nt", + "author": "inference-kernels", + "description": "DeepGEMM Blackwell BF16 GEMM with FP32 accumulation, for every (N, K). Selected by local B200 comparison against FlashInfer CUTLASS, cuTile, and small-row CuTe DSL; the CuTe DSL branch for 1-4 or 32 rows went with those rows. Merged by KDA from the per-shape solutions when the per-shape definitions were merged. Upstream: https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d (MIT).", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B200" + ], + "entry_point": "main.py::run", + "dependencies": [ + "torch>=2.11", + "deep-gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@1fc9c66975ee322d628b3288534ca0e9ce12a64d" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "import torch\nimport deep_gemm\n\ndef run(A, W):\n output = torch.empty((A.shape[0], W.shape[0]), dtype=torch.bfloat16, device=A.device)\n deep_gemm.bf16_gemm_nt(A, W, output)\n return output\n" + } + ] +} diff --git a/solutions/gemm/deepgemm_bf16_n12288_k4096.json b/solutions/gemm/deepgemm_bf16_n12288_k4096.json deleted file mode 100644 index c1200e57b64adbe807576f292a122960b0738a77..0000000000000000000000000000000000000000 --- a/solutions/gemm/deepgemm_bf16_n12288_k4096.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "deepgemm_bf16_n12288_k4096", - "definition": "dense_gemm_bf16_nt_n12288_k4096", - "author": "inference-kernels", - "description": "DeepGEMM Blackwell BF16 GEMM with FP32 accumulation. Selected by local B200 comparison against FlashInfer CUTLASS, cuTile, and small-row CuTe DSL. Upstream: https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "deep-gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@1fc9c66975ee322d628b3288534ca0e9ce12a64d"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "import torch\nimport deep_gemm\n\ndef run(A, W):\n output = torch.empty((A.shape[0], W.shape[0]), dtype=torch.bfloat16, device=A.device)\n deep_gemm.bf16_gemm_nt(A, W, output)\n return output\n"}] -} diff --git a/solutions/gemm/deepgemm_bf16_n4096_k12288.json b/solutions/gemm/deepgemm_bf16_n4096_k12288.json deleted file mode 100644 index 7b5660eda40a624135e4eb7383adb8f4a59b8118..0000000000000000000000000000000000000000 --- a/solutions/gemm/deepgemm_bf16_n4096_k12288.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "deepgemm_bf16_n4096_k12288", - "definition": "dense_gemm_bf16_nt_n4096_k12288", - "author": "inference-kernels", - "description": "DeepGEMM Blackwell BF16 GEMM with FP32 accumulation. Selected by local B200 comparison against FlashInfer CUTLASS, cuTile, and small-row CuTe DSL. Upstream: https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "deep-gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@1fc9c66975ee322d628b3288534ca0e9ce12a64d"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "import torch\nimport deep_gemm\n\ndef run(A, W):\n output = torch.empty((A.shape[0], W.shape[0]), dtype=torch.bfloat16, device=A.device)\n deep_gemm.bf16_gemm_nt(A, W, output)\n return output\n"}] -} diff --git a/solutions/gemm/hybrid_bf16_n4096_k4096.json b/solutions/gemm/hybrid_bf16_n4096_k4096.json deleted file mode 100644 index 6e03ae0ebb992f6ab2bcf9f1f8b0d36b389b237c..0000000000000000000000000000000000000000 --- a/solutions/gemm/hybrid_bf16_n4096_k4096.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "hybrid_bf16_n4096_k4096", - "definition": "dense_gemm_bf16_nt_n4096_k4096", - "author": "inference-kernels", - "description": "Shape-based B200 dispatch between FlashInfer CuTe DSL for 1-4 or 32 rows and DeepGEMM otherwise. Selected by local comparisons on actual captured shapes. Upstreams: https://github.com/flashinfer-ai/flashinfer (Apache-2.0), https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "flashinfer-python==0.7.2", "nvidia-cutlass-dsl[cu13]==4.7.1", "deep-gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@1fc9c66975ee322d628b3288534ca0e9ce12a64d"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "import torch\nimport deep_gemm\nfrom flashinfer.gemm import mm_bf16\n\ndef run(A, W):\n rows = A.shape[0]\n if rows <= 4 or rows == 32:\n return mm_bf16(A, W.T, backend='cute-dsl')\n output = torch.empty((rows, W.shape[0]), dtype=torch.bfloat16, device=A.device)\n deep_gemm.bf16_gemm_nt(A, W, output)\n return output\n"}] -} diff --git a/solutions/gemm/sglang_dense_gemm_bf16_nt.json b/solutions/gemm/sglang_dense_gemm_bf16_nt.json new file mode 100644 index 0000000000000000000000000000000000000000..ac87a627b8a0a05812804f554655fa865a593cdf --- /dev/null +++ b/solutions/gemm/sglang_dense_gemm_bf16_nt.json @@ -0,0 +1,23 @@ +{ + "name": "sglang_dense_gemm_bf16_nt", + "definition": "dense_gemm_bf16_nt", + "author": "kda", + "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B300" + ], + "entry_point": "main.py::run", + "dependencies": [ + "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for dense_gemm_bf16_nt: C = A @ W.T in BF16.\n\nSGLang diffusion runtime at commit 684fea8a, paths under\npython/sglang/multimodal_gen/runtime/. Every Qwen-Image-2.1 projection below\nends in UnquantizedLinearMethod.apply (layers/linear.py:203) ->\napply_unquantized_linear(x, weight, None) (:155) -> F.linear(x, weight, None)\n(:169):\n- (N, K) = (4096, 4096): ``attn.to_q`` ColumnParallelLinear\n (models/dits/qwen_image21.py:369, called at :487/:723) and ``attn.to_out.0``\n RowParallelLinear (:380, called at :741), via layers/linear.py:518/:1185.\n The nn.Linear layers ``txt_in.in_layer`` (:294) and\n ``timestep_embedder.linear_2`` (:308) issue the same F.linear call.\n- (12288, 4096): ``img_mlp.gate_layer`` ColumnParallelLinear (:331, called at\n :347), via layers/linear.py:518. The batch-1 packed Q/K/V projection,\n torch.mm(x.view(seq, C), packed.t(), out=buffer[0, prefix:]) (:579), gives\n the same bytes.\n- (4096, 12288): ``img_mlp.out`` RowParallelLinear (:338, called at\n :356/:360), via layers/linear.py:1185.\n\nAdapter: none. A is SGLang's x as [M, K] rows (the engine's [1, M, K]\nactivation gives the same bytes), W is layer.weight, bias is None. The\nworker's numerics policy (utils/numerics_policy.py:33, applied at\nmanagers/gpu_worker.py:420 with the server_args.py:510-511 defaults) keeps\nPyTorch's defaults, so nothing is set here.\n\"\"\"\n\nfrom sglang.multimodal_gen.runtime.layers.linear import apply_unquantized_linear\n\n\ndef run(A, W):\n return apply_unquantized_linear(A, W, None)\n" + } + ] +} diff --git a/solutions/gemm/sglang_dense_gemm_bf16_nt_n12288_k4096.json b/solutions/gemm/sglang_dense_gemm_bf16_nt_n12288_k4096.json deleted file mode 100644 index 134fc9835c310d98025af4e8114786fec1bd1cdf..0000000000000000000000000000000000000000 --- a/solutions/gemm/sglang_dense_gemm_bf16_nt_n12288_k4096.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_dense_gemm_bf16_nt_n12288_k4096", - "definition": "dense_gemm_bf16_nt_n12288_k4096", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for dense_gemm_bf16_nt_n12288_k4096: C = A @ W.T in BF16.\n\nSGLang diffusion runtime at commit 684fea8a, paths under\npython/sglang/multimodal_gen/runtime/: the Qwen-Image-2.1 ``img_mlp.gate_layer``\nColumnParallelLinear (models/dits/qwen_image21.py:331, called at :347) ->\nColumnParallelLinear.forward (layers/linear.py:518) ->\nUnquantizedLinearMethod.apply (:203) -> apply_unquantized_linear(x, weight,\nNone) (:155) -> F.linear(x, weight, None) (:169). The batch-1 packed Q/K/V\nprojection, torch.mm(x.view(seq, C), packed.t(), out=buffer[0, prefix:])\n(models/dits/qwen_image21.py:579), gives the same bytes.\n\nAdapter: none. A is SGLang's x as [M, K] rows (the engine's [1, M, K]\nactivation gives the same bytes), W is layer.weight, bias is None. The\nworker's numerics policy (utils/numerics_policy.py:33, applied at\nmanagers/gpu_worker.py:420 with the server_args.py:510-511 defaults) keeps\nPyTorch's defaults, so nothing is set here.\n\"\"\"\n\nfrom sglang.multimodal_gen.runtime.layers.linear import apply_unquantized_linear\n\n\ndef run(A, W):\n return apply_unquantized_linear(A, W, None)\n" - } - ] -} diff --git a/solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k12288.json b/solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k12288.json deleted file mode 100644 index aa60eef32f6c5cbb260fa670bb699202d9f71be6..0000000000000000000000000000000000000000 --- a/solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k12288.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_dense_gemm_bf16_nt_n4096_k12288", - "definition": "dense_gemm_bf16_nt_n4096_k12288", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for dense_gemm_bf16_nt_n4096_k12288: C = A @ W.T in BF16.\n\nSGLang diffusion runtime at commit 684fea8a, paths under\npython/sglang/multimodal_gen/runtime/: the Qwen-Image-2.1 ``img_mlp.out``\nRowParallelLinear (models/dits/qwen_image21.py:338, called at :356/:360) ->\nRowParallelLinear.forward (layers/linear.py:1185) ->\nUnquantizedLinearMethod.apply (:203) -> apply_unquantized_linear(x, weight,\nNone) (:155) -> F.linear(x, weight, None) (:169).\n\nAdapter: none. A is SGLang's x as [M, K] rows (the engine's [1, M, K]\nactivation gives the same bytes), W is layer.weight, bias is None. The\nworker's numerics policy (utils/numerics_policy.py:33, applied at\nmanagers/gpu_worker.py:420 with the server_args.py:510-511 defaults) keeps\nPyTorch's defaults, so nothing is set here.\n\"\"\"\n\nfrom sglang.multimodal_gen.runtime.layers.linear import apply_unquantized_linear\n\n\ndef run(A, W):\n return apply_unquantized_linear(A, W, None)\n" - } - ] -} diff --git a/solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k4096.json b/solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k4096.json deleted file mode 100644 index 61da369d12664e7f6822781e3ca3beeb57da8656..0000000000000000000000000000000000000000 --- a/solutions/gemm/sglang_dense_gemm_bf16_nt_n4096_k4096.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_dense_gemm_bf16_nt_n4096_k4096", - "definition": "dense_gemm_bf16_nt_n4096_k4096", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for dense_gemm_bf16_nt_n4096_k4096: C = A @ W.T in BF16.\n\nSGLang diffusion runtime at commit 684fea8a, paths under\npython/sglang/multimodal_gen/runtime/: the Qwen-Image-2.1 ``attn.to_q``\nColumnParallelLinear (models/dits/qwen_image21.py:369, called at :487/:723)\nand ``attn.to_out.0`` RowParallelLinear (:380, called at :741) ->\nlayers/linear.py:518/:1185 -> UnquantizedLinearMethod.apply (:203) ->\napply_unquantized_linear(x, weight, None) (:155) -> F.linear(x, weight, None)\n(:169). The nn.Linear layers ``txt_in.in_layer`` (models/dits/qwen_image21.py:294)\nand ``timestep_embedder.linear_2`` (:308) issue the same F.linear call.\n\nAdapter: none. A is SGLang's x as [M, K] rows (the engine's batched\nactivation gives the same bytes), W is layer.weight, bias is None. The\nworker's numerics policy (utils/numerics_policy.py:33, applied at\nmanagers/gpu_worker.py:420 with the server_args.py:510-511 defaults) keeps\nPyTorch's defaults, so nothing is set here.\n\"\"\"\n\nfrom sglang.multimodal_gen.runtime.layers.linear import apply_unquantized_linear\n\n\ndef run(A, W):\n return apply_unquantized_linear(A, W, None)\n" - } - ] -} diff --git a/solutions/grouped_gemm/deepgemm_fp8_fp4.json b/solutions/grouped_gemm/deepgemm_fp8_fp4.json new file mode 100644 index 0000000000000000000000000000000000000000..d47dc044f0aa419f4ce13c8d4ea619c5dd22e72c --- /dev/null +++ b/solutions/grouped_gemm/deepgemm_fp8_fp4.json @@ -0,0 +1,24 @@ +{ + "name": "deepgemm_fp8_fp4", + "definition": "grouped_gemm_fp8_fp4_e384_g32", + "author": "inference-kernels", + "description": "DeepGEMM MXFP8 x MXFP4 grouped GEMM. Aligns expert segments, packs scales without requantization, and restores original row order. Upstream: https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d (MIT). Merged by KDA from the per-shape solutions when the per-shape definitions were merged.", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B200" + ], + "entry_point": "main.py::run", + "dependencies": [ + "torch>=2.11", + "deep-gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@1fc9c66975ee322d628b3288534ca0e9ce12a64d" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "import torch\nimport deep_gemm\n\ndef run(Aq, Wq_packed, scale_A, scale_W, expert_id):\n m, k = Aq.shape\n experts, n, _ = Wq_packed.shape\n alignment = deep_gemm.get_mk_alignment_for_contiguous_layout()\n capacity = ((m + min(m, experts) * (alignment - 1) + alignment - 1) // alignment) * alignment\n boundaries = torch.searchsorted(expert_id, torch.arange(experts + 1, device=Aq.device, dtype=torch.int32))\n counts = boundaries[1:] - boundaries[:-1]\n padded_counts = ((counts + alignment - 1) // alignment) * alignment\n offsets = padded_counts.cumsum(0) - padded_counts\n ids = expert_id.long()\n row_map = offsets[ids] + torch.arange(m, device=Aq.device) - boundaries[ids]\n a = torch.zeros((capacity, k), device=Aq.device, dtype=Aq.dtype)\n a.view(torch.uint8).index_copy_(0, row_map, Aq.view(torch.uint8))\n sa = torch.full((capacity, k // 32), 127, device=Aq.device, dtype=torch.int8)\n sa.index_copy_(0, row_map, scale_A)\n sa = sa.view(torch.int32).t().contiguous().t()\n sw = scale_W.view(torch.int32).transpose(-1, -2).contiguous().transpose(-1, -2)\n layout = torch.full((capacity,), -1, device=Aq.device, dtype=torch.int32)\n layout.index_copy_(0, row_map, expert_id)\n output = torch.empty((capacity, n), device=Aq.device, dtype=torch.bfloat16)\n deep_gemm.m_grouped_fp8_fp4_gemm_nt_contiguous(\n (a, sa), (Wq_packed, sw), output, layout,\n recipe_a=(1, 32), recipe_b=(1, 32),\n )\n return output.index_select(0, row_map)\n" + } + ] +} diff --git a/solutions/grouped_gemm/deepgemm_n4608_k5120.json b/solutions/grouped_gemm/deepgemm_n4608_k5120.json deleted file mode 100644 index 28e0db26c95a343aae319334f13355fa939d3658..0000000000000000000000000000000000000000 --- a/solutions/grouped_gemm/deepgemm_n4608_k5120.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "deepgemm_n4608_k5120", - "definition": "grouped_gemm_fp8_fp4_e384_n4608_k5120_g32", - "author": "inference-kernels", - "description": "DeepGEMM MXFP8 x MXFP4 grouped GEMM. Aligns expert segments, packs scales without requantization, and restores original row order. Upstream: https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "deep-gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@1fc9c66975ee322d628b3288534ca0e9ce12a64d"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "import torch\nimport deep_gemm\n\ndef run(Aq, Wq_packed, scale_A, scale_W, expert_id):\n m, k = Aq.shape\n experts, n, _ = Wq_packed.shape\n alignment = deep_gemm.get_mk_alignment_for_contiguous_layout()\n capacity = ((m + min(m, experts) * (alignment - 1) + alignment - 1) // alignment) * alignment\n boundaries = torch.searchsorted(expert_id, torch.arange(experts + 1, device=Aq.device, dtype=torch.int32))\n counts = boundaries[1:] - boundaries[:-1]\n padded_counts = ((counts + alignment - 1) // alignment) * alignment\n offsets = padded_counts.cumsum(0) - padded_counts\n ids = expert_id.long()\n row_map = offsets[ids] + torch.arange(m, device=Aq.device) - boundaries[ids]\n a = torch.zeros((capacity, k), device=Aq.device, dtype=Aq.dtype)\n a.view(torch.uint8).index_copy_(0, row_map, Aq.view(torch.uint8))\n sa = torch.full((capacity, k // 32), 127, device=Aq.device, dtype=torch.int8)\n sa.index_copy_(0, row_map, scale_A)\n sa = sa.view(torch.int32).t().contiguous().t()\n sw = scale_W.view(torch.int32).transpose(-1, -2).contiguous().transpose(-1, -2)\n layout = torch.full((capacity,), -1, device=Aq.device, dtype=torch.int32)\n layout.index_copy_(0, row_map, expert_id)\n output = torch.empty((capacity, n), device=Aq.device, dtype=torch.bfloat16)\n deep_gemm.m_grouped_fp8_fp4_gemm_nt_contiguous(\n (a, sa), (Wq_packed, sw), output, layout,\n recipe_a=(1, 32), recipe_b=(1, 32),\n )\n return output.index_select(0, row_map)\n"}] -} diff --git a/solutions/grouped_gemm/deepgemm_n5120_k2304.json b/solutions/grouped_gemm/deepgemm_n5120_k2304.json deleted file mode 100644 index 755f86c0ecdeb0ee4a07fd869ba398ebf03a49ba..0000000000000000000000000000000000000000 --- a/solutions/grouped_gemm/deepgemm_n5120_k2304.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "deepgemm_n5120_k2304", - "definition": "grouped_gemm_fp8_fp4_e384_n5120_k2304_g32", - "author": "inference-kernels", - "description": "DeepGEMM MXFP8 x MXFP4 grouped GEMM. Aligns expert segments, packs scales without requantization, and restores original row order. Upstream: https://github.com/deepseek-ai/DeepGEMM/tree/1fc9c66975ee322d628b3288534ca0e9ce12a64d (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "deep-gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@1fc9c66975ee322d628b3288534ca0e9ce12a64d"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "import torch\nimport deep_gemm\n\ndef run(Aq, Wq_packed, scale_A, scale_W, expert_id):\n m, k = Aq.shape\n experts, n, _ = Wq_packed.shape\n alignment = deep_gemm.get_mk_alignment_for_contiguous_layout()\n capacity = ((m + min(m, experts) * (alignment - 1) + alignment - 1) // alignment) * alignment\n boundaries = torch.searchsorted(expert_id, torch.arange(experts + 1, device=Aq.device, dtype=torch.int32))\n counts = boundaries[1:] - boundaries[:-1]\n padded_counts = ((counts + alignment - 1) // alignment) * alignment\n offsets = padded_counts.cumsum(0) - padded_counts\n ids = expert_id.long()\n row_map = offsets[ids] + torch.arange(m, device=Aq.device) - boundaries[ids]\n a = torch.zeros((capacity, k), device=Aq.device, dtype=Aq.dtype)\n a.view(torch.uint8).index_copy_(0, row_map, Aq.view(torch.uint8))\n sa = torch.full((capacity, k // 32), 127, device=Aq.device, dtype=torch.int8)\n sa.index_copy_(0, row_map, scale_A)\n sa = sa.view(torch.int32).t().contiguous().t()\n sw = scale_W.view(torch.int32).transpose(-1, -2).contiguous().transpose(-1, -2)\n layout = torch.full((capacity,), -1, device=Aq.device, dtype=torch.int32)\n layout.index_copy_(0, row_map, expert_id)\n output = torch.empty((capacity, n), device=Aq.device, dtype=torch.bfloat16)\n deep_gemm.m_grouped_fp8_fp4_gemm_nt_contiguous(\n (a, sa), (Wq_packed, sw), output, layout,\n recipe_a=(1, 32), recipe_b=(1, 32),\n )\n return output.index_select(0, row_map)\n"}] -} diff --git a/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_g32.json b/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_g32.json new file mode 100644 index 0000000000000000000000000000000000000000..7645e0038f05b0ec0a932375dd05dfd0d751fb4d --- /dev/null +++ b/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_g32.json @@ -0,0 +1,23 @@ +{ + "name": "sglang_grouped_gemm_fp8_fp4_e384_g32", + "definition": "grouped_gemm_fp8_fp4_e384_g32", + "author": "kda", + "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B300" + ], + "entry_point": "main.py::run", + "dependencies": [ + "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang's DeepGEMM MoE expert GEMMs: the fused gate/up projection (N=4608,\nK=5120) and the down projection (N=5120, K=2304).\n\nSGLang 684fea8a (paths under ``python/sglang/``), captured with ``--tp-size 4\n--ep-size 4 --moe-runner-backend deep_gemm --moe-a2a-backend none``: each of\nthe 4 EP ranks holds 96 of the 384 experts and runs, for its rows,\n\n- once at load: ``Fp8MoEMethod.process_weights_after_loading_block_quant``\n packs the fp32 group-32 weight scales with\n ``deep_gemm.transform_sf_into_required_layout(recipe=(1, 32),\n num_groups=96)`` (srt/layers/quantization/fp8.py:2132-2147);\n- per batch: ``pre_permute_standard_to_deep_gemm``\n (srt/layers/moe/moe_runner/deep_gemm.py:988) sizes the compact contiguous\n layout (``get_contiguous_layout_alignment`` :1082, ``_get_compact_all_tokens``\n :1085, ``pad_expert_counts`` :1093) and ``ep_scatter`` (:1166) writes\n ``m_indices`` (-1 on padding rows) and, for gate/up, the FP8 rows and the\n MN-major packed UE8M0 scales (:1149); for the down projection\n ``_run_contiguous_gemm`` quantizes the gate/up activation into the same\n padded rows. ``DeepGemmRunnerCore.run`` enters\n ``contiguous_layout_alignment_scope(block_e)`` (:378) and\n ``_run_contiguous_gemm`` calls (gate/up :456, down :631)\n ``deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig``\n (srt/layers/deep_gemm_wrapper/entrypoint.py:132), i.e.\n ``deep_gemm.m_grouped_fp8_gemm_nt_contiguous`` (:157), with recipe_a =\n recipe_b = (1, 32): the capture's engine_adapter passes the actual\n activation group instead of the FP4-expert default (1, 128) (:337).\n\nInterface adapter: the definition's rows are already sorted by expert, so\n``prepare`` derives the layout from ``expert_id`` (each rank's padded\nsegments, row destinations, ``m_indices``), allocates the per-rank buffers,\nand does the load-time weight work; ``launch`` scatters the current FP8 rows\nand scale bytes (four UE8M0 bytes per int32) into the padded layout, runs the\nfour rank GEMMs and gathers the valid rows.\n\"\"\"\n\nimport deep_gemm\nimport torch\nfrom sglang.kernels.ops.moe.triton_pad_expert_counts import pad_expert_counts\nfrom sglang.srt.layers.deep_gemm_wrapper.entrypoint import (\n contiguous_layout_alignment_scope,\n get_contiguous_layout_alignment,\n)\nfrom sglang.srt.layers.moe.moe_runner.deep_gemm import _get_compact_all_tokens\n\nEP_SIZE = 4 # capture --ep-size\nRECIPE = (1, 32) # capture engine_adapter: actual activation group\n\n\n@torch.no_grad()\ndef prepare(Aq, Wq_packed, scale_A, scale_W, expert_id):\n m, k = Aq.shape\n num_experts, n, _ = Wq_packed.shape\n local = num_experts // EP_SIZE\n weights = [\n (\n Wq_packed[e0 : e0 + local],\n deep_gemm.transform_sf_into_required_layout(\n torch.exp2(scale_W[e0 : e0 + local].view(torch.uint8).float() - 127),\n mn=n,\n k=k,\n recipe=RECIPE,\n num_groups=local,\n disable_ue8m0_cast=False,\n ),\n )\n for e0 in range(0, num_experts, local)\n ]\n\n block_e = get_contiguous_layout_alignment(m, local)\n all_tokens = _get_compact_all_tokens(m, local, block_e)\n eid = expert_id.long()\n counts = torch.bincount(eid, minlength=num_experts)\n padded = torch.stack(\n [pad_expert_counts(c, block_e, all_tokens) for c in counts.view(-1, local)]\n ).flatten()\n rank, local_eid = eid // local, eid % local\n dst = (\n (torch.cumsum(padded, 0) - padded)[eid]\n + torch.arange(m, device=eid.device)\n - (torch.cumsum(counts, 0) - counts)[eid]\n )\n m_indices = torch.full(\n (EP_SIZE * all_tokens,), -1, dtype=torch.int32, device=Aq.device\n )\n m_indices[dst] = local_eid.int()\n a = torch.zeros(EP_SIZE * all_tokens, k, dtype=Aq.dtype, device=Aq.device)\n sfa = torch.zeros(\n EP_SIZE, k // 128, all_tokens, dtype=torch.int32, device=Aq.device\n )\n out = torch.empty(EP_SIZE * all_tokens, n, dtype=torch.bfloat16, device=Aq.device)\n y = torch.empty(m, n, dtype=torch.bfloat16, device=Aq.device)\n rows = [slice(r * all_tokens, (r + 1) * all_tokens) for r in range(EP_SIZE)]\n local_dst = dst - rank * all_tokens\n\n def launch():\n a.view(torch.uint8).index_copy_(0, dst, Aq.view(torch.uint8))\n sfa.transpose(1, 2)[rank, local_dst] = scale_A.view(torch.int32)\n with contiguous_layout_alignment_scope(block_e):\n for r, (w, sfw) in enumerate(weights):\n deep_gemm.m_grouped_fp8_gemm_nt_contiguous(\n (a[rows[r]], sfa[r].T),\n (w, sfw),\n out[rows[r]],\n m_indices[rows[r]],\n recipe_a=RECIPE,\n recipe_b=RECIPE,\n )\n torch.index_select(out, 0, dst, out=y)\n return y\n\n return launch\n\n\ndef run(Aq, Wq_packed, scale_A, scale_W, expert_id):\n return prepare(Aq, Wq_packed, scale_A, scale_W, expert_id)()\n" + } + ] +} diff --git a/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json b/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json deleted file mode 100644 index 26d19dcba5ea19d281192af1fbdcb457585983eb..0000000000000000000000000000000000000000 --- a/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n4608_k5120_g32.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_grouped_gemm_fp8_fp4_e384_n4608_k5120_g32", - "definition": "grouped_gemm_fp8_fp4_e384_n4608_k5120_g32", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang's DeepGEMM MoE expert GEMM, fused gate/up projection (N=4608, K=5120).\n\nSGLang 684fea8a (paths under ``python/sglang/``), captured with ``--tp-size 4\n--ep-size 4 --moe-runner-backend deep_gemm --moe-a2a-backend none``: each of\nthe 4 EP ranks holds 96 of the 384 experts and runs, for its rows,\n\n- once at load: ``Fp8MoEMethod.process_weights_after_loading_block_quant``\n packs the fp32 group-32 weight scales with\n ``deep_gemm.transform_sf_into_required_layout(recipe=(1, 32),\n num_groups=96)`` (srt/layers/quantization/fp8.py:2132-2147);\n- per batch: ``pre_permute_standard_to_deep_gemm``\n (srt/layers/moe/moe_runner/deep_gemm.py:988) sizes the compact contiguous\n layout (``get_contiguous_layout_alignment`` :1082, ``_get_compact_all_tokens``\n :1085, ``pad_expert_counts`` :1093) and ``ep_scatter`` (:1166) writes the FP8\n rows, the MN-major packed UE8M0 scales (:1149) and ``m_indices`` (-1 on\n padding rows); ``DeepGemmRunnerCore.run`` enters\n ``contiguous_layout_alignment_scope(block_e)`` (:378) and\n ``_run_contiguous_gemm`` (:456) calls\n ``deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig``\n (srt/layers/deep_gemm_wrapper/entrypoint.py:132), i.e.\n ``deep_gemm.m_grouped_fp8_gemm_nt_contiguous`` (:157), with recipe_a =\n recipe_b = (1, 32): the capture's engine_adapter passes the actual\n activation group instead of the FP4-expert default (1, 128) (:337).\n\nInterface adapter: the definition's rows are already sorted by expert, so\n``prepare`` derives the layout from ``expert_id`` (each rank's padded\nsegments, row destinations, ``m_indices``), allocates the per-rank buffers,\nand does the load-time weight work; ``launch`` scatters the current FP8 rows\nand scale bytes (four UE8M0 bytes per int32) into the padded layout, runs the\nfour rank GEMMs and gathers the valid rows.\n\"\"\"\n\nimport deep_gemm\nimport torch\nfrom sglang.kernels.ops.moe.triton_pad_expert_counts import pad_expert_counts\nfrom sglang.srt.layers.deep_gemm_wrapper.entrypoint import (\n contiguous_layout_alignment_scope,\n get_contiguous_layout_alignment,\n)\nfrom sglang.srt.layers.moe.moe_runner.deep_gemm import _get_compact_all_tokens\n\nEP_SIZE = 4 # capture --ep-size\nRECIPE = (1, 32) # capture engine_adapter: actual activation group\n\n\n@torch.no_grad()\ndef prepare(Aq, Wq_packed, scale_A, scale_W, expert_id):\n m, k = Aq.shape\n num_experts, n, _ = Wq_packed.shape\n local = num_experts // EP_SIZE\n weights = [\n (\n Wq_packed[e0 : e0 + local],\n deep_gemm.transform_sf_into_required_layout(\n torch.exp2(scale_W[e0 : e0 + local].view(torch.uint8).float() - 127),\n mn=n,\n k=k,\n recipe=RECIPE,\n num_groups=local,\n disable_ue8m0_cast=False,\n ),\n )\n for e0 in range(0, num_experts, local)\n ]\n\n block_e = get_contiguous_layout_alignment(m, local)\n all_tokens = _get_compact_all_tokens(m, local, block_e)\n eid = expert_id.long()\n counts = torch.bincount(eid, minlength=num_experts)\n padded = torch.stack(\n [pad_expert_counts(c, block_e, all_tokens) for c in counts.view(-1, local)]\n ).flatten()\n rank, local_eid = eid // local, eid % local\n dst = (\n (torch.cumsum(padded, 0) - padded)[eid]\n + torch.arange(m, device=eid.device)\n - (torch.cumsum(counts, 0) - counts)[eid]\n )\n m_indices = torch.full(\n (EP_SIZE * all_tokens,), -1, dtype=torch.int32, device=Aq.device\n )\n m_indices[dst] = local_eid.int()\n a = torch.zeros(EP_SIZE * all_tokens, k, dtype=Aq.dtype, device=Aq.device)\n sfa = torch.zeros(\n EP_SIZE, k // 128, all_tokens, dtype=torch.int32, device=Aq.device\n )\n out = torch.empty(EP_SIZE * all_tokens, n, dtype=torch.bfloat16, device=Aq.device)\n y = torch.empty(m, n, dtype=torch.bfloat16, device=Aq.device)\n rows = [slice(r * all_tokens, (r + 1) * all_tokens) for r in range(EP_SIZE)]\n local_dst = dst - rank * all_tokens\n\n def launch():\n a.view(torch.uint8).index_copy_(0, dst, Aq.view(torch.uint8))\n sfa.transpose(1, 2)[rank, local_dst] = scale_A.view(torch.int32)\n with contiguous_layout_alignment_scope(block_e):\n for r, (w, sfw) in enumerate(weights):\n deep_gemm.m_grouped_fp8_gemm_nt_contiguous(\n (a[rows[r]], sfa[r].T),\n (w, sfw),\n out[rows[r]],\n m_indices[rows[r]],\n recipe_a=RECIPE,\n recipe_b=RECIPE,\n )\n torch.index_select(out, 0, dst, out=y)\n return y\n\n return launch\n\n\ndef run(Aq, Wq_packed, scale_A, scale_W, expert_id):\n return prepare(Aq, Wq_packed, scale_A, scale_W, expert_id)()\n" - } - ] -} diff --git a/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json b/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json deleted file mode 100644 index 0f6fb100247e564c27ca0d4da3d1eac67df5eb1f..0000000000000000000000000000000000000000 --- a/solutions/grouped_gemm/sglang_grouped_gemm_fp8_fp4_e384_n5120_k2304_g32.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_grouped_gemm_fp8_fp4_e384_n5120_k2304_g32", - "definition": "grouped_gemm_fp8_fp4_e384_n5120_k2304_g32", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang's DeepGEMM MoE expert GEMM, down projection (N=5120, K=2304).\n\nSGLang 684fea8a (paths under ``python/sglang/``), captured with ``--tp-size 4\n--ep-size 4 --moe-runner-backend deep_gemm --moe-a2a-backend none``: each of\nthe 4 EP ranks holds 96 of the 384 experts and runs, for its rows,\n\n- once at load: ``Fp8MoEMethod.process_weights_after_loading_block_quant``\n packs the fp32 group-32 weight scales with\n ``deep_gemm.transform_sf_into_required_layout(recipe=(1, 32),\n num_groups=96)`` (srt/layers/quantization/fp8.py:2132-2147);\n- per batch: ``pre_permute_standard_to_deep_gemm``\n (srt/layers/moe/moe_runner/deep_gemm.py:988) sizes the compact contiguous\n layout (``get_contiguous_layout_alignment`` :1082, ``_get_compact_all_tokens``\n :1085, ``pad_expert_counts`` :1093) and ``ep_scatter`` (:1166) writes\n ``m_indices`` (-1 on padding rows); ``DeepGemmRunnerCore.run`` enters\n ``contiguous_layout_alignment_scope(block_e)`` (:378) and\n ``_run_contiguous_gemm`` quantizes the gate/up activation into the same\n padded rows (FP8 rows, MN-major packed UE8M0 scales) and, for the down\n projection, calls (:631) ``deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig``\n (srt/layers/deep_gemm_wrapper/entrypoint.py:132), i.e.\n ``deep_gemm.m_grouped_fp8_gemm_nt_contiguous`` (:157), with recipe_a =\n recipe_b = (1, 32): the capture's engine_adapter passes the actual\n activation group instead of the FP4-expert default (1, 128) (:337).\n\nInterface adapter: the definition's rows are already sorted by expert, so\n``prepare`` derives the layout from ``expert_id`` (each rank's padded\nsegments, row destinations, ``m_indices``), allocates the per-rank buffers,\nand does the load-time weight work; ``launch`` scatters the current FP8 rows\nand scale bytes (four UE8M0 bytes per int32) into the padded layout, runs the\nfour rank GEMMs and gathers the valid rows.\n\"\"\"\n\nimport deep_gemm\nimport torch\nfrom sglang.kernels.ops.moe.triton_pad_expert_counts import pad_expert_counts\nfrom sglang.srt.layers.deep_gemm_wrapper.entrypoint import (\n contiguous_layout_alignment_scope,\n get_contiguous_layout_alignment,\n)\nfrom sglang.srt.layers.moe.moe_runner.deep_gemm import _get_compact_all_tokens\n\nEP_SIZE = 4 # capture --ep-size\nRECIPE = (1, 32) # capture engine_adapter: actual activation group\n\n\n@torch.no_grad()\ndef prepare(Aq, Wq_packed, scale_A, scale_W, expert_id):\n m, k = Aq.shape\n num_experts, n, _ = Wq_packed.shape\n local = num_experts // EP_SIZE\n weights = [\n (\n Wq_packed[e0 : e0 + local],\n deep_gemm.transform_sf_into_required_layout(\n torch.exp2(scale_W[e0 : e0 + local].view(torch.uint8).float() - 127),\n mn=n,\n k=k,\n recipe=RECIPE,\n num_groups=local,\n disable_ue8m0_cast=False,\n ),\n )\n for e0 in range(0, num_experts, local)\n ]\n\n block_e = get_contiguous_layout_alignment(m, local)\n all_tokens = _get_compact_all_tokens(m, local, block_e)\n eid = expert_id.long()\n counts = torch.bincount(eid, minlength=num_experts)\n padded = torch.stack(\n [pad_expert_counts(c, block_e, all_tokens) for c in counts.view(-1, local)]\n ).flatten()\n rank, local_eid = eid // local, eid % local\n dst = (\n (torch.cumsum(padded, 0) - padded)[eid]\n + torch.arange(m, device=eid.device)\n - (torch.cumsum(counts, 0) - counts)[eid]\n )\n m_indices = torch.full(\n (EP_SIZE * all_tokens,), -1, dtype=torch.int32, device=Aq.device\n )\n m_indices[dst] = local_eid.int()\n a = torch.zeros(EP_SIZE * all_tokens, k, dtype=Aq.dtype, device=Aq.device)\n sfa = torch.zeros(\n EP_SIZE, k // 128, all_tokens, dtype=torch.int32, device=Aq.device\n )\n out = torch.empty(EP_SIZE * all_tokens, n, dtype=torch.bfloat16, device=Aq.device)\n y = torch.empty(m, n, dtype=torch.bfloat16, device=Aq.device)\n rows = [slice(r * all_tokens, (r + 1) * all_tokens) for r in range(EP_SIZE)]\n local_dst = dst - rank * all_tokens\n\n def launch():\n a.view(torch.uint8).index_copy_(0, dst, Aq.view(torch.uint8))\n sfa.transpose(1, 2)[rank, local_dst] = scale_A.view(torch.int32)\n with contiguous_layout_alignment_scope(block_e):\n for r, (w, sfw) in enumerate(weights):\n deep_gemm.m_grouped_fp8_gemm_nt_contiguous(\n (a[rows[r]], sfa[r].T),\n (w, sfw),\n out[rows[r]],\n m_indices[rows[r]],\n recipe_a=RECIPE,\n recipe_b=RECIPE,\n )\n torch.index_select(out, 0, dst, out=y)\n return y\n\n return launch\n\n\ndef run(Aq, Wq_packed, scale_A, scale_W, expert_id):\n return prepare(Aq, Wq_packed, scale_A, scale_W, expert_id)()\n" - } - ] -} diff --git a/solutions/neighborhood_attention/natten_blackwell.json b/solutions/neighborhood_attention/natten_blackwell.json new file mode 100644 index 0000000000000000000000000000000000000000..41622aac7531950680f94f48f8621f149803c46a --- /dev/null +++ b/solutions/neighborhood_attention/natten_blackwell.json @@ -0,0 +1,24 @@ +{ + "name": "natten_blackwell", + "definition": "neighborhood_attention_3d_d64", + "author": "inference-kernels", + "description": "NATTEN 0.21.7 Blackwell fused neighborhood attention with inward-shifted boundary windows, for every stage's window. Queries already include the model scale. Merged by KDA from the per-shape solutions when the per-shape definitions were merged. Upstream: https://github.com/SHI-Labs/NATTEN/tree/39375ea1d2f3b1bb284315fa5f79f6ecbebb6382 (MIT).", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B200" + ], + "entry_point": "main.py::run", + "dependencies": [ + "torch>=2.11", + "natten==0.21.7" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "from natten.functional import na3d\n\ndef run(Q, K, V, Kt, Ky, Kx):\n return na3d(Q, K, V, kernel_size=(int(Kt), int(Ky), int(Kx)), scale=1.0, backend='blackwell-fna')\n" + } + ] +} diff --git a/solutions/neighborhood_attention/natten_blackwell_h16_3x7x7.json b/solutions/neighborhood_attention/natten_blackwell_h16_3x7x7.json deleted file mode 100644 index fd5496b286b0e80354b9faea18b6cc99a3587b91..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/natten_blackwell_h16_3x7x7.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "natten_blackwell_h16_3x7x7", - "definition": "neighborhood_attention_3d_h16_d64_kt3_ky7_kx7", - "author": "inference-kernels", - "description": "NATTEN 0.21.7 Blackwell fused neighborhood attention with inward-shifted boundary windows. Queries already include the model scale. Upstream: https://github.com/SHI-Labs/NATTEN/tree/39375ea1d2f3b1bb284315fa5f79f6ecbebb6382 (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "natten==0.21.7"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "from natten.functional import na3d\n\ndef run(Q, K, V):\n return na3d(Q, K, V, kernel_size=(3, 7, 7), scale=1.0, backend='blackwell-fna')\n"}] -} diff --git a/solutions/neighborhood_attention/natten_blackwell_h32_3x7x7.json b/solutions/neighborhood_attention/natten_blackwell_h32_3x7x7.json deleted file mode 100644 index 9b9622008ca25df88da5c39a15a90de15c0e6790..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/natten_blackwell_h32_3x7x7.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "natten_blackwell_h32_3x7x7", - "definition": "neighborhood_attention_3d_h32_d64_kt3_ky7_kx7", - "author": "inference-kernels", - "description": "NATTEN 0.21.7 Blackwell fused neighborhood attention with inward-shifted boundary windows. Queries already include the model scale. Upstream: https://github.com/SHI-Labs/NATTEN/tree/39375ea1d2f3b1bb284315fa5f79f6ecbebb6382 (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "natten==0.21.7"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "from natten.functional import na3d\n\ndef run(Q, K, V):\n return na3d(Q, K, V, kernel_size=(3, 7, 7), scale=1.0, backend='blackwell-fna')\n"}] -} diff --git a/solutions/neighborhood_attention/natten_blackwell_h4_11x11x11.json b/solutions/neighborhood_attention/natten_blackwell_h4_11x11x11.json deleted file mode 100644 index e9b1831193ac5f4b2f1dda35da7b4d801b84e3f4..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/natten_blackwell_h4_11x11x11.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "natten_blackwell_h4_11x11x11", - "definition": "neighborhood_attention_3d_h4_d64_kt11_ky11_kx11", - "author": "inference-kernels", - "description": "NATTEN 0.21.7 Blackwell fused neighborhood attention with inward-shifted boundary windows. Queries already include the model scale. Upstream: https://github.com/SHI-Labs/NATTEN/tree/39375ea1d2f3b1bb284315fa5f79f6ecbebb6382 (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "natten==0.21.7"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "from natten.functional import na3d\n\ndef run(Q, K, V):\n return na3d(Q, K, V, kernel_size=(11, 11, 11), scale=1.0, backend='blackwell-fna')\n"}] -} diff --git a/solutions/neighborhood_attention/natten_blackwell_h8_3x5x5.json b/solutions/neighborhood_attention/natten_blackwell_h8_3x5x5.json deleted file mode 100644 index a10e46f94822e92088a79657ba19aa5a6eb4312e..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/natten_blackwell_h8_3x5x5.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "natten_blackwell_h8_3x5x5", - "definition": "neighborhood_attention_3d_h8_d64_kt3_ky5_kx5", - "author": "inference-kernels", - "description": "NATTEN 0.21.7 Blackwell fused neighborhood attention with inward-shifted boundary windows. Queries already include the model scale. Upstream: https://github.com/SHI-Labs/NATTEN/tree/39375ea1d2f3b1bb284315fa5f79f6ecbebb6382 (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "natten==0.21.7"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "from natten.functional import na3d\n\ndef run(Q, K, V):\n return na3d(Q, K, V, kernel_size=(3, 5, 5), scale=1.0, backend='blackwell-fna')\n"}] -} diff --git a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_d64.json b/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_d64.json new file mode 100644 index 0000000000000000000000000000000000000000..8f8374a22427840f84516ad1dc8495ab89fa779b --- /dev/null +++ b/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_d64.json @@ -0,0 +1,23 @@ +{ + "name": "sglang_neighborhood_attention_3d_d64", + "definition": "neighborhood_attention_3d_d64", + "author": "kda", + "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B300" + ], + "entry_point": "main.py::run", + "dependencies": [ + "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for neighborhood_attention_3d_d64, every LTX-2.5 decoder stage.\n\nSGLang diffusion runtime at commit 684fea8a,\npython/sglang/multimodal_gen/runtime/models/decoders/ltx_2_5_diffusion_decoder.py:\nLTX2VideoVaeNeighborhoodAttention.forward (:425) after project_qkv (:435).\nNATTEN is absent from the image, so _na3d() (:49) is None and forward takes the\nFlexAttention branch: Q/K/V viewed as (B, heads, S, head_dim) (:449-459), the\nblock mask from build_block_mask (:413) -> _neighborhood_block_mask(T, H, W,\nkernel_size, device) (:170), and _flex_attention_fn() (:104, torch.compile of\nflex_attention, dynamic=False) called with scale=1.0 (:465-467).\n\nAdapter: Q/K/V arrive as project_qkv returns them, [B, T, Y, X, H, D].\nkernel_size is the stage's (Kt, Ky, Kx), which the definition passes as inputs. The decoder builds the block mask\nonce per stage (:771, :785, :835) and passes it to every block, so prepare\nbuilds it and launch runs only the attention. The output keeps the heads\nsplit: :468-470 merges them into channels for to_out, which is not part of\nthis op; that reshape is a view of the flex output, so launch detaches it\n(no copy) to return a tensor that owns its storage.\n\"\"\"\n\nimport torch\nfrom sglang.multimodal_gen.runtime.models.decoders import (\n ltx_2_5_diffusion_decoder as ltx,\n)\n\n# The engine meets a few grids per process; one judge process meets every\n# workload's shape. Past dynamo's default limit of 8 recompiles flex_attention\n# would run uncompiled, materializing the dense S x S scores.\ntorch._dynamo.config.recompile_limit = 1024\ntorch._dynamo.config.accumulated_recompile_limit = 4096\n# The judge rejects a candidate that leaves child processes running, and\n# inductor's parallel compile pool is one; compile in-process instead.\ntorch._inductor.config.compile_threads = 1\n\n\ndef prepare(Q, K, V, Kt, Ky, Kx):\n batch_size, num_frames, height, width, heads, head_dim = Q.shape\n seq_len = num_frames * height * width\n # :451-459\n query = Q.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n key = K.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n value = V.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n # build_block_mask, :420-423\n block_mask = ltx._neighborhood_block_mask(\n num_frames, height, width, (int(Kt), int(Ky), int(Kx)), Q.device\n )\n flex_attention = ltx._flex_attention_fn()\n\n def launch():\n # :465-467\n hidden_states = flex_attention(\n query, key, value, block_mask=block_mask, scale=1.0\n )\n # :468-470, heads kept split. The reshape is a view of flex_attention's\n # own exact-sized output; detach() hands it over as a tensor of its own.\n return hidden_states.transpose(1, 2).reshape(Q.shape).detach()\n\n return launch\n\n\ndef run(Q, K, V, Kt, Ky, Kx):\n return prepare(Q, K, V, Kt, Ky, Kx)()\n" + } + ] +} diff --git a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json b/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json deleted file mode 100644 index a5ddea11002b091191e3e6d2f7e7afab813ccc36..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_neighborhood_attention_3d_h16_d64_kt3_ky7_kx7", - "definition": "neighborhood_attention_3d_h16_d64_kt3_ky7_kx7", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for neighborhood_attention_3d_h16_d64_kt3_ky7_kx7.\n\nSGLang diffusion runtime at commit 684fea8a,\npython/sglang/multimodal_gen/runtime/models/decoders/ltx_2_5_diffusion_decoder.py:\nLTX2VideoVaeNeighborhoodAttention.forward (:425) after project_qkv (:435).\nNATTEN is absent from the image, so _na3d() (:49) is None and forward takes the\nFlexAttention branch: Q/K/V viewed as (B, heads, S, head_dim) (:449-459), the\nblock mask from build_block_mask (:413) -> _neighborhood_block_mask(T, H, W,\nkernel_size, device) (:170), and _flex_attention_fn() (:104, torch.compile of\nflex_attention, dynamic=False) called with scale=1.0 (:465-467).\n\nAdapter: Q/K/V arrive as project_qkv returns them, [B, T, Y, X, H, D].\nkernel_size is the module's (Kt, Ky, Kx). The decoder builds the block mask\nonce per stage (:771, :785, :835) and passes it to every block, so prepare\nbuilds it and launch runs only the attention. The output keeps the heads\nsplit: :468-470 merges them into channels for to_out, which is not part of\nthis op; that reshape is a view of the flex output, so launch detaches it\n(no copy) to return a tensor that owns its storage.\n\"\"\"\n\nimport torch\nfrom sglang.multimodal_gen.runtime.models.decoders import (\n ltx_2_5_diffusion_decoder as ltx,\n)\n\nKERNEL_SIZE = (3, 7, 7)\n\n# The engine meets a few grids per process; one judge process meets every\n# workload's shape. Past dynamo's default limit of 8 recompiles flex_attention\n# would run uncompiled, materializing the dense S x S scores.\ntorch._dynamo.config.recompile_limit = 1024\ntorch._dynamo.config.accumulated_recompile_limit = 4096\n# The judge rejects a candidate that leaves child processes running, and\n# inductor's parallel compile pool is one; compile in-process instead.\ntorch._inductor.config.compile_threads = 1\n\n\ndef prepare(Q, K, V):\n batch_size, num_frames, height, width, heads, head_dim = Q.shape\n seq_len = num_frames * height * width\n # :451-459\n query = Q.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n key = K.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n value = V.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n # build_block_mask, :420-423\n block_mask = ltx._neighborhood_block_mask(\n num_frames, height, width, KERNEL_SIZE, Q.device\n )\n flex_attention = ltx._flex_attention_fn()\n\n def launch():\n # :465-467\n hidden_states = flex_attention(\n query, key, value, block_mask=block_mask, scale=1.0\n )\n # :468-470, heads kept split. The reshape is a view of flex_attention's\n # own exact-sized output; detach() hands it over as a tensor of its own.\n return hidden_states.transpose(1, 2).reshape(Q.shape).detach()\n\n return launch\n\n\ndef run(Q, K, V):\n return prepare(Q, K, V)()\n" - } - ] -} diff --git a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json b/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json deleted file mode 100644 index 8b9470880036db80584989a5858078cac391d768..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_neighborhood_attention_3d_h32_d64_kt3_ky7_kx7", - "definition": "neighborhood_attention_3d_h32_d64_kt3_ky7_kx7", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for neighborhood_attention_3d_h32_d64_kt3_ky7_kx7.\n\nSGLang diffusion runtime at commit 684fea8a,\npython/sglang/multimodal_gen/runtime/models/decoders/ltx_2_5_diffusion_decoder.py:\nLTX2VideoVaeNeighborhoodAttention.forward (:425) after project_qkv (:435).\nNATTEN is absent from the image, so _na3d() (:49) is None and forward takes the\nFlexAttention branch: Q/K/V viewed as (B, heads, S, head_dim) (:449-459), the\nblock mask from build_block_mask (:413) -> _neighborhood_block_mask(T, H, W,\nkernel_size, device) (:170), and _flex_attention_fn() (:104, torch.compile of\nflex_attention, dynamic=False) called with scale=1.0 (:465-467).\n\nAdapter: Q/K/V arrive as project_qkv returns them, [B, T, Y, X, H, D].\nkernel_size is the module's (Kt, Ky, Kx). The decoder builds the block mask\nonce per stage (:771, :785, :835) and passes it to every block, so prepare\nbuilds it and launch runs only the attention. The output keeps the heads\nsplit: :468-470 merges them into channels for to_out, which is not part of\nthis op; that reshape is a view of the flex output, so launch detaches it\n(no copy) to return a tensor that owns its storage.\n\"\"\"\n\nimport torch\nfrom sglang.multimodal_gen.runtime.models.decoders import (\n ltx_2_5_diffusion_decoder as ltx,\n)\n\nKERNEL_SIZE = (3, 7, 7)\n\n# The engine meets a few grids per process; one judge process meets every\n# workload's shape. Past dynamo's default limit of 8 recompiles flex_attention\n# would run uncompiled, materializing the dense S x S scores.\ntorch._dynamo.config.recompile_limit = 1024\ntorch._dynamo.config.accumulated_recompile_limit = 4096\n# The judge rejects a candidate that leaves child processes running, and\n# inductor's parallel compile pool is one; compile in-process instead.\ntorch._inductor.config.compile_threads = 1\n\n\ndef prepare(Q, K, V):\n batch_size, num_frames, height, width, heads, head_dim = Q.shape\n seq_len = num_frames * height * width\n # :451-459\n query = Q.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n key = K.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n value = V.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n # build_block_mask, :420-423\n block_mask = ltx._neighborhood_block_mask(\n num_frames, height, width, KERNEL_SIZE, Q.device\n )\n flex_attention = ltx._flex_attention_fn()\n\n def launch():\n # :465-467\n hidden_states = flex_attention(\n query, key, value, block_mask=block_mask, scale=1.0\n )\n # :468-470, heads kept split. The reshape is a view of flex_attention's\n # own exact-sized output; detach() hands it over as a tensor of its own.\n return hidden_states.transpose(1, 2).reshape(Q.shape).detach()\n\n return launch\n\n\ndef run(Q, K, V):\n return prepare(Q, K, V)()\n" - } - ] -} diff --git a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json b/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json deleted file mode 100644 index e9f99086b9e878fd32b8d305fbce77d23b915e90..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_neighborhood_attention_3d_h4_d64_kt11_ky11_kx11", - "definition": "neighborhood_attention_3d_h4_d64_kt11_ky11_kx11", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for neighborhood_attention_3d_h4_d64_kt11_ky11_kx11.\n\nSGLang diffusion runtime at commit 684fea8a,\npython/sglang/multimodal_gen/runtime/models/decoders/ltx_2_5_diffusion_decoder.py:\nLTX2VideoVaeNeighborhoodAttention.forward (:425) after project_qkv (:435).\nNATTEN is absent from the image, so _na3d() (:49) is None and forward takes the\nFlexAttention branch: Q/K/V viewed as (B, heads, S, head_dim) (:449-459), the\nblock mask from build_block_mask (:413) -> _neighborhood_block_mask(T, H, W,\nkernel_size, device) (:170), and _flex_attention_fn() (:104, torch.compile of\nflex_attention, dynamic=False) called with scale=1.0 (:465-467).\n\nAdapter: Q/K/V arrive as project_qkv returns them, [B, T, Y, X, H, D].\nkernel_size is the module's (Kt, Ky, Kx). The decoder builds the block mask\nonce per stage (:771, :785, :835) and passes it to every block, so prepare\nbuilds it and launch runs only the attention. The output keeps the heads\nsplit: :468-470 merges them into channels for to_out, which is not part of\nthis op; that reshape is a view of the flex output, so launch detaches it\n(no copy) to return a tensor that owns its storage.\n\"\"\"\n\nimport torch\nfrom sglang.multimodal_gen.runtime.models.decoders import (\n ltx_2_5_diffusion_decoder as ltx,\n)\n\nKERNEL_SIZE = (11, 11, 11)\n\n# The engine meets a few grids per process; one judge process meets every\n# workload's shape. Past dynamo's default limit of 8 recompiles flex_attention\n# would run uncompiled, materializing the dense S x S scores.\ntorch._dynamo.config.recompile_limit = 1024\ntorch._dynamo.config.accumulated_recompile_limit = 4096\n# The judge rejects a candidate that leaves child processes running, and\n# inductor's parallel compile pool is one; compile in-process instead.\ntorch._inductor.config.compile_threads = 1\n\n\ndef prepare(Q, K, V):\n batch_size, num_frames, height, width, heads, head_dim = Q.shape\n seq_len = num_frames * height * width\n # :451-459\n query = Q.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n key = K.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n value = V.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n # build_block_mask, :420-423\n block_mask = ltx._neighborhood_block_mask(\n num_frames, height, width, KERNEL_SIZE, Q.device\n )\n flex_attention = ltx._flex_attention_fn()\n\n def launch():\n # :465-467\n hidden_states = flex_attention(\n query, key, value, block_mask=block_mask, scale=1.0\n )\n # :468-470, heads kept split. The reshape is a view of flex_attention's\n # own exact-sized output; detach() hands it over as a tensor of its own.\n return hidden_states.transpose(1, 2).reshape(Q.shape).detach()\n\n return launch\n\n\ndef run(Q, K, V):\n return prepare(Q, K, V)()\n" - } - ] -} diff --git a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json b/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json deleted file mode 100644 index d9434afd793a8666c8d97c704660419033b51f8c..0000000000000000000000000000000000000000 --- a/solutions/neighborhood_attention/sglang_neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_neighborhood_attention_3d_h8_d64_kt3_ky5_kx5", - "definition": "neighborhood_attention_3d_h8_d64_kt3_ky5_kx5", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for neighborhood_attention_3d_h8_d64_kt3_ky5_kx5.\n\nSGLang diffusion runtime at commit 684fea8a,\npython/sglang/multimodal_gen/runtime/models/decoders/ltx_2_5_diffusion_decoder.py:\nLTX2VideoVaeNeighborhoodAttention.forward (:425) after project_qkv (:435).\nNATTEN is absent from the image, so _na3d() (:49) is None and forward takes the\nFlexAttention branch: Q/K/V viewed as (B, heads, S, head_dim) (:449-459), the\nblock mask from build_block_mask (:413) -> _neighborhood_block_mask(T, H, W,\nkernel_size, device) (:170), and _flex_attention_fn() (:104, torch.compile of\nflex_attention, dynamic=False) called with scale=1.0 (:465-467).\n\nAdapter: Q/K/V arrive as project_qkv returns them, [B, T, Y, X, H, D].\nkernel_size is the module's (Kt, Ky, Kx). The decoder builds the block mask\nonce per stage (:771, :785, :835) and passes it to every block, so prepare\nbuilds it and launch runs only the attention. The output keeps the heads\nsplit: :468-470 merges them into channels for to_out, which is not part of\nthis op; that reshape is a view of the flex output, so launch detaches it\n(no copy) to return a tensor that owns its storage.\n\"\"\"\n\nimport torch\nfrom sglang.multimodal_gen.runtime.models.decoders import (\n ltx_2_5_diffusion_decoder as ltx,\n)\n\nKERNEL_SIZE = (3, 5, 5)\n\n# The engine meets a few grids per process; one judge process meets every\n# workload's shape. Past dynamo's default limit of 8 recompiles flex_attention\n# would run uncompiled, materializing the dense S x S scores.\ntorch._dynamo.config.recompile_limit = 1024\ntorch._dynamo.config.accumulated_recompile_limit = 4096\n# The judge rejects a candidate that leaves child processes running, and\n# inductor's parallel compile pool is one; compile in-process instead.\ntorch._inductor.config.compile_threads = 1\n\n\ndef prepare(Q, K, V):\n batch_size, num_frames, height, width, heads, head_dim = Q.shape\n seq_len = num_frames * height * width\n # :451-459\n query = Q.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n key = K.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n value = V.reshape(batch_size, seq_len, heads, head_dim).transpose(1, 2)\n # build_block_mask, :420-423\n block_mask = ltx._neighborhood_block_mask(\n num_frames, height, width, KERNEL_SIZE, Q.device\n )\n flex_attention = ltx._flex_attention_fn()\n\n def launch():\n # :465-467\n hidden_states = flex_attention(\n query, key, value, block_mask=block_mask, scale=1.0\n )\n # :468-470, heads kept split. The reshape is a view of flex_attention's\n # own exact-sized output; detach() hands it over as a tensor of its own.\n return hidden_states.transpose(1, 2).reshape(Q.shape).detach()\n\n return launch\n\n\ndef run(Q, K, V):\n return prepare(Q, K, V)()\n" - } - ] -} diff --git a/solutions/sparse_mla/flashmla_prefill.json b/solutions/sparse_mla/flashmla_prefill.json new file mode 100644 index 0000000000000000000000000000000000000000..c22f87df8f05fd53227fa1c6c8db425c9cc9baca --- /dev/null +++ b/solutions/sparse_mla/flashmla_prefill.json @@ -0,0 +1,24 @@ +{ + "name": "flashmla_prefill", + "definition": "sparse_mla_prefill_h64_d512", + "author": "inference-kernels", + "description": "FlashMLA Blackwell BF16 sparse prefill with 128 or 640 index slots, per-query sparse lengths and attention sinks. Merged by KDA from the per-shape solutions when the per-shape definitions were merged. Upstream: https://github.com/deepseek-ai/FlashMLA/tree/0f11e178da1f67becb087a324b400854931ec3e3 (MIT).", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B200" + ], + "entry_point": "main.py::run", + "dependencies": [ + "torch>=2.11", + "flash-mla @ git+https://github.com/deepseek-ai/FlashMLA.git@0f11e178da1f67becb087a324b400854931ec3e3" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "from flash_mla import flash_mla_sparse_fwd\n\ndef run(Q, KV, indices, topk_length, attn_sink, sm_scale):\n return flash_mla_sparse_fwd(\n Q, KV, indices, sm_scale, d_v=512,\n attn_sink=attn_sink, topk_length=topk_length,\n )[0]\n" + } + ] +} diff --git a/solutions/sparse_mla/flashmla_prefill_k128.json b/solutions/sparse_mla/flashmla_prefill_k128.json deleted file mode 100644 index a443b0fd8f001c3dbe6bb6eb9d5719bfeaed7bfc..0000000000000000000000000000000000000000 --- a/solutions/sparse_mla/flashmla_prefill_k128.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "flashmla_prefill_k128", - "definition": "sparse_mla_prefill_h64_d512_k128", - "author": "inference-kernels", - "description": "FlashMLA Blackwell BF16 sparse prefill with 128 index slots, per-query sparse lengths and attention sinks. Upstream: https://github.com/deepseek-ai/FlashMLA/tree/0f11e178da1f67becb087a324b400854931ec3e3 (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "flash-mla @ git+https://github.com/deepseek-ai/FlashMLA.git@0f11e178da1f67becb087a324b400854931ec3e3"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "from flash_mla import flash_mla_sparse_fwd\n\ndef run(Q, KV, indices, topk_length, attn_sink, sm_scale):\n return flash_mla_sparse_fwd(\n Q, KV, indices, sm_scale, d_v=512,\n attn_sink=attn_sink, topk_length=topk_length,\n )[0]\n"}] -} diff --git a/solutions/sparse_mla/flashmla_prefill_k640.json b/solutions/sparse_mla/flashmla_prefill_k640.json deleted file mode 100644 index cabcb2ae2d2554b06a004d80ab804d6444abcd94..0000000000000000000000000000000000000000 --- a/solutions/sparse_mla/flashmla_prefill_k640.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "name": "flashmla_prefill_k640", - "definition": "sparse_mla_prefill_h64_d512_k640", - "author": "inference-kernels", - "description": "FlashMLA Blackwell BF16 sparse prefill with 640 index slots, per-query sparse lengths and attention sinks. Upstream: https://github.com/deepseek-ai/FlashMLA/tree/0f11e178da1f67becb087a324b400854931ec3e3 (MIT).", - "spec": {"language": "python", "target_hardware": ["NVIDIA_B200"], "entry_point": "main.py::run", "dependencies": ["torch>=2.11", "flash-mla @ git+https://github.com/deepseek-ai/FlashMLA.git@0f11e178da1f67becb087a324b400854931ec3e3"], "destination_passing_style": false}, - "sources": [{"path": "main.py", "content": "from flash_mla import flash_mla_sparse_fwd\n\ndef run(Q, KV, indices, topk_length, attn_sink, sm_scale):\n return flash_mla_sparse_fwd(\n Q, KV, indices, sm_scale, d_v=512,\n attn_sink=attn_sink, topk_length=topk_length,\n )[0]\n"}] -} diff --git a/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512.json b/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512.json new file mode 100644 index 0000000000000000000000000000000000000000..f99a304c07cc86dd0110f79255914988828aff36 --- /dev/null +++ b/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512.json @@ -0,0 +1,23 @@ +{ + "name": "sglang_sparse_mla_prefill_h64_d512", + "definition": "sparse_mla_prefill_h64_d512", + "author": "kda", + "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", + "spec": { + "language": "python", + "target_hardware": [ + "NVIDIA_B300" + ], + "entry_point": "main.py::run", + "dependencies": [ + "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" + ], + "destination_passing_style": false + }, + "sources": [ + { + "path": "main.py", + "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for sparse_mla_prefill_h64_d512 (TopK 128 or 640).\n\nSGLang 684fea8a, DeepSeek-V4.1 sparse prefill with ``--tp-size 4``:\n``DeepseekV4AttnBackend._forward_prefill_sparse``\n(sglang/srt/layers/attention/deepseek_v4_backend.py:3848) calls\n``sgl_kernel.flash_mla.flash_mla_sparse_fwd(q, kv, indices, sm_scale, d_v,\nattn_sink, topk_length)`` (:3916) on the BF16 workspace. Each rank passes its\n16 query heads in a zero-filled 64-head buffer (models/deepseek_v4.py:2342,\nwidth :1054) with ``attn_sink`` sliced to the rank and zero-padded the same way\n(:1056-1079), and keeps its first 16 output heads (:2492).\n\nAdapter: the four ranks run one after another and their heads are\nconcatenated in rank order, as the capture gathers them.\n\"\"\"\n\nimport torch\nfrom sgl_kernel.flash_mla import flash_mla_sparse_fwd\n\nTP = 4 # capture: --tp-size 4\n\n\ndef prepare(Q, KV, indices, topk_length, attn_sink, sm_scale):\n heads = Q.shape[1] // TP\n q_padded = torch.zeros_like(Q)\n sink = torch.zeros_like(attn_sink)\n\n def launch():\n out = []\n for rank in range(TP):\n local = slice(rank * heads, (rank + 1) * heads)\n q_padded[:, :heads] = Q[:, local]\n sink[:heads] = attn_sink[local]\n o, _, _ = flash_mla_sparse_fwd(\n q=q_padded,\n kv=KV,\n indices=indices,\n sm_scale=float(sm_scale),\n d_v=Q.shape[-1],\n attn_sink=sink,\n topk_length=topk_length,\n )\n out.append(o[:, :heads])\n return torch.cat(out, 1)\n\n return launch\n\n\ndef run(Q, KV, indices, topk_length, attn_sink, sm_scale):\n return prepare(Q, KV, indices, topk_length, attn_sink, sm_scale)()\n" + } + ] +} diff --git a/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k128.json b/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k128.json deleted file mode 100644 index 5dfa3aa17314a60403a7a174afed7ed1b2bef461..0000000000000000000000000000000000000000 --- a/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k128.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_sparse_mla_prefill_h64_d512_k128", - "definition": "sparse_mla_prefill_h64_d512_k128", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for sparse_mla_prefill_h64_d512_k128.\n\nSGLang 684fea8a, DeepSeek-V4.1 sparse prefill with ``--tp-size 4``:\n``DeepseekV4AttnBackend._forward_prefill_sparse``\n(sglang/srt/layers/attention/deepseek_v4_backend.py:3848) calls\n``sgl_kernel.flash_mla.flash_mla_sparse_fwd(q, kv, indices, sm_scale, d_v,\nattn_sink, topk_length)`` (:3916) on the BF16 workspace. Each rank passes its\n16 query heads in a zero-filled 64-head buffer (models/deepseek_v4.py:2342,\nwidth :1054) with ``attn_sink`` sliced to the rank and zero-padded the same way\n(:1056-1079), and keeps its first 16 output heads (:2492).\n\nAdapter: the four ranks run one after another and their heads are\nconcatenated in rank order, as the capture gathers them.\n\"\"\"\n\nimport torch\nfrom sgl_kernel.flash_mla import flash_mla_sparse_fwd\n\nTP = 4 # capture: --tp-size 4\n\n\ndef prepare(Q, KV, indices, topk_length, attn_sink, sm_scale):\n heads = Q.shape[1] // TP\n q_padded = torch.zeros_like(Q)\n sink = torch.zeros_like(attn_sink)\n\n def launch():\n out = []\n for rank in range(TP):\n local = slice(rank * heads, (rank + 1) * heads)\n q_padded[:, :heads] = Q[:, local]\n sink[:heads] = attn_sink[local]\n o, _, _ = flash_mla_sparse_fwd(\n q=q_padded,\n kv=KV,\n indices=indices,\n sm_scale=float(sm_scale),\n d_v=Q.shape[-1],\n attn_sink=sink,\n topk_length=topk_length,\n )\n out.append(o[:, :heads])\n return torch.cat(out, 1)\n\n return launch\n\n\ndef run(Q, KV, indices, topk_length, attn_sink, sm_scale):\n return prepare(Q, KV, indices, topk_length, attn_sink, sm_scale)()\n" - } - ] -} diff --git a/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k640.json b/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k640.json deleted file mode 100644 index d970832ddca9352e67c3d2286e84a1db00cdb3f5..0000000000000000000000000000000000000000 --- a/solutions/sparse_mla/sglang_sparse_mla_prefill_h64_d512_k640.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "sglang_sparse_mla_prefill_h64_d512_k640", - "definition": "sparse_mla_prefill_h64_d512_k640", - "author": "kda", - "description": "SGLang's own implementation at the capture commit 684fea8a, called the way the engine calls it (see the module docstring). Run it in lmsysorg/sglang@sha256:ddae0e399d3d94f7827f16183301cf8dac4cf0eae0ef25590b7dac5881d713d8. KDA (Kernel Design Agents) times it live as the baseline: prepare(*inputs) does the host-side setup and returns a zero-argument launch, and only launch is timed; run(*inputs) does both.", - "spec": { - "language": "python", - "target_hardware": [ - "NVIDIA_B300" - ], - "entry_point": "main.py::run", - "dependencies": [ - "sglang @ git+https://github.com/sgl-project/sglang.git@684fea8af3b11a5b51ec9966c02959a32ad3421c#subdirectory=python" - ], - "destination_passing_style": false - }, - "sources": [ - { - "path": "main.py", - "content": "# Copyright 2026 Co-developed by KDA (Kernel Design Agents)\n\"\"\"SGLang baseline for sparse_mla_prefill_h64_d512_k640.\n\nSGLang 684fea8a, DeepSeek-V4.1 sparse prefill with ``--tp-size 4``:\n``DeepseekV4AttnBackend._forward_prefill_sparse``\n(sglang/srt/layers/attention/deepseek_v4_backend.py:3848) calls\n``sgl_kernel.flash_mla.flash_mla_sparse_fwd(q, kv, indices, sm_scale, d_v,\nattn_sink, topk_length)`` (:3916) on the BF16 workspace. Each rank passes its\n16 query heads in a zero-filled 64-head buffer (models/deepseek_v4.py:2342,\nwidth :1054) with ``attn_sink`` sliced to the rank and zero-padded the same way\n(:1056-1079), and keeps its first 16 output heads (:2492).\n\nAdapter: the four ranks run one after another and their heads are\nconcatenated in rank order, as the capture gathers them.\n\"\"\"\n\nimport torch\nfrom sgl_kernel.flash_mla import flash_mla_sparse_fwd\n\nTP = 4 # capture: --tp-size 4\n\n\ndef prepare(Q, KV, indices, topk_length, attn_sink, sm_scale):\n heads = Q.shape[1] // TP\n q_padded = torch.zeros_like(Q)\n sink = torch.zeros_like(attn_sink)\n\n def launch():\n out = []\n for rank in range(TP):\n local = slice(rank * heads, (rank + 1) * heads)\n q_padded[:, :heads] = Q[:, local]\n sink[:heads] = attn_sink[local]\n o, _, _ = flash_mla_sparse_fwd(\n q=q_padded,\n kv=KV,\n indices=indices,\n sm_scale=float(sm_scale),\n d_v=Q.shape[-1],\n attn_sink=sink,\n topk_length=topk_length,\n )\n out.append(o[:, :heads])\n return torch.cat(out, 1)\n\n return launch\n\n\ndef run(Q, KV, indices, topk_length, attn_sink, sm_scale):\n return prepare(Q, KV, indices, topk_length, attn_sink, sm_scale)()\n" - } - ] -} diff --git a/workloads/dense_attention/noncausal_dense_attention_h32.jsonl b/workloads/dense_attention/noncausal_dense_attention_h32.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a3a56902f2a99f70534afd256e793cd2fba64ccf --- /dev/null +++ b/workloads/dense_attention/noncausal_dense_attention_h32.jsonl @@ -0,0 +1,162 @@ +{"definition": "noncausal_dense_attention_h32", "workload": {"axes": {"B": 1, "D": 64, "Nk": 51, "Nq": 51}, "inputs": {"K": {"path": "blob/workloads/noncausal_dense_attention_h32/f6db002ee61f456a3a4d0ad4974f29ece53ddad35964552e21cac134444d396b.safetensors", "tensor_key": "tensor", "type": "safetensors"}, "Q": {"path": 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