FIT-side companion to two-mode timing PR: setup-hook solutions + first traces

solutions/kernel-arena/gqa_paged/gqa_paged_prefill_causal_h16_kv1_d128_ps64/fa3_gqa_prefill_setuphook_v1.json

@@ -0,0 +1,22 @@
+{
+  "name": "fa3_gqa_prefill_setuphook_v1",
+  "definition": "gqa_paged_prefill_causal_h16_kv1_d128_ps64",
+  "author": "kernel-arena",
+  "description": "FA3 paged-prefill wrapper for gqa_paged_prefill_causal_h16_kv1_d128_ps64, exposing menyu's setup-hook contract (setup() returns a dict that is splatted as kwargs into run()). Canonical FIT-side example for the kernel-agnostic two-mode timing engine \u2014 kernel_ms captures only the run() FA3 call while setup() handles all per-workload page-table and scheduler-metadata precomputation.",
+  "spec": {
+    "language": "python",
+    "target_hardware": [
+      "NVIDIA H100",
+      "NVIDIA H200"
+    ],
+    "entry_point": "main.py::run",
+    "dependencies": [],
+    "destination_passing_style": false
+  },
+  "sources": [
+    {
+      "path": "main.py",
+      "content": "\"\"\"\nfa3_gqa_prefill_setuphook_v1 \u2014 FA3 paged-prefill wrapper for\ngqa_paged_prefill_causal_h16_kv1_d128_ps64, written in the menyu setup-hook\ncontract used by the flashinfer-bench kernel-agnostic two-mode timing engine.\n\nCompared to `fa3_gqa_prefill_v1.json` (the original plain-run version), this\nsolution exposes two top-level symbols:\n\n  setup(*args) -> dict\n      Precomputes page_table, cu_seqlens_q, cache_seqlens, max_seqlen_q, and\n      FA3 scheduler_metadata from the meta inputs (qo_indptr / kv_indptr /\n      kv_indices / kv_last_page_len). Runs ONCE outside the timer in two-mode\n      kernel measurements; runs per-iter inside the timer for e2e.\n\n  run(*args, **state)\n      Kernel hot path. Reads all meta state from the setup-injected kwargs\n      and touches only the data inputs (q / k_cache / v_cache) at GPU level.\n      This is what `time_runnable_two_mode.kernel_ms` captures into a CUDA\n      graph.\n\nReference flashinfer-bench validation: PR feat/two-mode-kernel-agnostic in\nnvidia-china-sae/flashinfer-bench-fork. Companion trace data lives at\n`traces/baseline/gqa_paged/` (48 new two-mode evaluations).\n\"\"\"\nimport math\nfrom typing import Tuple\n\nimport torch\nfrom flash_attn_interface import flash_attn_with_kvcache\n\ntry:\n    from flash_attn_interface import get_scheduler_metadata as _get_sched_meta\nexcept Exception:\n    _get_sched_meta = None\n\n_LOG2 = math.log(2.0)\n\n# Definition-specific constants (gqa_paged_prefill_causal_h16_kv1_d128_ps64).\nH_KV = 1\nHEAD_DIM = 128\nPAGE_SIZE = 64\n\n\ndef setup(q, k_cache, v_cache, qo_indptr, kv_indptr, kv_indices, kv_last_page_len, sm_scale):\n    \"\"\"Build per-workload meta state.\n\n    Inputs:\n        q / k_cache / v_cache    : data; ignored by setup\n        qo_indptr / kv_indptr / kv_indices / kv_last_page_len : meta\n        sm_scale                 : scalar\n\n    Returns a dict whose keys become keyword-only args of run().\n    \"\"\"\n    B = qo_indptr.shape[0] - 1\n    device = q.device\n\n    pages_per_seq = (kv_indptr[1:] - kv_indptr[:-1]).to(torch.int32)\n    max_pages = int(pages_per_seq.max().item())\n    page_table = torch.zeros((B, max_pages), dtype=torch.int32, device=device)\n    for b in range(B):\n        s = int(kv_indptr[b].item())\n        e = int(kv_indptr[b + 1].item())\n        page_table[b, : e - s] = kv_indices[s:e]\n\n    cache_seqlens = (pages_per_seq - 1) * PAGE_SIZE + kv_last_page_len[:B].to(torch.int32)\n    cu_seqlens_q = qo_indptr.to(torch.int32)\n    max_seqlen_q = int((qo_indptr[1:] - qo_indptr[:-1]).max().item())\n\n    sched_meta = None\n    if _get_sched_meta is not None:\n        try:\n            num_pages = k_cache.shape[0]\n            kv_len_max = int(cache_seqlens.max().item())\n            sched_meta = _get_sched_meta(\n                B, max_seqlen_q, kv_len_max, num_pages, H_KV, HEAD_DIM,\n                cache_seqlens, torch.bfloat16, headdim_v=HEAD_DIM,\n                page_size=PAGE_SIZE, causal=True,\n            )\n        except Exception:\n            sched_meta = None\n\n    return {\n        \"page_table\": page_table,\n        \"cache_seqlens\": cache_seqlens,\n        \"cu_seqlens_q\": cu_seqlens_q,\n        \"max_seqlen_q\": max_seqlen_q,\n        \"scheduler_metadata\": sched_meta,\n    }\n\n\ndef run(\n    q, k_cache, v_cache, qo_indptr, kv_indptr, kv_indices, kv_last_page_len, sm_scale,\n    *, page_table, cache_seqlens, cu_seqlens_q, max_seqlen_q, scheduler_metadata,\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Kernel hot path. All meta state arrives pre-computed via kwargs.\"\"\"\n    kwargs = dict(\n        page_table=page_table,\n        cache_seqlens=cache_seqlens,\n        cu_seqlens_q=cu_seqlens_q,\n        max_seqlen_q=max_seqlen_q,\n        softmax_scale=float(sm_scale),\n        causal=True,\n        return_softmax_lse=True,\n    )\n    if scheduler_metadata is not None:\n        kwargs[\"scheduler_metadata\"] = scheduler_metadata\n\n    out, lse_natural, *_ = flash_attn_with_kvcache(q, k_cache, v_cache, **kwargs)\n    output = out.contiguous()\n    if lse_natural.dim() == 2 and lse_natural.shape[0] == output.shape[1]:\n        lse_natural = lse_natural.transpose(0, 1)\n    lse_b2 = lse_natural * (1.0 / _LOG2)\n    return output, lse_b2.contiguous()\n"
+    }
+  ]
+}

solutions/kernel-arena/mla_paged/mla_paged_prefill_causal_h16_ckv512_kpe64_ps1/flashinfer_mla_setuphook_v1.json

@@ -0,0 +1,22 @@
+{
+  "name": "flashinfer_mla_setuphook_v1",
+  "definition": "mla_paged_prefill_causal_h16_ckv512_kpe64_ps1",
+  "author": "kernel-arena",
+  "description": "FlashInfer MLA paged-prefill wrapper for mla_paged_prefill_causal_h16_ckv512_kpe64_ps1, exposing menyu's setup-hook contract (setup() builds + plans the BatchMLAPagedAttentionWrapper and returns it as {wrapper: ...} which run() consumes as a kwarg). Canonical FIT-side example for the kernel-agnostic two-mode timing engine \u2014 kernel_ms captures only the wrapper.run() call while setup() handles the heavy plan() metadata build.",
+  "spec": {
+    "language": "python",
+    "target_hardware": [
+      "NVIDIA H100",
+      "NVIDIA H200"
+    ],
+    "entry_point": "main.py::run",
+    "dependencies": [],
+    "destination_passing_style": false
+  },
+  "sources": [
+    {
+      "path": "main.py",
+      "content": "\"\"\"\nflashinfer_mla_setuphook_v1 \u2014 FlashInfer MLA paged-attention wrapper for\nmla_paged_prefill_causal_h16_ckv512_kpe64_ps1, written in the menyu setup-hook\ncontract used by the flashinfer-bench kernel-agnostic two-mode timing engine.\n\nsetup() builds + plans a BatchMLAPagedAttentionWrapper from the meta inputs\n(qo_indptr / kv_indptr / kv_indices) and stashes the wrapper into the\nreturned dict. run() reads the wrapper as a setup-injected kwarg and calls\nwrapper.run() with the data inputs (q_nope / q_pe / ckv_cache / kpe_cache).\n\nThis is the wrapper-shape solution corresponding to the original\n`flashinfer_wrapper_ea3787` baseline, restructured so that the heavy\nwrapper.plan() cost is no longer captured by the kernel-only metric.\n\"\"\"\nimport torch\nimport flashinfer\nfrom flashinfer.mla import BatchMLAPagedAttentionWrapper\n\n_WORKSPACE_SIZE_BYTES = 128 * 1024 * 1024\n\n# Definition-specific constants (mla_paged_prefill_causal_h16_ckv512_kpe64_ps1).\nNUM_QO_HEADS = 16\nHEAD_DIM_CKV = 512\nHEAD_DIM_KPE = 64\nPAGE_SIZE = 1\n\n\ndef setup(q_nope, q_pe, ckv_cache, kpe_cache, qo_indptr, kv_indptr, kv_indices, sm_scale):\n    \"\"\"Build + plan the MLA wrapper from per-workload meta inputs.\"\"\"\n    device = q_nope.device\n    workspace = torch.empty(_WORKSPACE_SIZE_BYTES, dtype=torch.int8, device=device)\n    wrapper = BatchMLAPagedAttentionWrapper(workspace)\n    kv_len_arr = (kv_indptr[1:] - kv_indptr[:-1]).to(torch.int32)\n    wrapper.plan(\n        qo_indptr=qo_indptr.to(torch.int32),\n        kv_indptr=kv_indptr.to(torch.int32),\n        kv_indices=kv_indices.to(torch.int32),\n        kv_len_arr=kv_len_arr,\n        num_heads=NUM_QO_HEADS,\n        head_dim_ckv=HEAD_DIM_CKV,\n        head_dim_kpe=HEAD_DIM_KPE,\n        page_size=PAGE_SIZE,\n        causal=True,\n        sm_scale=float(sm_scale),\n        q_data_type=torch.bfloat16,\n        kv_data_type=torch.bfloat16,\n    )\n    return {\"wrapper\": wrapper}\n\n\ndef run(\n    q_nope, q_pe, ckv_cache, kpe_cache, qo_indptr, kv_indptr, kv_indices, sm_scale,\n    *, wrapper,\n):\n    \"\"\"Kernel hot path. Pre-planned wrapper arrives via setup-injected kwarg.\"\"\"\n    return wrapper.run(q_nope, q_pe, ckv_cache, kpe_cache, return_lse=True)\n"
+    }
+  ]
+}

traces/baseline/gqa_paged/gqa_paged_decode_h32_kv8_d128_ps1.jsonl

@@ -46,3 +46,51 @@
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