{"type":"system","subtype":"init","cwd":"/workspace","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","tools":["Task","Bash","CronCreate","CronDelete","CronList","Edit","EnterWorktree","ExitWorktree","ListAgents","NotebookEdit","Read","ReportFindings","ScheduleWakeup","SendMessage","Skill","TaskStop","ToolSearch","Workflow","Write"],"mcp_servers":[],"model":"claude-opus-5-5","permissionMode":"bypassPermissions","slash_commands":["deep-research","dataviz","update-config","verify","debug","code-review","simplify","batch","fewer-permission-prompts","doctor","loop","claude-api","workflow-authoring","run","run-skill-generator","plugin-authoring","agents","auto-mode-setup","autocompact","clear","color","compact","config","output-style","context","effort","fast","focus","heapdump","init","mcp","model","__remote-workflow","workflow-launch-exec","reload-plugins","reload-skills","rename","security-review","usage","insights","recap","goal","list-agents","team-onboarding"],"terminal_slash_commands":["doctor","color","focus","reload-plugins"],"apiKeySource":"ANTHROPIC_API_KEY","claude_code_version":"2.1.302","output_style":"default","agents":["claude","Explore","general-purpose","Plan","statusline-setup"],"skills":["deep-research","dataviz","update-config","verify","debug","code-review","simplify","batch","fewer-permission-prompts","doctor","loop","claude-api","workflow-authoring","run","run-skill-generator","plugin-authoring"],"plugins":[{"name":"cc-plugin-agents-md","path":"builtin","source":"cc-plugin-agents-md@builtin"},{"name":"cc-plugin-plugin-authoring","path":"builtin","source":"cc-plugin-plugin-authoring@builtin"}],"capabilities":["interrupt_receipt_v1","interrupt_cancel_queued_v1","interrupt_send_now_v1","msg_lifecycle_v1","request_marker_lists_v1","sdk_mcp_tools_list_changed","sdk_mcp_manifests","mcp_read_resource_v1","mcp_tool_ui_meta_v1","ui_surface_v1","mcp_call_result_not_for_model_v1"],"analytics_disabled":true,"product_feedback_disabled":false,"uuid":"a3a6ca70-dfd6-4218-9e91-0e143c51a4d1","memory_paths":{"auto":"/logs/agent/sessions/projects/-workspace/memory/"},"original_cwd":"/workspace","additional_directories":[],"fast_mode_state":"off","fast_mode_disabled_reason":"sdk_opt_in_required","per_turn_effort_active":true,"view_mode":"default"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9ovMXjVqn75ZKGh5Vza","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6859,"cache_read_input_tokens":12056,"cache_creation":{"ephemeral_5m_input_tokens":6859,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"50c8ec01-2abc-4ac6-a68d-90c7af05f0f3","timestamp":"2026-10-18T13:49:57.285Z","request_id":"req_011Cg9ovMCPpBb59Kyiv9Eaa","thinking_duration_ms":726,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9ovMXjVqn75ZKGh5Vza","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TfwAdENceDSaNBfuHeMn8U","name":"Bash","input":{"command":"ls -la && cat task.py && cat starting_kernel.py && cat check.py && cat static_check.py","description":"Inspect workspace files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6859,"cache_read_input_tokens":12056,"cache_creation":{"ephemeral_5m_input_tokens":6859,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"cb41b88f-574b-46ad-b20c-859681f7a2e1","timestamp":"2026-10-18T13:49:57.854Z","request_id":"req_011Cg9ovMCPpBb59Kyiv9Eaa"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TfwAdENceDSaNBfuHeMn8U","type":"tool_result","content":"<persisted-output>\nOutput too large (40.8KB). Full output saved to: /logs/agent/sessions/projects/-workspace/711e70e5-71bc-4971-97a3-904cce35aad5/tool-results/b024e3pd9.txt\n\nPreview (first 2KB):\ntotal 53\ndrwxr-xr-x 1 agent agent   130 Oct 18 11:07 .\ndrwxr-xr-x 1 root  root    220 Oct 18 13:49 ..\n-rw-r--r-- 1 agent agent  3034 Oct 18 11:07 check.py\n-rw-r--r-- 1 agent agent  4816 Oct 18 11:07 nodus_autosave.py\n-rw-r--r-- 1 agent agent  5666 Oct 18 11:07 starting_kernel.py\n-rw-r--r-- 1 agent agent 13355 Oct 18 11:07 static_check.py\n-rw-r--r-- 1 agent agent 19226 Oct 18 11:07 task.py\n-rw-r--r-- 1 agent agent  5143 Oct 18 11:07 task_api.py\n\"\"\"Public task definition for xpr_hd256_attn_bwd: reference, check, timed shapes, standard normal inputs. The verifier also uses edge shapes and hidden input distributions that are not in this file.\"\"\"\n\"\"\"Causal attention backward with head dim 256 (dq, dk, dv) on Blackwell, GQA, sliding windows and packed documents\n(expert PR task).\n\nSource: Dao-AILab/flash-attention PRs #2964, #2965 and #2966 \"[CuTe, SM100] Unify hd256 backward (1/5-3/5)\" (Johnson,\nmerged 2026-10-06, 10-15 and 10-16), which rebuilt FlashAttention-4's head-dim-256 training backward on Blackwell on\ntop of #2807, #2891 and #2952. Upstream main (965c921, the #2966 merge, 2026-10-16) has two public hd256 backwards: the\ndefault \"dedicated\" pair (a dQ kernel and a dKdV kernel that each recompute S and dP, 2-CTA, no sliding window) and\nthe opt-in general kernel FlashAttentionBackwardSm100 (one kernel, 64-row KV tile per CTA, 2-CTA, any window,\nFLASH_ATTENTION_HD256_GENERIC_BWD=1), which the authors measured at parity to 11% faster than the pair on most shapes\nand 22-29% slower on 2K GQA (B200). Open PR #2967 (head 90419de, 2026-10-18) packs a KV head's query heads into the\ngeneral kernel's Q tile, 1-11% faster than the pair on hd256 GQA per its author. The bar on each shape is the fastest\nof the three (vendored in kernel_lab/expert_pr/fa4_v2966 and fa4_v2967; the route per shape is in fa4_v2966/b300.py),\nat 1.0x its speed in the same run.\n\nShapes: training of head-dim-256 models per GPU: Gemma-3-style global and sliding-window-1024 local layers (16 query\n...\n</persisted-output>","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"caa36326-1113-4377-bbc7-23fc9b321c3d","timestamp":"2026-10-18T13:49:57.971Z","tool_use_result":{"stdout":"total 53\ndrwxr-xr-x 1 agent agent   130 Oct 18 11:07 .\ndrwxr-xr-x 1 root  root    220 Oct 18 13:49 ..\n-rw-r--r-- 1 agent agent  3034 Oct 18 11:07 check.py\n-rw-r--r-- 1 agent agent  4816 Oct 18 11:07 nodus_autosave.py\n-rw-r--r-- 1 agent agent  5666 Oct 18 11:07 starting_kernel.py\n-rw-r--r-- 1 agent agent 13355 Oct 18 11:07 static_check.py\n-rw-r--r-- 1 agent agent 19226 Oct 18 11:07 task.py\n-rw-r--r-- 1 agent agent  5143 Oct 18 11:07 task_api.py\n\"\"\"Public task definition for xpr_hd256_attn_bwd: reference, check, timed shapes, standard normal inputs. The verifier also uses edge shapes and hidden input distributions that are not in this file.\"\"\"\n\"\"\"Causal attention backward with head dim 256 (dq, dk, dv) on Blackwell, GQA, sliding windows and packed documents\n(expert PR task).\n\nSource: Dao-AILab/flash-attention PRs #2964, #2965 and #2966 \"[CuTe, SM100] Unify hd256 backward (1/5-3/5)\" (Johnson,\nmerged 2026-10-06, 10-15 and 10-16), which rebuilt FlashAttention-4's head-dim-256 training backward on Blackwell on\ntop of #2807, #2891 and #2952. Upstream main (965c921, the #2966 merge, 2026-10-16) has two public hd256 backwards: the\ndefault \"dedicated\" pair (a dQ kernel and a dKdV kernel that each recompute S and dP, 2-CTA, no sliding window) and\nthe opt-in general kernel FlashAttentionBackwardSm100 (one kernel, 64-row KV tile per CTA, 2-CTA, any window,\nFLASH_ATTENTION_HD256_GENERIC_BWD=1), which the authors measured at parity to 11% faster than the pair on most shapes\nand 22-29% slower on 2K GQA (B200). Open PR #2967 (head 90419de, 2026-10-18) packs a KV head's query heads into the\ngeneral kernel's Q tile, 1-11% faster than the pair on hd256 GQA per its author. The bar on each shape is the fastest\nof the three (vendored in kernel_lab/expert_pr/fa4_v2966 and fa4_v2967; the route per shape is in fa4_v2966/b300.py),\nat 1.0x its speed in the same run.\n\nShapes: training of head-dim-256 models per GPU: Gemma-3-style global and sliding-window-1024 local layers (16 query\nand 8 KV heads, or 8 and 4), a Qwen3-Next-style full-attention layer (16 query heads, 2 KV heads) and packed\ndocuments of mixed lengths, 8K-16K tokens per step. o and lse come from an fp32 forward over the same inputs.\n\"\"\"\nfrom task_api import Case, Task\nAGENT_MINUTES = 120\nD = 256\nSCALE = D ** (-0.5)\nGAMMA = 2.0 ** (-17)\nOUTS = ((9, 'dq'), (10, 'dk'), (11, 'dv'))\n\ndef seq_lens(shape, g, device):\n    \"\"\"n sequences of length L, or with 'ragged' n of mean length L in [L/2, 3L/2], built in pairs (L + d, L - d) with\n    the first pair fixed at (3L/2, L/2), so the token count (n * L) and the longest sequence depend on the shape only;\n    'long' puts one extra sequence of that length first.\"\"\"\n    import torch\n    n, L = (shape['n'], shape['L'])\n    if shape.get('ragged'):\n        h = L // 2\n        d = torch.randint(-h, h + 1, (n // 2,), generator=g, device=device, dtype=torch.int64)\n        if n >= 2:\n            d[0] = h\n        lens = torch.stack([L + d, L - d], 1).reshape(-1)\n        if n % 2:\n            lens = torch.cat([lens, torch.full((1,), L, device=device, dtype=torch.int64)])\n    else:\n        lens = torch.full((n,), L, device=device, dtype=torch.int64)\n    if shape.get('long'):\n        lens = torch.cat([torch.tensor([shape['long']], device=device, dtype=torch.int64), lens])\n    return lens\n\ndef _visible(t0, t1, k0, k1, W, device):\n    \"\"\"[t1 - t0, k1 - k0] bool: key j is visible to query i of the same sequence when j <= i and, with a window W >= 0,\n    i - j <= W.\"\"\"\n    import torch\n    i = torch.arange(t0, t1, device=device).view(-1, 1)\n    j = torch.arange(k0, k1, device=device).view(1, -1)\n    m = j <= i\n    return m & (i - j <= W) if W >= 0 else m\n\ndef _chunks(cu, W, rows):\n    \"\"\"(t0, t1, k0) per query chunk of every sequence: keys k0 .. t1 - 1 hold every key a query of the chunk sees.\"\"\"\n    bounds = cu.tolist()\n    for s0, s1 in zip(bounds[:-1], bounds[1:]):\n        for t0 in range(s0, s1, rows):\n            t1 = min(s1, t0 + rows)\n            yield (t0, t1, s0 if W < 0 else max(s0, t0 - W))\n\ndef _rows(H, n_keys):\n    \"\"\"Query tokens per reference chunk: the [H, tokens, keys] fp32 intermediates stay at 64 MiB.\"\"\"\n    return max(16, 2 ** 24 // (H * max(1, n_keys)))\n\ndef _mm(eq, a, b):\n    \"\"\"einsum(eq, a, b) at fp32 accuracy for fp32 a and bf16-valued b, also when matmuls run in TF32: a splits into a\n    TF32-exact head (11 significant bits) and an exact fp32 tail, and every bf16 x head product is exact.\"\"\"\n    import torch\n    hi = (a.view(torch.int32) & -8192).view(torch.float32)\n    return torch.einsum(eq, hi, b) + torch.einsum(eq, a - hi, b)\n\ndef _tf32(fn, args):\n    \"\"\"Runs fn(args) with TF32 matmuls on CUDA (every product is split or exact, see _mm), restoring the setting.\"\"\"\n    import torch\n    prev = torch.get_float32_matmul_precision()\n    if args[0].is_cuda:\n        torch.set_float32_matmul_precision('high')\n    try:\n        return fn(args)\n    finally:\n        torch.set_float32_matmul_precision(prev)\n\ndef _forward(args):\n    \"\"\"The training forward in fp32: o = bf16(softmax(q k^T / 16) v) and lse = log(sum_j exp(q . k_j / 16)), written\n    into args[3] and args[5].\"\"\"\n    import torch\n    q, k, v, o, _, lse, cu, max_len, W = args[:9]\n    T, Hq, _ = q.shape\n    Hkv = k.shape[1]\n    G = Hq // Hkv\n    rows = _rows(Hq, max_len if W < 0 else min(max_len, W + 1))\n    for t0, t1, k0 in _chunks(cu, W, rows):\n        n = t1 - t0\n        qc = q[t0:t1].float().view(n, Hkv, G, D)\n        kc, vc = (k[k0:t1].float(), v[k0:t1].float())\n        s = torch.einsum('nhgd,mhd->hgnm', qc, kc) * SCALE\n        s = s.masked_fill(~_visible(t0, t1, k0, t1, W, q.device), float('-inf'))\n        L = torch.logsumexp(s, -1, keepdim=True)\n        O = _mm('hgnm,mhd->nhgd', torch.exp(s - L), vc)\n        o[t0:t1] = O.reshape(n, Hq, D).to(torch.bfloat16)\n        lse[:, t0:t1] = L.reshape(Hq, n)\n\ndef make_case(shape, seed, device, dist='normal'):\n    \"\"\"q, k, v and dO from the input distribution, generated as [tokens, heads * 256] so row-wise distributions act\n    per token; o and lse from the fp32 forward over the same inputs.\"\"\"\n    import torch\n    from task_api import gen, randn\n    g = gen(device, seed)\n    Hq, Hkv, W = (shape['Hq'], shape['Hkv'], shape['window'])\n    lens = seq_lens(shape, g, device)\n    T = int(lens.sum().item())\n    cu = torch.zeros((lens.numel() + 1,), device=device, dtype=torch.int32)\n    cu[1:] = torch.cumsum(lens, 0).to(torch.int32)\n    bf = torch.bfloat16\n    q = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n    k = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n    v = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n    do = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n    o = torch.empty((T, Hq, D), device=device, dtype=bf)\n    lse = torch.empty((Hq, T), device=device, dtype=torch.float32)\n    max_len = int(lens.max().item())\n    _tf32(_forward, [q, k, v, o, do, lse, cu, max_len, W])\n    dq = torch.empty((T, Hq, D), device=device, dtype=bf)\n    dk = torch.empty((T, Hkv, D), device=device, dtype=bf)\n    dv = torch.empty((T, Hkv, D), device=device, dtype=bf)\n    return Case(args=[q, k, v, o, do, lse, cu, max_len, W, dq, dk, dv], outputs=(9, 10, 11), shape=shape, dist=dist)\n\ndef _reference(args):\n    import torch\n    q, k, v, o, do, lse, cu, max_len, W = args[:9]\n    T, Hq, _ = q.shape\n    Hkv = k.shape[1]\n    G = Hq // Hkv\n    dev, f32 = (q.device, torch.float32)\n    DQ, TQ = (torch.empty((T, Hq, D), device=dev, dtype=f32), torch.empty((T, Hq, D), device=dev, dtype=f32))\n    DK, TK = (torch.zeros((T, Hkv, D), device=dev, dtype=f32), torch.zeros((T, Hkv, D), device=dev, dtype=f32))\n    DV, TV = (torch.zeros_like(DK), torch.zeros_like(DK))\n    kf, vf = (k.float(), v.float())\n    nk, nv = (kf.pow(2).sum(-1).sqrt(), vf.pow(2).sum(-1).sqrt())\n    rows = _rows(Hq, max_len if W < 0 else min(max_len, W + 1))\n    for t0, t1, k0 in _chunks(cu, W, rows):\n        n = t1 - t0\n        qc = q[t0:t1].float().view(n, Hkv, G, D)\n        kc, vc = (kf[k0:t1], vf[k0:t1])\n        dO = do[t0:t1].float().view(n, Hkv, G, D)\n        L = lse[:, t0:t1].view(Hkv, G, n, 1)\n        vis = _visible(t0, t1, k0, t1, W, dev)\n        s = torch.einsum('nhgd,mhd->hgnm', qc, kc)\n        P = torch.exp(s * SCALE - L).masked_fill_(~vis, 0.0)\n        dP = torch.einsum('nhgd,mhd->hgnm', dO, vc)\n        Od = o[t0:t1].double().view(n, Hkv, G, D)\n        delta = (Od * dO.double()).sum(-1).permute(1, 2, 0).unsqueeze(-1)\n        dmd = dP.sub_(delta.float())\n        dS = P * dmd\n        DQ[t0:t1] = (SCALE * _mm('hgnm,mhd->nhgd', dS, kc)).reshape(n, Hq, D)\n        DK[k0:t1] += SCALE * _mm('hgnm,nhgd->mhd', dS, qc)\n        DV[k0:t1] += _mm('hgnm,nhgd->mhd', P, dO)\n        nq = qc.pow(2).sum(-1).sqrt().permute(1, 2, 0).unsqueeze(-1)\n        ndo = dO.pow(2).sum(-1).sqrt().permute(1, 2, 0).unsqueeze(-1)\n        d1 = (Od.abs() * dO.double().abs()).sum(-1).permute(1, 2, 0).unsqueeze(-1).float()\n        kmax = nk[k0:t1].max(0).values.view(Hkv, 1, 1, 1)\n        vmax = nv[k0:t1].max(0).values.view(Hkv, 1, 1, 1)\n        e = (2 * GAMMA + 2.0 ** (-20)) * SCALE * nq * kmax + 2.0 ** (-20) * L.abs() + 2.0 ** (-21)\n        eta = torch.expm1(e)\n        a = 2.0 ** (-7) + eta\n        b = (1 + eta) * (2 * GAMMA) * (ndo * vmax + d1)\n        A = dS.abs_().mul_(a).add_(b * P)\n        TQ[t0:t1] = (SCALE * torch.einsum('hgnm,mhd->nhgd', A, kc.abs())).reshape(n, Hq, D)\n        TK[k0:t1] += SCALE * torch.einsum('hgnm,nhgd->mhd', A, qc.abs())\n        TV[k0:t1] += torch.einsum('hgnm,nhgd->mhd', P.mul_(a), dO.abs())\n    exact = {'dq': DQ, 'dk': DK, 'dv': DV}\n    tol = {'dq': TQ, 'dk': TK, 'dv': TV}\n    res = {}\n    for i, nm in OUTS:\n        x = exact[nm]\n        res[i] = x.to(torch.bfloat16)\n        res[nm] = x\n        res['tol_' + nm] = tol[nm] * (1 + 2.0 ** (-8)) + 2.0 ** (-7) * x.abs() + 2.0 ** (-100)\n    return res\n\ndef reference(args):\n    \"\"\"The contract in fp32 from the bf16 inputs (every product exact or split to fp32 accuracy, delta in float64), in\n    chunks of query tokens per sequence: P from the given lse, dS = P (dP - delta) with delta = sum(dO * o), dq per\n    token, dk and dv summed over every query (and query head of the KV head's group) that sees the key. Returns the\n    outputs in bf16 (int keys) and, for the check, the exact values in fp32 plus per-element tolerances 'tol_<name>'.\"\"\"\n    return _tf32(_reference, args)\n\ndef check(got, exp):\n    \"\"\"Per element, every output must lie within its tolerance of the exact value: |got - exact| <= tol. The tolerance\n    follows the error a correct kernel can make, term by term, summed over the (query, key) pairs that feed the element:\n    - logits: e = (2 GAMMA + 2^-20) |q| max|k| / 16 + 2^-20 |lse| + 2^-21, with GAMMA = 2^-17 for an fp32 dot product\n      of exact bf16 products (blocked or tensor-core sums reach about 2^-21), the score, lse and scale roundings and an\n      approximate exp2 (max|k| over the keys of the reference's query chunk); P may be off by expm1(e) of itself;\n    - dP - delta: eps = 2 GAMMA (|dO| max|v| + sum |dO o|), an fp32 dot product and an fp32 delta;\n    - per pair, A = |dS| (2^-7 + expm1(e)) + P (1 + expm1(e)) eps: dS rounded once to bf16 (2^-8, the rest headroom\n      for fp32 sums over up to 16K keys) and the P error;\n    - dq: sum over keys of A |k| / 16; dk: the sum over (query, query head of the group) of A |q| / 16; dv: the same sum\n      of P (2^-7 + expm1(e)) |dO| (P rounded once to bf16 for the dV product); each output also gets 2^-7 of its value\n      for the final bf16 rounding.\n    Non-finite values fail. The bounds take delta from the given o (bf16): that is the contract (delta = sum(dO * o)),\n    as every FlashAttention backward computes it. The FlashAttention-4 numerics (P recomputed in fp32 with exp2, bf16 P\n    for dV, dS formed in fp32 and rounded once to bf16, fp32 dq/dk/dv sums, bf16 outputs), modeled on CPU in\n    tests/test_hd256_attn_bwd.py, use under half of any tolerance on every loop and holdout distribution. A window off\n    by one key, a key of the wrong KV head, keys of another packed sequence, delta left out of dS, dP rounded to bf16\n    before subtracting delta or a missing softmax scale on dk fails.\"\"\"\n    import torch\n    stats = {}\n    ok = True\n    for i, nm in OUTS:\n        g, e, tol = (got[i], exp[nm], exp['tol_' + nm])\n        if tuple(g.shape) != tuple(e.shape):\n            return (False, {'error': f'{nm}: shape {tuple(g.shape)} != {tuple(e.shape)}'})\n        if g.dtype != exp[i].dtype:\n            return (False, {'error': f'{nm}: dtype {g.dtype} is not {exp[i].dtype}'})\n        gf = g.float()\n        finite = torch.isfinite(gf)\n        diff = torch.where(finite, (gf - e).abs(), torch.full_like(gf, float('inf')))\n        bad = int((diff > tol).sum().item())\n        fin = torch.where(finite, diff, torch.zeros_like(diff))\n        over = fin / (tol + 1e-30)\n        stats[nm] = {'bad': bad, 'nonfinite': int((~finite).sum().item()), 'max_abs': float(fin.max().item()) if g.numel() else 0.0, 'max_over_tol': float(over.max().item()) if g.numel() else 0.0}\n        ok = ok and bad == 0\n    return (ok, stats)\n\ndef _bytes(s):\n    \"\"\"Lower bound: q, o, dO and dq (query heads), k, v, dk and dv (KV heads) moved once in bf16, lse read once.\"\"\"\n    T = s['n'] * s['L'] + s.get('long', 0)\n    return T * D * 2 * 4 * (s['Hq'] + s['Hkv']) + T * s['Hq'] * 4\n\ndef vllm():\n    \"\"\"The bar: FlashAttention-4's hd256 backward, on each shape the fastest public route (expert_pr/fa4_v2966/b300.py\n    ROUTE): the dedicated pair or the general kernel at upstream 965c921 (the #2966 merge, expert_pr/fa4_v2966), or the\n    general kernel with GQA head packing at open PR #2967's head (expert_pr/fa4_v2967); called dense for batches of\n    equal-length sequences and varlen otherwise, writing dq, dk and dv into the output buffers.\"\"\"\n    from kernel_lab.expert_pr.fa4_v2966 import b300, utils as utils_2966\n    from kernel_lab.expert_pr.fa4_v2967 import utils as utils_2967\n    from kernel_lab.expert_pr.fa4_v2966.interface import _flash_attn_bwd as bwd_2966\n    from kernel_lab.expert_pr.fa4_v2967.interface import _flash_attn_bwd as bwd_2967\n\n    def run(q, k, v, o, do, lse, cu, max_len, W, dq, dk, dv):\n        T, Hq, _ = q.shape\n        n = cu.shape[0] - 1\n        kernel, mode = b300.route(T, Hq, k.shape[1], n, max_len, W)\n        if kernel == 'packed':\n            bwd = bwd_2967\n            utils_2967.HD256_GENERIC_BWD = utils_2967.BWD_PACK_GQA = True\n        else:\n            bwd = bwd_2966\n            utils_2966.HD256_GENERIC_BWD = kernel == 'generic'\n        win = {} if W < 0 else {'window_size_left': W, 'window_size_right': 0}\n        if mode == 'dense':\n            S = T // n\n            x4 = [t.view(n, S, t.shape[1], D) for t in (q, k, v, o, do, dq, dk, dv)]\n            bwd(*x4[:5], lse.view(Hq, n, S).transpose(0, 1), softmax_scale=SCALE, causal=True, dq=x4[5], dk=x4[6], dv=x4[7], **win)\n        else:\n            bwd(q, k, v, o, do, lse, softmax_scale=SCALE, causal=True, cu_seqlens_q=cu, cu_seqlens_k=cu, max_seqlen_q=max_len, max_seqlen_k=max_len, dq=dq, dk=dk, dv=dv, **win)\n    return run\nTASK = Task(name='xpr_hd256_attn_bwd', title='Causal attention backward (dq, dk, dv), head dim 256, GQA, sliding windows and packed documents, 8K-16K tokens (bar: FlashAttention-4 after PR #2966, with open PR #2967, October 2026)', models='Training of head-dim-256 models on Blackwell, per GPU: Gemma-3-style global layers and sliding-window-1024 local layers (16 query / 8 KV heads, or 8 / 4), Qwen3-Next-style full-attention layers (16 query heads, 2 KV heads) and packed batches of documents of mixed lengths, 8K-16K tokens per step.', semantics=\"q is [T, Hq, 256] bf16, k and v are [T, Hkv, 256] bf16 (Hq a multiple of Hkv; query head h uses KV head h // (Hq / Hkv)): the tokens of all sequences packed back to back. o is [T, Hq, 256] bf16, the forward output rounded to bf16; do is [T, Hq, 256] bf16, the gradient of the output; lse is [Hq, T] float32, the forward log-sum-exp (natural log, scale included). cu_seqlens is [n + 1] int32 with cu_seqlens[0] = 0 and sequence s occupying tokens cu_seqlens[s] .. cu_seqlens[s + 1] - 1; max_seqlen is the longest sequence (a python int); window is a python int, -1 or W >= 0. All tensors contiguous. Query i of a sequence sees key j of the same sequence when j <= i and, if window >= 0, i - j <= window. For each query i, head h and visible key j: score = q[i,h] . k[j,h'] / 16, P = exp(score - lse[h,i]), dP = do[i,h] . v[j,h'], delta = sum over the 256 columns of do[i,h] * o[i,h], dS = P (dP - delta). Write dq[i,h] = sum_j dS k[j,h'] / 16, dk[j,h'] = sum over every (i, h) that sees j with h // (Hq / Hkv) = h' of dS q[i,h] / 16, and dv[j,h'] = the same sum of P do[i,h], fp32 (or better) math rounded to bf16, into dq [T, Hq, 256], dk and dv [T, Hkv, 256] (contiguous). dq, dk and dv arrive filled with NaN like every output. P and dS may each be rounded once to bf16 as tensor-core operands; scores, P, dP, delta and dS are otherwise fp32 or better from the given values (no extra rounding of the scores or dP); summation order is free.\", signature='def kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv) -> None  # writes dq, dk, dv', shapes=[{'model': 'Gemma-3-12B-style global layer (16 query / 8 KV heads), one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': -1}, {'model': 'Gemma-3-12B-style local layer, sliding window 1024, one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023}, {'model': 'Qwen3-Next-style full-attention layer (16 query / 2 KV heads), 2 x 4K sequences', 'n': 2, 'L': 4096, 'Hq': 16, 'Hkv': 2, 'window': -1}, {'model': 'packed documents (16 / 8 heads): 8 documents of 1K-3K tokens, 16K tokens', 'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1, 'ragged': True}, {'model': 'Gemma-3-4B-style global layer (8 query / 4 KV heads), 4 x 2K sequences', 'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1}, {'model': 'Gemma-3-4B-style local layer (8 / 4 heads), sliding window 1024, packed documents of 2K-6K tokens, 16K tokens', 'n': 4, 'L': 4096, 'Hq': 8, 'Hkv': 4, 'window': 1023, 'ragged': True}], edge_shapes=[], dists=[], make_case=make_case, reference=reference, check=check, vllm=vllm, bytes_moved=_bytes, notes=\"Per visible (query, key) pair and query head, 5 x 256 multiply-adds: recompute the score (q . k), dP (dO . v), and the three products dV += P dO, dQ += dS k and dK += dS q, about 1.4 TFLOP for a causal 8K sequence with 16 query heads. Compute bound; skip key blocks the causal mask or the window hides entirely, and the tiles of other packed sequences. Head dim 256 doubles the register, shared-memory and tensor-memory footprint of each tile compared with 128: one 128 x 256 bf16 tile is 64 KiB, and fp32 dK and dV for 128 keys fill a whole SM's 512 tensor-memory columns. With GQA every key gets dK and dV contributions from all Hq / Hkv query heads of its group; summing them inside one program avoids fp32 atomics over heads. The expert runs, per shape, two kernels (dQ, then dK and dV, each recomputing S and dP) or one kernel with a 64-row KV tile and fp32 dQ partial sums, with or without the group's query heads packed into one tile; none is best on every shape. delta = rowsum(dO * o) needs only o and dO. Expert times on B300 (us, cold L2): 1x8K 1518, window 1024 510, 2x4K with 2 KV heads 980, 8 packed documents 1124-1206 (by lengths), 4x2K 302, packed 16K window 1024 520 (the pair before the series, which has no window: 1774, -, 1118, 1336, 356, -). The bar is 1.0x the expert speed (geometric mean over the timed and hidden shapes, each hidden shape at least 0.8x).\", weight=0.5, gpus=('B300',))\n# Starting kernel: correct and deliberately simple. A preprocess writes delta = rowsum(dO * o) and each token's\n# sequence bounds; one program per (16 query tokens, query head) walks every key its rows can see in 16-key steps and\n# accumulates dq; one program per (16 keys, KV head) walks every query head of its group and every query row that can\n# see its keys and accumulates dk and dv. P is recomputed from lse with tl.dot; tiny tiles, no pipelining, S and dP\n# computed twice: much slower than the expert kernels.\nimport torch\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef _prep(O, DO, DELTA, CU, START, END, T, H, D: tl.constexpr, BT: tl.constexpr):\n    s = tl.program_id(0)\n    h = tl.program_id(1)\n    st = tl.load(CU + s)\n    en = tl.load(CU + s + 1)\n    rd = tl.arange(0, D)\n    for t0 in range(st, en, BT):\n        rt = t0 + tl.arange(0, BT)\n        m = rt < en\n        row = (rt.to(tl.int64) * H + h) * D\n        o = tl.load(O + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n        do = tl.load(DO + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n        tl.store(DELTA + h.to(tl.int64) * T + rt, tl.sum(o * do, 1), mask=m)\n        if h == 0:\n            tl.store(START + rt, tl.zeros((BT,), tl.int32) + st, mask=m)\n            tl.store(END + rt, tl.zeros((BT,), tl.int32) + en, mask=m)\n\n\n@triton.jit\ndef _dq(Q, K, V, DO, LSE, DELTA, START, DQ, sm_scale, T, H, HKV, W, D: tl.constexpr, BM: tl.constexpr,\n        BN: tl.constexpr):\n    i0 = tl.program_id(0) * BM\n    h = tl.program_id(1)\n    hk = h // (H // HKV)\n    ri = i0 + tl.arange(0, BM)\n    mi = ri < T\n    rd = tl.arange(0, D)\n    rn = tl.arange(0, BN)\n    qrow = (ri.to(tl.int64) * H + h) * D\n    q = tl.load(Q + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n    do = tl.load(DO + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n    lse = tl.load(LSE + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n    delta = tl.load(DELTA + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n    st = tl.load(START + ri, mask=mi, other=T)\n    lo = st\n    if W >= 0:\n        lo = tl.maximum(st, ri - W)\n    j_lo = tl.min(tl.where(mi, lo, T), 0)\n    j_hi = tl.minimum(i0 + BM, T)\n    dq = tl.zeros((BM, D), tl.float32)\n    for j0 in range(j_lo, j_hi, BN):\n        rj = j0 + rn\n        mj = rj < j_hi\n        krow = (rj.to(tl.int64) * HKV + hk) * D\n        k = tl.load(K + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n        v = tl.load(V + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n        vis = (rj[None, :] >= lo[:, None]) & (rj[None, :] <= ri[:, None]) & mi[:, None] & mj[None, :]\n        s = tl.dot(q, tl.trans(k))\n        p = tl.where(vis, tl.exp(s * sm_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        ds = (p * (dp - delta[:, None])).to(tl.bfloat16)\n        dq = tl.dot(ds, k, dq)\n    tl.store(DQ + qrow[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=mi[:, None])\n\n\n@triton.jit\ndef _dkdv(Q, K, V, DO, LSE, DELTA, END, DK, DV, sm_scale, T, H, HKV, W, D: tl.constexpr, BM: tl.constexpr,\n          BN: tl.constexpr):\n    j0 = tl.program_id(0) * BN\n    hk = tl.program_id(1)\n    G = H // HKV\n    rj = j0 + tl.arange(0, BN)\n    mj = rj < T\n    rd = tl.arange(0, D)\n    rm = tl.arange(0, BM)\n    krow = (rj.to(tl.int64) * HKV + hk) * D\n    k = tl.load(K + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n    v = tl.load(V + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n    en = tl.load(END + rj, mask=mj, other=0)\n    hi = en\n    if W >= 0:\n        hi = tl.minimum(en, rj + W + 1)\n    i_hi = tl.max(tl.where(mj, hi, 0), 0)\n    dk = tl.zeros((BN, D), tl.float32)\n    dv = tl.zeros((BN, D), tl.float32)\n    for g in range(0, G):\n        h = hk * G + g\n        for i0 in range(j0, i_hi, BM):\n            ri = i0 + rm\n            mi = ri < i_hi\n            qrow = (ri.to(tl.int64) * H + h) * D\n            q = tl.load(Q + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n            do = tl.load(DO + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n            lse = tl.load(LSE + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n            delta = tl.load(DELTA + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n            vis = (ri[:, None] >= rj[None, :]) & (ri[:, None] < hi[None, :]) & mi[:, None] & mj[None, :]\n            s = tl.dot(q, tl.trans(k))\n            p = tl.where(vis, tl.exp(s * sm_scale - lse[:, None]), 0.0)\n            dp = tl.dot(do, tl.trans(v))\n            ds = (p * (dp - delta[:, None])).to(tl.bfloat16)\n            dv = tl.dot(tl.trans(p.to(tl.bfloat16)), do, dv)\n            dk = tl.dot(tl.trans(ds), q, dk)\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    start = torch.empty((T,), device=q.device, dtype=torch.int32)\n    end = torch.empty((T,), device=q.device, dtype=torch.int32)\n    _prep[(n, H)](o, do, delta, cu_seqlens, start, end, T, H, D=D, BT=16, num_warps=4)\n    _dq[(triton.cdiv(T, 16), H)](q, k, v, do, lse, delta, start, dq, D ** -0.5, T, H, HKV, window, D=D, BM=16,\n                                 BN=16, num_warps=4, num_stages=1)\n    _dkdv[(triton.cdiv(T, 16), HKV)](q, k, v, do, lse, delta, end, dk, dv, D ** -0.5, T, H, HKV, window, D=D,\n                                     BM=16, BN=16, num_warps=4, num_stages=1)\n\"\"\"Check a kernel on the public shapes before you submit it:  python check.py [solution.py]\n\nCorrectness against the reference on the public shapes (standard normal inputs), then median CUDA-event times of\nyour kernel, the starting kernel and vLLM's kernel. The verifier checks much more: edge shapes, hidden input\ndistributions and cheats (torch math, cached results, timers, streams). It times you against vLLM in a paired,\nL2-flushed run in a separate container.\n\"\"\"\nimport importlib.util\nimport sys\n\nimport torch\n\nimport static_check\nimport task as T\n\n\ndef load(path, name):\n    spec = importlib.util.spec_from_file_location(name, path)\n    mod = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(mod)\n    return mod\n\n\ndef median_us(fn, args, n=50):\n    for _ in range(3):\n        fn(*args)\n    torch.cuda.synchronize()\n    ts = []\n    for _ in range(n):\n        s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n        s.record()\n        fn(*args)\n        e.record()\n        torch.cuda.synchronize()\n        ts.append(s.elapsed_time(e) * 1000.0)\n    return sorted(ts)[n // 2]\n\n\ndef main():\n    t = T.TASK\n    path = sys.argv[1] if len(sys.argv) > 1 else '/workspace/solution.py'\n    with open(path) as f:\n        ok, why = static_check.check(f.read(), getattr(t, 'langs', ('triton',)))\n    if not ok:                                  # the verifier rejects the file before running it\n        print(f'REJECTED by the static check: {why}')\n        return\n    sol = load(path, 'solution')\n    base = load('/workspace/starting_kernel.py', 'starting_kernel')\n    try:\n        vllm = t.vllm()\n    except Exception as e:  # noqa: BLE001\n        vllm, why = None, e\n    for shape in t.shapes:\n        case = t.make_case(shape, 1, 'cuda')\n        exp = t.reference([a.clone() if torch.is_tensor(a) else a for a in case.args])\n        sol.kernel_fn(*case.args)\n        torch.cuda.synchronize()\n        ok, stats = t.check({i: case.args[i] for i in case.checked}, exp)\n        line = f\"{T.shape_key(shape) if hasattr(T, 'shape_key') else shape}: \" + ('correct' if ok else f'WRONG {stats}')\n        if ok:\n            line += f', {median_us(sol.kernel_fn, case.args):.2f} us'\n            line += f', starting kernel {median_us(base.kernel_fn, t.make_case(shape, 1, \"cuda\").args):.2f} us'\n            if vllm:\n                label = getattr(T, 'BASELINE_SHORT', 'vLLM')\n                line += f', {label} {median_us(vllm, t.make_case(shape, 1, \"cuda\").args):.2f} us'\n        print(line[:400])\n\n\nif __name__ == '__main__':\n    import contextlib\n    import io\n\n    import nodus_autosave           # best-version autosave (hardened export): /workspace/.best\n\n    class _Tee(io.StringIO):\n        def write(self, s):\n            sys.__stdout__.write(s)\n            return super().write(s)\n    _out = _Tee()\n    with contextlib.redirect_stdout(_out):\n        main()\n    sys.stdout.flush()\n    nodus_autosave.record(sys.argv[1] if len(sys.argv) > 1 else '/workspace/solution.py', _out.getvalue())\n\"\"\"Static admission for candidate kernels. Runs on the controller before any GPU time is spent, and again in the\njudge. The contract is deliberately narrow: a candidate is a Triton module that defines kernel_fn. torch may be used\nto allocate and reshape tensors. All arithmetic must run inside Triton kernels (the judge also enforces this at\nruntime by recording every torch op the candidate executes).\n\nA task whose langs include 'cute' also admits CuTe DSL kernels (the judge image's `cutlass` 4.7.1 package): the\nmodules in CUTE_IMPORTS, and no cutlass name whose path has a private part or a part in CUTE_BLOCKED (loaders of\nprebuilt or exported binaries, external linking, the CUDA driver, compiler internals, prebuilt operator packages,\ntest and framework helpers, pointers made from integers). Internals are entered only through CUTE_ENTRY. Such a\nmodule may also use `with` on cutlass context managers (cute.arch.elect_one) and declare shared memory as @cute.struct\nclasses of annotated fields.\n\"\"\"\nimport ast\n\nMAX_BYTES = 48 * 1024\nCUTE_MAX_BYTES = 128 * 1024        # CuTe spells out the layouts, pipelines and warp roles that Triton infers\nALLOWED_IMPORTS = {'torch', 'triton', 'triton.language', 'triton.language.extra', 'triton.language.extra.libdevice',\n                   'math', 'typing'}\nTORCH_CHAINS = {\n    'torch', 'torch.empty', 'torch.empty_like', 'torch.empty_strided', 'torch.zeros', 'torch.zeros_like', 'torch.full',\n    'torch.float32', 'torch.float16', 'torch.bfloat16', 'torch.float8_e4m3fn', 'torch.float8_e5m2', 'torch.int8',\n    'torch.int16', 'torch.int32', 'torch.int64', 'torch.uint8', 'torch","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false,"persistedOutputPath":"/logs/agent/sessions/projects/-workspace/711e70e5-71bc-4971-97a3-904cce35aad5/tool-results/b024e3pd9.txt","persistedOutputSize":41729},"tool_result_meta":[{"id":"toolu_01TfwAdENceDSaNBfuHeMn8U","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9ovMXjVqn75ZKGh5Vza","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012yP2J8may9nKmciCGx6jFz","name":"Bash","input":{"command":"ls /logs/agent/sessions/projects/-workspace/memory/ 2>/dev/null && cat /logs/agent/sessions/projects/-workspace/memory/MEMORY.md 2>/dev/null; nvidia-smi; python -c \"import triton, torch; print(triton.__version__, torch.__version__)\"","description":"Check memory, GPU and versions"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6859,"cache_read_input_tokens":12056,"cache_creation":{"ephemeral_5m_input_tokens":6859,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"c07efbca-d023-4351-b659-8f7a1c1e11ef","timestamp":"2026-10-18T13:49:59.196Z","request_id":"req_011Cg9ovMCPpBb59Kyiv9Eaa"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012yP2J8may9nKmciCGx6jFz","type":"tool_result","content":"Sun Oct 18 13:49:59 2026       \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 610.57.04              KMD Version: 610.57.04     CUDA UMD Version: 13.3     |\n+-----------------------------------------+------------------------+----------------------+\n| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n|                                         |                        |               MIG M. |\n|=========================================+========================+======================|\n|   0  NVIDIA B300 SXM6 AC            On  |   00000000:B2:00.0 Off |                    0 |\n| N/A   33C    P0            180W / 1100W |       4MiB / 275040MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes:                                                                              |\n|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n|        ID   ID                                                               Usage      |\n|=========================================================================================|\n|  No running processes found                                                             |\n+-----------------------------------------------------------------------------------------+\n3.7.1 2.13.0+cu130","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"cad747a5-b479-4c60-a0f0-b55da276e5fb","timestamp":"2026-10-18T13:50:03.959Z","tool_use_result":{"stdout":"Sun Oct 18 13:49:59 2026       \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 610.57.04              KMD Version: 610.57.04     CUDA UMD Version: 13.3     |\n+-----------------------------------------+------------------------+----------------------+\n| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n|                                         |                        |               MIG M. |\n|=========================================+========================+======================|\n|   0  NVIDIA B300 SXM6 AC            On  |   00000000:B2:00.0 Off |                    0 |\n| N/A   33C    P0            180W / 1100W |       4MiB / 275040MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes:                                                                              |\n|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n|        ID   ID                                                               Usage      |\n|=========================================================================================|\n|  No running processes found                                                             |\n+-----------------------------------------------------------------------------------------+\n3.7.1 2.13.0+cu130","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_012yP2J8may9nKmciCGx6jFz","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AM7g8YpuQGod4modRtSzBj","type":"tool_result","content":"\"\"\"Public task definition for xpr_hd256_attn_bwd: reference, check, timed shapes, standard normal inputs. The verifier also uses edge shapes and hidden input distributions that are not in this file.\"\"\"\n\"\"\"Causal attention backward with head dim 256 (dq, dk, dv) on Blackwell, GQA, sliding windows and packed documents\n(expert PR task).\n\nSource: Dao-AILab/flash-attention PRs #2964, #2965 and #2966 \"[CuTe, SM100] Unify hd256 backward (1/5-3/5)\" (Johnson,\nmerged 2026-10-06, 10-15 and 10-16), which rebuilt FlashAttention-4's head-dim-256 training backward on Blackwell on\ntop of #2807, #2891 and #2952. Upstream main (965c921, the #2966 merge, 2026-10-16) has two public hd256 backwards: the\ndefault \"dedicated\" pair (a dQ kernel and a dKdV kernel that each recompute S and dP, 2-CTA, no sliding window) and\nthe opt-in general kernel FlashAttentionBackwardSm100 (one kernel, 64-row KV tile per CTA, 2-CTA, any window,\nFLASH_ATTENTION_HD256_GENERIC_BWD=1), which the authors measured at parity to 11% faster than the pair on most shapes\nand 22-29% slower on 2K GQA (B200). Open PR #2967 (head 90419de, 2026-10-18) packs a KV head's query heads into the\ngeneral kernel's Q tile, 1-11% faster than the pair on hd256 GQA per its author. The bar on each shape is the fastest\nof the three (vendored in kernel_lab/expert_pr/fa4_v2966 and fa4_v2967; the route per shape is in fa4_v2966/b300.py),\nat 1.0x its speed in the same run.\n\nShapes: training of head-dim-256 models per GPU: Gemma-3-style global and sliding-window-1024 local layers (16 query\nand 8 KV heads, or 8 and 4), a Qwen3-Next-style full-attention layer (16 query heads, 2 KV heads) and packed\ndocuments of mixed lengths, 8K-16K tokens per step. o and lse come from an fp32 forward over the same inputs.\n\"\"\"\nfrom task_api import Case, Task\nAGENT_MINUTES = 120\nD = 256\nSCALE = D ** (-0.5)\nGAMMA = 2.0 ** (-17)\nOUTS = ((9, 'dq'), (10, 'dk'), (11, 'dv'))\n\ndef seq_lens(shape, g, device):\n    \"\"\"n sequences of length L, or with 'ragged' n of mean length L in [L/2, 3L/2], built in pairs (L + d, L - d) with\n    the first pair fixed at (3L/2, L/2), so the token count (n * L) and the longest sequence depend on the shape only;\n    'long' puts one extra sequence of that length first.\"\"\"\n    import torch\n    n, L = (shape['n'], shape['L'])\n    if shape.get('ragged'):\n        h = L // 2\n        d = torch.randint(-h, h + 1, (n // 2,), generator=g, device=device, dtype=torch.int64)\n        if n >= 2:\n            d[0] = h\n        lens = torch.stack([L + d, L - d], 1).reshape(-1)\n        if n % 2:\n            lens = torch.cat([lens, torch.full((1,), L, device=device, dtype=torch.int64)])\n    else:\n        lens = torch.full((n,), L, device=device, dtype=torch.int64)\n    if shape.get('long'):\n        lens = torch.cat([torch.tensor([shape['long']], device=device, dtype=torch.int64), lens])\n    return lens\n\ndef _visible(t0, t1, k0, k1, W, device):\n    \"\"\"[t1 - t0, k1 - k0] bool: key j is visible to query i of the same sequence when j <= i and, with a window W >= 0,\n    i - j <= W.\"\"\"\n    import torch\n    i = torch.arange(t0, t1, device=device).view(-1, 1)\n    j = torch.arange(k0, k1, device=device).view(1, -1)\n    m = j <= i\n    return m & (i - j <= W) if W >= 0 else m\n\ndef _chunks(cu, W, rows):\n    \"\"\"(t0, t1, k0) per query chunk of every sequence: keys k0 .. t1 - 1 hold every key a query of the chunk sees.\"\"\"\n    bounds = cu.tolist()\n    for s0, s1 in zip(bounds[:-1], bounds[1:]):\n        for t0 in range(s0, s1, rows):\n            t1 = min(s1, t0 + rows)\n            yield (t0, t1, s0 if W < 0 else max(s0, t0 - W))\n\ndef _rows(H, n_keys):\n    \"\"\"Query tokens per reference chunk: the [H, tokens, keys] fp32 intermediates stay at 64 MiB.\"\"\"\n    return max(16, 2 ** 24 // (H * max(1, n_keys)))\n\ndef _mm(eq, a, b):\n    \"\"\"einsum(eq, a, b) at fp32 accuracy for fp32 a and bf16-valued b, also when matmuls run in TF32: a splits into a\n    TF32-exact head (11 significant bits) and an exact fp32 tail, and every bf16 x head product is exact.\"\"\"\n    import torch\n    hi = (a.view(torch.int32) & -8192).view(torch.float32)\n    return torch.einsum(eq, hi, b) + torch.einsum(eq, a - hi, b)\n\ndef _tf32(fn, args):\n    \"\"\"Runs fn(args) with TF32 matmuls on CUDA (every product is split or exact, see _mm), restoring the setting.\"\"\"\n    import torch\n    prev = torch.get_float32_matmul_precision()\n    if args[0].is_cuda:\n        torch.set_float32_matmul_precision('high')\n    try:\n        return fn(args)\n    finally:\n        torch.set_float32_matmul_precision(prev)\n\ndef _forward(args):\n    \"\"\"The training forward in fp32: o = bf16(softmax(q k^T / 16) v) and lse = log(sum_j exp(q . k_j / 16)), written\n    into args[3] and args[5].\"\"\"\n    import torch\n    q, k, v, o, _, lse, cu, max_len, W = args[:9]\n    T, Hq, _ = q.shape\n    Hkv = k.shape[1]\n    G = Hq // Hkv\n    rows = _rows(Hq, max_len if W < 0 else min(max_len, W + 1))\n    for t0, t1, k0 in _chunks(cu, W, rows):\n        n = t1 - t0\n        qc = q[t0:t1].float().view(n, Hkv, G, D)\n        kc, vc = (k[k0:t1].float(), v[k0:t1].float())\n        s = torch.einsum('nhgd,mhd->hgnm', qc, kc) * SCALE\n        s = s.masked_fill(~_visible(t0, t1, k0, t1, W, q.device), float('-inf'))\n        L = torch.logsumexp(s, -1, keepdim=True)\n        O = _mm('hgnm,mhd->nhgd', torch.exp(s - L), vc)\n        o[t0:t1] = O.reshape(n, Hq, D).to(torch.bfloat16)\n        lse[:, t0:t1] = L.reshape(Hq, n)\n\ndef make_case(shape, seed, device, dist='normal'):\n    \"\"\"q, k, v and dO from the input distribution, generated as [tokens, heads * 256] so row-wise distributions act\n    per token; o and lse from the fp32 forward over the same inputs.\"\"\"\n    import torch\n    from task_api import gen, randn\n    g = gen(device, seed)\n    Hq, Hkv, W = (shape['Hq'], shape['Hkv'], shape['window'])\n    lens = seq_lens(shape, g, device)\n    T = int(lens.sum().item())\n    cu = torch.zeros((lens.numel() + 1,), device=device, dtype=torch.int32)\n    cu[1:] = torch.cumsum(lens, 0).to(torch.int32)\n    bf = torch.bfloat16\n    q = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n    k = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n    v = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n    do = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n    o = torch.empty((T, Hq, D), device=device, dtype=bf)\n    lse = torch.empty((Hq, T), device=device, dtype=torch.float32)\n    max_len = int(lens.max().item())\n    _tf32(_forward, [q, k, v, o, do, lse, cu, max_len, W])\n    dq = torch.empty((T, Hq, D), device=device, dtype=bf)\n    dk = torch.empty((T, Hkv, D), device=device, dtype=bf)\n    dv = torch.empty((T, Hkv, D), device=device, dtype=bf)\n    return Case(args=[q, k, v, o, do, lse, cu, max_len, W, dq, dk, dv], outputs=(9, 10, 11), shape=shape, dist=dist)\n\ndef _reference(args):\n    import torch\n    q, k, v, o, do, lse, cu, max_len, W = args[:9]\n    T, Hq, _ = q.shape\n    Hkv = k.shape[1]\n    G = Hq // Hkv\n    dev, f32 = (q.device, torch.float32)\n    DQ, TQ = (torch.empty((T, Hq, D), device=dev, dtype=f32), torch.empty((T, Hq, D), device=dev, dtype=f32))\n    DK, TK = (torch.zeros((T, Hkv, D), device=dev, dtype=f32), torch.zeros((T, Hkv, D), device=dev, dtype=f32))\n    DV, TV = (torch.zeros_like(DK), torch.zeros_like(DK))\n    kf, vf = (k.float(), v.float())\n    nk, nv = (kf.pow(2).sum(-1).sqrt(), vf.pow(2).sum(-1).sqrt())\n    rows = _rows(Hq, max_len if W < 0 else min(max_len, W + 1))\n    for t0, t1, k0 in _chunks(cu, W, rows):\n        n = t1 - t0\n        qc = q[t0:t1].float().view(n, Hkv, G, D)\n        kc, vc = (kf[k0:t1], vf[k0:t1])\n        dO = do[t0:t1].float().view(n, Hkv, G, D)\n        L = lse[:, t0:t1].view(Hkv, G, n, 1)\n        vis = _visible(t0, t1, k0, t1, W, dev)\n        s = torch.einsum('nhgd,mhd->hgnm', qc, kc)\n        P = torch.exp(s * SCALE - L).masked_fill_(~vis, 0.0)\n        dP = torch.einsum('nhgd,mhd->hgnm', dO, vc)\n        Od = o[t0:t1].double().view(n, Hkv, G, D)\n        delta = (Od * dO.double()).sum(-1).permute(1, 2, 0).unsqueeze(-1)\n        dmd = dP.sub_(delta.float())\n        dS = P * dmd\n        DQ[t0:t1] = (SCALE * _mm('hgnm,mhd->nhgd', dS, kc)).reshape(n, Hq, D)\n        DK[k0:t1] += SCALE * _mm('hgnm,nhgd->mhd', dS, qc)\n        DV[k0:t1] += _mm('hgnm,nhgd->mhd', P, dO)\n        nq = qc.pow(2).sum(-1).sqrt().permute(1, 2, 0).unsqueeze(-1)\n        ndo = dO.pow(2).sum(-1).sqrt().permute(1, 2, 0).unsqueeze(-1)\n        d1 = (Od.abs() * dO.double().abs()).sum(-1).permute(1, 2, 0).unsqueeze(-1).float()\n        kmax = nk[k0:t1].max(0).values.view(Hkv, 1, 1, 1)\n        vmax = nv[k0:t1].max(0).values.view(Hkv, 1, 1, 1)\n        e = (2 * GAMMA + 2.0 ** (-20)) * SCALE * nq * kmax + 2.0 ** (-20) * L.abs() + 2.0 ** (-21)\n        eta = torch.expm1(e)\n        a = 2.0 ** (-7) + eta\n        b = (1 + eta) * (2 * GAMMA) * (ndo * vmax + d1)\n        A = dS.abs_().mul_(a).add_(b * P)\n        TQ[t0:t1] = (SCALE * torch.einsum('hgnm,mhd->nhgd', A, kc.abs())).reshape(n, Hq, D)\n        TK[k0:t1] += SCALE * torch.einsum('hgnm,nhgd->mhd', A, qc.abs())\n        TV[k0:t1] += torch.einsum('hgnm,nhgd->mhd', P.mul_(a), dO.abs())\n    exact = {'dq': DQ, 'dk': DK, 'dv': DV}\n    tol = {'dq': TQ, 'dk': TK, 'dv': TV}\n    res = {}\n    for i, nm in OUTS:\n        x = exact[nm]\n        res[i] = x.to(torch.bfloat16)\n        res[nm] = x\n        res['tol_' + nm] = tol[nm] * (1 + 2.0 ** (-8)) + 2.0 ** (-7) * x.abs() + 2.0 ** (-100)\n    return res\n\ndef reference(args):\n    \"\"\"The contract in fp32 from the bf16 inputs (every product exact or split to fp32 accuracy, delta in float64), in\n    chunks of query tokens per sequence: P from the given lse, dS = P (dP - delta) with delta = sum(dO * o), dq per\n    token, dk and dv summed over every query (and query head of the KV head's group) that sees the key. Returns the\n    outputs in bf16 (int keys) and, for the check, the exact values in fp32 plus per-element tolerances 'tol_<name>'.\"\"\"\n    return _tf32(_reference, args)\n\ndef check(got, exp):\n    \"\"\"Per element, every output must lie within its tolerance of the exact value: |got - exact| <= tol. The tolerance\n    follows the error a correct kernel can make, term by term, summed over the (query, key) pairs that feed the element:\n    - logits: e = (2 GAMMA + 2^-20) |q| max|k| / 16 + 2^-20 |lse| + 2^-21, with GAMMA = 2^-17 for an fp32 dot product\n      of exact bf16 products (blocked or tensor-core sums reach about 2^-21), the score, lse and scale roundings and an\n      approximate exp2 (max|k| over the keys of the reference's query chunk); P may be off by expm1(e) of itself;\n    - dP - delta: eps = 2 GAMMA (|dO| max|v| + sum |dO o|), an fp32 dot product and an fp32 delta;\n    - per pair, A = |dS| (2^-7 + expm1(e)) + P (1 + expm1(e)) eps: dS rounded once to bf16 (2^-8, the rest headroom\n      for fp32 sums over up to 16K keys) and the P error;\n    - dq: sum over keys of A |k| / 16; dk: the sum over (query, query head of the group) of A |q| / 16; dv: the same sum\n      of P (2^-7 + expm1(e)) |dO| (P rounded once to bf16 for the dV product); each output also gets 2^-7 of its value\n      for the final bf16 rounding.\n    Non-finite values fail. The bounds take delta from the given o (bf16): that is the contract (delta = sum(dO * o)),\n    as every FlashAttention backward computes it. The FlashAttention-4 numerics (P recomputed in fp32 with exp2, bf16 P\n    for dV, dS formed in fp32 and rounded once to bf16, fp32 dq/dk/dv sums, bf16 outputs), modeled on CPU in\n    tests/test_hd256_attn_bwd.py, use under half of any tolerance on every loop and holdout distribution. A window off\n    by one key, a key of the wrong KV head, keys of another packed sequence, delta left out of dS, dP rounded to bf16\n    before subtracting delta or a missing softmax scale on dk fails.\"\"\"\n    import torch\n    stats = {}\n    ok = True\n    for i, nm in OUTS:\n        g, e, tol = (got[i], exp[nm], exp['tol_' + nm])\n        if tuple(g.shape) != tuple(e.shape):\n            return (False, {'error': f'{nm}: shape {tuple(g.shape)} != {tuple(e.shape)}'})\n        if g.dtype != exp[i].dtype:\n            return (False, {'error': f'{nm}: dtype {g.dtype} is not {exp[i].dtype}'})\n        gf = g.float()\n        finite = torch.isfinite(gf)\n        diff = torch.where(finite, (gf - e).abs(), torch.full_like(gf, float('inf')))\n        bad = int((diff > tol).sum().item())\n        fin = torch.where(finite, diff, torch.zeros_like(diff))\n        over = fin / (tol + 1e-30)\n        stats[nm] = {'bad': bad, 'nonfinite': int((~finite).sum().item()), 'max_abs': float(fin.max().item()) if g.numel() else 0.0, 'max_over_tol': float(over.max().item()) if g.numel() else 0.0}\n        ok = ok and bad == 0\n    return (ok, stats)\n\ndef _bytes(s):\n    \"\"\"Lower bound: q, o, dO and dq (query heads), k, v, dk and dv (KV heads) moved once in bf16, lse read once.\"\"\"\n    T = s['n'] * s['L'] + s.get('long', 0)\n    return T * D * 2 * 4 * (s['Hq'] + s['Hkv']) + T * s['Hq'] * 4\n\ndef vllm():\n    \"\"\"The bar: FlashAttention-4's hd256 backward, on each shape the fastest public route (expert_pr/fa4_v2966/b300.py\n    ROUTE): the dedicated pair or the general kernel at upstream 965c921 (the #2966 merge, expert_pr/fa4_v2966), or the\n    general kernel with GQA head packing at open PR #2967's head (expert_pr/fa4_v2967); called dense for batches of\n    equal-length sequences and varlen otherwise, writing dq, dk and dv into the output buffers.\"\"\"\n    from kernel_lab.expert_pr.fa4_v2966 import b300, utils as utils_2966\n    from kernel_lab.expert_pr.fa4_v2967 import utils as utils_2967\n    from kernel_lab.expert_pr.fa4_v2966.interface import _flash_attn_bwd as bwd_2966\n    from kernel_lab.expert_pr.fa4_v2967.interface import _flash_attn_bwd as bwd_2967\n\n    def run(q, k, v, o, do, lse, cu, max_len, W, dq, dk, dv):\n        T, Hq, _ = q.shape\n        n = cu.shape[0] - 1\n        kernel, mode = b300.route(T, Hq, k.shape[1], n, max_len, W)\n        if kernel == 'packed':\n            bwd = bwd_2967\n            utils_2967.HD256_GENERIC_BWD = utils_2967.BWD_PACK_GQA = True\n        else:\n            bwd = bwd_2966\n            utils_2966.HD256_GENERIC_BWD = kernel == 'generic'\n        win = {} if W < 0 else {'window_size_left': W, 'window_size_right': 0}\n        if mode == 'dense':\n            S = T // n\n            x4 = [t.view(n, S, t.shape[1], D) for t in (q, k, v, o, do, dq, dk, dv)]\n            bwd(*x4[:5], lse.view(Hq, n, S).transpose(0, 1), softmax_scale=SCALE, causal=True, dq=x4[5], dk=x4[6], dv=x4[7], **win)\n        else:\n            bwd(q, k, v, o, do, lse, softmax_scale=SCALE, causal=True, cu_seqlens_q=cu, cu_seqlens_k=cu, max_seqlen_q=max_len, max_seqlen_k=max_len, dq=dq, dk=dk, dv=dv, **win)\n    return run\nTASK = Task(name='xpr_hd256_attn_bwd', title='Causal attention backward (dq, dk, dv), head dim 256, GQA, sliding windows and packed documents, 8K-16K tokens (bar: FlashAttention-4 after PR #2966, with open PR #2967, October 2026)', models='Training of head-dim-256 models on Blackwell, per GPU: Gemma-3-style global layers and sliding-window-1024 local layers (16 query / 8 KV heads, or 8 / 4), Qwen3-Next-style full-attention layers (16 query heads, 2 KV heads) and packed batches of documents of mixed lengths, 8K-16K tokens per step.', semantics=\"q is [T, Hq, 256] bf16, k and v are [T, Hkv, 256] bf16 (Hq a multiple of Hkv; query head h uses KV head h // (Hq / Hkv)): the tokens of all sequences packed back to back. o is [T, Hq, 256] bf16, the forward output rounded to bf16; do is [T, Hq, 256] bf16, the gradient of the output; lse is [Hq, T] float32, the forward log-sum-exp (natural log, scale included). cu_seqlens is [n + 1] int32 with cu_seqlens[0] = 0 and sequence s occupying tokens cu_seqlens[s] .. cu_seqlens[s + 1] - 1; max_seqlen is the longest sequence (a python int); window is a python int, -1 or W >= 0. All tensors contiguous. Query i of a sequence sees key j of the same sequence when j <= i and, if window >= 0, i - j <= window. For each query i, head h and visible key j: score = q[i,h] . k[j,h'] / 16, P = exp(score - lse[h,i]), dP = do[i,h] . v[j,h'], delta = sum over the 256 columns of do[i,h] * o[i,h], dS = P (dP - delta). Write dq[i,h] = sum_j dS k[j,h'] / 16, dk[j,h'] = sum over every (i, h) that sees j with h // (Hq / Hkv) = h' of dS q[i,h] / 16, and dv[j,h'] = the same sum of P do[i,h], fp32 (or better) math rounded to bf16, into dq [T, Hq, 256], dk and dv [T, Hkv, 256] (contiguous). dq, dk and dv arrive filled with NaN like every output. P and dS may each be rounded once to bf16 as tensor-core operands; scores, P, dP, delta and dS are otherwise fp32 or better from the given values (no extra rounding of the scores or dP); summation order is free.\", signature='def kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv) -> None  # writes dq, dk, dv', shapes=[{'model': 'Gemma-3-12B-style global layer (16 query / 8 KV heads), one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': -1}, {'model': 'Gemma-3-12B-style local layer, sliding window 1024, one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023}, {'model': 'Qwen3-Next-style full-attention layer (16 query / 2 KV heads), 2 x 4K sequences', 'n': 2, 'L': 4096, 'Hq': 16, 'Hkv': 2, 'window': -1}, {'model': 'packed documents (16 / 8 heads): 8 documents of 1K-3K tokens, 16K tokens', 'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1, 'ragged': True}, {'model': 'Gemma-3-4B-style global layer (8 query / 4 KV heads), 4 x 2K sequences', 'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1}, {'model': 'Gemma-3-4B-style local layer (8 / 4 heads), sliding window 1024, packed documents of 2K-6K tokens, 16K tokens', 'n': 4, 'L': 4096, 'Hq': 8, 'Hkv': 4, 'window': 1023, 'ragged': True}], edge_shapes=[], dists=[], make_case=make_case, reference=reference, check=check, vllm=vllm, bytes_moved=_bytes, notes=\"Per visible (query, key) pair and query head, 5 x 256 multiply-adds: recompute the score (q . k), dP (dO . v), and the three products dV += P dO, dQ += dS k and dK += dS q, about 1.4 TFLOP for a causal 8K sequence with 16 query heads. Compute bound; skip key blocks the causal mask or the window hides entirely, and the tiles of other packed sequences. Head dim 256 doubles the register, shared-memory and tensor-memory footprint of each tile compared with 128: one 128 x 256 bf16 tile is 64 KiB, and fp32 dK and dV for 128 keys fill a whole SM's 512 tensor-memory columns. With GQA every key gets dK and dV contributions from all Hq / Hkv query heads of its group; summing them inside one program avoids fp32 atomics over heads. The expert runs, per shape, two kernels (dQ, then dK and dV, each recomputing S and dP) or one kernel with a 64-row KV tile and fp32 dQ partial sums, with or without the group's query heads packed into one tile; none is best on every shape. delta = rowsum(dO * o) needs only o and dO. Expert times on B300 (us, cold L2): 1x8K 1518, window 1024 510, 2x4K with 2 KV heads 980, 8 packed documents 1124-1206 (by lengths), 4x2K 302, packed 16K window 1024 520 (the pair before the series, which has no window: 1774, -, 1118, 1336, 356, -). The bar is 1.0x the expert speed (geometric mean over the timed and hidden shapes, each hidden shape at least 0.8x).\", weight=0.5, gpus=('B300',))","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"dd176c61-327f-466d-9d16-a9c5fc849373","timestamp":"2026-10-18T13:50:05.385Z","tool_use_result":{"stdout":"\"\"\"Public task definition for xpr_hd256_attn_bwd: reference, check, timed shapes, standard normal inputs. The verifier also uses edge shapes and hidden input distributions that are not in this file.\"\"\"\n\"\"\"Causal attention backward with head dim 256 (dq, dk, dv) on Blackwell, GQA, sliding windows and packed documents\n(expert PR task).\n\nSource: Dao-AILab/flash-attention PRs #2964, #2965 and #2966 \"[CuTe, SM100] Unify hd256 backward (1/5-3/5)\" (Johnson,\nmerged 2026-10-06, 10-15 and 10-16), which rebuilt FlashAttention-4's head-dim-256 training backward on Blackwell on\ntop of #2807, #2891 and #2952. Upstream main (965c921, the #2966 merge, 2026-10-16) has two public hd256 backwards: the\ndefault \"dedicated\" pair (a dQ kernel and a dKdV kernel that each recompute S and dP, 2-CTA, no sliding window) and\nthe opt-in general kernel FlashAttentionBackwardSm100 (one kernel, 64-row KV tile per CTA, 2-CTA, any window,\nFLASH_ATTENTION_HD256_GENERIC_BWD=1), which the authors measured at parity to 11% faster than the pair on most shapes\nand 22-29% slower on 2K GQA (B200). Open PR #2967 (head 90419de, 2026-10-18) packs a KV head's query heads into the\ngeneral kernel's Q tile, 1-11% faster than the pair on hd256 GQA per its author. The bar on each shape is the fastest\nof the three (vendored in kernel_lab/expert_pr/fa4_v2966 and fa4_v2967; the route per shape is in fa4_v2966/b300.py),\nat 1.0x its speed in the same run.\n\nShapes: training of head-dim-256 models per GPU: Gemma-3-style global and sliding-window-1024 local layers (16 query\nand 8 KV heads, or 8 and 4), a Qwen3-Next-style full-attention layer (16 query heads, 2 KV heads) and packed\ndocuments of mixed lengths, 8K-16K tokens per step. o and lse come from an fp32 forward over the same inputs.\n\"\"\"\nfrom task_api import Case, Task\nAGENT_MINUTES = 120\nD = 256\nSCALE = D ** (-0.5)\nGAMMA = 2.0 ** (-17)\nOUTS = ((9, 'dq'), (10, 'dk'), (11, 'dv'))\n\ndef seq_lens(shape, g, device):\n    \"\"\"n sequences of length L, or with 'ragged' n of mean length L in [L/2, 3L/2], built in pairs (L + d, L - d) with\n    the first pair fixed at (3L/2, L/2), so the token count (n * L) and the longest sequence depend on the shape only;\n    'long' puts one extra sequence of that length first.\"\"\"\n    import torch\n    n, L = (shape['n'], shape['L'])\n    if shape.get('ragged'):\n        h = L // 2\n        d = torch.randint(-h, h + 1, (n // 2,), generator=g, device=device, dtype=torch.int64)\n        if n >= 2:\n            d[0] = h\n        lens = torch.stack([L + d, L - d], 1).reshape(-1)\n        if n % 2:\n            lens = torch.cat([lens, torch.full((1,), L, device=device, dtype=torch.int64)])\n    else:\n        lens = torch.full((n,), L, device=device, dtype=torch.int64)\n    if shape.get('long'):\n        lens = torch.cat([torch.tensor([shape['long']], device=device, dtype=torch.int64), lens])\n    return lens\n\ndef _visible(t0, t1, k0, k1, W, device):\n    \"\"\"[t1 - t0, k1 - k0] bool: key j is visible to query i of the same sequence when j <= i and, with a window W >= 0,\n    i - j <= W.\"\"\"\n    import torch\n    i = torch.arange(t0, t1, device=device).view(-1, 1)\n    j = torch.arange(k0, k1, device=device).view(1, -1)\n    m = j <= i\n    return m & (i - j <= W) if W >= 0 else m\n\ndef _chunks(cu, W, rows):\n    \"\"\"(t0, t1, k0) per query chunk of every sequence: keys k0 .. t1 - 1 hold every key a query of the chunk sees.\"\"\"\n    bounds = cu.tolist()\n    for s0, s1 in zip(bounds[:-1], bounds[1:]):\n        for t0 in range(s0, s1, rows):\n            t1 = min(s1, t0 + rows)\n            yield (t0, t1, s0 if W < 0 else max(s0, t0 - W))\n\ndef _rows(H, n_keys):\n    \"\"\"Query tokens per reference chunk: the [H, tokens, keys] fp32 intermediates stay at 64 MiB.\"\"\"\n    return max(16, 2 ** 24 // (H * max(1, n_keys)))\n\ndef _mm(eq, a, b):\n    \"\"\"einsum(eq, a, b) at fp32 accuracy for fp32 a and bf16-valued b, also when matmuls run in TF32: a splits into a\n    TF32-exact head (11 significant bits) and an exact fp32 tail, and every bf16 x head product is exact.\"\"\"\n    import torch\n    hi = (a.view(torch.int32) & -8192).view(torch.float32)\n    return torch.einsum(eq, hi, b) + torch.einsum(eq, a - hi, b)\n\ndef _tf32(fn, args):\n    \"\"\"Runs fn(args) with TF32 matmuls on CUDA (every product is split or exact, see _mm), restoring the setting.\"\"\"\n    import torch\n    prev = torch.get_float32_matmul_precision()\n    if args[0].is_cuda:\n        torch.set_float32_matmul_precision('high')\n    try:\n        return fn(args)\n    finally:\n        torch.set_float32_matmul_precision(prev)\n\ndef _forward(args):\n    \"\"\"The training forward in fp32: o = bf16(softmax(q k^T / 16) v) and lse = log(sum_j exp(q . k_j / 16)), written\n    into args[3] and args[5].\"\"\"\n    import torch\n    q, k, v, o, _, lse, cu, max_len, W = args[:9]\n    T, Hq, _ = q.shape\n    Hkv = k.shape[1]\n    G = Hq // Hkv\n    rows = _rows(Hq, max_len if W < 0 else min(max_len, W + 1))\n    for t0, t1, k0 in _chunks(cu, W, rows):\n        n = t1 - t0\n        qc = q[t0:t1].float().view(n, Hkv, G, D)\n        kc, vc = (k[k0:t1].float(), v[k0:t1].float())\n        s = torch.einsum('nhgd,mhd->hgnm', qc, kc) * SCALE\n        s = s.masked_fill(~_visible(t0, t1, k0, t1, W, q.device), float('-inf'))\n        L = torch.logsumexp(s, -1, keepdim=True)\n        O = _mm('hgnm,mhd->nhgd', torch.exp(s - L), vc)\n        o[t0:t1] = O.reshape(n, Hq, D).to(torch.bfloat16)\n        lse[:, t0:t1] = L.reshape(Hq, n)\n\ndef make_case(shape, seed, device, dist='normal'):\n    \"\"\"q, k, v and dO from the input distribution, generated as [tokens, heads * 256] so row-wise distributions act\n    per token; o and lse from the fp32 forward over the same inputs.\"\"\"\n    import torch\n    from task_api import gen, randn\n    g = gen(device, seed)\n    Hq, Hkv, W = (shape['Hq'], shape['Hkv'], shape['window'])\n    lens = seq_lens(shape, g, device)\n    T = int(lens.sum().item())\n    cu = torch.zeros((lens.numel() + 1,), device=device, dtype=torch.int32)\n    cu[1:] = torch.cumsum(lens, 0).to(torch.int32)\n    bf = torch.bfloat16\n    q = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n    k = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n    v = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n    do = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n    o = torch.empty((T, Hq, D), device=device, dtype=bf)\n    lse = torch.empty((Hq, T), device=device, dtype=torch.float32)\n    max_len = int(lens.max().item())\n    _tf32(_forward, [q, k, v, o, do, lse, cu, max_len, W])\n    dq = torch.empty((T, Hq, D), device=device, dtype=bf)\n    dk = torch.empty((T, Hkv, D), device=device, dtype=bf)\n    dv = torch.empty((T, Hkv, D), device=device, dtype=bf)\n    return Case(args=[q, k, v, o, do, lse, cu, max_len, W, dq, dk, dv], outputs=(9, 10, 11), shape=shape, dist=dist)\n\ndef _reference(args):\n    import torch\n    q, k, v, o, do, lse, cu, max_len, W = args[:9]\n    T, Hq, _ = q.shape\n    Hkv = k.shape[1]\n    G = Hq // Hkv\n    dev, f32 = (q.device, torch.float32)\n    DQ, TQ = (torch.empty((T, Hq, D), device=dev, dtype=f32), torch.empty((T, Hq, D), device=dev, dtype=f32))\n    DK, TK = (torch.zeros((T, Hkv, D), device=dev, dtype=f32), torch.zeros((T, Hkv, D), device=dev, dtype=f32))\n    DV, TV = (torch.zeros_like(DK), torch.zeros_like(DK))\n    kf, vf = (k.float(), v.float())\n    nk, nv = (kf.pow(2).sum(-1).sqrt(), vf.pow(2).sum(-1).sqrt())\n    rows = _rows(Hq, max_len if W < 0 else min(max_len, W + 1))\n    for t0, t1, k0 in _chunks(cu, W, rows):\n        n = t1 - t0\n        qc = q[t0:t1].float().view(n, Hkv, G, D)\n        kc, vc = (kf[k0:t1], vf[k0:t1])\n        dO = do[t0:t1].float().view(n, Hkv, G, D)\n        L = lse[:, t0:t1].view(Hkv, G, n, 1)\n        vis = _visible(t0, t1, k0, t1, W, dev)\n        s = torch.einsum('nhgd,mhd->hgnm', qc, kc)\n        P = torch.exp(s * SCALE - L).masked_fill_(~vis, 0.0)\n        dP = torch.einsum('nhgd,mhd->hgnm', dO, vc)\n        Od = o[t0:t1].double().view(n, Hkv, G, D)\n        delta = (Od * dO.double()).sum(-1).permute(1, 2, 0).unsqueeze(-1)\n        dmd = dP.sub_(delta.float())\n        dS = P * dmd\n        DQ[t0:t1] = (SCALE * _mm('hgnm,mhd->nhgd', dS, kc)).reshape(n, Hq, D)\n        DK[k0:t1] += SCALE * _mm('hgnm,nhgd->mhd', dS, qc)\n        DV[k0:t1] += _mm('hgnm,nhgd->mhd', P, dO)\n        nq = qc.pow(2).sum(-1).sqrt().permute(1, 2, 0).unsqueeze(-1)\n        ndo = dO.pow(2).sum(-1).sqrt().permute(1, 2, 0).unsqueeze(-1)\n        d1 = (Od.abs() * dO.double().abs()).sum(-1).permute(1, 2, 0).unsqueeze(-1).float()\n        kmax = nk[k0:t1].max(0).values.view(Hkv, 1, 1, 1)\n        vmax = nv[k0:t1].max(0).values.view(Hkv, 1, 1, 1)\n        e = (2 * GAMMA + 2.0 ** (-20)) * SCALE * nq * kmax + 2.0 ** (-20) * L.abs() + 2.0 ** (-21)\n        eta = torch.expm1(e)\n        a = 2.0 ** (-7) + eta\n        b = (1 + eta) * (2 * GAMMA) * (ndo * vmax + d1)\n        A = dS.abs_().mul_(a).add_(b * P)\n        TQ[t0:t1] = (SCALE * torch.einsum('hgnm,mhd->nhgd', A, kc.abs())).reshape(n, Hq, D)\n        TK[k0:t1] += SCALE * torch.einsum('hgnm,nhgd->mhd', A, qc.abs())\n        TV[k0:t1] += torch.einsum('hgnm,nhgd->mhd', P.mul_(a), dO.abs())\n    exact = {'dq': DQ, 'dk': DK, 'dv': DV}\n    tol = {'dq': TQ, 'dk': TK, 'dv': TV}\n    res = {}\n    for i, nm in OUTS:\n        x = exact[nm]\n        res[i] = x.to(torch.bfloat16)\n        res[nm] = x\n        res['tol_' + nm] = tol[nm] * (1 + 2.0 ** (-8)) + 2.0 ** (-7) * x.abs() + 2.0 ** (-100)\n    return res\n\ndef reference(args):\n    \"\"\"The contract in fp32 from the bf16 inputs (every product exact or split to fp32 accuracy, delta in float64), in\n    chunks of query tokens per sequence: P from the given lse, dS = P (dP - delta) with delta = sum(dO * o), dq per\n    token, dk and dv summed over every query (and query head of the KV head's group) that sees the key. Returns the\n    outputs in bf16 (int keys) and, for the check, the exact values in fp32 plus per-element tolerances 'tol_<name>'.\"\"\"\n    return _tf32(_reference, args)\n\ndef check(got, exp):\n    \"\"\"Per element, every output must lie within its tolerance of the exact value: |got - exact| <= tol. The tolerance\n    follows the error a correct kernel can make, term by term, summed over the (query, key) pairs that feed the element:\n    - logits: e = (2 GAMMA + 2^-20) |q| max|k| / 16 + 2^-20 |lse| + 2^-21, with GAMMA = 2^-17 for an fp32 dot product\n      of exact bf16 products (blocked or tensor-core sums reach about 2^-21), the score, lse and scale roundings and an\n      approximate exp2 (max|k| over the keys of the reference's query chunk); P may be off by expm1(e) of itself;\n    - dP - delta: eps = 2 GAMMA (|dO| max|v| + sum |dO o|), an fp32 dot product and an fp32 delta;\n    - per pair, A = |dS| (2^-7 + expm1(e)) + P (1 + expm1(e)) eps: dS rounded once to bf16 (2^-8, the rest headroom\n      for fp32 sums over up to 16K keys) and the P error;\n    - dq: sum over keys of A |k| / 16; dk: the sum over (query, query head of the group) of A |q| / 16; dv: the same sum\n      of P (2^-7 + expm1(e)) |dO| (P rounded once to bf16 for the dV product); each output also gets 2^-7 of its value\n      for the final bf16 rounding.\n    Non-finite values fail. The bounds take delta from the given o (bf16): that is the contract (delta = sum(dO * o)),\n    as every FlashAttention backward computes it. The FlashAttention-4 numerics (P recomputed in fp32 with exp2, bf16 P\n    for dV, dS formed in fp32 and rounded once to bf16, fp32 dq/dk/dv sums, bf16 outputs), modeled on CPU in\n    tests/test_hd256_attn_bwd.py, use under half of any tolerance on every loop and holdout distribution. A window off\n    by one key, a key of the wrong KV head, keys of another packed sequence, delta left out of dS, dP rounded to bf16\n    before subtracting delta or a missing softmax scale on dk fails.\"\"\"\n    import torch\n    stats = {}\n    ok = True\n    for i, nm in OUTS:\n        g, e, tol = (got[i], exp[nm], exp['tol_' + nm])\n        if tuple(g.shape) != tuple(e.shape):\n            return (False, {'error': f'{nm}: shape {tuple(g.shape)} != {tuple(e.shape)}'})\n        if g.dtype != exp[i].dtype:\n            return (False, {'error': f'{nm}: dtype {g.dtype} is not {exp[i].dtype}'})\n        gf = g.float()\n        finite = torch.isfinite(gf)\n        diff = torch.where(finite, (gf - e).abs(), torch.full_like(gf, float('inf')))\n        bad = int((diff > tol).sum().item())\n        fin = torch.where(finite, diff, torch.zeros_like(diff))\n        over = fin / (tol + 1e-30)\n        stats[nm] = {'bad': bad, 'nonfinite': int((~finite).sum().item()), 'max_abs': float(fin.max().item()) if g.numel() else 0.0, 'max_over_tol': float(over.max().item()) if g.numel() else 0.0}\n        ok = ok and bad == 0\n    return (ok, stats)\n\ndef _bytes(s):\n    \"\"\"Lower bound: q, o, dO and dq (query heads), k, v, dk and dv (KV heads) moved once in bf16, lse read once.\"\"\"\n    T = s['n'] * s['L'] + s.get('long', 0)\n    return T * D * 2 * 4 * (s['Hq'] + s['Hkv']) + T * s['Hq'] * 4\n\ndef vllm():\n    \"\"\"The bar: FlashAttention-4's hd256 backward, on each shape the fastest public route (expert_pr/fa4_v2966/b300.py\n    ROUTE): the dedicated pair or the general kernel at upstream 965c921 (the #2966 merge, expert_pr/fa4_v2966), or the\n    general kernel with GQA head packing at open PR #2967's head (expert_pr/fa4_v2967); called dense for batches of\n    equal-length sequences and varlen otherwise, writing dq, dk and dv into the output buffers.\"\"\"\n    from kernel_lab.expert_pr.fa4_v2966 import b300, utils as utils_2966\n    from kernel_lab.expert_pr.fa4_v2967 import utils as utils_2967\n    from kernel_lab.expert_pr.fa4_v2966.interface import _flash_attn_bwd as bwd_2966\n    from kernel_lab.expert_pr.fa4_v2967.interface import _flash_attn_bwd as bwd_2967\n\n    def run(q, k, v, o, do, lse, cu, max_len, W, dq, dk, dv):\n        T, Hq, _ = q.shape\n        n = cu.shape[0] - 1\n        kernel, mode = b300.route(T, Hq, k.shape[1], n, max_len, W)\n        if kernel == 'packed':\n            bwd = bwd_2967\n            utils_2967.HD256_GENERIC_BWD = utils_2967.BWD_PACK_GQA = True\n        else:\n            bwd = bwd_2966\n            utils_2966.HD256_GENERIC_BWD = kernel == 'generic'\n        win = {} if W < 0 else {'window_size_left': W, 'window_size_right': 0}\n        if mode == 'dense':\n            S = T // n\n            x4 = [t.view(n, S, t.shape[1], D) for t in (q, k, v, o, do, dq, dk, dv)]\n            bwd(*x4[:5], lse.view(Hq, n, S).transpose(0, 1), softmax_scale=SCALE, causal=True, dq=x4[5], dk=x4[6], dv=x4[7], **win)\n        else:\n            bwd(q, k, v, o, do, lse, softmax_scale=SCALE, causal=True, cu_seqlens_q=cu, cu_seqlens_k=cu, max_seqlen_q=max_len, max_seqlen_k=max_len, dq=dq, dk=dk, dv=dv, **win)\n    return run\nTASK = Task(name='xpr_hd256_attn_bwd', title='Causal attention backward (dq, dk, dv), head dim 256, GQA, sliding windows and packed documents, 8K-16K tokens (bar: FlashAttention-4 after PR #2966, with open PR #2967, October 2026)', models='Training of head-dim-256 models on Blackwell, per GPU: Gemma-3-style global layers and sliding-window-1024 local layers (16 query / 8 KV heads, or 8 / 4), Qwen3-Next-style full-attention layers (16 query heads, 2 KV heads) and packed batches of documents of mixed lengths, 8K-16K tokens per step.', semantics=\"q is [T, Hq, 256] bf16, k and v are [T, Hkv, 256] bf16 (Hq a multiple of Hkv; query head h uses KV head h // (Hq / Hkv)): the tokens of all sequences packed back to back. o is [T, Hq, 256] bf16, the forward output rounded to bf16; do is [T, Hq, 256] bf16, the gradient of the output; lse is [Hq, T] float32, the forward log-sum-exp (natural log, scale included). cu_seqlens is [n + 1] int32 with cu_seqlens[0] = 0 and sequence s occupying tokens cu_seqlens[s] .. cu_seqlens[s + 1] - 1; max_seqlen is the longest sequence (a python int); window is a python int, -1 or W >= 0. All tensors contiguous. Query i of a sequence sees key j of the same sequence when j <= i and, if window >= 0, i - j <= window. For each query i, head h and visible key j: score = q[i,h] . k[j,h'] / 16, P = exp(score - lse[h,i]), dP = do[i,h] . v[j,h'], delta = sum over the 256 columns of do[i,h] * o[i,h], dS = P (dP - delta). Write dq[i,h] = sum_j dS k[j,h'] / 16, dk[j,h'] = sum over every (i, h) that sees j with h // (Hq / Hkv) = h' of dS q[i,h] / 16, and dv[j,h'] = the same sum of P do[i,h], fp32 (or better) math rounded to bf16, into dq [T, Hq, 256], dk and dv [T, Hkv, 256] (contiguous). dq, dk and dv arrive filled with NaN like every output. P and dS may each be rounded once to bf16 as tensor-core operands; scores, P, dP, delta and dS are otherwise fp32 or better from the given values (no extra rounding of the scores or dP); summation order is free.\", signature='def kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv) -> None  # writes dq, dk, dv', shapes=[{'model': 'Gemma-3-12B-style global layer (16 query / 8 KV heads), one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': -1}, {'model': 'Gemma-3-12B-style local layer, sliding window 1024, one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023}, {'model': 'Qwen3-Next-style full-attention layer (16 query / 2 KV heads), 2 x 4K sequences', 'n': 2, 'L': 4096, 'Hq': 16, 'Hkv': 2, 'window': -1}, {'model': 'packed documents (16 / 8 heads): 8 documents of 1K-3K tokens, 16K tokens', 'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1, 'ragged': True}, {'model': 'Gemma-3-4B-style global layer (8 query / 4 KV heads), 4 x 2K sequences', 'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1}, {'model': 'Gemma-3-4B-style local layer (8 / 4 heads), sliding window 1024, packed documents of 2K-6K tokens, 16K tokens', 'n': 4, 'L': 4096, 'Hq': 8, 'Hkv': 4, 'window': 1023, 'ragged': True}], edge_shapes=[], dists=[], make_case=make_case, reference=reference, check=check, vllm=vllm, bytes_moved=_bytes, notes=\"Per visible (query, key) pair and query head, 5 x 256 multiply-adds: recompute the score (q . k), dP (dO . v), and the three products dV += P dO, dQ += dS k and dK += dS q, about 1.4 TFLOP for a causal 8K sequence with 16 query heads. Compute bound; skip key blocks the causal mask or the window hides entirely, and the tiles of other packed sequences. Head dim 256 doubles the register, shared-memory and tensor-memory footprint of each tile compared with 128: one 128 x 256 bf16 tile is 64 KiB, and fp32 dK and dV for 128 keys fill a whole SM's 512 tensor-memory columns. With GQA every key gets dK and dV contributions from all Hq / Hkv query heads of its group; summing them inside one program avoids fp32 atomics over heads. The expert runs, per shape, two kernels (dQ, then dK and dV, each recomputing S and dP) or one kernel with a 64-row KV tile and fp32 dQ partial sums, with or without the group's query heads packed into one tile; none is best on every shape. delta = rowsum(dO * o) needs only o and dO. Expert times on B300 (us, cold L2): 1x8K 1518, window 1024 510, 2x4K with 2 KV heads 980, 8 packed documents 1124-1206 (by lengths), 4x2K 302, packed 16K window 1024 520 (the pair before the series, which has no window: 1774, -, 1118, 1336, 356, -). The bar is 1.0x the expert speed (geometric mean over the timed and hidden shapes, each hidden shape at least 0.8x).\", weight=0.5, gpus=('B300',))","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01AM7g8YpuQGod4modRtSzBj","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DoYExWPxnSDfeRcBUfWQmL","type":"tool_result","content":"# Starting kernel: correct and deliberately simple. A preprocess writes delta = rowsum(dO * o) and each token's\n# sequence bounds; one program per (16 query tokens, query head) walks every key its rows can see in 16-key steps and\n# accumulates dq; one program per (16 keys, KV head) walks every query head of its group and every query row that can\n# see its keys and accumulates dk and dv. P is recomputed from lse with tl.dot; tiny tiles, no pipelining, S and dP\n# computed twice: much slower than the expert kernels.\nimport torch\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef _prep(O, DO, DELTA, CU, START, END, T, H, D: tl.constexpr, BT: tl.constexpr):\n    s = tl.program_id(0)\n    h = tl.program_id(1)\n    st = tl.load(CU + s)\n    en = tl.load(CU + s + 1)\n    rd = tl.arange(0, D)\n    for t0 in range(st, en, BT):\n        rt = t0 + tl.arange(0, BT)\n        m = rt < en\n        row = (rt.to(tl.int64) * H + h) * D\n        o = tl.load(O + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n        do = tl.load(DO + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n        tl.store(DELTA + h.to(tl.int64) * T + rt, tl.sum(o * do, 1), mask=m)\n        if h == 0:\n            tl.store(START + rt, tl.zeros((BT,), tl.int32) + st, mask=m)\n            tl.store(END + rt, tl.zeros((BT,), tl.int32) + en, mask=m)\n\n\n@triton.jit\ndef _dq(Q, K, V, DO, LSE, DELTA, START, DQ, sm_scale, T, H, HKV, W, D: tl.constexpr, BM: tl.constexpr,\n        BN: tl.constexpr):\n    i0 = tl.program_id(0) * BM\n    h = tl.program_id(1)\n    hk = h // (H // HKV)\n    ri = i0 + tl.arange(0, BM)\n    mi = ri < T\n    rd = tl.arange(0, D)\n    rn = tl.arange(0, BN)\n    qrow = (ri.to(tl.int64) * H + h) * D\n    q = tl.load(Q + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n    do = tl.load(DO + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n    lse = tl.load(LSE + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n    delta = tl.load(DELTA + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n    st = tl.load(START + ri, mask=mi, other=T)\n    lo = st\n    if W >= 0:\n        lo = tl.maximum(st, ri - W)\n    j_lo = tl.min(tl.where(mi, lo, T), 0)\n    j_hi = tl.minimum(i0 + BM, T)\n    dq = tl.zeros((BM, D), tl.float32)\n    for j0 in range(j_lo, j_hi, BN):\n        rj = j0 + rn\n        mj = rj < j_hi\n        krow = (rj.to(tl.int64) * HKV + hk) * D\n        k = tl.load(K + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n        v = tl.load(V + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n        vis = (rj[None, :] >= lo[:, None]) & (rj[None, :] <= ri[:, None]) & mi[:, None] & mj[None, :]\n        s = tl.dot(q, tl.trans(k))\n        p = tl.where(vis, tl.exp(s * sm_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        ds = (p * (dp - delta[:, None])).to(tl.bfloat16)\n        dq = tl.dot(ds, k, dq)\n    tl.store(DQ + qrow[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=mi[:, None])\n\n\n@triton.jit\ndef _dkdv(Q, K, V, DO, LSE, DELTA, END, DK, DV, sm_scale, T, H, HKV, W, D: tl.constexpr, BM: tl.constexpr,\n          BN: tl.constexpr):\n    j0 = tl.program_id(0) * BN\n    hk = tl.program_id(1)\n    G = H // HKV\n    rj = j0 + tl.arange(0, BN)\n    mj = rj < T\n    rd = tl.arange(0, D)\n    rm = tl.arange(0, BM)\n    krow = (rj.to(tl.int64) * HKV + hk) * D\n    k = tl.load(K + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n    v = tl.load(V + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n    en = tl.load(END + rj, mask=mj, other=0)\n    hi = en\n    if W >= 0:\n        hi = tl.minimum(en, rj + W + 1)\n    i_hi = tl.max(tl.where(mj, hi, 0), 0)\n    dk = tl.zeros((BN, D), tl.float32)\n    dv = tl.zeros((BN, D), tl.float32)\n    for g in range(0, G):\n        h = hk * G + g\n        for i0 in range(j0, i_hi, BM):\n            ri = i0 + rm\n            mi = ri < i_hi\n            qrow = (ri.to(tl.int64) * H + h) * D\n            q = tl.load(Q + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n            do = tl.load(DO + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n            lse = tl.load(LSE + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n            delta = tl.load(DELTA + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n            vis = (ri[:, None] >= rj[None, :]) & (ri[:, None] < hi[None, :]) & mi[:, None] & mj[None, :]\n            s = tl.dot(q, tl.trans(k))\n            p = tl.where(vis, tl.exp(s * sm_scale - lse[:, None]), 0.0)\n            dp = tl.dot(do, tl.trans(v))\n            ds = (p * (dp - delta[:, None])).to(tl.bfloat16)\n            dv = tl.dot(tl.trans(p.to(tl.bfloat16)), do, dv)\n            dk = tl.dot(tl.trans(ds), q, dk)\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    start = torch.empty((T,), device=q.device, dtype=torch.int32)\n    end = torch.empty((T,), device=q.device, dtype=torch.int32)\n    _prep[(n, H)](o, do, delta, cu_seqlens, start, end, T, H, D=D, BT=16, num_warps=4)\n    _dq[(triton.cdiv(T, 16), H)](q, k, v, do, lse, delta, start, dq, D ** -0.5, T, H, HKV, window, D=D, BM=16,\n                                 BN=16, num_warps=4, num_stages=1)\n    _dkdv[(triton.cdiv(T, 16), HKV)](q, k, v, do, lse, delta, end, dk, dv, D ** -0.5, T, H, HKV, window, D=D,\n                                     BM=16, BN=16, num_warps=4, num_stages=1)\n\"\"\"Check a kernel on the public shapes before you submit it:  python check.py [solution.py]\n\nCorrectness against the reference on the public shapes (standard normal inputs), then median CUDA-event times of\nyour kernel, the starting kernel and vLLM's kernel. The verifier checks much more: edge shapes, hidden input\ndistributions and cheats (torch math, cached results, timers, streams). It times you against vLLM in a paired,\nL2-flushed run in a separate container.\n\"\"\"\nimport importlib.util\nimport sys\n\nimport torch\n\nimport static_check\nimport task as T\n\n\ndef load(path, name):\n    spec = importlib.util.spec_from_file_location(name, path)\n    mod = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(mod)\n    return mod\n\n\ndef median_us(fn, args, n=50):\n    for _ in range(3):\n        fn(*args)\n    torch.cuda.synchronize()\n    ts = []\n    for _ in range(n):\n        s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n        s.record()\n        fn(*args)\n        e.record()\n        torch.cuda.synchronize()\n        ts.append(s.elapsed_time(e) * 1000.0)\n    return sorted(ts)[n // 2]\n\n\ndef main():\n    t = T.TASK\n    path = sys.argv[1] if len(sys.argv) > 1 else '/workspace/solution.py'\n    with open(path) as f:\n        ok, why = static_check.check(f.read(), getattr(t, 'langs', ('triton',)))\n    if not ok:                                  # the verifier rejects the file before running it\n        print(f'REJECTED by the static check: {why}')\n        return\n    sol = load(path, 'solution')\n    base = load('/workspace/starting_kernel.py', 'starting_kernel')\n    try:\n        vllm = t.vllm()\n    except Exception as e:  # noqa: BLE001\n        vllm, why = None, e\n    for shape in t.shapes:\n        case = t.make_case(shape, 1, 'cuda')\n        exp = t.reference([a.clone() if torch.is_tensor(a) else a for a in case.args])\n        sol.kernel_fn(*case.args)\n        torch.cuda.synchronize()\n        ok, stats = t.check({i: case.args[i] for i in case.checked}, exp)\n        line = f\"{T.shape_key(shape) if hasattr(T, 'shape_key') else shape}: \" + ('correct' if ok else f'WRONG {stats}')\n        if ok:\n            line += f', {median_us(sol.kernel_fn, case.args):.2f} us'\n            line += f', starting kernel {median_us(base.kernel_fn, t.make_case(shape, 1, \"cuda\").args):.2f} us'\n            if vllm:\n                label = getattr(T, 'BASELINE_SHORT', 'vLLM')\n                line += f', {label} {median_us(vllm, t.make_case(shape, 1, \"cuda\").args):.2f} us'\n        print(line[:400])\n\n\nif __name__ == '__main__':\n    import contextlib\n    import io\n\n    import nodus_autosave           # best-version autosave (hardened export): /workspace/.best\n\n    class _Tee(io.StringIO):\n        def write(self, s):\n            sys.__stdout__.write(s)\n            return super().write(s)\n    _out = _Tee()\n    with contextlib.redirect_stdout(_out):\n        main()\n    sys.stdout.flush()\n    nodus_autosave.record(sys.argv[1] if len(sys.argv) > 1 else '/workspace/solution.py', _out.getvalue())","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"838dfaf9-11cb-4021-95cd-7215a07c7ee5","timestamp":"2026-10-18T13:50:07.394Z","tool_use_result":{"stdout":"# Starting kernel: correct and deliberately simple. A preprocess writes delta = rowsum(dO * o) and each token's\n# sequence bounds; one program per (16 query tokens, query head) walks every key its rows can see in 16-key steps and\n# accumulates dq; one program per (16 keys, KV head) walks every query head of its group and every query row that can\n# see its keys and accumulates dk and dv. P is recomputed from lse with tl.dot; tiny tiles, no pipelining, S and dP\n# computed twice: much slower than the expert kernels.\nimport torch\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef _prep(O, DO, DELTA, CU, START, END, T, H, D: tl.constexpr, BT: tl.constexpr):\n    s = tl.program_id(0)\n    h = tl.program_id(1)\n    st = tl.load(CU + s)\n    en = tl.load(CU + s + 1)\n    rd = tl.arange(0, D)\n    for t0 in range(st, en, BT):\n        rt = t0 + tl.arange(0, BT)\n        m = rt < en\n        row = (rt.to(tl.int64) * H + h) * D\n        o = tl.load(O + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n        do = tl.load(DO + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n        tl.store(DELTA + h.to(tl.int64) * T + rt, tl.sum(o * do, 1), mask=m)\n        if h == 0:\n            tl.store(START + rt, tl.zeros((BT,), tl.int32) + st, mask=m)\n            tl.store(END + rt, tl.zeros((BT,), tl.int32) + en, mask=m)\n\n\n@triton.jit\ndef _dq(Q, K, V, DO, LSE, DELTA, START, DQ, sm_scale, T, H, HKV, W, D: tl.constexpr, BM: tl.constexpr,\n        BN: tl.constexpr):\n    i0 = tl.program_id(0) * BM\n    h = tl.program_id(1)\n    hk = h // (H // HKV)\n    ri = i0 + tl.arange(0, BM)\n    mi = ri < T\n    rd = tl.arange(0, D)\n    rn = tl.arange(0, BN)\n    qrow = (ri.to(tl.int64) * H + h) * D\n    q = tl.load(Q + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n    do = tl.load(DO + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n    lse = tl.load(LSE + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n    delta = tl.load(DELTA + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n    st = tl.load(START + ri, mask=mi, other=T)\n    lo = st\n    if W >= 0:\n        lo = tl.maximum(st, ri - W)\n    j_lo = tl.min(tl.where(mi, lo, T), 0)\n    j_hi = tl.minimum(i0 + BM, T)\n    dq = tl.zeros((BM, D), tl.float32)\n    for j0 in range(j_lo, j_hi, BN):\n        rj = j0 + rn\n        mj = rj < j_hi\n        krow = (rj.to(tl.int64) * HKV + hk) * D\n        k = tl.load(K + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n        v = tl.load(V + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n        vis = (rj[None, :] >= lo[:, None]) & (rj[None, :] <= ri[:, None]) & mi[:, None] & mj[None, :]\n        s = tl.dot(q, tl.trans(k))\n        p = tl.where(vis, tl.exp(s * sm_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        ds = (p * (dp - delta[:, None])).to(tl.bfloat16)\n        dq = tl.dot(ds, k, dq)\n    tl.store(DQ + qrow[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=mi[:, None])\n\n\n@triton.jit\ndef _dkdv(Q, K, V, DO, LSE, DELTA, END, DK, DV, sm_scale, T, H, HKV, W, D: tl.constexpr, BM: tl.constexpr,\n          BN: tl.constexpr):\n    j0 = tl.program_id(0) * BN\n    hk = tl.program_id(1)\n    G = H // HKV\n    rj = j0 + tl.arange(0, BN)\n    mj = rj < T\n    rd = tl.arange(0, D)\n    rm = tl.arange(0, BM)\n    krow = (rj.to(tl.int64) * HKV + hk) * D\n    k = tl.load(K + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n    v = tl.load(V + krow[:, None] + rd[None, :], mask=mj[:, None], other=0.0)\n    en = tl.load(END + rj, mask=mj, other=0)\n    hi = en\n    if W >= 0:\n        hi = tl.minimum(en, rj + W + 1)\n    i_hi = tl.max(tl.where(mj, hi, 0), 0)\n    dk = tl.zeros((BN, D), tl.float32)\n    dv = tl.zeros((BN, D), tl.float32)\n    for g in range(0, G):\n        h = hk * G + g\n        for i0 in range(j0, i_hi, BM):\n            ri = i0 + rm\n            mi = ri < i_hi\n            qrow = (ri.to(tl.int64) * H + h) * D\n            q = tl.load(Q + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n            do = tl.load(DO + qrow[:, None] + rd[None, :], mask=mi[:, None], other=0.0)\n            lse = tl.load(LSE + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n            delta = tl.load(DELTA + h.to(tl.int64) * T + ri, mask=mi, other=0.0)\n            vis = (ri[:, None] >= rj[None, :]) & (ri[:, None] < hi[None, :]) & mi[:, None] & mj[None, :]\n            s = tl.dot(q, tl.trans(k))\n            p = tl.where(vis, tl.exp(s * sm_scale - lse[:, None]), 0.0)\n            dp = tl.dot(do, tl.trans(v))\n            ds = (p * (dp - delta[:, None])).to(tl.bfloat16)\n            dv = tl.dot(tl.trans(p.to(tl.bfloat16)), do, dv)\n            dk = tl.dot(tl.trans(ds), q, dk)\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    start = torch.empty((T,), device=q.device, dtype=torch.int32)\n    end = torch.empty((T,), device=q.device, dtype=torch.int32)\n    _prep[(n, H)](o, do, delta, cu_seqlens, start, end, T, H, D=D, BT=16, num_warps=4)\n    _dq[(triton.cdiv(T, 16), H)](q, k, v, do, lse, delta, start, dq, D ** -0.5, T, H, HKV, window, D=D, BM=16,\n                                 BN=16, num_warps=4, num_stages=1)\n    _dkdv[(triton.cdiv(T, 16), HKV)](q, k, v, do, lse, delta, end, dk, dv, D ** -0.5, T, H, HKV, window, D=D,\n                                     BM=16, BN=16, num_warps=4, num_stages=1)\n\"\"\"Check a kernel on the public shapes before you submit it:  python check.py [solution.py]\n\nCorrectness against the reference on the public shapes (standard normal inputs), then median CUDA-event times of\nyour kernel, the starting kernel and vLLM's kernel. The verifier checks much more: edge shapes, hidden input\ndistributions and cheats (torch math, cached results, timers, streams). It times you against vLLM in a paired,\nL2-flushed run in a separate container.\n\"\"\"\nimport importlib.util\nimport sys\n\nimport torch\n\nimport static_check\nimport task as T\n\n\ndef load(path, name):\n    spec = importlib.util.spec_from_file_location(name, path)\n    mod = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(mod)\n    return mod\n\n\ndef median_us(fn, args, n=50):\n    for _ in range(3):\n        fn(*args)\n    torch.cuda.synchronize()\n    ts = []\n    for _ in range(n):\n        s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n        s.record()\n        fn(*args)\n        e.record()\n        torch.cuda.synchronize()\n        ts.append(s.elapsed_time(e) * 1000.0)\n    return sorted(ts)[n // 2]\n\n\ndef main():\n    t = T.TASK\n    path = sys.argv[1] if len(sys.argv) > 1 else '/workspace/solution.py'\n    with open(path) as f:\n        ok, why = static_check.check(f.read(), getattr(t, 'langs', ('triton',)))\n    if not ok:                                  # the verifier rejects the file before running it\n        print(f'REJECTED by the static check: {why}')\n        return\n    sol = load(path, 'solution')\n    base = load('/workspace/starting_kernel.py', 'starting_kernel')\n    try:\n        vllm = t.vllm()\n    except Exception as e:  # noqa: BLE001\n        vllm, why = None, e\n    for shape in t.shapes:\n        case = t.make_case(shape, 1, 'cuda')\n        exp = t.reference([a.clone() if torch.is_tensor(a) else a for a in case.args])\n        sol.kernel_fn(*case.args)\n        torch.cuda.synchronize()\n        ok, stats = t.check({i: case.args[i] for i in case.checked}, exp)\n        line = f\"{T.shape_key(shape) if hasattr(T, 'shape_key') else shape}: \" + ('correct' if ok else f'WRONG {stats}')\n        if ok:\n            line += f', {median_us(sol.kernel_fn, case.args):.2f} us'\n            line += f', starting kernel {median_us(base.kernel_fn, t.make_case(shape, 1, \"cuda\").args):.2f} us'\n            if vllm:\n                label = getattr(T, 'BASELINE_SHORT', 'vLLM')\n                line += f', {label} {median_us(vllm, t.make_case(shape, 1, \"cuda\").args):.2f} us'\n        print(line[:400])\n\n\nif __name__ == '__main__':\n    import contextlib\n    import io\n\n    import nodus_autosave           # best-version autosave (hardened export): /workspace/.best\n\n    class _Tee(io.StringIO):\n        def write(self, s):\n            sys.__stdout__.write(s)\n            return super().write(s)\n    _out = _Tee()\n    with contextlib.redirect_stdout(_out):\n        main()\n    sys.stdout.flush()\n    nodus_autosave.record(sys.argv[1] if len(sys.argv) > 1 else '/workspace/solution.py', _out.getvalue())","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01DoYExWPxnSDfeRcBUfWQmL","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9ow5Bkw9syptMsaJba1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01B4c1vuirEMPYyCFTni6zZ5","name":"Bash","input":{"command":"cat /workspace/static_check.py | head -150; cat /workspace/task_api.py","description":"Read static check and task api"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9500,"cache_read_input_tokens":20904,"cache_creation":{"ephemeral_5m_input_tokens":9500,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"118635b0-46b3-4c8e-ae3a-8486b92e79e4","timestamp":"2026-10-18T13:50:07.757Z","request_id":"req_011Cg9ow4qArdQUFEiWsCteU"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01B4c1vuirEMPYyCFTni6zZ5","type":"tool_result","content":"\"\"\"Static admission for candidate kernels. Runs on the controller before any GPU time is spent, and again in the\njudge. The contract is deliberately narrow: a candidate is a Triton module that defines kernel_fn. torch may be used\nto allocate and reshape tensors. All arithmetic must run inside Triton kernels (the judge also enforces this at\nruntime by recording every torch op the candidate executes).\n\nA task whose langs include 'cute' also admits CuTe DSL kernels (the judge image's `cutlass` 4.7.1 package): the\nmodules in CUTE_IMPORTS, and no cutlass name whose path has a private part or a part in CUTE_BLOCKED (loaders of\nprebuilt or exported binaries, external linking, the CUDA driver, compiler internals, prebuilt operator packages,\ntest and framework helpers, pointers made from integers). Internals are entered only through CUTE_ENTRY. Such a\nmodule may also use `with` on cutlass context managers (cute.arch.elect_one) and declare shared memory as @cute.struct\nclasses of annotated fields.\n\"\"\"\nimport ast\n\nMAX_BYTES = 48 * 1024\nCUTE_MAX_BYTES = 128 * 1024        # CuTe spells out the layouts, pipelines and warp roles that Triton infers\nALLOWED_IMPORTS = {'torch', 'triton', 'triton.language', 'triton.language.extra', 'triton.language.extra.libdevice',\n                   'math', 'typing'}\nTORCH_CHAINS = {\n    'torch', 'torch.empty', 'torch.empty_like', 'torch.empty_strided', 'torch.zeros', 'torch.zeros_like', 'torch.full',\n    'torch.float32', 'torch.float16', 'torch.bfloat16', 'torch.float8_e4m3fn', 'torch.float8_e5m2', 'torch.int8',\n    'torch.int16', 'torch.int32', 'torch.int64', 'torch.uint8', 'torch.bool', 'torch.Tensor', 'torch.dtype',\n    'torch.device', 'torch.Size', 'torch.finfo', 'torch.iinfo', 'torch.cuda.get_device_properties',\n    'torch.cuda.current_device', 'torch.cuda.is_available',\n}\nTRITON_CHAINS = {'triton.jit', 'triton.autotune', 'triton.Config', 'triton.cdiv', 'triton.next_power_of_2',\n                 'triton.heuristics', 'triton.set_allocator'}   # set_allocator enables TMA descriptors\nCUTE_IMPORTS = {\n    'cutlass', 'cutlass.cute', 'cutlass.cute.arch', 'cutlass.cute.math', 'cutlass.cute.runtime', 'cutlass.cute.nvgpu',\n    'cutlass.cute.nvgpu.cpasync', 'cutlass.cute.nvgpu.tcgen05', 'cutlass.cute.nvgpu.warp',\n    'cutlass.cute.nvgpu.warpgroup', 'cutlass.pipeline', 'cutlass.utils', 'cutlass.utils.blackwell_helpers',\n    'cutlass.utils.hopper_helpers', 'cutlass.cutlass_dsl', 'cutlass._mlir.dialects', 'cutlass._mlir.dialects.nvvm',\n    'cuda.bindings.driver',\n}\n# The only ways into internals: device-only inline PTX (nvvm), and the stream handle that wraps torch's current stream.\n# The llvm dialect is not admitted: @cute.jit host functions run as native host code, where llvm ops (inline\n# assembly, external calls, raw loads and stores) escape every check here.\nCUTE_ENTRY = ('cutlass.cutlass_dsl.T', 'cutlass.cutlass_dsl.dsl_user_op', 'cutlass._mlir.dialects.nvvm',\n              'cuda.bindings.driver.CUstream')\n# No part of a cutlass name may be one of these. cutlass modules re-export what they import, so the CUDA driver\n# (cutlass.cuda: load_cubin_module, launch_kernel, stream_create) is reachable as an attribute of kernel modules.\nCUTE_BLOCKED = {\n    'export', 'load_module', 'find_runtime_libraries', 'ffi', 'extern', 'base_extern', 'BitCode', 'LinkLibraries',\n    'compile_to', 'make_ptr', 'cuda', 'cuda_helpers', 'driver', 'runtime', 'base_dsl', 'cutlass_dsl', 'compiler',\n    'ir', 'spec', 'BaseDSL', 'CuTeDSL', 'CuteExperimentalDSL', 'FFI', 'ExecutionArgs', 'JitArgAdapterRegistry',\n    'register_jit_arg_adapter', 'operators', 'kernels', 'testing', 'experimental', 'jax', 'torch', 'tvm_ffi',\n    'ctypes', 'os', 'sys', 'subprocess', 'importlib', 'builtins', 'inspect', 'json', 'tempfile', 'pathlib', 'shutil',\n    'pickle', 'threading', 'weakref',\n}\nCUTE_TORCH_CHAINS = {'torch.cuda.current_stream'}      # its .cuda_stream goes into cuda.bindings.driver.CUstream\nFORBIDDEN_NAMES = {'eval', 'exec', 'compile', 'open', '__import__', 'globals', 'locals', 'vars', 'getattr', 'setattr',\n                   'delattr', 'breakpoint', 'input', 'id', 'memoryview', 'help', 'exit', 'quit', 'sys', 'os',\n                   'print', 'super', 'type', 'object'}\nFORBIDDEN_NODES = (ast.ClassDef, ast.Global, ast.Nonlocal, ast.AsyncFunctionDef, ast.Await, ast.Yield, ast.YieldFrom,\n                   ast.Try, ast.With, ast.AsyncWith)\n\n\ndef _chain(node):\n    parts = []\n    while isinstance(node, ast.Attribute):\n        parts.append(node.attr)\n        node = node.value\n    if isinstance(node, ast.Name):\n        parts.append(node.id)\n        return list(reversed(parts))\n    return None\n\n\ndef _canon(node, alias):\n    c = _chain(node)\n    return '.'.join(alias[c[0]].split('.') + c[1:]) if c and c[0] in alias else None\n\n\ndef _cute_bad(full):\n    \"\"\"Why a dotted cutlass or cuda name is not admitted, or None. cutlass.cute.runtime is the one runtime module.\"\"\"\n    head = next((h for h in CUTE_ENTRY if full == h or full.startswith(h + '.')), None)\n    if full == 'cuda.bindings.driver':          # the module itself; only CUstream may be taken from it\n        return None\n    if full.startswith('cuda') and not head:\n        return f'{full} is not allowed (from cuda.bindings.driver only CUstream)'\n    parts = full.split('.')\n    for i in range(len(head.split('.')) if head else 1, len(parts)):\n        if parts[:i + 1] != ['cutlass', 'cute', 'runtime'] and (parts[i].startswith('_') or parts[i] in CUTE_BLOCKED):\n            return f'{full} is not allowed: {parts[i]} is private or not kernel writing (CUTE_BLOCKED, static_check.py)'\n    return None\n\n\ndef _launders(node, alias, parents):\n    \"\"\"Whether a CUTE_BLOCKED attribute sits in a chain that does not start at an imported name (a module kept in a\n    variable, a list item, a call result): the chain checks cannot see where such a chain leads, so it is refused.\"\"\"\n    while isinstance(parents.get(id(node)), ast.Attribute) and parents[id(node)].value is node:\n        node = parents[id(node)]\n    return _canon(node, alias) is None\n\n\ndef _cute_exempt(node, alias):\n    \"\"\"A with statement on cutlass context managers, or a @cute.struct class of annotated fields only.\"\"\"\n    if isinstance(node, ast.With):\n        return all(isinstance(i.context_expr, ast.Call) and (_canon(i.context_expr.func, alias) or '').startswith(\n            'cutlass.') and (i.optional_vars is None or isinstance(i.optional_vars, ast.Name)) for i in node.items)\n    if isinstance(node, ast.ClassDef):\n        return (not node.bases and not node.keywords and len(node.decorator_list) == 1\n                and _canon(node.decorator_list[0], alias) == 'cutlass.cute.struct'\n                and all(isinstance(s, ast.AnnAssign) and isinstance(s.target, ast.Name) and s.value is None\n                        or isinstance(s, ast.Pass) or isinstance(s, ast.Expr) and isinstance(s.value, ast.Constant)\n                        for s in node.body))\n    return False\n\n\ndef check(code, langs=('triton',)):\n    \"\"\"Return (ok, reason). The reason names the first violation, in words the proposer can act on. langs is the\n    task's tuple of admitted kernel languages: ('triton',) or ('triton', 'cute').\"\"\"\n    cute = 'cute' in langs\n    limit = CUTE_MAX_BYTES if cute else MAX_BYTES\n    imports = ALLOWED_IMPORTS | CUTE_IMPORTS if cute else ALLOWED_IMPORTS\n    torch_ok = TORCH_CHAINS | CUTE_TORCH_CHAINS if cute else TORCH_CHAINS\n    if not isinstance(code, str) or not code.strip():\n        return False, 'empty candidate'\n    if len(code.encode()) > limit:\n        return False, f'candidate exceeds {limit} bytes'\n    try:\n        tree = ast.parse(code)\n    except SyntaxError as e:\n        return False, f'syntax error on line {e.lineno}: {e.msg}'\n    parents = {}\n    for p in ast.walk(tree):\n        for ch in ast.iter_child_nodes(p):\n            parents[id(ch)] = p\n    alias = {}\n    for node in ast.walk(tree):\n        if isinstance(node, ast.Import):\n            for a in node.names:\n                if a.name not in imports:\n                    return False, f'import of {a.name} is not allowed (allowed: torch, triton, triton.language, math' + (\n                        ' and the CuTe DSL modules in CUTE_IMPORTS)' if cute else ')')\n                alias[a.asname or a.name.split('.')[0]] = a.name if a.asname else a.name.split('.')[0]\n        elif isinstance(node, ast.ImportFrom):\n            mod = node.module or ''\n            if node.level or mod not in imports:\n                return False, f'import from {mod or \".\"} is not allowed'\n            for a in node.names:\n                if a.name == '*':\n                    return False, 'star imports are not allowed'\n                full = f'{mod}.{a.name}'\n                if mod == 'torch' and full not in torch_ok:\n                    return False, f'from torch import {a.name} is not allowed'\n                if mod == 'triton' and full not in TRITON_CHAINS and a.name != 'language':\n                    return False, f'from triton import {a.name} is not allowed'\n                if cute and mod in CUTE_IMPORTS and _cute_bad(full):\n                    return False, _cute_bad(full)\n\"\"\"Task API for the agent: Case, Task and input helpers (public copy, standard normal inputs only).\"\"\"\nfrom dataclasses import dataclass, field\n\n@dataclass\nclass Case:\n    args: list\n    outputs: tuple = ()\n    inplace: tuple = ()\n    shape: dict = field(default_factory=dict)\n    dist: str = 'normal'\n\n    @property\n    def checked(self):\n        return tuple(self.outputs) + tuple(self.inplace)\n\n@dataclass\nclass Task:\n    name: str\n    title: str\n    models: str\n    semantics: str\n    signature: str\n    shapes: list\n    edge_shapes: list\n    dists: list\n    make_case: object\n    reference: object\n    check: object\n    bytes_moved: object\n    vllm: object = None\n    notes: str = ''\n    weight: float = 1.0\n    gpus: tuple = ()\n    ceilings: dict = None\n    langs: tuple = ('triton',)\n    timing: str = 'eager'\n\n    def meta(self):\n        return {'name': self.name, 'title': self.title, 'models': self.models, 'semantics': self.semantics, 'signature': self.signature, 'shapes': self.shapes, 'edge_shapes': self.edge_shapes, 'dists': self.dists, 'notes': self.notes, 'weight': self.weight}\n\ndef shape_key(shape):\n    return ','.join((f'{k}={v}' for k, v in shape.items() if k not in ('model', 'seq')))\nimport torch\n\ndef gen(device, seed):\n    g = torch.Generator(device=device)\n    g.manual_seed(int(seed) & 140737488355327)\n    return g\n\ndef randn(shape, g, device, dist='normal', scale=1.0):\n    \"\"\"Public inputs: standard normal values times scale. The verifier also checks hidden input distributions.\"\"\"\n    return torch.randn(shape, generator=g, device=device, dtype=torch.float32) * scale\n\ndef close(got, exp, atol, rtol, scale_atol=False):\n    \"\"\"Elementwise |got - exp| <= atol + rtol*|exp| with NaN/Inf handling. Returns (ok, stats).\n    scale_atol multiplies atol by the RMS of the expected values, so near-zero outputs of large inputs (cancellation)\n    are judged at the precision of their inputs.\"\"\"\n    if tuple(got.shape) != tuple(exp.shape):\n        return (False, {'error': f'shape {tuple(got.shape)} != {tuple(exp.shape)}'})\n    g, e = (got.float(), exp.float())\n    fin_e = torch.isfinite(e)\n    if scale_atol and e.numel():\n        atol = atol * max(1.0, float(torch.where(fin_e, e, torch.zeros_like(e)).pow(2).mean().sqrt().item()))\n    bad_nonfinite = int((~torch.isfinite(g) & fin_e).sum().item())\n    diff = (g - e).abs()\n    diff = torch.where(fin_e & torch.isfinite(g), diff, torch.zeros_like(diff))\n    tol = atol + rtol * e.abs()\n    bad = int(((diff > tol) & fin_e).sum().item()) + bad_nonfinite\n    n = max(1, e.numel())\n    stats = {'max_abs': float(diff.max().item()) if diff.numel() else 0.0, 'max_rel': float((diff / e.abs().clamp_min(1e-06)).max().item()) if diff.numel() else 0.0, 'bad': bad, 'bad_frac': bad / n, 'nonfinite': bad_nonfinite}\n    return (bad == 0, stats)\n\ndef mag_close(got, exp, mag, rtol, mag_tol):\n    \"\"\"|got - exp| <= rtol * |exp| + mag_tol * mag per element, where mag is the sum of the absolute values of the\n    terms that make up each output (the natural scale of summation error). Non-finite outputs fail.\"\"\"\n    g, e, m = (got.float(), exp.float(), mag.float())\n    if g.shape != e.shape:\n        return (False, {'error': f'shape {tuple(g.shape)} != {tuple(e.shape)}'})\n    tol = rtol * e.abs() + mag_tol * m + 1e-30\n    finite = torch.isfinite(g)\n    diff = torch.where(finite, (g - e).abs(), torch.full_like(g, float('inf')))\n    bad = int((diff > tol).sum().item())\n    fin = torch.where(finite, diff, torch.zeros_like(diff))\n    return (bad == 0, {'max_abs': float(fin.max().item()) if g.numel() else 0.0, 'max_over_tol': float((fin / tol).max().item()) if g.numel() else 0.0, 'bad': bad, 'bad_frac': bad / max(1, e.numel()), 'nonfinite': int((~finite).sum().item())})\n\ndef fp8_ulp(v):\n    \"\"\"Spacing of float8_e4m3fn values around |v| (3 mantissa bits, min normal 2^-6, subnormal step 2^-9).\"\"\"\n    a = v.abs().clamp_min(2.0 ** (-6))\n    return torch.exp2(torch.floor(torch.log2(a)) - 3.0)\n\ndef rope_cache(max_pos, dim, base, device):\n    inv = 1.0 / base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)\n    t = torch.arange(max_pos, device=device, dtype=torch.float32)\n    f = torch.outer(t, inv)\n    return torch.cat([f.cos(), f.sin()], dim=-1).to(torch.bfloat16)\n\ndef positions(T, max_pos, g, device, sequential):\n    if sequential:\n        start = int(torch.randint(0, max(1, max_pos - T), (1,), generator=g, device=device).item())\n        return torch.arange(start, start + T, device=device, dtype=torch.int64)\n    return torch.randint(0, max_pos, (T,), generator=g, device=device, dtype=torch.int64)\n\ndef rope_neox(x, cos, sin):\n    \"\"\"x [T, H, D] any float dtype, cos/sin [T, D/2] float32. Returns float32.\"\"\"\n    half = x.shape[-1] // 2\n    x1, x2 = (x[..., :half].float(), x[..., half:].float())\n    c, s = (cos[:, None, :], sin[:, None, :])\n    return torch.cat([x1 * c - x2 * s, x2 * c + x1 * s], dim=-1)\n\ndef ensure_vllm():\n    \"\"\"Import vLLM's compiled ops so torch.ops._C and torch.ops._moe_C are registered.\"\"\"\n    import vllm._custom_ops\n    try:\n        import vllm._moe_C\n    except Exception:\n        pass\n    return torch.ops","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"c0f84530-eeda-44ef-a813-f4fd2afe02cc","timestamp":"2026-10-18T13:50:07.798Z","tool_use_result":{"stdout":"\"\"\"Static admission for candidate kernels. Runs on the controller before any GPU time is spent, and again in the\njudge. The contract is deliberately narrow: a candidate is a Triton module that defines kernel_fn. torch may be used\nto allocate and reshape tensors. All arithmetic must run inside Triton kernels (the judge also enforces this at\nruntime by recording every torch op the candidate executes).\n\nA task whose langs include 'cute' also admits CuTe DSL kernels (the judge image's `cutlass` 4.7.1 package): the\nmodules in CUTE_IMPORTS, and no cutlass name whose path has a private part or a part in CUTE_BLOCKED (loaders of\nprebuilt or exported binaries, external linking, the CUDA driver, compiler internals, prebuilt operator packages,\ntest and framework helpers, pointers made from integers). Internals are entered only through CUTE_ENTRY. Such a\nmodule may also use `with` on cutlass context managers (cute.arch.elect_one) and declare shared memory as @cute.struct\nclasses of annotated fields.\n\"\"\"\nimport ast\n\nMAX_BYTES = 48 * 1024\nCUTE_MAX_BYTES = 128 * 1024        # CuTe spells out the layouts, pipelines and warp roles that Triton infers\nALLOWED_IMPORTS = {'torch', 'triton', 'triton.language', 'triton.language.extra', 'triton.language.extra.libdevice',\n                   'math', 'typing'}\nTORCH_CHAINS = {\n    'torch', 'torch.empty', 'torch.empty_like', 'torch.empty_strided', 'torch.zeros', 'torch.zeros_like', 'torch.full',\n    'torch.float32', 'torch.float16', 'torch.bfloat16', 'torch.float8_e4m3fn', 'torch.float8_e5m2', 'torch.int8',\n    'torch.int16', 'torch.int32', 'torch.int64', 'torch.uint8', 'torch.bool', 'torch.Tensor', 'torch.dtype',\n    'torch.device', 'torch.Size', 'torch.finfo', 'torch.iinfo', 'torch.cuda.get_device_properties',\n    'torch.cuda.current_device', 'torch.cuda.is_available',\n}\nTRITON_CHAINS = {'triton.jit', 'triton.autotune', 'triton.Config', 'triton.cdiv', 'triton.next_power_of_2',\n                 'triton.heuristics', 'triton.set_allocator'}   # set_allocator enables TMA descriptors\nCUTE_IMPORTS = {\n    'cutlass', 'cutlass.cute', 'cutlass.cute.arch', 'cutlass.cute.math', 'cutlass.cute.runtime', 'cutlass.cute.nvgpu',\n    'cutlass.cute.nvgpu.cpasync', 'cutlass.cute.nvgpu.tcgen05', 'cutlass.cute.nvgpu.warp',\n    'cutlass.cute.nvgpu.warpgroup', 'cutlass.pipeline', 'cutlass.utils', 'cutlass.utils.blackwell_helpers',\n    'cutlass.utils.hopper_helpers', 'cutlass.cutlass_dsl', 'cutlass._mlir.dialects', 'cutlass._mlir.dialects.nvvm',\n    'cuda.bindings.driver',\n}\n# The only ways into internals: device-only inline PTX (nvvm), and the stream handle that wraps torch's current stream.\n# The llvm dialect is not admitted: @cute.jit host functions run as native host code, where llvm ops (inline\n# assembly, external calls, raw loads and stores) escape every check here.\nCUTE_ENTRY = ('cutlass.cutlass_dsl.T', 'cutlass.cutlass_dsl.dsl_user_op', 'cutlass._mlir.dialects.nvvm',\n              'cuda.bindings.driver.CUstream')\n# No part of a cutlass name may be one of these. cutlass modules re-export what they import, so the CUDA driver\n# (cutlass.cuda: load_cubin_module, launch_kernel, stream_create) is reachable as an attribute of kernel modules.\nCUTE_BLOCKED = {\n    'export', 'load_module', 'find_runtime_libraries', 'ffi', 'extern', 'base_extern', 'BitCode', 'LinkLibraries',\n    'compile_to', 'make_ptr', 'cuda', 'cuda_helpers', 'driver', 'runtime', 'base_dsl', 'cutlass_dsl', 'compiler',\n    'ir', 'spec', 'BaseDSL', 'CuTeDSL', 'CuteExperimentalDSL', 'FFI', 'ExecutionArgs', 'JitArgAdapterRegistry',\n    'register_jit_arg_adapter', 'operators', 'kernels', 'testing', 'experimental', 'jax', 'torch', 'tvm_ffi',\n    'ctypes', 'os', 'sys', 'subprocess', 'importlib', 'builtins', 'inspect', 'json', 'tempfile', 'pathlib', 'shutil',\n    'pickle', 'threading', 'weakref',\n}\nCUTE_TORCH_CHAINS = {'torch.cuda.current_stream'}      # its .cuda_stream goes into cuda.bindings.driver.CUstream\nFORBIDDEN_NAMES = {'eval', 'exec', 'compile', 'open', '__import__', 'globals', 'locals', 'vars', 'getattr', 'setattr',\n                   'delattr', 'breakpoint', 'input', 'id', 'memoryview', 'help', 'exit', 'quit', 'sys', 'os',\n                   'print', 'super', 'type', 'object'}\nFORBIDDEN_NODES = (ast.ClassDef, ast.Global, ast.Nonlocal, ast.AsyncFunctionDef, ast.Await, ast.Yield, ast.YieldFrom,\n                   ast.Try, ast.With, ast.AsyncWith)\n\n\ndef _chain(node):\n    parts = []\n    while isinstance(node, ast.Attribute):\n        parts.append(node.attr)\n        node = node.value\n    if isinstance(node, ast.Name):\n        parts.append(node.id)\n        return list(reversed(parts))\n    return None\n\n\ndef _canon(node, alias):\n    c = _chain(node)\n    return '.'.join(alias[c[0]].split('.') + c[1:]) if c and c[0] in alias else None\n\n\ndef _cute_bad(full):\n    \"\"\"Why a dotted cutlass or cuda name is not admitted, or None. cutlass.cute.runtime is the one runtime module.\"\"\"\n    head = next((h for h in CUTE_ENTRY if full == h or full.startswith(h + '.')), None)\n    if full == 'cuda.bindings.driver':          # the module itself; only CUstream may be taken from it\n        return None\n    if full.startswith('cuda') and not head:\n        return f'{full} is not allowed (from cuda.bindings.driver only CUstream)'\n    parts = full.split('.')\n    for i in range(len(head.split('.')) if head else 1, len(parts)):\n        if parts[:i + 1] != ['cutlass', 'cute', 'runtime'] and (parts[i].startswith('_') or parts[i] in CUTE_BLOCKED):\n            return f'{full} is not allowed: {parts[i]} is private or not kernel writing (CUTE_BLOCKED, static_check.py)'\n    return None\n\n\ndef _launders(node, alias, parents):\n    \"\"\"Whether a CUTE_BLOCKED attribute sits in a chain that does not start at an imported name (a module kept in a\n    variable, a list item, a call result): the chain checks cannot see where such a chain leads, so it is refused.\"\"\"\n    while isinstance(parents.get(id(node)), ast.Attribute) and parents[id(node)].value is node:\n        node = parents[id(node)]\n    return _canon(node, alias) is None\n\n\ndef _cute_exempt(node, alias):\n    \"\"\"A with statement on cutlass context managers, or a @cute.struct class of annotated fields only.\"\"\"\n    if isinstance(node, ast.With):\n        return all(isinstance(i.context_expr, ast.Call) and (_canon(i.context_expr.func, alias) or '').startswith(\n            'cutlass.') and (i.optional_vars is None or isinstance(i.optional_vars, ast.Name)) for i in node.items)\n    if isinstance(node, ast.ClassDef):\n        return (not node.bases and not node.keywords and len(node.decorator_list) == 1\n                and _canon(node.decorator_list[0], alias) == 'cutlass.cute.struct'\n                and all(isinstance(s, ast.AnnAssign) and isinstance(s.target, ast.Name) and s.value is None\n                        or isinstance(s, ast.Pass) or isinstance(s, ast.Expr) and isinstance(s.value, ast.Constant)\n                        for s in node.body))\n    return False\n\n\ndef check(code, langs=('triton',)):\n    \"\"\"Return (ok, reason). The reason names the first violation, in words the proposer can act on. langs is the\n    task's tuple of admitted kernel languages: ('triton',) or ('triton', 'cute').\"\"\"\n    cute = 'cute' in langs\n    limit = CUTE_MAX_BYTES if cute else MAX_BYTES\n    imports = ALLOWED_IMPORTS | CUTE_IMPORTS if cute else ALLOWED_IMPORTS\n    torch_ok = TORCH_CHAINS | CUTE_TORCH_CHAINS if cute else TORCH_CHAINS\n    if not isinstance(code, str) or not code.strip():\n        return False, 'empty candidate'\n    if len(code.encode()) > limit:\n        return False, f'candidate exceeds {limit} bytes'\n    try:\n        tree = ast.parse(code)\n    except SyntaxError as e:\n        return False, f'syntax error on line {e.lineno}: {e.msg}'\n    parents = {}\n    for p in ast.walk(tree):\n        for ch in ast.iter_child_nodes(p):\n            parents[id(ch)] = p\n    alias = {}\n    for node in ast.walk(tree):\n        if isinstance(node, ast.Import):\n            for a in node.names:\n                if a.name not in imports:\n                    return False, f'import of {a.name} is not allowed (allowed: torch, triton, triton.language, math' + (\n                        ' and the CuTe DSL modules in CUTE_IMPORTS)' if cute else ')')\n                alias[a.asname or a.name.split('.')[0]] = a.name if a.asname else a.name.split('.')[0]\n        elif isinstance(node, ast.ImportFrom):\n            mod = node.module or ''\n            if node.level or mod not in imports:\n                return False, f'import from {mod or \".\"} is not allowed'\n            for a in node.names:\n                if a.name == '*':\n                    return False, 'star imports are not allowed'\n                full = f'{mod}.{a.name}'\n                if mod == 'torch' and full not in torch_ok:\n                    return False, f'from torch import {a.name} is not allowed'\n                if mod == 'triton' and full not in TRITON_CHAINS and a.name != 'language':\n                    return False, f'from triton import {a.name} is not allowed'\n                if cute and mod in CUTE_IMPORTS and _cute_bad(full):\n                    return False, _cute_bad(full)\n\"\"\"Task API for the agent: Case, Task and input helpers (public copy, standard normal inputs only).\"\"\"\nfrom dataclasses import dataclass, field\n\n@dataclass\nclass Case:\n    args: list\n    outputs: tuple = ()\n    inplace: tuple = ()\n    shape: dict = field(default_factory=dict)\n    dist: str = 'normal'\n\n    @property\n    def checked(self):\n        return tuple(self.outputs) + tuple(self.inplace)\n\n@dataclass\nclass Task:\n    name: str\n    title: str\n    models: str\n    semantics: str\n    signature: str\n    shapes: list\n    edge_shapes: list\n    dists: list\n    make_case: object\n    reference: object\n    check: object\n    bytes_moved: object\n    vllm: object = None\n    notes: str = ''\n    weight: float = 1.0\n    gpus: tuple = ()\n    ceilings: dict = None\n    langs: tuple = ('triton',)\n    timing: str = 'eager'\n\n    def meta(self):\n        return {'name': self.name, 'title': self.title, 'models': self.models, 'semantics': self.semantics, 'signature': self.signature, 'shapes': self.shapes, 'edge_shapes': self.edge_shapes, 'dists': self.dists, 'notes': self.notes, 'weight': self.weight}\n\ndef shape_key(shape):\n    return ','.join((f'{k}={v}' for k, v in shape.items() if k not in ('model', 'seq')))\nimport torch\n\ndef gen(device, seed):\n    g = torch.Generator(device=device)\n    g.manual_seed(int(seed) & 140737488355327)\n    return g\n\ndef randn(shape, g, device, dist='normal', scale=1.0):\n    \"\"\"Public inputs: standard normal values times scale. The verifier also checks hidden input distributions.\"\"\"\n    return torch.randn(shape, generator=g, device=device, dtype=torch.float32) * scale\n\ndef close(got, exp, atol, rtol, scale_atol=False):\n    \"\"\"Elementwise |got - exp| <= atol + rtol*|exp| with NaN/Inf handling. Returns (ok, stats).\n    scale_atol multiplies atol by the RMS of the expected values, so near-zero outputs of large inputs (cancellation)\n    are judged at the precision of their inputs.\"\"\"\n    if tuple(got.shape) != tuple(exp.shape):\n        return (False, {'error': f'shape {tuple(got.shape)} != {tuple(exp.shape)}'})\n    g, e = (got.float(), exp.float())\n    fin_e = torch.isfinite(e)\n    if scale_atol and e.numel():\n        atol = atol * max(1.0, float(torch.where(fin_e, e, torch.zeros_like(e)).pow(2).mean().sqrt().item()))\n    bad_nonfinite = int((~torch.isfinite(g) & fin_e).sum().item())\n    diff = (g - e).abs()\n    diff = torch.where(fin_e & torch.isfinite(g), diff, torch.zeros_like(diff))\n    tol = atol + rtol * e.abs()\n    bad = int(((diff > tol) & fin_e).sum().item()) + bad_nonfinite\n    n = max(1, e.numel())\n    stats = {'max_abs': float(diff.max().item()) if diff.numel() else 0.0, 'max_rel': float((diff / e.abs().clamp_min(1e-06)).max().item()) if diff.numel() else 0.0, 'bad': bad, 'bad_frac': bad / n, 'nonfinite': bad_nonfinite}\n    return (bad == 0, stats)\n\ndef mag_close(got, exp, mag, rtol, mag_tol):\n    \"\"\"|got - exp| <= rtol * |exp| + mag_tol * mag per element, where mag is the sum of the absolute values of the\n    terms that make up each output (the natural scale of summation error). Non-finite outputs fail.\"\"\"\n    g, e, m = (got.float(), exp.float(), mag.float())\n    if g.shape != e.shape:\n        return (False, {'error': f'shape {tuple(g.shape)} != {tuple(e.shape)}'})\n    tol = rtol * e.abs() + mag_tol * m + 1e-30\n    finite = torch.isfinite(g)\n    diff = torch.where(finite, (g - e).abs(), torch.full_like(g, float('inf')))\n    bad = int((diff > tol).sum().item())\n    fin = torch.where(finite, diff, torch.zeros_like(diff))\n    return (bad == 0, {'max_abs': float(fin.max().item()) if g.numel() else 0.0, 'max_over_tol': float((fin / tol).max().item()) if g.numel() else 0.0, 'bad': bad, 'bad_frac': bad / max(1, e.numel()), 'nonfinite': int((~finite).sum().item())})\n\ndef fp8_ulp(v):\n    \"\"\"Spacing of float8_e4m3fn values around |v| (3 mantissa bits, min normal 2^-6, subnormal step 2^-9).\"\"\"\n    a = v.abs().clamp_min(2.0 ** (-6))\n    return torch.exp2(torch.floor(torch.log2(a)) - 3.0)\n\ndef rope_cache(max_pos, dim, base, device):\n    inv = 1.0 / base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)\n    t = torch.arange(max_pos, device=device, dtype=torch.float32)\n    f = torch.outer(t, inv)\n    return torch.cat([f.cos(), f.sin()], dim=-1).to(torch.bfloat16)\n\ndef positions(T, max_pos, g, device, sequential):\n    if sequential:\n        start = int(torch.randint(0, max(1, max_pos - T), (1,), generator=g, device=device).item())\n        return torch.arange(start, start + T, device=device, dtype=torch.int64)\n    return torch.randint(0, max_pos, (T,), generator=g, device=device, dtype=torch.int64)\n\ndef rope_neox(x, cos, sin):\n    \"\"\"x [T, H, D] any float dtype, cos/sin [T, D/2] float32. Returns float32.\"\"\"\n    half = x.shape[-1] // 2\n    x1, x2 = (x[..., :half].float(), x[..., half:].float())\n    c, s = (cos[:, None, :], sin[:, None, :])\n    return torch.cat([x1 * c - x2 * s, x2 * c + x1 * s], dim=-1)\n\ndef ensure_vllm():\n    \"\"\"Import vLLM's compiled ops so torch.ops._C and torch.ops._moe_C are registered.\"\"\"\n    import vllm._custom_ops\n    try:\n        import vllm._moe_C\n    except Exception:\n        pass\n    return torch.ops","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01B4c1vuirEMPYyCFTni6zZ5","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9owFVNMmNmk5SGByJjE","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10885,"cache_read_input_tokens":30404,"cache_creation":{"ephemeral_5m_input_tokens":10885,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"58f7fe57-20d9-4d17-a067-8ecd84fa0bbf","timestamp":"2026-10-18T13:50:09.067Z","request_id":"req_011Cg9owF9nAyBJo5TbCcTag","thinking_duration_ms":490,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9owFVNMmNmk5SGByJjE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012Dd84AxVGNrLfDnm4AtKjW","name":"Bash","input":{"command":"sed -n 150,400p /workspace/static_check.py; cat /workspace/nodus_autosave.py | head -50","description":"Read rest of static check"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":10885,"cache_read_input_tokens":30404,"cache_creation":{"ephemeral_5m_input_tokens":10885,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"522583f7-decd-4e9b-8229-7492ddc18ad9","timestamp":"2026-10-18T13:50:09.316Z","request_id":"req_011Cg9owF9nAyBJo5TbCcTag"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012Dd84AxVGNrLfDnm4AtKjW","type":"tool_result","content":"                    return False, _cute_bad(full)\n                alias[a.asname or a.name] = full\n    has_kernel_fn = False\n    has_jit = False\n    has_cute_kernel = False\n    for node in ast.walk(tree):\n        if isinstance(node, FORBIDDEN_NODES) and not (cute and _cute_exempt(node, alias)):\n            return False, f'{type(node).__name__} is not allowed in candidates'\n        if isinstance(node, ast.FunctionDef):\n            if node.name.startswith('__'):\n                return False, f'dunder function {node.name} is not allowed'\n            if node.name == 'kernel_fn' and node in tree.body:\n                has_kernel_fn = True\n            for d in node.decorator_list:\n                c = _chain(d.func if isinstance(d, ast.Call) else d)\n                if c:\n                    canon = alias.get(c[0], c[0]).split('.') + c[1:]\n                    if canon[:2] == ['triton', 'jit']:\n                        has_jit = True\n                    if canon[:3] == ['cutlass', 'cute', 'kernel']:\n                        has_cute_kernel = True\n        if isinstance(node, ast.Name) and node.id in FORBIDDEN_NAMES:\n            return False, f'use of {node.id} is not allowed'\n        if isinstance(node, ast.Attribute) and node.attr.startswith('__'):\n            return False, f'dunder attribute {node.attr} is not allowed'\n        if cute and isinstance(node, ast.Attribute) and node.attr.startswith('_') and node.attr != '_mlir':\n            return False, f'private attribute {node.attr} is not allowed'\n        if cute and isinstance(node, ast.Attribute) and node.attr in CUTE_BLOCKED and _launders(node, alias, parents):\n            return False, (f'attribute {node.attr} is not allowed here: in CuTe modules the names in CUTE_BLOCKED may '\n                           'only appear in a chain that starts at an import')\n        if cute and isinstance(node, ast.keyword) and node.arg is None:\n            return False, 'keyword unpacking (**) is not allowed in CuTe modules: pass compile options by name'\n        if cute and isinstance(node, ast.keyword) and node.arg == 'options' and not (\n                isinstance(node.value, ast.Constant) and isinstance(node.value.value, str)\n                and 'link' not in node.value.value.lower()):\n            return False, 'compile options must be a string literal and must not link libraries'\n        if isinstance(node, (ast.Assign, ast.AugAssign, ast.AnnAssign, ast.Delete)):\n            targets = node.targets if isinstance(node, (ast.Assign, ast.Delete)) else [node.target]\n            for t in targets:\n                for sub in ast.walk(t):\n                    if isinstance(sub, ast.Attribute):\n                        return False, 'assigning to attributes is not allowed'\n    for node in ast.walk(tree):\n        if not isinstance(node, (ast.Attribute, ast.Name)):\n            continue\n        p = parents.get(id(node))\n        if isinstance(p, ast.Attribute) and p.value is node:\n            continue  # only check the longest chain\n        c = _chain(node)\n        if not c or c[0] not in alias:\n            continue\n        canon = alias[c[0]].split('.') + c[1:]\n        full = '.'.join(canon)\n        if canon[0] == 'torch':\n            if full not in torch_ok:\n                return False, f'{full} is not allowed: allocate with torch, compute in Triton'\n        elif canon[0] == 'triton':\n            if canon[:2] == ['triton', 'language']:\n                continue\n            if len(canon) > 1 and full not in TRITON_CHAINS:\n                return False, f'{full} is not allowed'\n        elif cute and canon[0] in ('cutlass', 'cuda'):\n            if _cute_bad(full):\n                return False, _cute_bad(full)\n            if full in CUTE_IMPORTS:       # a module object passed around would escape these checks\n                return False, f'the module {full} may only be used through its attributes'\n    if not has_kernel_fn:\n        return False, 'the module must define a top-level function named kernel_fn'\n    if not (has_jit or cute and has_cute_kernel):\n        return False, 'the module must contain at least one ' + ('@triton.jit or @cute.kernel kernel' if cute else\n                                                                 '@triton.jit kernel')\n    return True, 'ok'\n\"\"\"Best-version autosave, the agent's side (hardened feature \"autosave\"; shipped as /workspace/nodus_autosave.py).\n\nEvery run of /workspace/check.py ends here. When the solution it checked was correct on every public shape, the run\nis logged in /workspace/.best/history.jsonl (time, measured speedup over the starting kernel, file hash), and when it\nis also faster than the best saved so far, the file is copied to /workspace/.best/solution.py. After the session the\ncollect step hands .best/solution.py and the history to the sealed verifier next to /workspace/solution.py. If\nsolution.py is missing or not correct, the verifier grades the best saved version instead (kernel_lab/judge/autosave.py).\nThe speeds here are check.py's quick measurements, not the verifier's: they decide what to keep and draw the\nscore-over-time curve. The grade always comes from the sealed verifier.\n\"\"\"\nimport hashlib\nimport json\nimport math\nimport os\nimport re\nimport time\n\nBEST = os.environ.get('NODUS_BEST_DIR', '/workspace/.best')\n_LINE = re.compile(r': correct, ([0-9.]+) us, starting kernel ([0-9.]+) us')\n\n\ndef parse(out):\n    \"\"\"(correct, speedup over the starting kernel) from check.py's printed lines (single-GPU check).\"\"\"\n    if 'WRONG' in out or 'REJECTED' in out or 'Traceback' in out:\n        return False, 0.0\n    pairs = [(float(a), float(b)) for a, b in _LINE.findall(out)]\n    if not pairs or any(a <= 0 for a, _ in pairs):\n        return False, 0.0\n    return True, math.exp(sum(math.log(b / a) for a, b in pairs) / len(pairs))\n\n\ndef parse_report(rep):\n    \"\"\"(correct, speedup over the starting kernel) from the 8-GPU check's worker report.\"\"\"\n    sp = ((rep or {}).get('summary') or {}).get('geo_vs_champion') or 0.0\n    return (rep or {}).get('status') == 'ok' and sp > 0, float(sp)\n\n\ndef _best_speed():\n    try:\n        with open(os.path.join(BEST, 'best.json')) as f:\n            return float(json.load(f).get('speedup') or 0.0)\n    except (OSError, ValueError):\n        return 0.0\n\n\ndef save(path, correct, speedup, now=None):\n    \"\"\"Log a correct check and keep the file when it beats the best so far. Returns True when it was saved.\"\"\"\n    if not correct or not speedup or not os.path.isfile(path) or os.path.islink(path):\n        return False\n    os.makedirs(BEST, exist_ok=True)","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"84d6fc45-a51c-4131-8fa9-9c9ee71d2544","timestamp":"2026-10-18T13:50:09.363Z","tool_use_result":{"stdout":"                    return False, _cute_bad(full)\n                alias[a.asname or a.name] = full\n    has_kernel_fn = False\n    has_jit = False\n    has_cute_kernel = False\n    for node in ast.walk(tree):\n        if isinstance(node, FORBIDDEN_NODES) and not (cute and _cute_exempt(node, alias)):\n            return False, f'{type(node).__name__} is not allowed in candidates'\n        if isinstance(node, ast.FunctionDef):\n            if node.name.startswith('__'):\n                return False, f'dunder function {node.name} is not allowed'\n            if node.name == 'kernel_fn' and node in tree.body:\n                has_kernel_fn = True\n            for d in node.decorator_list:\n                c = _chain(d.func if isinstance(d, ast.Call) else d)\n                if c:\n                    canon = alias.get(c[0], c[0]).split('.') + c[1:]\n                    if canon[:2] == ['triton', 'jit']:\n                        has_jit = True\n                    if canon[:3] == ['cutlass', 'cute', 'kernel']:\n                        has_cute_kernel = True\n        if isinstance(node, ast.Name) and node.id in FORBIDDEN_NAMES:\n            return False, f'use of {node.id} is not allowed'\n        if isinstance(node, ast.Attribute) and node.attr.startswith('__'):\n            return False, f'dunder attribute {node.attr} is not allowed'\n        if cute and isinstance(node, ast.Attribute) and node.attr.startswith('_') and node.attr != '_mlir':\n            return False, f'private attribute {node.attr} is not allowed'\n        if cute and isinstance(node, ast.Attribute) and node.attr in CUTE_BLOCKED and _launders(node, alias, parents):\n            return False, (f'attribute {node.attr} is not allowed here: in CuTe modules the names in CUTE_BLOCKED may '\n                           'only appear in a chain that starts at an import')\n        if cute and isinstance(node, ast.keyword) and node.arg is None:\n            return False, 'keyword unpacking (**) is not allowed in CuTe modules: pass compile options by name'\n        if cute and isinstance(node, ast.keyword) and node.arg == 'options' and not (\n                isinstance(node.value, ast.Constant) and isinstance(node.value.value, str)\n                and 'link' not in node.value.value.lower()):\n            return False, 'compile options must be a string literal and must not link libraries'\n        if isinstance(node, (ast.Assign, ast.AugAssign, ast.AnnAssign, ast.Delete)):\n            targets = node.targets if isinstance(node, (ast.Assign, ast.Delete)) else [node.target]\n            for t in targets:\n                for sub in ast.walk(t):\n                    if isinstance(sub, ast.Attribute):\n                        return False, 'assigning to attributes is not allowed'\n    for node in ast.walk(tree):\n        if not isinstance(node, (ast.Attribute, ast.Name)):\n            continue\n        p = parents.get(id(node))\n        if isinstance(p, ast.Attribute) and p.value is node:\n            continue  # only check the longest chain\n        c = _chain(node)\n        if not c or c[0] not in alias:\n            continue\n        canon = alias[c[0]].split('.') + c[1:]\n        full = '.'.join(canon)\n        if canon[0] == 'torch':\n            if full not in torch_ok:\n                return False, f'{full} is not allowed: allocate with torch, compute in Triton'\n        elif canon[0] == 'triton':\n            if canon[:2] == ['triton', 'language']:\n                continue\n            if len(canon) > 1 and full not in TRITON_CHAINS:\n                return False, f'{full} is not allowed'\n        elif cute and canon[0] in ('cutlass', 'cuda'):\n            if _cute_bad(full):\n                return False, _cute_bad(full)\n            if full in CUTE_IMPORTS:       # a module object passed around would escape these checks\n                return False, f'the module {full} may only be used through its attributes'\n    if not has_kernel_fn:\n        return False, 'the module must define a top-level function named kernel_fn'\n    if not (has_jit or cute and has_cute_kernel):\n        return False, 'the module must contain at least one ' + ('@triton.jit or @cute.kernel kernel' if cute else\n                                                                 '@triton.jit kernel')\n    return True, 'ok'\n\"\"\"Best-version autosave, the agent's side (hardened feature \"autosave\"; shipped as /workspace/nodus_autosave.py).\n\nEvery run of /workspace/check.py ends here. When the solution it checked was correct on every public shape, the run\nis logged in /workspace/.best/history.jsonl (time, measured speedup over the starting kernel, file hash), and when it\nis also faster than the best saved so far, the file is copied to /workspace/.best/solution.py. After the session the\ncollect step hands .best/solution.py and the history to the sealed verifier next to /workspace/solution.py. If\nsolution.py is missing or not correct, the verifier grades the best saved version instead (kernel_lab/judge/autosave.py).\nThe speeds here are check.py's quick measurements, not the verifier's: they decide what to keep and draw the\nscore-over-time curve. The grade always comes from the sealed verifier.\n\"\"\"\nimport hashlib\nimport json\nimport math\nimport os\nimport re\nimport time\n\nBEST = os.environ.get('NODUS_BEST_DIR', '/workspace/.best')\n_LINE = re.compile(r': correct, ([0-9.]+) us, starting kernel ([0-9.]+) us')\n\n\ndef parse(out):\n    \"\"\"(correct, speedup over the starting kernel) from check.py's printed lines (single-GPU check).\"\"\"\n    if 'WRONG' in out or 'REJECTED' in out or 'Traceback' in out:\n        return False, 0.0\n    pairs = [(float(a), float(b)) for a, b in _LINE.findall(out)]\n    if not pairs or any(a <= 0 for a, _ in pairs):\n        return False, 0.0\n    return True, math.exp(sum(math.log(b / a) for a, b in pairs) / len(pairs))\n\n\ndef parse_report(rep):\n    \"\"\"(correct, speedup over the starting kernel) from the 8-GPU check's worker report.\"\"\"\n    sp = ((rep or {}).get('summary') or {}).get('geo_vs_champion') or 0.0\n    return (rep or {}).get('status') == 'ok' and sp > 0, float(sp)\n\n\ndef _best_speed():\n    try:\n        with open(os.path.join(BEST, 'best.json')) as f:\n            return float(json.load(f).get('speedup') or 0.0)\n    except (OSError, ValueError):\n        return 0.0\n\n\ndef save(path, correct, speedup, now=None):\n    \"\"\"Log a correct check and keep the file when it beats the best so far. Returns True when it was saved.\"\"\"\n    if not correct or not speedup or not os.path.isfile(path) or os.path.islink(path):\n        return False\n    os.makedirs(BEST, exist_ok=True)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_012Dd84AxVGNrLfDnm4AtKjW","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016rBHv8y85WAaxyZeKuZeSN","type":"tool_result","content":"108:    num_ctas: int = 1\n186:        if args.get(\"num_ctas\", 1) > 1 and capability < 90:\n187:            raise ValueError((f\"num_ctas > 1 requires NVIDIA SM90+ (Hopper). \"\n189:                              f\"Please set num_ctas=1 or target an SM90+ GPU.\"))\n211:            metadata.num_ctas,\n260:        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n358:        nvidia.passes.ttnvgpuir.add_check_matmul_two_cta(pm)\n{'model': 'Gemma-3-12B-style global layer (16 query / 8 KV heads), one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': -1}: correct, 20832.86 us, starting kernel 20783.36 us\n{'model': 'Gemma-3-12B-style local layer, sliding window 1024, one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023}: correct, 5690.59 us, starting kernel 5684.26 us\n{'model': 'Qwen3-Next-style full-attention layer (16 query / 2 KV heads), 2 x 4K sequences', 'n': 2, 'L': 4096, 'Hq': 16, 'Hkv': 2, 'window': -1}: correct, 11427.07 us, starting kernel 11421.12 us\n{'model': 'packed documents (16 / 8 heads): 8 documents of 1K-3K tokens, 16K tokens', 'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1, 'ragged': True}: correct, 11164.03 us, starting kernel 11172.61 us\n{'model': 'Gemma-3-4B-style global layer (8 query / 4 KV heads), 4 x 2K sequences', 'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1}: correct, 3025.22 us, starting kernel 3025.41 us\n{'model': 'Gemma-3-4B-style local layer (8 / 4 heads), sliding window 1024, packed documents of 2K-6K tokens, 16K tokens', 'n': 4, 'L': 4096, 'Hq': 8, 'Hkv': 4, 'window': 1023, 'ragged': True}: correct, 5170.11 us, starting kernel 5175.17 us\nautosave: kept this version in /workspace/.best/solution.py (1.000x the starting kernel on the public shapes)","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"18e710e2-0d00-4df8-ac0c-77e1ad642d31","timestamp":"2026-10-18T13:53:10.160Z","tool_use_result":{"stdout":"108:    num_ctas: int = 1\n186:        if args.get(\"num_ctas\", 1) > 1 and capability < 90:\n187:            raise ValueError((f\"num_ctas > 1 requires NVIDIA SM90+ (Hopper). \"\n189:                              f\"Please set num_ctas=1 or target an SM90+ GPU.\"))\n211:            metadata.num_ctas,\n260:        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n358:        nvidia.passes.ttnvgpuir.add_check_matmul_two_cta(pm)\n{'model': 'Gemma-3-12B-style global layer (16 query / 8 KV heads), one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': -1}: correct, 20832.86 us, starting kernel 20783.36 us\n{'model': 'Gemma-3-12B-style local layer, sliding window 1024, one 8K sequence', 'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023}: correct, 5690.59 us, starting kernel 5684.26 us\n{'model': 'Qwen3-Next-style full-attention layer (16 query / 2 KV heads), 2 x 4K sequences', 'n': 2, 'L': 4096, 'Hq': 16, 'Hkv': 2, 'window': -1}: correct, 11427.07 us, starting kernel 11421.12 us\n{'model': 'packed documents (16 / 8 heads): 8 documents of 1K-3K tokens, 16K tokens', 'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1, 'ragged': True}: correct, 11164.03 us, starting kernel 11172.61 us\n{'model': 'Gemma-3-4B-style global layer (8 query / 4 KV heads), 4 x 2K sequences', 'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1}: correct, 3025.22 us, starting kernel 3025.41 us\n{'model': 'Gemma-3-4B-style local layer (8 / 4 heads), sliding window 1024, packed documents of 2K-6K tokens, 16K tokens', 'n': 4, 'L': 4096, 'Hq': 8, 'Hkv': 4, 'window': 1023, 'ragged': True}: correct, 5170.11 us, starting kernel 5175.17 us\nautosave: kept this version in /workspace/.best/solution.py (1.000x the starting kernel on the public shapes)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_016rBHv8y85WAaxyZeKuZeSN","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pAhEHmFS1hn53Ag3A5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018SBBEcfgQ61mj7gYQMaRcG","name":"Bash","input":{"command":"TR=/usr/local/lib/python3.12/dist-packages/triton; sed -n 330,380p $TR/backends/nvidia/compiler.py; grep -rn \"two_cta\\|twoCTA\\|TwoCTA\" $TR/language/*.py $TR/experimental/gluon/language/nvidia/blackwell/__init__.py | head -20","description":"Inspect two-CTA support in Triton"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1201,"cache_read_input_tokens":59049,"cache_creation":{"ephemeral_5m_input_tokens":1201,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"42c2386f-20c9-46bd-a798-600ecf14f216","timestamp":"2026-10-18T13:53:13.198Z","request_id":"req_011Cg9pAgrisNUZGsBsZ6dvs"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018SBBEcfgQ61mj7gYQMaRcG","type":"tool_result","content":"        passes.gluon.add_inliner(pm)\n        passes.gluon.add_infer_coalesced_encodings(pm)\n        passes.gluon.add_resolve_auto_encodings(pm)\n        nvidia.passes.ttnvgpuir.add_tma_lowering(pm)\n        passes.gluon.add_canonicalizer(pm)\n        passes.common.add_sccp(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        passes.gluon.add_canonicalizer(pm)\n        passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n\n        pm.run(mod, 'gluon_to_ttgir')\n        metadata[\"tensordesc_meta\"] = mod.get_tensordesc_metadata()\n        return mod\n\n    def make_llir(self, src, metadata, options, capability):\n        ptx_version = get_ptx_version_from_options(options, self.target.arch)\n\n        mod = src\n        # TritonGPU -> LLVM-IR (MLIR)\n        pm = ir.pass_manager(mod.context)\n        pm.enable_debug()\n\n        passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n        passes.ttgpuir.add_allocate_warp_groups(pm)\n        passes.convert.add_scf_to_cf(pm)\n        passes.gluon.add_inliner(pm)\n        nvidia.passes.ttgpuir.add_allocate_shared_memory_nv(pm, capability, ptx_version)\n        nvidia.passes.ttnvgpuir.add_allocate_tensor_memory(pm)\n        nvidia.passes.ttnvgpuir.add_check_matmul_two_cta(pm)\n        if \"consan\" in options.instrumentation_mode:\n            # Call ConcurrencySanitizerPass here, before allocating global scratch memory but after allocating tensor and shared\n            passes.ttgpuir.add_concurrency_sanitizer(pm)\n        passes.ttgpuir.add_allocate_global_scratch_memory(pm)\n        nvidia.passes.ttnvgpuir.add_proxy_fence_insertion(pm, capability)\n        # instrumentation point here so we can override IRs above (e.g., ttir and ttgir)\n        if CUDABackend.instrumentation:\n            CUDABackend.instrumentation.patch(\"ttgpuir_to_llvmir\", pm, mod.context)\n        nvidia.passes.ttgpuir.add_to_llvmir(pm, capability, ptx_version)\n        passes.common.add_canonicalizer(pm)\n        passes.common.add_cse(pm)\n        nvidia.passes.ttnvgpuir.add_nvgpu_to_llvm(pm)\n        nvidia.passes.ttnvgpuir.add_warp_specialize_to_llvm(pm)\n        passes.common.add_canonicalizer(pm)\n        passes.common.add_cse(pm)\n        passes.common.add_symbol_dce(pm)\n        passes.convert.add_nvvm_to_llvm(pm)\n\n        if not knobs.compilation.disable_line_info and not knobs.compilation.dump_ir_extract_di_local_variables:\n            passes.llvmir.add_di_scope(pm)\n\n        if CUDABackend.instrumentation:\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:46:        two_ctas (bool): Whether the layout is for two-CTA mode. Defaults to False.\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:51:    two_ctas: bool = False\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:57:        super().__setattr__(\"two_ctas\", _unwrap_if_constexpr(self.two_ctas))\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:69:            self.two_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:76:        two_ctas_str = \"2CT\" if self.two_ctas else \"\"\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:77:        return f\"TL{block_str}{stride_str}{cta_split_str}{two_ctas_str}TL\"\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:80:        return hash((self.block, self.col_stride, self.cta_split_num, self.two_ctas))\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:343:            layout.two_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:464:                                         mbarrier_preds, acc.layout.two_ctas, multicast)","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"a7301425-6644-4d81-8a49-bc2413956d60","timestamp":"2026-10-18T13:53:13.262Z","tool_use_result":{"stdout":"        passes.gluon.add_inliner(pm)\n        passes.gluon.add_infer_coalesced_encodings(pm)\n        passes.gluon.add_resolve_auto_encodings(pm)\n        nvidia.passes.ttnvgpuir.add_tma_lowering(pm)\n        passes.gluon.add_canonicalizer(pm)\n        passes.common.add_sccp(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        passes.gluon.add_canonicalizer(pm)\n        passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n\n        pm.run(mod, 'gluon_to_ttgir')\n        metadata[\"tensordesc_meta\"] = mod.get_tensordesc_metadata()\n        return mod\n\n    def make_llir(self, src, metadata, options, capability):\n        ptx_version = get_ptx_version_from_options(options, self.target.arch)\n\n        mod = src\n        # TritonGPU -> LLVM-IR (MLIR)\n        pm = ir.pass_manager(mod.context)\n        pm.enable_debug()\n\n        passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n        passes.ttgpuir.add_allocate_warp_groups(pm)\n        passes.convert.add_scf_to_cf(pm)\n        passes.gluon.add_inliner(pm)\n        nvidia.passes.ttgpuir.add_allocate_shared_memory_nv(pm, capability, ptx_version)\n        nvidia.passes.ttnvgpuir.add_allocate_tensor_memory(pm)\n        nvidia.passes.ttnvgpuir.add_check_matmul_two_cta(pm)\n        if \"consan\" in options.instrumentation_mode:\n            # Call ConcurrencySanitizerPass here, before allocating global scratch memory but after allocating tensor and shared\n            passes.ttgpuir.add_concurrency_sanitizer(pm)\n        passes.ttgpuir.add_allocate_global_scratch_memory(pm)\n        nvidia.passes.ttnvgpuir.add_proxy_fence_insertion(pm, capability)\n        # instrumentation point here so we can override IRs above (e.g., ttir and ttgir)\n        if CUDABackend.instrumentation:\n            CUDABackend.instrumentation.patch(\"ttgpuir_to_llvmir\", pm, mod.context)\n        nvidia.passes.ttgpuir.add_to_llvmir(pm, capability, ptx_version)\n        passes.common.add_canonicalizer(pm)\n        passes.common.add_cse(pm)\n        nvidia.passes.ttnvgpuir.add_nvgpu_to_llvm(pm)\n        nvidia.passes.ttnvgpuir.add_warp_specialize_to_llvm(pm)\n        passes.common.add_canonicalizer(pm)\n        passes.common.add_cse(pm)\n        passes.common.add_symbol_dce(pm)\n        passes.convert.add_nvvm_to_llvm(pm)\n\n        if not knobs.compilation.disable_line_info and not knobs.compilation.dump_ir_extract_di_local_variables:\n            passes.llvmir.add_di_scope(pm)\n\n        if CUDABackend.instrumentation:\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:46:        two_ctas (bool): Whether the layout is for two-CTA mode. Defaults to False.\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:51:    two_ctas: bool = False\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:57:        super().__setattr__(\"two_ctas\", _unwrap_if_constexpr(self.two_ctas))\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:69:            self.two_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:76:        two_ctas_str = \"2CT\" if self.two_ctas else \"\"\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:77:        return f\"TL{block_str}{stride_str}{cta_split_str}{two_ctas_str}TL\"\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:80:        return hash((self.block, self.col_stride, self.cta_split_num, self.two_ctas))\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:343:            layout.two_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py:464:                                         mbarrier_preds, acc.layout.two_ctas, multicast)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_018SBBEcfgQ61mj7gYQMaRcG","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"system","subtype":"thinking_tokens","estimated_tokens":150,"estimated_tokens_delta":100,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"cb4256b4-4b2a-4b81-b063-20bd1a000da3"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":300,"estimated_tokens_delta":150,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"50f9592c-bb60-4357-9be9-4c17b61f8888"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":400,"estimated_tokens_delta":100,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"7f1294ee-1a45-4fae-95af-60d8bbc46391"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":550,"estimated_tokens_delta":150,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"a1aef2b2-55e5-480b-b34e-1d640b9c2324"}
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{"type":"system","subtype":"thinking_tokens","estimated_tokens":1400,"estimated_tokens_delta":100,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"43b441e3-0a29-4263-af07-e50fa67f38d9"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1500,"estimated_tokens_delta":100,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"31d0bd52-de5f-4d73-a961-e20869eb9ee5"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pAvNa6AxHFiVz2grXQ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The current kernel runs about 13x slower than the expert benchmark, so I'll rebuild it as a two-kernel FlashAttention-2 design in Triton with TMA-loaded tiles, then tune tiling, pipelining, and warp specialization.\n\n","signature":"CAQSlggKEQgSGAI4AUIJbmFycmF0aW9uEgx5FGFIyQQqkqCaKgYaDBDYlwX1htwVFRWAeyIwnmrBtmT3rmrdPNrgrPEpEFoXJb6EU3V8nuGA2Wr+BKl3M9L3Zy14Evk12zgbJcYsKrIHMusaJ+kMzwBt6eRoJOLnzy4hdOt7KwxIIj1gyJj43rma1c2UBOp4VTonJZD2BKwdU8xDaLTKAn851HyARemXMvb7UKH0gDIH+UQuhUlac7QxDcd5SVfkHcerfysF9DfPSHY3AsFwgMRKUIhJGCY2Odh79Nzgm31MbieUMs1cxIvuWuzi6U9z5PhBxHm2IDWeU8a+ilMAYD0Qzi80/7F3HAeFHwofTWWElltj24C1WZ7LL2C624V+Jjsz1kXgW2eZY00YPwl/rvtUB4rZl93+arqpg3zgVE/AwJVjW7k8ggoPDPeswo83FGdr+2KG37QrUuAOGxIMk0ITfU9xDrgVfn2CjEId4Ifqvzi+lAAvKRxFtAaUtKyX7K2oeZ2cfv2q7AQ0LdCN+JWNrmvWitD4bWLVGRWnmklb8Mo/FQzfOn+SWr0POZ1sRi/TNeGJFciEE7oU7BoPhFeMZDCj1015LqrzYwod+upIPbzPQ1/wdAOYNqOtIjZMFElY9YhgWMNXfi5ARzzjQS38SkQo+41QvxkuENqe8vtJWeUqeYOX0alAqRFZobZczGa0fdNWSEEeCcfOo9oN+zoL+AMBj4JBhyDaJiSS/8QXYKUT0qEg8NSvIrZuwYgRhlBzCoCLEGF+Zdy/+HPUElfCsAJnDJHnCAfnnsBjmvhQUoo2IX49SCf+ZOKOmL8n3MGmd1FHXpqdCCxoS3kh9NABnQg2oG90YGCaUppmUn+2QrVdIEOGdRk8Umhl+bHDrKcE1EqVM6S9l8YIenKXdxkxFuFBQjH8fMfxIVLN7H/K8HgRZTKjFEDMzP0jrIyr2SPxv3p9yj0BySJQJ0zDrYlDWdRcbyl/IlktCSsSiw4bj58yiY+XJea/R0xLNNT5ZkYl58DPeC8gwry8zF6T2nXa2gMhi/zdAX37uWcCPaq6RRzC2zQb1ipr2DEmgZLQoDrjcKIPR/PCjfv8Z6Ywfbs9qVbv/fIzD7ajEBgGpDQWUytKWdLC1yduBwmcrzpvJnCA/TZHJm3paSMNfo1NTuCBqJg4TfhVLCPXt/PZliJAS9c50EKRo4xGsKFirmKCruEnKtxNigxCjaYTMPW3exKc/cbmX/TdEoY0R7s+vgcviO1zAZGCroORWa1ndgP//PxGLibYNXr4yRSC0FS/y5UytIk1mD0QSb83Jr4Iq6KrOgdFAC8ADNwJyF6DckXH61mrQfi/boJSBwYyn1446eFt4Z/uLJurssJkT63ltL6Zu/5v9+GX0jTZCxgB"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2168,"cache_read_input_tokens":60250,"cache_creation":{"ephemeral_5m_input_tokens":2168,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"63fcc73e-2353-4cfd-9812-fe8418f28335","timestamp":"2026-10-18T13:53:53.657Z","request_id":"req_011Cg9pAv7wWGzqqAjY31hKW","narration_block_indexes":[0],"thinking_duration_ms":2,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pAvNa6AxHFiVz2grXQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EPe4dqudmoSw6xtbGRVYvw","name":"Write","input":{"file_path":"/workspace/sol_v1.py","content":"import torch\nimport triton\nimport triton.language as tl\n\nLOG2E = tl.constexpr(1.4426950408889634)\n\n\n@triton.jit\ndef _prep(O, DO, LSE, DELTA, LSE2, T, H, D: tl.constexpr, BT: tl.constexpr):\n    t0 = tl.program_id(0) * BT\n    h = tl.program_id(1)\n    rt = t0 + tl.arange(0, BT)\n    m = rt < T\n    rd = tl.arange(0, D)\n    row = (rt.to(tl.int64) * H + h) * D\n    o = tl.load(O + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n    do = tl.load(DO + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n    off = h.to(tl.int64) * T + rt\n    tl.store(DELTA + off, tl.sum(o * do, 1), mask=m)\n    lse = tl.load(LSE + off, mask=m, other=0.0)\n    tl.store(LSE2 + off, lse * LOG2E, mask=m)\n\n\n@triton.jit\ndef _dkdv(q_desc, k_desc, v_desc, do_desc, LSE2, DELTA, CU, DK, DV, qk_scale, sm_scale, T, W,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          STAGES: tl.constexpr, WS: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    pid_h = tl.program_id(0)\n    hk = pid_h % HKV\n    sid = pid_h // HKV\n    kb = tl.program_id(1)\n    s0 = tl.load(CU + sid)\n    Ls = tl.load(CU + sid + 1) - s0\n    j0 = kb * BN\n    if j0 >= Ls:\n        return\n    k = k_desc.load([s0 + j0, hk * D])\n    v = v_desc.load([s0 + j0, hk * D])\n    rj = j0 + tl.arange(0, BN)\n    q_lo = (j0 // BM) * BM\n    q_hi = Ls\n    if W >= 0:\n        q_hi = tl.minimum(Ls, j0 + BN + W)\n    nq = tl.cdiv(q_hi - q_lo, BM)\n    dk = tl.zeros((BN, D), tl.float32)\n    dv = tl.zeros((BN, D), tl.float32)\n    rm = tl.arange(0, BM)\n    for it in tl.range(0, nq * G, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + g\n        q = q_desc.load([s0 + i0, h * D])\n        do = do_desc.load([s0 + i0, h * D])\n        ri = i0 + rm\n        mi = ri < Ls\n        off = h.to(tl.int64) * T + s0 + ri\n        lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n        delta = tl.load(DELTA + off, mask=mi, other=0.0)\n        sT = tl.dot(k, tl.trans(q))\n        vis = (rj[:, None] <= ri[None, :]) & mi[None, :]\n        if W >= 0:\n            vis = vis & (ri[None, :] - rj[:, None] <= W)\n        pT = tl.where(vis, tl.exp2(sT * qk_scale - lse[None, :]), 0.0)\n        dpT = tl.dot(v, tl.trans(do))\n        dv = tl.dot(pT.to(tl.bfloat16), do, dv)\n        dsT = pT * (dpT - delta[None, :])\n        dk = tl.dot(dsT.to(tl.bfloat16), q, dk)\n    mj = rj < Ls\n    krow = ((s0 + rj).to(tl.int64) * HKV + hk) * D\n    rd = tl.arange(0, D)\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n\n\n@triton.jit\ndef _dq(q_desc, k_desc, v_desc, do_desc, LSE2, DELTA, CU, DQ, qk_scale, sm_scale, T, W, NQB,\n        H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n        STAGES: tl.constexpr, WS: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    pid_h = tl.program_id(0)\n    h = pid_h % H\n    sid = pid_h // H\n    hk = h // G\n    qb = NQB - 1 - tl.program_id(1)\n    s0 = tl.load(CU + sid)\n    Ls = tl.load(CU + sid + 1) - s0\n    i0 = qb * BM\n    if i0 >= Ls:\n        return\n    q = q_desc.load([s0 + i0, h * D])\n    do = do_desc.load([s0 + i0, h * D])\n    ri = i0 + tl.arange(0, BM)\n    mi = ri < Ls\n    off = h.to(tl.int64) * T + s0 + ri\n    lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n    delta = tl.load(DELTA + off, mask=mi, other=0.0)\n    k_lo = 0\n    if W >= 0:\n        k_lo = tl.maximum(0, i0 - W)\n        k_lo = (k_lo // BN) * BN\n    k_hi = tl.minimum(i0 + BM, Ls)\n    dq = tl.zeros((BM, D), tl.float32)\n    rn = tl.arange(0, BN)\n    for j0 in tl.range(k_lo, k_hi, BN, num_stages=STAGES, warp_specialize=WS):\n        k = k_desc.load([s0 + j0, hk * D])\n        v = v_desc.load([s0 + j0, hk * D])\n        rj = j0 + rn\n        s = tl.dot(q, tl.trans(k))\n        vis = (rj[None, :] <= ri[:, None]) & mi[:, None]\n        if W >= 0:\n            vis = vis & (ri[:, None] - rj[None, :] <= W)\n        p = tl.where(vis, tl.exp2(s * qk_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        ds = p * (dp - delta[:, None])\n        dq = tl.dot(ds.to(tl.bfloat16), k, dq)\n    rd = tl.arange(0, D)\n    qrow = ((s0 + ri).to(tl.int64) * H + h) * D\n    tl.store(DQ + qrow[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=mi[:, None])\n\n\ndef _alloc(size, align, stream):\n    return torch.empty(size, dtype=torch.int8, device='cuda')\n\n\nCFG = dict(KV_BM=64, KV_BN=64, KV_W=4, KV_S=2, KV_WS=False, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2, Q_WS=False)\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, cfg=CFG):\n    triton.set_allocator(_alloc)\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    lse2 = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    _prep[(triton.cdiv(T, 32), H)](o, do, lse, delta, lse2, T, H, D=D, BT=32, num_warps=4)\n    sm_scale = D ** -0.5\n    qk_scale = sm_scale * 1.4426950408889634\n    q2 = q.view(T, H * D)\n    do2 = do.view(T, H * D)\n    k2 = k.view(T, HKV * D)\n    v2 = v.view(T, HKV * D)\n    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n    qd = tl.make_tensor_descriptor(q2, [T, H * D], [H * D, 1], [BM, D])\n    dod = tl.make_tensor_descriptor(do2, [T, H * D], [H * D, 1], [BM, D])\n    kd = tl.make_tensor_descriptor(k2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    vd = tl.make_tensor_descriptor(v2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    _dkdv[(n * HKV, triton.cdiv(max_seqlen, BN))](\n        qd, kd, vd, dod, lse2, delta, cu_seqlens, dk, dv, qk_scale, sm_scale, T, window,\n        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'])\n    BM, BN = cfg['Q_BM'], cfg['Q_BN']\n    qd = tl.make_tensor_descriptor(q2, [T, H * D], [H * D, 1], [BM, D])\n    dod = tl.make_tensor_descriptor(do2, [T, H * D], [H * D, 1], [BM, D])\n    kd = tl.make_tensor_descriptor(k2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    vd = tl.make_tensor_descriptor(v2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    nqb = triton.cdiv(max_seqlen, BM)\n    _dq[(n * H, nqb)](\n        qd, kd, vd, dod, lse2, delta, cu_seqlens, dq, qk_scale, sm_scale, T, window, nqb,\n        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['Q_S'], WS=cfg['Q_WS'], num_warps=cfg['Q_W'])\n"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2168,"cache_read_input_tokens":60250,"cache_creation":{"ephemeral_5m_input_tokens":2168,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"e1edaf95-1691-46d1-af94-d86c07dbd23a","timestamp":"2026-10-18T13:54:17.208Z","request_id":"req_011Cg9pAv7wWGzqqAjY31hKW"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EPe4dqudmoSw6xtbGRVYvw","type":"tool_result","content":"File created successfully at: /workspace/sol_v1.py (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"515007df-fde3-4978-b78f-caaf17cc2375","timestamp":"2026-10-18T13:54:17.241Z","tool_use_result":{"type":"create","filePath":"/workspace/sol_v1.py","content":"import torch\nimport triton\nimport triton.language as tl\n\nLOG2E = tl.constexpr(1.4426950408889634)\n\n\n@triton.jit\ndef _prep(O, DO, LSE, DELTA, LSE2, T, H, D: tl.constexpr, BT: tl.constexpr):\n    t0 = tl.program_id(0) * BT\n    h = tl.program_id(1)\n    rt = t0 + tl.arange(0, BT)\n    m = rt < T\n    rd = tl.arange(0, D)\n    row = (rt.to(tl.int64) * H + h) * D\n    o = tl.load(O + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n    do = tl.load(DO + row[:, None] + rd[None, :], mask=m[:, None], other=0.0).to(tl.float32)\n    off = h.to(tl.int64) * T + rt\n    tl.store(DELTA + off, tl.sum(o * do, 1), mask=m)\n    lse = tl.load(LSE + off, mask=m, other=0.0)\n    tl.store(LSE2 + off, lse * LOG2E, mask=m)\n\n\n@triton.jit\ndef _dkdv(q_desc, k_desc, v_desc, do_desc, LSE2, DELTA, CU, DK, DV, qk_scale, sm_scale, T, W,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          STAGES: tl.constexpr, WS: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    pid_h = tl.program_id(0)\n    hk = pid_h % HKV\n    sid = pid_h // HKV\n    kb = tl.program_id(1)\n    s0 = tl.load(CU + sid)\n    Ls = tl.load(CU + sid + 1) - s0\n    j0 = kb * BN\n    if j0 >= Ls:\n        return\n    k = k_desc.load([s0 + j0, hk * D])\n    v = v_desc.load([s0 + j0, hk * D])\n    rj = j0 + tl.arange(0, BN)\n    q_lo = (j0 // BM) * BM\n    q_hi = Ls\n    if W >= 0:\n        q_hi = tl.minimum(Ls, j0 + BN + W)\n    nq = tl.cdiv(q_hi - q_lo, BM)\n    dk = tl.zeros((BN, D), tl.float32)\n    dv = tl.zeros((BN, D), tl.float32)\n    rm = tl.arange(0, BM)\n    for it in tl.range(0, nq * G, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + g\n        q = q_desc.load([s0 + i0, h * D])\n        do = do_desc.load([s0 + i0, h * D])\n        ri = i0 + rm\n        mi = ri < Ls\n        off = h.to(tl.int64) * T + s0 + ri\n        lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n        delta = tl.load(DELTA + off, mask=mi, other=0.0)\n        sT = tl.dot(k, tl.trans(q))\n        vis = (rj[:, None] <= ri[None, :]) & mi[None, :]\n        if W >= 0:\n            vis = vis & (ri[None, :] - rj[:, None] <= W)\n        pT = tl.where(vis, tl.exp2(sT * qk_scale - lse[None, :]), 0.0)\n        dpT = tl.dot(v, tl.trans(do))\n        dv = tl.dot(pT.to(tl.bfloat16), do, dv)\n        dsT = pT * (dpT - delta[None, :])\n        dk = tl.dot(dsT.to(tl.bfloat16), q, dk)\n    mj = rj < Ls\n    krow = ((s0 + rj).to(tl.int64) * HKV + hk) * D\n    rd = tl.arange(0, D)\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n\n\n@triton.jit\ndef _dq(q_desc, k_desc, v_desc, do_desc, LSE2, DELTA, CU, DQ, qk_scale, sm_scale, T, W, NQB,\n        H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n        STAGES: tl.constexpr, WS: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    pid_h = tl.program_id(0)\n    h = pid_h % H\n    sid = pid_h // H\n    hk = h // G\n    qb = NQB - 1 - tl.program_id(1)\n    s0 = tl.load(CU + sid)\n    Ls = tl.load(CU + sid + 1) - s0\n    i0 = qb * BM\n    if i0 >= Ls:\n        return\n    q = q_desc.load([s0 + i0, h * D])\n    do = do_desc.load([s0 + i0, h * D])\n    ri = i0 + tl.arange(0, BM)\n    mi = ri < Ls\n    off = h.to(tl.int64) * T + s0 + ri\n    lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n    delta = tl.load(DELTA + off, mask=mi, other=0.0)\n    k_lo = 0\n    if W >= 0:\n        k_lo = tl.maximum(0, i0 - W)\n        k_lo = (k_lo // BN) * BN\n    k_hi = tl.minimum(i0 + BM, Ls)\n    dq = tl.zeros((BM, D), tl.float32)\n    rn = tl.arange(0, BN)\n    for j0 in tl.range(k_lo, k_hi, BN, num_stages=STAGES, warp_specialize=WS):\n        k = k_desc.load([s0 + j0, hk * D])\n        v = v_desc.load([s0 + j0, hk * D])\n        rj = j0 + rn\n        s = tl.dot(q, tl.trans(k))\n        vis = (rj[None, :] <= ri[:, None]) & mi[:, None]\n        if W >= 0:\n            vis = vis & (ri[:, None] - rj[None, :] <= W)\n        p = tl.where(vis, tl.exp2(s * qk_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        ds = p * (dp - delta[:, None])\n        dq = tl.dot(ds.to(tl.bfloat16), k, dq)\n    rd = tl.arange(0, D)\n    qrow = ((s0 + ri).to(tl.int64) * H + h) * D\n    tl.store(DQ + qrow[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=mi[:, None])\n\n\ndef _alloc(size, align, stream):\n    return torch.empty(size, dtype=torch.int8, device='cuda')\n\n\nCFG = dict(KV_BM=64, KV_BN=64, KV_W=4, KV_S=2, KV_WS=False, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2, Q_WS=False)\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, cfg=CFG):\n    triton.set_allocator(_alloc)\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    lse2 = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    _prep[(triton.cdiv(T, 32), H)](o, do, lse, delta, lse2, T, H, D=D, BT=32, num_warps=4)\n    sm_scale = D ** -0.5\n    qk_scale = sm_scale * 1.4426950408889634\n    q2 = q.view(T, H * D)\n    do2 = do.view(T, H * D)\n    k2 = k.view(T, HKV * D)\n    v2 = v.view(T, HKV * D)\n    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n    qd = tl.make_tensor_descriptor(q2, [T, H * D], [H * D, 1], [BM, D])\n    dod = tl.make_tensor_descriptor(do2, [T, H * D], [H * D, 1], [BM, D])\n    kd = tl.make_tensor_descriptor(k2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    vd = tl.make_tensor_descriptor(v2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    _dkdv[(n * HKV, triton.cdiv(max_seqlen, BN))](\n        qd, kd, vd, dod, lse2, delta, cu_seqlens, dk, dv, qk_scale, sm_scale, T, window,\n        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'])\n    BM, BN = cfg['Q_BM'], cfg['Q_BN']\n    qd = tl.make_tensor_descriptor(q2, [T, H * D], [H * D, 1], [BM, D])\n    dod = tl.make_tensor_descriptor(do2, [T, H * D], [H * D, 1], [BM, D])\n    kd = tl.make_tensor_descriptor(k2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    vd = tl.make_tensor_descriptor(v2, [T, HKV * D], [HKV * D, 1], [BN, D])\n    nqb = triton.cdiv(max_seqlen, BM)\n    _dq[(n * H, nqb)](\n        qd, kd, vd, dod, lse2, delta, cu_seqlens, dq, qk_scale, sm_scale, T, window, nqb,\n        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['Q_S'], WS=cfg['Q_WS'], num_warps=cfg['Q_W'])\n","structuredPatch":[],"originalFile":null,"userModified":false},"tool_result_meta":[{"id":"toolu_01EPe4dqudmoSw6xtbGRVYvw","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"239c612d-c93d-401e-956c-442cfd6a1ab0"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":200,"estimated_tokens_delta":150,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"b4bcb324-52f0-4d4b-b684-ec16490281a9"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":390,"estimated_tokens_delta":190,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"d10c521e-d565-4f88-b49d-20348b0af4ef"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pFe2hHkBPKoe25dzUb","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":7678,"cache_read_input_tokens":62418,"cache_creation":{"ephemeral_5m_input_tokens":7678,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"68c15c3e-a603-455a-82dc-adf6206d803d","timestamp":"2026-10-18T13:54:20.642Z","request_id":"req_011Cg9pFdfNeKB7q9aTYFT3C","thinking_duration_ms":2182,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pFe2hHkBPKoe25dzUb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NbaHxxWRrCtTLske1pfNDb","name":"Bash","input":{"command":"python - <<'EOF'\nimport re\ns = open('sol_v1.py').read()\ns = s.replace(\"\"\"def _dkdv(q_desc, k_desc, v_desc, do_desc, LSE2\"\"\", \"\"\"def _dkdv(Q, K, V, DO, LSE2\"\"\")\ns = s.replace(\"\"\"    G: tl.constexpr = H // HKV\n    pid_h = tl.program_id(0)\n    hk = pid_h % HKV\"\"\", \"\"\"    G: tl.constexpr = H // HKV\n    q_desc = tl.make_tensor_descriptor(Q, [T, H * D], [H * D, 1], [BM, D])\n    do_desc = tl.make_tensor_descriptor(DO, [T, H * D], [H * D, 1], [BM, D])\n    k_desc = tl.make_tensor_descriptor(K, [T, HKV * D], [HKV * D, 1], [BN, D])\n    v_desc = tl.make_tensor_descriptor(V, [T, HKV * D], [HKV * D, 1], [BN, D])\n    pid_h = tl.program_id(0)\n    hk = pid_h % HKV\"\"\")\ns = s.replace(\"\"\"def _dq(q_desc, k_desc, v_desc, do_desc, LSE2\"\"\", \"\"\"def _dq(Q, K, V, DO, LSE2\"\"\")\ns = s.replace(\"\"\"    G: tl.constexpr = H // HKV\n    pid_h = tl.program_id(0)\n    h = pid_h % H\"\"\", \"\"\"    G: tl.constexpr = H // HKV\n    q_desc = tl.make_tensor_descriptor(Q, [T, H * D], [H * D, 1], [BM, D])\n    do_desc = tl.make_tensor_descriptor(DO, [T, H * D], [H * D, 1], [BM, D])\n    k_desc = tl.make_tensor_descriptor(K, [T, HKV * D], [HKV * D, 1], [BN, D])\n    v_desc = tl.make_tensor_descriptor(V, [T, HKV * D], [HKV * D, 1], [BN, D])\n    pid_h = tl.program_id(0)\n    h = pid_h % H\"\"\")\n# host side\ns = re.sub(r\"    q2 = q.view\\(T, H \\* D\\)\\n.*?    _dkdv\\[\", \"    BM, BN = cfg['KV_BM'], cfg['KV_BN']\\n    _dkdv[\", s, flags=re.S)\ns = s.replace(\"qd, kd, vd, dod, lse2, delta, cu_seqlens, dk, dv\", \"q, k, v, do, lse2, delta, cu_seqlens, dk, dv\")\ns = re.sub(r\"    BM, BN = cfg\\['Q_BM'\\], cfg\\['Q_BN'\\]\\n.*?    nqb =\", \"    BM, BN = cfg['Q_BM'], cfg['Q_BN']\\n    nqb =\", s, flags=re.S)\ns = s.replace(\"qd, kd, vd, dod, lse2, delta, cu_seqlens, dq\", \"q, k, v, do, lse2, delta, cu_seqlens, dq\")\nopen('sol_v1.py','w').write(s)\nEOF\nsed -n 95,130p sol_v1.py","description":"Move descriptor creation into kernels"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":7678,"cache_read_input_tokens":62418,"cache_creation":{"ephemeral_5m_input_tokens":7678,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"aea320b5-f4a4-4296-aaac-1363c9747a4b","timestamp":"2026-10-18T13:54:28.324Z","request_id":"req_011Cg9pFdfNeKB7q9aTYFT3C"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NbaHxxWRrCtTLske1pfNDb","type":"tool_result","content":"    Ls = tl.load(CU + sid + 1) - s0\n    i0 = qb * BM\n    if i0 >= Ls:\n        return\n    q = q_desc.load([s0 + i0, h * D])\n    do = do_desc.load([s0 + i0, h * D])\n    ri = i0 + tl.arange(0, BM)\n    mi = ri < Ls\n    off = h.to(tl.int64) * T + s0 + ri\n    lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n    delta = tl.load(DELTA + off, mask=mi, other=0.0)\n    k_lo = 0\n    if W >= 0:\n        k_lo = tl.maximum(0, i0 - W)\n        k_lo = (k_lo // BN) * BN\n    k_hi = tl.minimum(i0 + BM, Ls)\n    dq = tl.zeros((BM, D), tl.float32)\n    rn = tl.arange(0, BN)\n    for j0 in tl.range(k_lo, k_hi, BN, num_stages=STAGES, warp_specialize=WS):\n        k = k_desc.load([s0 + j0, hk * D])\n        v = v_desc.load([s0 + j0, hk * D])\n        rj = j0 + rn\n        s = tl.dot(q, tl.trans(k))\n        vis = (rj[None, :] <= ri[:, None]) & mi[:, None]\n        if W >= 0:\n            vis = vis & (ri[:, None] - rj[None, :] <= W)\n        p = tl.where(vis, tl.exp2(s * qk_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        ds = p * (dp - delta[:, None])\n        dq = tl.dot(ds.to(tl.bfloat16), k, dq)\n    rd = tl.arange(0, D)\n    qrow = ((s0 + ri).to(tl.int64) * H + h) * D\n    tl.store(DQ + qrow[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=mi[:, None])\n\n\ndef _alloc(size, align, stream):","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"76be0629-c386-49ad-a9d8-edf1a0208f70","timestamp":"2026-10-18T13:54:28.390Z","tool_use_result":{"stdout":"    Ls = tl.load(CU + sid + 1) - s0\n    i0 = qb * BM\n    if i0 >= Ls:\n        return\n    q = q_desc.load([s0 + i0, h * D])\n    do = do_desc.load([s0 + i0, h * D])\n    ri = i0 + tl.arange(0, BM)\n    mi = ri < Ls\n    off = h.to(tl.int64) * T + s0 + ri\n    lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n    delta = tl.load(DELTA + off, mask=mi, other=0.0)\n    k_lo = 0\n    if W >= 0:\n        k_lo = tl.maximum(0, i0 - W)\n        k_lo = (k_lo // BN) * BN\n    k_hi = tl.minimum(i0 + BM, Ls)\n    dq = tl.zeros((BM, D), tl.float32)\n    rn = tl.arange(0, BN)\n    for j0 in tl.range(k_lo, k_hi, BN, num_stages=STAGES, warp_specialize=WS):\n        k = k_desc.load([s0 + j0, hk * D])\n        v = v_desc.load([s0 + j0, hk * D])\n        rj = j0 + rn\n        s = tl.dot(q, tl.trans(k))\n        vis = (rj[None, :] <= ri[:, None]) & mi[:, None]\n        if W >= 0:\n            vis = vis & (ri[:, None] - rj[None, :] <= W)\n        p = tl.where(vis, tl.exp2(s * qk_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        ds = p * (dp - delta[:, None])\n        dq = tl.dot(ds.to(tl.bfloat16), k, dq)\n    rd = tl.arange(0, D)\n    qrow = ((s0 + ri).to(tl.int64) * H + h) * D\n    tl.store(DQ + qrow[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=mi[:, None])\n\n\ndef _alloc(size, align, stream):","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01NbaHxxWRrCtTLske1pfNDb","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pGTgt6Af6GUjH76yKi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018dUNN13brw6HYwnptDbiaS","name":"Bash","input":{"command":"mkdir -p /workspace/tools && cat > /workspace/tools/bench.py <<'EOF'\nimport sys, json, importlib.util, torch\nsys.path.insert(0, '/workspace')\nimport task as T\n\ndef load(path):\n    spec = importlib.util.spec_from_file_location('sol', path)\n    m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m); return m\n\nEXPERT = [1518, 510, 980, 1165, 302, 520]\n\ndef time_fn(fn, n=20):\n    for _ in range(3): fn()\n    torch.cuda.synchronize()\n    flush = torch.empty(256 * 1024 * 1024, dtype=torch.int8, device='cuda')\n    ts = []\n    for _ in range(n):\n        flush.zero_()\n        s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n        s.record(); fn(); e.record(); torch.cuda.synchronize()\n        ts.append(s.elapsed_time(e) * 1000)\n    return sorted(ts)[n // 2]\n\ndef main():\n    path = sys.argv[1]\n    cfg = json.loads(sys.argv[2]) if len(sys.argv) > 2 and sys.argv[2] else {}\n    which = [int(x) for x in sys.argv[3].split(',')] if len(sys.argv) > 3 else list(range(6))\n    check = '--nocheck' not in sys.argv\n    m = load(path)\n    full = dict(m.CFG); full.update(cfg)\n    import math\n    lg = []\n    for si in which:\n        shape = T.TASK.shapes[si]\n        case = T.make_case(shape, 1, 'cuda')\n        args = case.args\n        ok = True\n        if check:\n            exp = T.reference([a.clone() if torch.is_tensor(a) else a for a in args])\n            for i in (9, 10, 11): args[i].fill_(float('nan'))\n            m.kernel_fn(*args, cfg=full)\n            torch.cuda.synchronize()\n            ok, stats = T.check({i: args[i] for i in (9, 10, 11)}, exp)\n        t = time_fn(lambda: m.kernel_fn(*args, cfg=full))\n        lg.append(math.log(EXPERT[si] / t))\n        print(f\"shape {si}: {'ok' if ok else 'WRONG ' + str(stats)} {t:.1f} us  expert {EXPERT[si]}  ratio {EXPERT[si]/t:.3f}\", flush=True)\n    print(f\"geomean ratio vs expert: {math.exp(sum(lg)/len(lg)):.3f}\")\n\nmain()\nEOF\ncd /workspace && timeout 900 python tools/bench.py sol_v1.py '{}' 2>&1 | tail -20","description":"Write bench script and test v1"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2368,"cache_read_input_tokens":70096,"cache_creation":{"ephemeral_5m_input_tokens":2368,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"7c560a56-b7b8-4f0a-b1a3-3dd84f30ab05","timestamp":"2026-10-18T13:54:37.142Z","request_id":"req_011Cg9pGTK4APHWfQjpgKdGj"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018dUNN13brw6HYwnptDbiaS","type":"tool_result","content":"Traceback (most recent call last):\n  File \"/workspace/tools/bench.py\", line 48, in <module>\n    main()\n  File \"/workspace/tools/bench.py\", line 40, in main\n    m.kernel_fn(*args, cfg=full)\n  File \"/workspace/sol_v1.py\", line 148, in kernel_fn\n    _dkdv[(n * HKV, triton.cdiv(max_seqlen, BN))](\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 370, in <lambda>\n    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)\n                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 760, in run\n    launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values())\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 485, in launch_metadata\n    self._init_handles()\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 459, in _init_handles\n    raise_(OutOfResources(self.metadata.shared, max_shared, \"shared memory\"))\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 451, in raise_\n    raise err\ntriton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 262752, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"f9d5ad24-f182-4c99-a483-2835c88d5f7b","timestamp":"2026-10-18T13:54:44.014Z","tool_use_result":{"stdout":"Traceback (most recent call last):\n  File \"/workspace/tools/bench.py\", line 48, in <module>\n    main()\n  File \"/workspace/tools/bench.py\", line 40, in main\n    m.kernel_fn(*args, cfg=full)\n  File \"/workspace/sol_v1.py\", line 148, in kernel_fn\n    _dkdv[(n * HKV, triton.cdiv(max_seqlen, BN))](\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 370, in <lambda>\n    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)\n                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 760, in run\n    launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values())\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 485, in launch_metadata\n    self._init_handles()\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 459, in _init_handles\n    raise_(OutOfResources(self.metadata.shared, max_shared, \"shared memory\"))\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 451, in raise_\n    raise err\ntriton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 262752, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_018dUNN13brw6HYwnptDbiaS","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UZZfhv9twZyPiyFPicprNv","type":"tool_result","content":"shape 0: ok 6155.2 us  expert 1518  ratio 0.247\nshape 4: ok 862.2 us  expert 302  ratio 0.350\ngeomean ratio vs expert: 0.294","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"3c01cc9e-6cab-4e8c-b9c4-9b8719af4b08","timestamp":"2026-10-18T13:54:57.696Z","tool_use_result":{"stdout":"shape 0: ok 6155.2 us  expert 1518  ratio 0.247\nshape 4: ok 862.2 us  expert 302  ratio 0.350\ngeomean ratio vs expert: 0.294","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01UZZfhv9twZyPiyFPicprNv","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Qsr4TtMamNsZ35pZD5t1Zg","type":"tool_result","content":"USDT:2026-10-18 13:55:13 5218:5218 SyncActivityProfilerHandler.cpp:52] profiler_start\nUSDT:2026-10-18 13:55:13 5218:5218 SyncActivityProfilerHandler.cpp:59] profiler_stop\nshape 0: _dkdv=3659.6, _dq=2453.7, _prep=29.6","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"6aa16b41-535b-416a-ba13-6e69e008dd81","timestamp":"2026-10-18T13:55:13.993Z","tool_use_result":{"stdout":"USDT:2026-10-18 13:55:13 5218:5218 SyncActivityProfilerHandler.cpp:52] profiler_start\nUSDT:2026-10-18 13:55:13 5218:5218 SyncActivityProfilerHandler.cpp:59] profiler_stop\nshape 0: _dkdv=3659.6, _dq=2453.7, _prep=29.6","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01Qsr4TtMamNsZ35pZD5t1Zg","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pKpfmze9KMWBhkor3w","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LMGP6GRyP1Ds1TUL66oxpu","name":"Bash","input":{"command":"cat > /workspace/tools/sweep.py <<'EOF'\nimport sys, json, importlib.util, torch, traceback\nsys.path.insert(0, '/workspace')\nimport task as T\nfrom torch.profiler import profile, ProfilerActivity\n\ndef load(path):\n    spec = importlib.util.spec_from_file_location('sol', path)\n    m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m); return m\n\npath = sys.argv[1]\ncfgs = json.loads(sys.argv[2])\nwhich = [int(x) for x in sys.argv[3].split(',')] if len(sys.argv) > 3 else [0]\nm = load(path)\ncases = {si: T.make_case(T.TASK.shapes[si], 1, 'cuda') for si in which}\nfor cfg in cfgs:\n    full = dict(m.CFG); full.update(cfg)\n    out = []\n    for si in which:\n        args = cases[si].args\n        try:\n            for _ in range(2): m.kernel_fn(*args, cfg=full)\n            torch.cuda.synchronize()\n        except Exception as e:\n            out.append(f\"s{si}: ERR {str(e)[:150]}\")\n            continue\n        with profile(activities=[ProfilerActivity.CUDA]) as prof:\n            for _ in range(3): m.kernel_fn(*args, cfg=full)\n            torch.cuda.synchronize()\n        res = {}\n        for e in prof.key_averages():\n            if e.self_device_time_total > 0:\n                res[e.key] = e.self_device_time_total / e.count\n        out.append(f\"s{si}: \" + \" \".join(f\"{k[:6]}={v:.0f}\" for k, v in sorted(res.items())))\n    print(json.dumps(cfg), \" | \".join(out), flush=True)\nEOF\ncd /workspace && timeout 1200 python tools/sweep.py sol_v1.py '[\n{\"KV_S\":1,\"Q_S\":1,\"Q_BM\":128,\"Q_BN\":64,\"Q_W\":4},\n{\"KV_S\":1,\"Q_S\":1,\"Q_BM\":128,\"Q_BN\":64,\"Q_W\":8},\n{\"KV_S\":1,\"Q_S\":2,\"Q_BM\":128,\"Q_BN\":32,\"Q_W\":8},\n{\"KV_S\":1,\"Q_S\":2,\"Q_BM\":64,\"Q_BN\":64,\"Q_W\":4},\n{\"KV_S\":1,\"KV_W\":8,\"Q_S\":1,\"Q_WS\":true},\n{\"KV_S\":1,\"KV_WS\":true,\"Q_S\":2,\"Q_WS\":true},\n{\"KV_S\":2,\"KV_BM\":32,\"KV_BN\":64,\"Q_S\":1},\n{\"KV_S\":1,\"KV_BM\":128,\"KV_BN\":64,\"KV_W\":8,\"Q_S\":1}\n]' 0 2>&1 | grep -v USDT","description":"Sweep a few configs for both kernels"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1053,"cache_read_input_tokens":74912,"cache_creation":{"ephemeral_5m_input_tokens":1053,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"00f721d2-d8d1-4e93-b22b-d8ffb013cd8a","timestamp":"2026-10-18T13:55:25.398Z","request_id":"req_011Cg9pKpJgmAMe3uGihMugu"}
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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2118,"cache_read_input_tokens":75965,"cache_creation":{"ephemeral_5m_input_tokens":2118,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"cec6149f-54ea-4c61-b3df-ce7edec4f818","timestamp":"2026-10-18T13:56:20.763Z","request_id":"req_011Cg9pMn1qgBRWGziCdhnqj","thinking_duration_ms":38917,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pMnLg5vytafU2zDM9m","type":"message","role":"assistant","content":[{"type":"text","text":"Correct v1 is ~2.3x slower than the expert (dK/dV kernel ~2100 us, dQ ~1300 us on the 8K shape). Next I'm sweeping tile sizes plus warp specialization."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2118,"cache_read_input_tokens":75965,"cache_creation":{"ephemeral_5m_input_tokens":2118,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"4fa4c92b-02f8-4adb-ac62-f157beb1dbce","timestamp":"2026-10-18T13:56:20.766Z","request_id":"req_011Cg9pMn1qgBRWGziCdhnqj"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CpMhFiVVeufRvRsXXEgqJh","type":"tool_result","content":"== CCJW6TPJOJ62RV76ELQ5TQK2W7Z7K443DKRQDZDQISZWG6Z3XJMQ/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 128, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 128, blockN = 32, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<128x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x32xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      1 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x32x256xbf16\n== J2MLOFCQEU2NLYIW6MWHSV3DEPCYT3GLGHDJPXP7FZY6YKVVKWKQ/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 64, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x64xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      1 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x64x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== 4DDVB7QRYOWJPK7ROXDEAKMCOTAN6N67OTXM5VRGELVLORYSHEGQ/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 32, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<2x32xf32\n      1 ttg.local_alloc : () -> !ttg.memdesc<4x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<4x32x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== CUOEE67F4GKM7CWJW6HZILGDPBCJQQPZB2JONDZ7U5JRXQ24ZG2Q/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 32, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x32xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      1 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x32x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== H3UOMW7FMH4NZK3Q3DNTTYILOXZIHMNXLVIBO2SD7Q5JPGRDU3BA/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 32, colStride = 1>\n25\n      8 ttg.local_alloc : () -> !ttg.memdesc<1x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x32xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x32x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== NIE55UP2YAVGSZ4CCHL2PLKDN2H524AZEK5TP7RDSK7WVDJTA5YA/_dkdv.ttgir\n      1 ttng.tmem_alloc %dk_81 : (tensor<64x128xbf16, #linear1>\n      1 ttng.tmem_alloc %dv_74 : (tensor<64x128xbf16, #linear1>\n      4 ttng.tmem_alloc : () ->\nShell cwd was reset to /workspace","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"235be10e-3861-444e-90dd-66a02cf6eca5","timestamp":"2026-10-18T13:56:50.620Z","tool_use_result":{"stdout":"== CCJW6TPJOJ62RV76ELQ5TQK2W7Z7K443DKRQDZDQISZWG6Z3XJMQ/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 128, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 128, blockN = 32, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<128x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x32xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      1 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x32x256xbf16\n== J2MLOFCQEU2NLYIW6MWHSV3DEPCYT3GLGHDJPXP7FZY6YKVVKWKQ/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 64, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x64xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      1 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x64x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== 4DDVB7QRYOWJPK7ROXDEAKMCOTAN6N67OTXM5VRGELVLORYSHEGQ/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 32, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<2x32xf32\n      1 ttg.local_alloc : () -> !ttg.memdesc<4x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<4x32x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== CUOEE67F4GKM7CWJW6HZILGDPBCJQQPZB2JONDZ7U5JRXQ24ZG2Q/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 32, colStride = 1>\n0\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x32xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      1 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x32x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== H3UOMW7FMH4NZK3Q3DNTTYILOXZIHMNXLVIBO2SD7Q5JPGRDU3BA/_dkdv.ttgir\n      6 ttng.tmem_alloc : () ->\ntensor_memory_encoding<blockM = 64, blockN = 256, colStride = 1>\ntensor_memory_encoding<blockM = 64, blockN = 32, colStride = 1>\n25\n      8 ttg.local_alloc : () -> !ttg.memdesc<1x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<1x32xf32\n      2 ttg.local_alloc : () -> !ttg.memdesc<1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<2x1xi64\n      4 ttg.local_alloc : () -> !ttg.memdesc<3x1xi64\n      2 ttg.local_alloc : () -> !ttg.memdesc<3x32x256xbf16\n      2 ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16\n== NIE55UP2YAVGSZ4CCHL2PLKDN2H524AZEK5TP7RDSK7WVDJTA5YA/_dkdv.ttgir\n      1 ttng.tmem_alloc %dk_81 : (tensor<64x128xbf16, #linear1>\n      1 ttng.tmem_alloc %dv_74 : (tensor<64x128xbf16, #linear1>\n      4 ttng.tmem_alloc : () ->","stderr":"\nShell cwd was reset to /workspace","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01CpMhFiVVeufRvRsXXEgqJh","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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SM90+ only.\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:314:    :type num_ctas: int\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:323:    def __init__(self, kwargs, num_warps=4, num_stages=3, num_ctas=1, maxnreg=None, pre_hook=None, ir_override=None):\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:326:        self.num_ctas = num_ctas\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:336:        self.num_ctas = state.get(\"num_ctas\", 1)\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:347:                    (\"num_ctas\", self.num_ctas),\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:360:        res.append(f\"num_ctas: {self.num_ctas}\")\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:108:    num_ctas: int = 1\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:186:        if args.get(\"num_ctas\", 1) > 1 and capability < 90:\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:187:            raise ValueError((f\"num_ctas > 1 requires NVIDIA SM90+ (Hopper). \"\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:189:                              f\"Please set num_ctas=1 or target an SM90+ GPU.\"))\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:211:            metadata.num_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:260:        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:358:        nvidia.passes.ttnvgpuir.add_check_matmul_two_cta(pm)\n/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py:633:        repr = f\"{name}[num_warps={options.num_warps}, num_ctas={options.num_ctas}, num_stages={options.num_stages}, enable_fp_fusion={options.enable_fp_fusion}, launch_cooperative_grid={options.launch_cooperative_grid}]({arg_reprs})\"\n/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py:644:            'num_ctas': options.num_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py:126:            num_ctas = self.module.get_int_attr(\"ttg.num-ctas\")\n/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py:127:            if num_ctas is not None:\n/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py:128:                options['num_ctas'] = num_ctas\n_ZNK4mlir6triton10nvidia_gpu24TensorMemoryEncodingAttr10getTwoCTAsEv\n_ZN4mlir6triton10nvidia_gpu11TCGen5MMAOp10getTwoCtasEv\n_ZN4mlir6triton10nvidia_gpu6detail29TCGen5MMAOpGenericAdaptorBase14getTwoCtasAttrEv\n_ZN4mlir6triton10nvidia_gpu6detail29TCGen5MMAOpGenericAdaptorBase10getTwoCtasEv\n_ZN4mlir6triton10nvidia_gpu11TCGen5MMAOp10setTwoCtasEb\n_ZN4mlir6triton10nvidia_gpu42createTritonNvidiaGPUCheckMatmulTwoCTAPassEv\nThe LHS operand's encoding must have twoCTA=true to be used in a twoCTA matmul\nInvalid attribute `two_ctas` in property conversion: \nadd_check_matmul_two_cta\ninconsistent two_ctas setting across matmuls; expected all matmuls to \ntwoCTAs\ntwo_ctas\nfailed to parse TTG_TensorMemoryEncodingAttr parameter 'twoCTAs' which is to be a `bool`\ntwoCTAs == op.getTwoCtas()\nThe returned value's encoding must have twoCTA=true to be used in a twoCTA matmul\ntwoCTA mode expects the RHS split along N to be twice the result split along N. Expected \ntwoCTA mode expects the LHS split along M to match the result split along M. Expected \n two_ctas.\ntwoCTAs = \nVerify consistent two_ctas usage across matmuls","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"6ac93c87-e3eb-4bf2-95c5-3ecd8b710fe9","timestamp":"2026-10-18T13:57:21.680Z","tool_use_result":{"stdout":"/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:33:            self.configs = [Config({}, num_warps=4, num_stages=3, num_ctas=1)]\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:313:    :ivar num_ctas: number of blocks in a block cluster. SM90+ only.\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:314:    :type num_ctas: int\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:323:    def __init__(self, kwargs, num_warps=4, num_stages=3, num_ctas=1, maxnreg=None, pre_hook=None, ir_override=None):\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:326:        self.num_ctas = num_ctas\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:336:        self.num_ctas = state.get(\"num_ctas\", 1)\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:347:                    (\"num_ctas\", self.num_ctas),\n/usr/local/lib/python3.12/dist-packages/triton/runtime/autotuner.py:360:        res.append(f\"num_ctas: {self.num_ctas}\")\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:108:    num_ctas: int = 1\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:186:        if args.get(\"num_ctas\", 1) > 1 and capability < 90:\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:187:            raise ValueError((f\"num_ctas > 1 requires NVIDIA SM90+ (Hopper). \"\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:189:                              f\"Please set num_ctas=1 or target an SM90+ GPU.\"))\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:211:            metadata.num_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:260:        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py:358:        nvidia.passes.ttnvgpuir.add_check_matmul_two_cta(pm)\n/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py:633:        repr = f\"{name}[num_warps={options.num_warps}, num_ctas={options.num_ctas}, num_stages={options.num_stages}, enable_fp_fusion={options.enable_fp_fusion}, launch_cooperative_grid={options.launch_cooperative_grid}]({arg_reprs})\"\n/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py:644:            'num_ctas': options.num_ctas,\n/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py:126:            num_ctas = self.module.get_int_attr(\"ttg.num-ctas\")\n/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py:127:            if num_ctas is not None:\n/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py:128:                options['num_ctas'] = num_ctas\n_ZNK4mlir6triton10nvidia_gpu24TensorMemoryEncodingAttr10getTwoCTAsEv\n_ZN4mlir6triton10nvidia_gpu11TCGen5MMAOp10getTwoCtasEv\n_ZN4mlir6triton10nvidia_gpu6detail29TCGen5MMAOpGenericAdaptorBase14getTwoCtasAttrEv\n_ZN4mlir6triton10nvidia_gpu6detail29TCGen5MMAOpGenericAdaptorBase10getTwoCtasEv\n_ZN4mlir6triton10nvidia_gpu11TCGen5MMAOp10setTwoCtasEb\n_ZN4mlir6triton10nvidia_gpu42createTritonNvidiaGPUCheckMatmulTwoCTAPassEv\nThe LHS operand's encoding must have twoCTA=true to be used in a twoCTA matmul\nInvalid attribute `two_ctas` in property conversion: \nadd_check_matmul_two_cta\ninconsistent two_ctas setting across matmuls; expected all matmuls to \ntwoCTAs\ntwo_ctas\nfailed to parse TTG_TensorMemoryEncodingAttr parameter 'twoCTAs' which is to be a `bool`\ntwoCTAs == op.getTwoCtas()\nThe returned value's encoding must have twoCTA=true to be used in a twoCTA matmul\ntwoCTA mode expects the RHS split along N to be twice the result split along N. Expected \ntwoCTA mode expects the LHS split along M to match the result split along M. Expected \n two_ctas.\ntwoCTAs = \nVerify consistent two_ctas usage across matmuls","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01KNaGHhskb7XR4shPPcufK9","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pVEZD32po26kHuSqLz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TUqgeAP2MJxAFxK7UiUtaP","name":"Bash","input":{"command":"cat > /workspace/tools/mm_bench.py <<'EOF'\nimport torch, triton, triton.language as tl\ntriton.set_allocator(lambda size, align, stream: torch.empty(size, dtype=torch.int8, device='cuda'))\n\n@triton.jit\ndef mm(A, B, C, K, BM: tl.constexpr, BN: tl.constexpr, BK: tl.constexpr, STAGES: tl.constexpr):\n    pid = tl.program_id(0)\n    a_desc = tl.make_tensor_descriptor(A, [K * 0 + 148 * 8 * 128, K], [K, 1], [BM, BK])\n    b_desc = tl.make_tensor_descriptor(B, [K, BN], [BN, 1], [BK, BN])\n    acc = tl.zeros((BM, BN), tl.float32)\n    for k in tl.range(0, K, BK, num_stages=STAGES):\n        a = a_desc.load([pid * BM, k])\n        b = b_desc.load([k, 0])\n        acc = tl.dot(a, b, acc)\n    rm = pid * BM + tl.arange(0, BM)\n    rn = tl.arange(0, BN)\n    tl.store(C + rm[:, None] * BN + rn[None, :], acc)\n\nK = 8192\nfor BM, BN, BK, ST in [(128, 256, 64, 3), (64, 256, 64, 4), (128, 128, 64, 4), (64, 128, 64, 4), (128, 64, 64, 4), (64, 64, 64, 4)]:\n    nblk = 148 * 8 * 128 // BM\n    A = torch.randn(nblk * BM, K, device='cuda', dtype=torch.bfloat16)\n    B = torch.randn(K, BN, device='cuda', dtype=torch.bfloat16)\n    C = torch.empty(nblk * BM, BN, device='cuda', dtype=torch.float32)\n    f = lambda: mm[(nblk,)](A, B, C, K, BM=BM, BN=BN, BK=BK, STAGES=ST, num_warps=4)\n    f(); torch.cuda.synchronize()\n    ms = triton.testing.do_bench(f)\n    print(BM, BN, BK, ST, f\"{2 * nblk * BM * BN * K / ms / 1e9:.0f} TFLOPS\")\nEOF\ncd /workspace && python tools/mm_bench.py","description":"Microbenchmark tcgen05 MMA throughput by tile M/N"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4849,"cache_read_input_tokens":87411,"cache_creation":{"ephemeral_5m_input_tokens":4849,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"207f84c0-4697-4d0a-a147-0b0f7c217af4","timestamp":"2026-10-18T13:57:45.321Z","request_id":"req_011Cg9pVEC8TqmaHPqmcV5zh"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TUqgeAP2MJxAFxK7UiUtaP","type":"tool_result","content":"128 256 64 3 950 TFLOPS\n64 256 64 4 741 TFLOPS\n128 128 64 4 620 TFLOPS\n64 128 64 4 614 TFLOPS\n128 64 64 4 379 TFLOPS\n64 64 64 4 360 TFLOPS","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"1033abb3-ff4f-4c5d-b16e-6830ff47d1d7","timestamp":"2026-10-18T13:57:52.164Z","tool_use_result":{"stdout":"128 256 64 3 950 TFLOPS\n64 256 64 4 741 TFLOPS\n128 128 64 4 620 TFLOPS\n64 128 64 4 614 TFLOPS\n128 64 64 4 379 TFLOPS\n64 64 64 4 360 TFLOPS","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01TUqgeAP2MJxAFxK7UiUtaP","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pYfXiMEGoZGu9yu4xP","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"I found that on B300, M=64 tcgen05 MMAs run at half the rate of M=128 ones, and the current dK/dV kernel uses M=64 throughout. I'll rewrite it to compute S = Q·Kᵀ with queries on the M=128 side instead.\n\n","signature":"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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1291,"cache_read_input_tokens":95050,"cache_creation":{"ephemeral_5m_input_tokens":1291,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"89c69f87-1548-473b-8667-6293a54310cd","timestamp":"2026-10-18T13:58:56.619Z","request_id":"req_011Cg9pYfBsRHFyZ3PUiavEV","narration_block_indexes":[0],"thinking_duration_ms":1,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pYfXiMEGoZGu9yu4xP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YXc2ytTgcXvpwsehwPVYFs","name":"Bash","input":{"command":"python - <<'EOF'\ns = open('sol_v1.py').read()\nadd = '''\n\n@triton.jit\ndef _dkdv2(Q, K, V, DO, LSE2, DELTA, CU, DK, DV, qk_scale, sm_scale, T, W,\n           H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n           STAGES: tl.constexpr, WS: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    q_desc = tl.make_tensor_descriptor(Q, [T, H * D], [H * D, 1], [BM, D])\n    do_desc = tl.make_tensor_descriptor(DO, [T, H * D], [H * D, 1], [BM, D])\n    k_desc = tl.make_tensor_descriptor(K, [T, HKV * D], [HKV * D, 1], [BN, D])\n    v_desc = tl.make_tensor_descriptor(V, [T, HKV * D], [HKV * D, 1], [BN, D])\n    pid_h = tl.program_id(0)\n    hk = pid_h % HKV\n    sid = pid_h // HKV\n    kb = tl.program_id(1)\n    s0 = tl.load(CU + sid)\n    Ls = tl.load(CU + sid + 1) - s0\n    j0 = kb * BN\n    if j0 >= Ls:\n        return\n    k = k_desc.load([s0 + j0, hk * D])\n    v = v_desc.load([s0 + j0, hk * D])\n    rj = j0 + tl.arange(0, BN)\n    q_lo = (j0 // BM) * BM\n    q_hi = Ls\n    if W >= 0:\n        q_hi = tl.minimum(Ls, j0 + BN + W)\n    nq = tl.cdiv(q_hi - q_lo, BM)\n    dk = tl.zeros((BN, D), tl.float32)\n    dv = tl.zeros((BN, D), tl.float32)\n    rm = tl.arange(0, BM)\n    for it in tl.range(0, nq * G, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + g\n        q = q_desc.load([s0 + i0, h * D])\n        do = do_desc.load([s0 + i0, h * D])\n        ri = i0 + rm\n        mi = ri < Ls\n        off = h.to(tl.int64) * T + s0 + ri\n        lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n        delta = tl.load(DELTA + off, mask=mi, other=0.0)\n        s = tl.dot(q, tl.trans(k))\n        vis = (rj[None, :] <= ri[:, None]) & mi[:, None]\n        if W >= 0:\n            vis = vis & (ri[:, None] - rj[None, :] <= W)\n        p = tl.where(vis, tl.exp2(s * qk_scale - lse[:, None]), 0.0)\n        dp = tl.dot(do, tl.trans(v))\n        dv = tl.dot(tl.trans(p.to(tl.bfloat16)), do, dv)\n        ds = p * (dp - delta[:, None])\n        dk = tl.dot(tl.trans(ds.to(tl.bfloat16)), q, dk)\n    mj = rj < Ls\n    krow = ((s0 + rj).to(tl.int64) * HKV + hk) * D\n    rd = tl.arange(0, D)\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n'''\ns = s.replace(\"\\n\\n@triton.jit\\ndef _dq(\", add + \"\\n\\n@triton.jit\\ndef _dq(\")\ns = s.replace(\"    _dkdv[(n * HKV\", \"    kern = _dkdv2 if cfg.get('KV_T', 0) else _dkdv\\n    kern[(n * HKV\")\nopen('sol_v1.py', 'w').write(s)\nEOF\ntimeout 1500 python tools/sweep.py sol_v1.py '[\n{\"KV_T\":1,\"KV_S\":1,\"KV_BM\":128,\"KV_BN\":64,\"KV_W\":4,\"KV_WS\":true,\"Q_S\":2,\"Q_BM\":128,\"Q_BN\":32,\"Q_W\":4,\"Q_WS\":true},\n{\"KV_T\":1,\"KV_S\":2,\"KV_BM\":64,\"KV_BN\":64,\"KV_W\":4,\"KV_WS\":true,\"Q_S\":1,\"Q_BM\":128,\"Q_BN\":64,\"Q_W\":4,\"Q_WS\":true},\n{\"KV_T\":1,\"KV_S\":1,\"KV_BM\":128,\"KV_BN\":64,\"KV_W\":8,\"KV_WS\":true,\"Q_S\":1,\"Q_BM\":128,\"Q_BN\":64,\"Q_W\":8,\"Q_WS\":true},\n{\"KV_T\":1,\"KV_S\":2,\"KV_BM\":64,\"KV_BN\":64,\"KV_W\":8,\"KV_WS\":true,\"Q_S\":2,\"Q_BM\":128,\"Q_BN\":32,\"Q_W\":8,\"Q_WS\":true}\n]' 0 2>&1 | grep -v USDT","description":"Add query-major dkdv kernel and sweep"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1291,"cache_read_input_tokens":95050,"cache_creation":{"ephemeral_5m_input_tokens":1291,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"b17c6350-01d0-4b3e-b2ed-ee912cba2bf5","timestamp":"2026-10-18T13:59:08.561Z","request_id":"req_011Cg9pYfBsRHFyZ3PUiavEV"}
{"type":"system","subtype":"task_started","task_id":"bhqmtktdv","run_id":"0mvdw60v4-b6a127b8","tool_use_id":"toolu_01YXc2ytTgcXvpwsehwPVYFs","description":"Add query-major dkdv kernel and sweep","is_backgrounded":false,"task_type":"local_bash","uuid":"5c134ac8-e97a-4eb1-9aec-26f683ea3db7","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
{"type":"system","subtype":"task_notification","task_id":"bhqmtktdv","run_id":"0mvdw60v4-b6a127b8","tool_use_id":"toolu_01YXc2ytTgcXvpwsehwPVYFs","status":"completed","output_file":"","summary":"Add query-major dkdv kernel and sweep","uuid":"f9257111-43ac-4236-af87-9659d63bd840","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YXc2ytTgcXvpwsehwPVYFs","type":"tool_result","content":"<persisted-output>\nOutput too large (49.5KB). Full output saved to: /logs/agent/sessions/projects/-workspace/711e70e5-71bc-4971-97a3-904cce35aad5/tool-results/bhqmtktdv.txt\n\nPreview (first 2KB):\npython: /__w/triton/triton/llvm-project/llvm/include/llvm/Support/Casting.h:650: decltype(auto) llvm::dyn_cast(From &) [To = mlir::DenseArrayAttr, From = mlir::Attribute]: Assertion `detail::isPresent(Val) && \"dyn_cast on a non-existent value\"' failed.\nmodule {\n  tt.func public @_dkdv2(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg8: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg9: f32, %arg10: f32, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32) attributes {noinline = false} {\n    %cst = arith.constant dense<0.000000e+00> : tensor<64x256xf32>\n    %c127_i32 = arith.constant 127 : i32\n    %cst_0 = arith.constant dense<256> : tensor<64xi64>\n    %cst_1 = arith.constant dense<8> : tensor<64xi64>\n    %cst_2 = arith.constant dense<0.000000e+00> : tensor<128x64xf32>\n    %cst_3 = arith.constant dense<0.000000e+00> : tensor<128xf32>\n    %c2_i32 = arith.constant 2 : i32\n    %c0_i32 = arith.constant 0 : i32\n    %c128_i32 = arith.constant 128 : i32\n    %c256_i32 = arith.constant 256 : i32\n    %c64_i32 = arith.constant 64 : i32\n    %c1_i32 = arith.constant 1 : i32\n    %c8_i32 = arith.constant 8 : i32\n    %c2048_i64 = arith.constant 2048 : i64\n    %c2048_i32 = arith.constant 2048 : i32\n    %c4096_i32 = arith.constant 4096 : i32\n    %c4096_i64 = arith.constant 4096 : i64\n    %c1_i64 = arith.constant 1 : i64\n    %0 = tt.make_tensor_descriptor %arg0, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<128x256xbf16>>\n    %1 = tt.make_tensor_descriptor %arg3, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<128x256xbf16>>\n...\n</persisted-output>","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"47eeec4e-92c7-43e5-b22c-00cae1694ca0","timestamp":"2026-10-18T13:59:14.419Z","tool_use_result":{"stdout":"python: /__w/triton/triton/llvm-project/llvm/include/llvm/Support/Casting.h:650: decltype(auto) llvm::dyn_cast(From &) [To = mlir::DenseArrayAttr, From = mlir::Attribute]: Assertion `detail::isPresent(Val) && \"dyn_cast on a non-existent value\"' failed.\nmodule {\n  tt.func public @_dkdv2(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg8: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg9: f32, %arg10: f32, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32) attributes {noinline = false} {\n    %cst = arith.constant dense<0.000000e+00> : tensor<64x256xf32>\n    %c127_i32 = arith.constant 127 : i32\n    %cst_0 = arith.constant dense<256> : tensor<64xi64>\n    %cst_1 = arith.constant dense<8> : tensor<64xi64>\n    %cst_2 = arith.constant dense<0.000000e+00> : tensor<128x64xf32>\n    %cst_3 = arith.constant dense<0.000000e+00> : tensor<128xf32>\n    %c2_i32 = arith.constant 2 : i32\n    %c0_i32 = arith.constant 0 : i32\n    %c128_i32 = arith.constant 128 : i32\n    %c256_i32 = arith.constant 256 : i32\n    %c64_i32 = arith.constant 64 : i32\n    %c1_i32 = arith.constant 1 : i32\n    %c8_i32 = arith.constant 8 : i32\n    %c2048_i64 = arith.constant 2048 : i64\n    %c2048_i32 = arith.constant 2048 : i32\n    %c4096_i32 = arith.constant 4096 : i32\n    %c4096_i64 = arith.constant 4096 : i64\n    %c1_i64 = arith.constant 1 : i64\n    %0 = tt.make_tensor_descriptor %arg0, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<128x256xbf16>>\n    %1 = tt.make_tensor_descriptor %arg3, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<128x256xbf16>>\n    %2 = tt.make_tensor_descriptor %arg1, [%arg11, %c2048_i32], [%c2048_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %3 = tt.make_tensor_descriptor %arg2, [%arg11, %c2048_i32], [%c2048_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %4 = tt.get_program_id x : i32\n    %5 = arith.remsi %4, %c8_i32 : i32\n    %6 = arith.divsi %4, %c8_i32 : i32\n    %7 = tt.get_program_id y : i32\n    %8 = tt.addptr %arg6, %6 : !tt.ptr<i32>, i32\n    %9 = tt.load %8 : !tt.ptr<i32>\n    %10 = tt.addptr %8, %c1_i32 : !tt.ptr<i32>, i32\n    %11 = tt.load %10 : !tt.ptr<i32>\n    %12 = arith.subi %11, %9 : i32\n    %13 = arith.muli %7, %c64_i32 : i32\n    %14 = arith.cmpi sge, %13, %12 : i32\n    cf.cond_br %14, ^bb1, ^bb2\n  ^bb1:  // pred: ^bb0\n    tt.return\n  ^bb2:  // pred: ^bb0\n    %15 = arith.addi %9, %13 : i32\n    %16 = arith.muli %5, %c256_i32 : i32\n    %17 = tt.descriptor_load %2[%15, %16] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n    %18 = tt.descriptor_load %3[%15, %16] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n    %19 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>\n    %20 = tt.splat %13 : i32 -> tensor<64xi32>\n    %21 = arith.addi %20, %19 : tensor<64xi32>\n    %22 = arith.divsi %13, %c128_i32 : i32\n    %23 = arith.muli %22, %c128_i32 : i32\n    %24 = arith.cmpi sge, %arg12, %c0_i32 : i32\n    %25 = scf.if %24 -> (i32) {\n      %60 = arith.addi %13, %c64_i32 : i32\n      %61 = arith.addi %60, %arg12 : i32\n      %62 = arith.minsi %12, %61 : i32\n      scf.yield %62 : i32\n    } else {\n      scf.yield %12 : i32\n    }\n    %26 = arith.subi %25, %23 : i32\n    %27 = arith.addi %26, %c127_i32 : i32\n    %28 = arith.divsi %27, %c128_i32 : i32\n    %29 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32>\n    %30 = arith.muli %28, %c2_i32 : i32\n    %31:2 = scf.for %arg13 = %c0_i32 to %30 step %c1_i32 iter_args(%arg14 = %cst, %arg15 = %cst) -> (tensor<64x256xf32>, tensor<64x256xf32>)  : i32 {\n      %60 = arith.divsi %arg13, %28 : i32\n      %61 = arith.muli %60, %28 : i32\n      %62 = arith.subi %arg13, %61 : i32\n      %63 = arith.muli %62, %c128_i32 : i32\n      %64 = arith.addi %23, %63 : i32\n      %65 = arith.muli %5, %c2_i32 : i32\n      %66 = arith.addi %65, %60 : i32\n      %67 = arith.addi %9, %64 : i32\n      %68 = arith.muli %66, %c256_i32 : i32\n      %69 = tt.descriptor_load %0[%67, %68] : !tt.tensordesc<tensor<128x256xbf16>> -> tensor<128x256xbf16>\n      %70 = tt.descriptor_load %1[%67, %68] : !tt.tensordesc<tensor<128x256xbf16>> -> tensor<128x256xbf16>\n      %71 = tt.splat %64 : i32 -> tensor<128xi32>\n      %72 = arith.addi %71, %29 : tensor<128xi32>\n      %73 = tt.splat %12 : i32 -> tensor<128xi32>\n      %74 = arith.cmpi slt, %72, %73 : tensor<128xi32>\n      %75 = arith.extsi %66 : i32 to i64\n      %76 = arith.extsi %arg11 : i32 to i64\n      %77 = arith.muli %75, %76 : i64\n      %78 = arith.extsi %9 : i32 to i64\n      %79 = arith.addi %77, %78 : i64\n      %80 = arith.extsi %72 : tensor<128xi32> to tensor<128xi64>\n      %81 = tt.splat %79 : i64 -> tensor<128xi64>\n      %82 = arith.addi %81, %80 : tensor<128xi64>\n      %83 = tt.splat %arg4 : !tt.ptr<f32> -> tensor<128x!tt.ptr<f32>>\n      %84 = tt.addptr %83, %82 : tensor<128x!tt.ptr<f32>>, tensor<128xi64>\n      %85 = tt.load %84, %74, %cst_3 : tensor<128x!tt.ptr<f32>>\n      %86 = tt.splat %arg5 : !tt.ptr<f32> -> tensor<128x!tt.ptr<f32>>\n      %87 = tt.addptr %86, %82 : tensor<128x!tt.ptr<f32>>, tensor<128xi64>\n      %88 = tt.load %87, %74, %cst_3 : tensor<128x!tt.ptr<f32>>\n      %89 = tt.trans %17 {order = array<i32: 1, 0>} : tensor<64x256xbf16> -> tensor<256x64xbf16>\n      %90 = tt.dot %69, %89, %cst_2, inputPrecision = tf32 : tensor<128x256xbf16> * tensor<256x64xbf16> -> tensor<128x64xf32>\n      %91 = tt.expand_dims %21 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>\n      %92 = tt.expand_dims %72 {axis = 1 : i32} : tensor<128xi32> -> tensor<128x1xi32>\n      %93 = tt.broadcast %91 : tensor<1x64xi32> -> tensor<128x64xi32>\n      %94 = tt.broadcast %92 : tensor<128x1xi32> -> tensor<128x64xi32>\n      %95 = arith.cmpi sle, %93, %94 : tensor<128x64xi32>\n      %96 = tt.expand_dims %74 {axis = 1 : i32} : tensor<128xi1> -> tensor<128x1xi1>\n      %97 = tt.broadcast %96 : tensor<128x1xi1> -> tensor<128x64xi1>\n      %98 = arith.andi %95, %97 : tensor<128x64xi1>\n      %99 = scf.if %24 -> (tensor<128x64xi1>) {\n        %119 = arith.subi %94, %93 : tensor<128x64xi32>\n        %120 = tt.splat %arg12 : i32 -> tensor<128x64xi32>\n        %121 = arith.cmpi sle, %119, %120 : tensor<128x64xi32>\n        %122 = arith.andi %98, %121 : tensor<128x64xi1>\n        scf.yield %122 : tensor<128x64xi1>\n      } else {\n        scf.yield %98 : tensor<128x64xi1>\n      }\n      %100 = tt.splat %arg9 : f32 -> tensor<128x64xf32>\n      %101 = arith.mulf %90, %100 : tensor<128x64xf32>\n      %102 = tt.expand_dims %85 {axis = 1 : i32} : tensor<128xf32> -> tensor<128x1xf32>\n      %103 = tt.broadcast %102 : tensor<128x1xf32> -> tensor<128x64xf32>\n      %104 = arith.subf %101, %103 : tensor<128x64xf32>\n      %105 = math.exp2 %104 : tensor<128x64xf32>\n      %106 = arith.select %99, %105, %cst_2 : tensor<128x64xi1>, tensor<128x64xf32>\n      %107 = tt.trans %18 {order = array<i32: 1, 0>} : tensor<64x256xbf16> -> tensor<256x64xbf16>\n      %108 = tt.dot %70, %107, %cst_2, inputPrecision = tf32 : tensor<128x256xbf16> * tensor<256x64xbf16> -> tensor<128x64xf32>\n      %109 = arith.truncf %106 : tensor<128x64xf32> to tensor<128x64xbf16>\n      %110 = tt.trans %109 {order = array<i32: 1, 0>} : tensor<128x64xbf16> -> tensor<64x128xbf16>\n      %111 = tt.dot %110, %70, %arg15, inputPrecision = tf32 : tensor<64x128xbf16> * tensor<128x256xbf16> -> tensor<64x256xf32>\n      %112 = tt.expand_dims %88 {axis = 1 : i32} : tensor<128xf32> -> tensor<128x1xf32>\n      %113 = tt.broadcast %112 : tensor<128x1xf32> -> tensor<128x64xf32>\n      %114 = arith.subf %108, %113 : tensor<128x64xf32>\n      %115 = arith.mulf %106, %114 : tensor<128x64xf32>\n      %116 = arith.truncf %115 : tensor<128x64xf32> to tensor<128x64xbf16>\n      %117 = tt.trans %116 {order = array<i32: 1, 0>} : tensor<128x64xbf16> -> tensor<64x128xbf16>\n      %118 = tt.dot %117, %69, %arg14, inputPrecision = tf32 : tensor<64x128xbf16> * tensor<128x256xbf16> -> tensor<64x256xf32>\n      scf.yield %118, %111 : tensor<64x256xf32>, tensor<64x256xf32>\n    } {tt.num_stages = 1 : i32, tt.warp_specialize}\n    %32 = tt.splat %12 : i32 -> tensor<64xi32>\n    %33 = arith.cmpi slt, %21, %32 : tensor<64xi32>\n    %34 = tt.splat %9 : i32 -> tensor<64xi32>\n    %35 = arith.addi %34, %21 : tensor<64xi32>\n    %36 = arith.extsi %35 : tensor<64xi32> to tensor<64xi64>\n    %37 = arith.muli %36, %cst_1 : tensor<64xi64>\n    %38 = arith.extsi %5 : i32 to i64\n    %39 = tt.splat %38 : i64 -> tensor<64xi64>\n    %40 = arith.addi %37, %39 : tensor<64xi64>\n    %41 = arith.muli %40, %cst_0 : tensor<64xi64>\n    %42 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32>\n    %43 = tt.expand_dims %33 {axis = 1 : i32} : tensor<64xi1> -> tensor<64x1xi1>\n    %44 = tt.expand_dims %41 {axis = 1 : i32} : tensor<64xi64> -> tensor<64x1xi64>\n    %45 = tt.splat %arg7 : !tt.ptr<bf16> -> tensor<64x1x!tt.ptr<bf16>>\n    %46 = tt.addptr %45, %44 : tensor<64x1x!tt.ptr<bf16>>, tensor<64x1xi64>\n    %47 = tt.expand_dims %42 {axis = 0 : i32} : tensor<256xi32> -> tensor<1x256xi32>\n    %48 = tt.broadcast %46 : tensor<64x1x!tt.ptr<bf16>> -> tensor<64x256x!tt.ptr<bf16>>\n    %49 = tt.broadcast %47 : tensor<1x256xi32> -> tensor<64x256xi32>\n    %50 = tt.addptr %48, %49 : tensor<64x256x!tt.ptr<bf16>>, tensor<64x256xi32>\n    %51 = tt.splat %arg10 : f32 -> tensor<64x256xf32>\n    %52 = arith.mulf %31#0, %51 : tensor<64x256xf32>\n    %53 = arith.truncf %52 : tensor<64x256xf32> to tensor<64x256xbf16>\n    %54 = tt.broadcast %43 : tensor<64x1xi1> -> tensor<64x256xi1>\n    tt.store %50, %53, %54 : tensor<64x256x!tt.ptr<bf16>>\n    %55 = tt.splat %arg8 : !tt.ptr<bf16> -> tensor<64x1x!tt.ptr<bf16>>\n    %56 = tt.addptr %55, %44 : tensor<64x1x!tt.ptr<bf16>>, tensor<64x1xi64>\n    %57 = tt.broadcast %56 : tensor<64x1x!tt.ptr<bf16>> -> tensor<64x256x!tt.ptr<bf16>>\n    %58 = tt.addptr %57, %49 : tensor<64x256x!tt.ptr<bf16>>, tensor<64x256xi32>\n    %59 = arith.truncf %31#1 : tensor<64x256xf32> to tensor<64x256xbf16>\n    tt.store %58, %59, %54 : tensor<64x256x!tt.ptr<bf16>>\n    tt.return\n  }\n}\n\n{-#\n  external_resources: {\n    mlir_reproducer: {\n      pipeline: \"builtin.module(convert-triton-to-tritongpu{enable-source-remat=false num-ctas=1 num-warps=4 target=cuda:103 threads-per-warp=32}, tritongpu-coalesce, tritongpu-F32DotTC{emu-tf32=true}, triton-nvidia-gpu-plan-cta, tritongpu-remove-layout-conversions, tritongpu-optimize-thread-locality, tritongpu-accelerate-matmul, tritongpu-remove-layout-conversions, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, triton-nvidia-optimize-descriptor-encoding, triton-loop-aware-cse, tritongpu-fuse-nested-loops, canonicalize{  max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-licm, tritongpu-optimize-accumulator-init, tritongpu-hoist-tmem-alloc{hoist-out-of-if=false}, tritongpu-promote-lhs-to-tmem, tritongpu-assign-latencies{num-stages=3}, tritongpu-schedule-loops, tritongpu-automatic-warp-specialization{num-stages=3}, tritongpu-pipeline{dump-intermediate-steps=false num-stages=3}, tritongpu-optimize-partition-warps, tritongpu-combine-tensor-select-and-if, tritongpu-hoist-tmem-alloc{hoist-out-of-if=true}, triton-nvidia-gpu-remove-tmem-tokens, canonicalize{  max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-loop-aware-cse, tritongpu-prefetch, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, tritongpu-coalesce-async-copy, triton-nvidia-optimize-tmem-layouts, triton-nvidia-tma-lowering, tritongpu-remove-layout-conversions, triton-nvidia-interleave-tmem, tritongpu-reduce-data-duplication, tritongpu-reorder-instructions, triton-loop-aware-cse, symbol-dce, triton-nvidia-gpu-fence-insertion{compute-capability=103}, triton-nvidia-mma-lowering, sccp, cse, canonicalize{  max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true})\",\n      disable_threading: true,\n      verify_each: true\n    }\n  }\n#-}\n/workspace/sol_v1.py:81:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/sol_v1.py:81:0: note: Pipeline failed while executing [`TritonGPUAutomaticWarpSpecialization` on 'builtin.module' operation, `NVWSInsertAref` on 'builtin.module' operation]: reproducer generated at `std::errs, please share the reproducer above with Triton project.`\n{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 128, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: ERR PassManager::run failed\npython: /__w/triton/triton/llvm-project/llvm/include/llvm/Support/Casting.h:650: decltype(auto) llvm::dyn_cast(From &) [To = mlir::DenseArrayAttr, From = mlir::Attribute]: Assertion `detail::isPresent(Val) && \"dyn_cast on a non-existent value\"' failed.\nmodule {\n  tt.func public @_dkdv2(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg8: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg9: f32, %arg10: f32, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32) attributes {noinline = false} {\n    %cst = arith.constant dense<0.000000e+00> : tensor<64x256xf32>\n    %c63_i32 = arith.constant 63 : i32\n    %cst_0 = arith.constant dense<256> : tensor<64xi64>\n    %cst_1 = arith.constant dense<8> : tensor<64xi64>\n    %cst_2 = arith.constant dense<0.000000e+00> : tensor<64x64xf32>\n    %cst_3 = arith.constant dense<0.000000e+00> : tensor<64xf32>\n    %c2_i32 = arith.constant 2 : i32\n    %c0_i32 = arith.constant 0 : i32\n    %c256_i32 = arith.constant 256 : i32\n    %c64_i32 = arith.constant 64 : i32\n    %c1_i32 = arith.constant 1 : i32\n    %c8_i32 = arith.constant 8 : i32\n    %c2048_i64 = arith.constant 2048 : i64\n    %c2048_i32 = arith.constant 2048 : i32\n    %c4096_i32 = arith.constant 4096 : i32\n    %c4096_i64 = arith.constant 4096 : i64\n    %c1_i64 = arith.constant 1 : i64\n    %0 = tt.make_tensor_descriptor %arg0, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %1 = tt.make_tensor_descriptor %arg3, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %2 = tt.make_tensor_descriptor %arg1, [%arg11, %c2048_i32], [%c2048_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %3 = tt.make_tensor_descriptor %arg2, [%arg11, %c2048_i32], [%c2048_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %4 = tt.get_program_id x : i32\n    %5 = arith.remsi %4, %c8_i32 : i32\n    %6 = arith.divsi %4, %c8_i32 : i32\n    %7 = tt.get_program_id y : i32\n    %8 = tt.addptr %arg6, %6 : !tt.ptr<i32>, i32\n    %9 = tt.load %8 : !tt.ptr<i32>\n    %10 = tt.addptr %8, %c1_i32 : !tt.ptr<i32>, i32\n    %11 = tt.load %10 : !tt.ptr<i32>\n    %12 = arith.subi %11, %9 : i32\n    %13 = arith.muli %7, %c64_i32 : i32\n    %14 = arith.cmpi sge, %13, %12 : i32\n    cf.cond_br %14, ^bb1, ^bb2\n  ^bb1:  // pred: ^bb0\n    tt.return\n  ^bb2:  // pred: ^bb0\n    %15 = arith.addi %9, %13 : i32\n    %16 = arith.muli %5, %c256_i32 : i32\n    %17 = tt.descriptor_load %2[%15, %16] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n    %18 = tt.descriptor_load %3[%15, %16] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n    %19 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>\n    %20 = tt.splat %13 : i32 -> tensor<64xi32>\n    %21 = arith.addi %20, %19 : tensor<64xi32>\n    %22 = arith.divsi %13, %c64_i32 : i32\n    %23 = arith.muli %22, %c64_i32 : i32\n    %24 = arith.cmpi sge, %arg12, %c0_i32 : i32\n    %25 = scf.if %24 -> (i32) {\n      %59 = arith.addi %13, %c64_i32 : i32\n      %60 = arith.addi %59, %arg12 : i32\n      %61 = arith.minsi %12, %60 : i32\n      scf.yield %61 : i32\n    } else {\n      scf.yield %12 : i32\n    }\n    %26 = arith.subi %25, %23 : i32\n    %27 = arith.addi %26, %c63_i32 : i32\n    %28 = arith.divsi %27, %c64_i32 : i32\n    %29 = arith.muli %28, %c2_i32 : i32\n    %30:2 = scf.for %arg13 = %c0_i32 to %29 step %c1_i32 iter_args(%arg14 = %cst, %arg15 = %cst) -> (tensor<64x256xf32>, tensor<64x256xf32>)  : i32 {\n      %59 = arith.divsi %arg13, %28 : i32\n      %60 = arith.muli %59, %28 : i32\n      %61 = arith.subi %arg13, %60 : i32\n      %62 = arith.muli %61, %c64_i32 : i32\n      %63 = arith.addi %23, %62 : i32\n      %64 = arith.muli %5, %c2_i32 : i32\n      %65 = arith.addi %64, %59 : i32\n      %66 = arith.addi %9, %63 : i32\n      %67 = arith.muli %65, %c256_i32 : i32\n      %68 = tt.descriptor_load %0[%66, %67] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n      %69 = tt.descriptor_load %1[%66, %67] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n      %70 = tt.splat %63 : i32 -> tensor<64xi32>\n      %71 = arith.addi %70, %19 : tensor<64xi32>\n      %72 = tt.splat %12 : i32 -> tensor<64xi32>\n      %73 = arith.cmpi slt, %71, %72 : tensor<64xi32>\n      %74 = arith.extsi %65 : i32 to i64\n      %75 = arith.extsi %arg11 : i32 to i64\n      %76 = arith.muli %74, %75 : i64\n      %77 = arith.extsi %9 : i32 to i64\n      %78 = arith.addi %76, %77 : i64\n      %79 = arith.extsi %71 : tensor<64xi32> to tensor<64xi64>\n      %80 = tt.splat %78 : i64 -> tensor<64xi64>\n      %81 = arith.addi %80, %79 : tensor<64xi64>\n      %82 = tt.splat %arg4 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>\n      %83 = tt.addptr %82, %81 : tensor<64x!tt.ptr<f32>>, tensor<64xi64>\n      %84 = tt.load %83, %73, %cst_3 : tensor<64x!tt.ptr<f32>>\n      %85 = tt.splat %arg5 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>\n      %86 = tt.addptr %85, %81 : tensor<64x!tt.ptr<f32>>, tensor<64xi64>\n      %87 = tt.load %86, %73, %cst_3 : tensor<64x!tt.ptr<f32>>\n      %88 = tt.trans %17 {order = array<i32: 1, 0>} : tensor<64x256xbf16> -> tensor<256x64xbf16>\n      %89 = tt.dot %68, %88, %cst_2, inputPrecision = tf32 : tensor<64x256xbf16> * tensor<256x64xbf16> -> tensor<64x64xf32>\n      %90 = tt.expand_dims %21 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>\n      %91 = tt.expand_dims %71 {axis = 1 : i32} : tensor<64xi32> -> tensor<64x1xi32>\n      %92 = tt.broadcast %90 : tensor<1x64xi32> -> tensor<64x64xi32>\n      %93 = tt.broadcast %91 : tensor<64x1xi32> -> tensor<64x64xi32>\n      %94 = arith.cmpi sle, %92, %93 : tensor<64x64xi32>\n      %95 = tt.expand_dims %73 {axis = 1 : i32} : tensor<64xi1> -> tensor<64x1xi1>\n      %96 = tt.broadcast %95 : tensor<64x1xi1> -> tensor<64x64xi1>\n      %97 = arith.andi %94, %96 : tensor<64x64xi1>\n      %98 = scf.if %24 -> (tensor<64x64xi1>) {\n        %118 = arith.subi %93, %92 : tensor<64x64xi32>\n        %119 = tt.splat %arg12 : i32 -> tensor<64x64xi32>\n        %120 = arith.cmpi sle, %118, %119 : tensor<64x64xi32>\n        %121 = arith.andi %97, %120 : tensor<64x64xi1>\n        scf.yield %121 : tensor<64x64xi1>\n      } else {\n        scf.yield %97 : tensor<64x64xi1>\n      }\n      %99 = tt.splat %arg9 : f32 -> tensor<64x64xf32>\n      %100 = arith.mulf %89, %99 : tensor<64x64xf32>\n      %101 = tt.expand_dims %84 {axis = 1 : i32} : tensor<64xf32> -> tensor<64x1xf32>\n      %102 = tt.broadcast %101 : tensor<64x1xf32> -> tensor<64x64xf32>\n      %103 = arith.subf %100, %102 : tensor<64x64xf32>\n      %104 = math.exp2 %103 : tensor<64x64xf32>\n      %105 = arith.select %98, %104, %cst_2 : tensor<64x64xi1>, tensor<64x64xf32>\n      %106 = tt.trans %18 {order = array<i32: 1, 0>} : tensor<64x256xbf16> -> tensor<256x64xbf16>\n      %107 = tt.dot %69, %106, %cst_2, inputPrecision = tf32 : tensor<64x256xbf16> * tensor<256x64xbf16> -> tensor<64x64xf32>\n      %108 = arith.truncf %105 : tensor<64x64xf32> to tensor<64x64xbf16>\n      %109 = tt.trans %108 {order = array<i32: 1, 0>} : tensor<64x64xbf16> -> tensor<64x64xbf16>\n      %110 = tt.dot %109, %69, %arg15, inputPrecision = tf32 : tensor<64x64xbf16> * tensor<64x256xbf16> -> tensor<64x256xf32>\n      %111 = tt.expand_dims %87 {axis = 1 : i32} : tensor<64xf32> -> tensor<64x1xf32>\n      %112 = tt.broadcast %111 : tensor<64x1xf32> -> tensor<64x64xf32>\n      %113 = arith.subf %107, %112 : tensor<64x64xf32>\n      %114 = arith.mulf %105, %113 : tensor<64x64xf32>\n      %115 = arith.truncf %114 : tensor<64x64xf32> to tensor<64x64xbf16>\n      %116 = tt.trans %115 {order = array<i32: 1, 0>} : tensor<64x64xbf16> -> tensor<64x64xbf16>\n      %117 = tt.dot %116, %68, %arg14, inputPrecision = tf32 : tensor<64x64xbf16> * tensor<64x256xbf16> -> tensor<64x256xf32>\n      scf.yield %117, %110 : tensor<64x256xf32>, tensor<64x256xf32>\n    } {tt.num_stages = 2 : i32, tt.warp_specialize}\n    %31 = tt.splat %12 : i32 -> tensor<64xi32>\n    %32 = arith.cmpi slt, %21, %31 : tensor<64xi32>\n    %33 = tt.splat %9 : i32 -> tensor<64xi32>\n    %34 = arith.addi %33, %21 : tensor<64xi32>\n    %35 = arith.extsi %34 : tensor<64xi32> to tensor<64xi64>\n    %36 = arith.muli %35, %cst_1 : tensor<64xi64>\n    %37 = arith.extsi %5 : i32 to i64\n    %38 = tt.splat %37 : i64 -> tensor<64xi64>\n    %39 = arith.addi %36, %38 : tensor<64xi64>\n    %40 = arith.muli %39, %cst_0 : tensor<64xi64>\n    %41 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32>\n    %42 = tt.expand_dims %32 {axis = 1 : i32} : tensor<64xi1> -> tensor<64x1xi1>\n    %43 = tt.expand_dims %40 {axis = 1 : i32} : tensor<64xi64> -> tensor<64x1xi64>\n    %44 = tt.splat %arg7 : !tt.ptr<bf16> -> tensor<64x1x!tt.ptr<bf16>>\n    %45 = tt.addptr %44, %43 : tensor<64x1x!tt.ptr<bf16>>, tensor<64x1xi64>\n    %46 = tt.expand_dims %41 {axis = 0 : i32} : tensor<256xi32> -> tensor<1x256xi32>\n    %47 = tt.broadcast %45 : tensor<64x1x!tt.ptr<bf16>> -> tensor<64x256x!tt.ptr<bf16>>\n    %48 = tt.broadcast %46 : tensor<1x256xi32> -> tensor<64x256xi32>\n    %49 = tt.addptr %47, %48 : tensor<64x256x!tt.ptr<bf16>>, tensor<64x256xi32>\n    %50 = tt.splat %arg10 : f32 -> tensor<64x256xf32>\n    %51 = arith.mulf %30#0, %50 : tensor<64x256xf32>\n    %52 = arith.truncf %51 : tensor<64x256xf32> to tensor<64x256xbf16>\n    %53 = tt.broadcast %42 : tensor<64x1xi1> -> tensor<64x256xi1>\n    tt.store %49, %52, %53 : tensor<64x256x!tt.ptr<bf16>>\n    %54 = tt.splat %arg8 : !tt.ptr<bf16> -> tensor<64x1x!tt.ptr<bf16>>\n    %55 = tt.addptr %54, %43 : tensor<64x1x!tt.ptr<bf16>>, tensor<64x1xi64>\n    %56 = tt.broadcast %55 : tensor<64x1x!tt.ptr<bf16>> -> tensor<64x256x!tt.ptr<bf16>>\n    %57 = tt.addptr %56, %48 : tensor<64x256x!tt.ptr<bf16>>, tensor<64x256xi32>\n    %58 = arith.truncf %30#1 : tensor<64x256xf32> to tensor<64x256xbf16>\n    tt.store %57, %58, %53 : tensor<64x256x!tt.ptr<bf16>>\n    tt.return\n  }\n}\n\n{-#\n  external_resources: {\n    mlir_reproducer: {\n      pipeline: \"builtin.module(convert-triton-to-tritongpu{enable-source-remat=false num-ctas=1 num-warps=4 target=cuda:103 threads-per-warp=32}, tritongpu-coalesce, tritongpu-F32DotTC{emu-tf32=true}, triton-nvidia-gpu-plan-cta, tritongpu-remove-layout-conversions, tritongpu-optimize-thread-locality, tritongpu-accelerate-matmul, tritongpu-remove-layout-conversions, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, triton-nvidia-optimize-descriptor-encoding, triton-loop-aware-cse, tritongpu-fuse-nested-loops, canonicalize{  max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-licm, tritongpu-optimize-accumulator-init, tritongpu-hoist-tmem-alloc{hoist-out-of-if=false}, tritongpu-promote-lhs-to-tmem, tritongpu-assign-latencies{num-stages=3}, tritongpu-schedule-loops, tritongpu-automatic-warp-specialization{num-stages=3}, tritongpu-pipeline{dump-intermediate-steps=false num-stages=3}, tritongpu-optimize-partition-warps, tritongpu-combine-tensor-select-and-if, tritongpu-hoist-tmem-alloc{hoist-out-of-if=true}, triton-nvidia-gpu-remove-tmem-tokens, canonicalize{  max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-loop-aware-cse, tritongpu-prefetch, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, tritongpu-coalesce-async-copy, triton-nvidia-optimize-tmem-layouts, triton-nvidia-tma-lowering, tritongpu-remove-layout-conversions, triton-nvidia-interleave-tmem, tritongpu-reduce-data-duplication, tritongpu-reorder-instructions, triton-loop-aware-cse, symbol-dce, triton-nvidia-gpu-fence-insertion{compute-capability=103}, triton-nvidia-mma-lowering, sccp, cse, canonicalize{  max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true})\",\n      disable_threading: true,\n      verify_each: true\n    }\n  }\n#-}\n/workspace/sol_v1.py:81:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/sol_v1.py:81:0: note: Pipeline failed while executing [`TritonGPUAutomaticWarpSpecialization` on 'builtin.module' operation, `NVWSInsertAref` on 'builtin.module' operation]: reproducer generated at `std::errs, please share the reproducer above with Triton project.`\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR PassManager::run failed\npython: /__w/triton/triton/llvm-project/llvm/include/llvm/Support/Casting.h:650: decltype(auto) llvm::dyn_cast(From &) [To = mlir::DenseArrayAttr, From = mlir::Attribute]: Assertion `detail::isPresent(Val) && \"dyn_cast on a non-existent value\"' failed.\nmodule {\n  tt.func public @_dkdv2(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg8: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg9: f32, %arg10: f32, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32) attributes {noinline = false} {\n    %cst = arith.constant dense<0.000000e+00> : tensor<64x256xf32>\n    %c127_i32 = arith.constant 127 : i32\n    %cst_0 = arith.constant dense<256> : tensor<64xi64>\n    %cst_1 = arith.constant dense<8> : tensor<64xi64>\n    %cst_2 = arith.constant dense<0.000000e+00> : tensor<128x64xf32>\n    %cst_3 = arith.constant dense<0.000000e+00> : tensor<128xf32>\n    %c2_i32 = arith.constant 2 : i32\n    %c0_i32 = arith.constant 0 : i32\n    %c128_i32 = arith.constant 128 : i32\n    %c256_i32 = arith.constant 256 : i32\n    %c64_i32 = arith.constant 64 : i32\n    %c1_i32 = arith.constant 1 : i32\n    %c8_i32 = arith.constant 8 : i32\n    %c2048_i64 = arith.constant 2048 : i64\n    %c2048_i32 = arith.constant 2048 : i32\n    %c4096_i32 = arith.constant 4096 : i32\n    %c4096_i64 = arith.constant 4096 : i64\n    %c1_i64 = arith.constant 1 : i64\n    %0 = tt.make_tensor_descriptor %arg0, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<128x256xbf16>>\n    %1 = tt.make_tensor_descriptor %arg3, [%arg11, %c4096_i32], [%c4096_i64, %c1_i64] : <bf16>, <tensor<128x256xbf16>>\n    %2 = tt.make_tensor_descriptor %arg1, [%arg11, %c2048_i32], [%c2048_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %3 = tt.make_tensor_descriptor %arg2, [%arg11, %c2048_i32], [%c2048_i64, %c1_i64] : <bf16>, <tensor<64x256xbf16>>\n    %4 = tt.get_program_id x : i32\n    %5 = arith.remsi %4, %c8_i32 : i32\n    %6 = arith.divsi %4, %c8_i32 : i32\n    %7 = tt.get_program_id y : i32\n    %8 = tt.addptr %arg6, %6 : !tt.ptr<i32>, i32\n    %9 = tt.load %8 : !tt.ptr<i32>\n    %10 = tt.addptr %8, %c1_i32 : !tt.ptr<i32>, i32\n    %11 = tt.load %10 : !tt.ptr<i32>\n    %12 = arith.subi %11, %9 : i32\n    %13 = arith.muli %7, %c64_i32 : i32\n    %14 = arith.cmpi sge, %13, %12 : i32\n    cf.cond_br %14, ^bb1, ^bb2\n  ^bb1:  // pred: ^bb0\n    tt.return\n  ^bb2:  // pred: ^bb0\n    %15 = arith.addi %9, %13 : i32\n    %16 = arith.muli %5, %c256_i32 : i32\n    %17 = tt.descriptor_load %2[%15, %16] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n    %18 = tt.descriptor_load %3[%15, %16] : !tt.tensordesc<tensor<64x256xbf16>> -> tensor<64x256xbf16>\n    %19 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>\n    %20 = tt.splat %13 : i32 -> tensor<64xi32>\n    %21 = arith.addi %20, %19 : tensor<64xi32>\n    %22 = arith.divsi %13, %c128_i32 : i32\n    %23 = arith.muli %22, %c128_i32 : i32\n    %24 = arith.cmpi sge, %arg12, %c0_i32 : i32\n    %25 = scf.if %24 -> (i32) {\n      %60 = arith.addi %13, %c64_i32 : i32\n      %61 = arith.addi %60, %arg12 : i32\n      %62 = arith.minsi %12, %61 : i32\n      scf.yield %62 : i32\n    } else {\n      scf.yield %12 : i32\n    }\n    %26 = arith.subi %25, %23 : i32\n    %27 = arith.addi %26, %c127_i32 : i32\n    %28 = arith.divsi %27, %c128_i32 : i32\n    %29 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32>\n    %30 = arith.muli %28, %c2_i32 : i32\n    %31:2 = scf.for %arg13 = %c0_i32 to %30 step %c1_i32 iter_args(%arg14 = %cst, %arg15 = %cst) -> (tensor<64x256xf32>, tensor<64x256xf32>)  : i32 {\n      %60 = arith.divsi %arg13, %28 : i32\n      %61 = arith.muli %60, %28 : i32\n      %62 = arith.subi %arg13, %61 : i32\n      %63 = arith.muli %62, %c128_i32 : i32\n      %64 = arith.addi %23, %63 : i32\n      %65 = arith.muli %5, %c2_i32 : i32\n      %66 = arith.addi %65, %60 : i32\n      %67 = arith.addi %9, %64 : i32\n      %68 = arith.muli %66, %c256_i32 : i32\n      %69 = tt.descriptor_load %0[%67, %68] : !tt.tensordesc<tensor<128x256xbf16>> -> tensor<128x256xbf16>\n      %70 = tt.descriptor_load %1[%67, %68] : !tt.tensordesc<tensor<128x256xbf16>> -> tensor<128x256xbf16>\n      %71 = tt.splat %64 : i32 -> tensor<128xi32>\n      %72 = arith.addi %71, %29 : tensor<128xi32>\n      %73 = tt.splat %12 : i32 -> tensor<128xi32>\n      %74 = arith.cmpi","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false,"persistedOutputPath":"/logs/agent/sessions/projects/-workspace/711e70e5-71bc-4971-97a3-904cce35aad5/tool-results/bhqmtktdv.txt","persistedOutputSize":50728},"tool_result_meta":[{"id":"toolu_01YXc2ytTgcXvpwsehwPVYFs","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"db45061d-0ba8-416f-9274-c8de11c2ef10"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":281,"estimated_tokens_delta":231,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"ad376b9d-c112-4549-b8b8-2417c76da040"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pdZ9ctPEh1bbfSdXjV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BrF2exeBerk8GBgmBPooXz","name":"Bash","input":{"command":"cat > /workspace/tools/msweep.sh <<'EOF'\n#!/bin/bash\n# usage: msweep.sh file shapes cfg1 cfg2 ...\nf=$1; shp=$2; shift 2\nfor c in \"$@\"; do\n  out=$(timeout 600 python /workspace/tools/sweep.py $f \"[$c]\" $shp 2>&1 | grep -v USDT | grep '^{' )\n  if [ -z \"$out\" ]; then echo \"$c CRASH\"; else echo \"$out\"; fi\ndone\nEOF\nchmod +x /workspace/tools/msweep.sh; cd /workspace && tools/msweep.sh sol_v1.py 0 \\\n'{\"KV_T\":1,\"KV_S\":2,\"KV_BM\":64,\"KV_BN\":64,\"KV_W\":4,\"KV_WS\":true,\"Q_S\":1,\"Q_BM\":128,\"Q_BN\":64,\"Q_W\":4,\"Q_WS\":true}' \\\n'{\"KV_T\":1,\"KV_S\":1,\"KV_BM\":128,\"KV_BN\":64,\"KV_W\":8,\"KV_WS\":false,\"Q_S\":1,\"Q_BM\":128,\"Q_BN\":64,\"Q_W\":8,\"Q_WS\":true}' \\\n'{\"KV_T\":1,\"KV_S\":2,\"KV_BM\":64,\"KV_BN\":64,\"KV_W\":8,\"KV_WS\":true,\"Q_S\":2,\"Q_BM\":128,\"Q_BN\":32,\"Q_W\":8,\"Q_WS\":true}' \\\n'{\"KV_T\":1,\"KV_S\":2,\"KV_BM\":64,\"KV_BN\":64,\"KV_W\":4,\"KV_WS\":false,\"Q_S\":2,\"Q_BM\":128,\"Q_BN\":32,\"Q_W\":4,\"Q_WS\":true}'","description":"Sweep configs in separate processes"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8248,"cache_read_input_tokens":96341,"cache_creation":{"ephemeral_5m_input_tokens":8248,"ephemeral_1h_input_tokens":0},"output_tokens":9,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"f10bc9de-6694-4fa9-86c5-aac32cb55888","timestamp":"2026-10-18T13:59:21.371Z","request_id":"req_011Cg9pdYF3mBkShuzAGoJ7g"}
{"type":"system","subtype":"task_started","task_id":"bhhcbb3ay","run_id":"0mvdw6ar9-7f1c6f8e","tool_use_id":"toolu_01BrF2exeBerk8GBgmBPooXz","description":"Sweep configs in separate processes","is_backgrounded":false,"task_type":"local_bash","uuid":"6fc96d6f-ec36-4f25-b0a4-c237ff4acf11","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
{"type":"system","subtype":"task_notification","task_id":"bhhcbb3ay","run_id":"0mvdw6ar9-7f1c6f8e","tool_use_id":"toolu_01BrF2exeBerk8GBgmBPooXz","status":"completed","output_file":"","summary":"Sweep configs in separate processes","uuid":"a83c1566-2bc3-4120-b746-9ee170029136","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BrF2exeBerk8GBgmBPooXz","type":"tool_result","content":"{-#\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR PassManager::run failed\n{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 128, \"KV_BN\": 64, \"KV_W\": 8, \"KV_WS\": false, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 8, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 328252, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{-#\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 8, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 8, \"Q_WS\": true} s0: ERR PassManager::run failed\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 279136, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"c22e1208-c07d-4811-b199-8947461e889b","timestamp":"2026-10-18T13:59:44.773Z","tool_use_result":{"stdout":"{-#\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR PassManager::run failed\n{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 128, \"KV_BN\": 64, \"KV_W\": 8, \"KV_WS\": false, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 8, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 328252, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{-#\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 8, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 8, \"Q_WS\": true} s0: ERR PassManager::run failed\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 279136, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01BrF2exeBerk8GBgmBPooXz","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"system","subtype":"task_started","task_id":"bsycayfv9","run_id":"0mvdw70z3-bfcdccec","tool_use_id":"toolu_01269Gozy2dQdZrdQez3G1fn","description":"Isolate failing kernel configs","is_backgrounded":false,"task_type":"local_bash","uuid":"9591ae38-722a-4bcc-ae6c-84393ed7bfd0","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01269Gozy2dQdZrdQez3G1fn","type":"tool_result","content":"{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: ERR PassManager::run failed\n{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv2=2919 _dq=944 _prep=31\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 32, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv2=5033 _dq=943 _prep=30\n{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 128, \"KV_BN\": 32, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv2=10009 _dq=944 _prep=30\n{\"KV_S\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_WS\": true, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 328252, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{\"KV_S\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_WS\": true, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": false} s0: _dkdv=1616 _dq=1530 _prep=30","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"ad58b3cc-02e1-4a0a-bdcd-a4ada46c1b17","timestamp":"2026-10-18T14:00:45.023Z","tool_use_result":{"stdout":"{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: ERR PassManager::run failed\n{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv2=2919 _dq=944 _prep=31\n{\"KV_T\": 1, \"KV_S\": 2, \"KV_BM\": 64, \"KV_BN\": 32, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv2=5033 _dq=943 _prep=30\n{\"KV_T\": 1, \"KV_S\": 1, \"KV_BM\": 128, \"KV_BN\": 32, \"KV_W\": 4, \"KV_WS\": false, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv2=10009 _dq=944 _prep=30\n{\"KV_S\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_WS\": true, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 328252, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{\"KV_S\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_WS\": true, \"Q_S\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": false} s0: _dkdv=1616 _dq=1530 _prep=30","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01269Gozy2dQdZrdQez3G1fn","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pkDz91ZTHssyosmT5M","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HKR4zKXMKKLvtXXCmAVeei","name":"Bash","input":{"command":"python - <<'EOF'\ns = open('sol_v1.py').read()\ns = s.replace(\"CFG = dict(KV_BM=64, KV_BN=64, KV_W=4, KV_S=2, KV_WS=False, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2, Q_WS=False)\",\n \"CFG = dict(KV_BM=32, KV_BN=64, KV_W=4, KV_S=2, KV_WS=True, Q_BM=128, Q_BN=32, Q_W=4, Q_S=2, Q_WS=True)\")\nopen('sol_v2.py', 'w').write(s)\nEOF\ncp sol_v2.py solution.py && timeout 1200 python check.py 2>&1 | grep -v USDT | cut -c1-300","description":"Save best config as solution and run check"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2035,"cache_read_input_tokens":106031,"cache_creation":{"ephemeral_5m_input_tokens":2035,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"87be700c-4a77-40f8-b252-175fe2fa7513","timestamp":"2026-10-18T14:01:15.241Z","request_id":"req_011Cg9pkDbLBVWt3V8KJUX4L"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pqysF88hCpfkBcYXN3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01StUmeCedxA1nrL2cUs3t6r","name":"Bash","input":{"command":"sed -i \"s/STAGES=cfg\\['KV_S'\\], WS=cfg\\['KV_WS'\\], num_warps=cfg\\['KV_W'\\])/STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))/; s/STAGES=cfg\\['Q_S'\\], WS=cfg\\['Q_WS'\\], num_warps=cfg\\['Q_W'\\])/STAGES=cfg['Q_S'], WS=cfg['Q_WS'], num_warps=cfg['Q_W'], num_stages=cfg.get('Q_NS', 3))/\" sol_v2.py && grep -n \"num_stages=cfg\" sol_v2.py && Q='\"Q_S\":2,\"Q_BM\":128,\"Q_BN\":32,\"Q_W\":4,\"Q_WS\":true' && tools/msweep.sh sol_v2.py 0 \\\n\"{\\\"KV_S\\\":2,\\\"KV_NS\\\":2,\\\"KV_BM\\\":64,\\\"KV_BN\\\":64,\\\"KV_W\\\":4,\\\"KV_WS\\\":true,$Q}\" \\\n\"{\\\"KV_S\\\":1,\\\"KV_NS\\\":1,\\\"KV_BM\\\":64,\\\"KV_BN\\\":64,\\\"KV_W\\\":4,\\\"KV_WS\\\":true,$Q}\" \\\n\"{\\\"KV_S\\\":2,\\\"KV_NS\\\":2,\\\"KV_BM\\\":64,\\\"KV_BN\\\":64,\\\"KV_W\\\":8,\\\"KV_WS\\\":true,$Q}\" \\\n\"{\\\"KV_S\\\":2,\\\"KV_NS\\\":2,\\\"KV_BM\\\":32,\\\"KV_BN\\\":64,\\\"KV_W\\\":4,\\\"KV_WS\\\":true,\\\"Q_S\\\":2,\\\"Q_NS\\\":2,\\\"Q_BM\\\":128,\\\"Q_BN\\\":64,\\\"Q_W\\\":4,\\\"Q_WS\\\":true}\" \\\n\"{\\\"KV_S\\\":2,\\\"KV_NS\\\":2,\\\"KV_BM\\\":32,\\\"KV_BN\\\":64,\\\"KV_W\\\":4,\\\"KV_WS\\\":true,\\\"Q_S\\\":1,\\\"Q_NS\\\":1,\\\"Q_BM\\\":128,\\\"Q_BN\\\":64,\\\"Q_W\\\":4,\\\"Q_WS\\\":true}\" 2>&1 | grep -v \"^{-#\"","description":"Try explicit num_stages with larger tiles"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3002,"cache_read_input_tokens":111999,"cache_creation":{"ephemeral_5m_input_tokens":3002,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"404beedf-b4c4-4379-9f97-624bafbda2b5","timestamp":"2026-10-18T14:02:21.488Z","request_id":"req_011Cg9pqyUwBMS88tuqKX72n"}
{"type":"system","subtype":"task_started","task_id":"bmuc4tczg","run_id":"0mvdwa5qk-4a344412","tool_use_id":"toolu_01StUmeCedxA1nrL2cUs3t6r","description":"Try explicit num_stages with larger tiles","is_backgrounded":false,"task_type":"local_bash","uuid":"b2add729-75fd-4e7a-a050-4b5303a334cd","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
{"type":"tool_progress","tool_use_id":"toolu_01StUmeCedxA1nrL2cUs3t6r-heartbeat-0","tool_name":"Bash","parent_tool_use_id":"toolu_01StUmeCedxA1nrL2cUs3t6r","elapsed_time_seconds":30,"heartbeat":true,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"62f0462c-cdbb-4d16-a0bc-877b66f0614a"}
{"type":"system","subtype":"task_notification","task_id":"bmuc4tczg","run_id":"0mvdwa5qk-4a344412","tool_use_id":"toolu_01StUmeCedxA1nrL2cUs3t6r","status":"completed","output_file":"","summary":"Try explicit num_stages with larger tiles","uuid":"f126b209-d303-433a-bc1d-f62562d19338","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01StUmeCedxA1nrL2cUs3t6r","type":"tool_result","content":"207:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))\n212:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['Q_S'], WS=cfg['Q_WS'], num_warps=cfg['Q_W'], num_stages=cfg.get('Q_NS', 3))\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv=1789 _dq=943 _prep=31\n{\"KV_S\": 1, \"KV_NS\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv=3233 _dq=944 _prep=30\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 8, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv=1783 _dq=943 _prep=31\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_NS\": 2, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 262652, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 1, \"Q_NS\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: tensor memory, Required: 544, Hardware limit: 512. Reducing block sizes or `num_stages` may help.","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"9b939aab-4149-4608-a340-99ade61e4f86","timestamp":"2026-10-18T14:03:02.511Z","tool_use_result":{"stdout":"207:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))\n212:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['Q_S'], WS=cfg['Q_WS'], num_warps=cfg['Q_W'], num_stages=cfg.get('Q_NS', 3))\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv=1789 _dq=943 _prep=31\n{\"KV_S\": 1, \"KV_NS\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv=3233 _dq=944 _prep=30\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_W\": 8, \"KV_WS\": true, \"Q_S\": 2, \"Q_BM\": 128, \"Q_BN\": 32, \"Q_W\": 4, \"Q_WS\": true} s0: _dkdv=1783 _dq=943 _prep=31\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 2, \"Q_NS\": 2, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: shared memory, Required: 262652, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{\"KV_S\": 2, \"KV_NS\": 2, \"KV_BM\": 32, \"KV_BN\": 64, \"KV_W\": 4, \"KV_WS\": true, \"Q_S\": 1, \"Q_NS\": 1, \"Q_BM\": 128, \"Q_BN\": 64, \"Q_W\": 4, \"Q_WS\": true} s0: ERR out of resource: tensor memory, Required: 544, Hardware limit: 512. Reducing block sizes or `num_stages` may help.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01StUmeCedxA1nrL2cUs3t6r","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pwjZC4W7wm44gaY2j4","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"dK/dV accounts for ~62% of runtime and its per-iteration time exceeds the MMA bound, likely due to exposed latency from lse/delta pointer loads and mask overhead. I'll try using TMA loads for those vectors and limit masking to just the diagonal and edge blocks.\n\n","signature":"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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1211,"cache_read_input_tokens":118199,"cache_creation":{"ephemeral_5m_input_tokens":1211,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"646e64d6-7ace-47b3-b508-3f1379329be6","timestamp":"2026-10-18T14:03:49.550Z","request_id":"req_011Cg9pwjCs67xTMG2voXyxR","narration_block_indexes":[0],"thinking_duration_ms":1,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9pwjZC4W7wm44gaY2j4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LoLrZKkC4KYpPDVQAgocCY","name":"Bash","input":{"command":"python - <<'EOF'\ns = open('sol_v2.py').read()\n# dkdv: TMA for lse/delta, conditional mask\nold = '''    for it in tl.range(0, nq * G, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + g\n        q = q_desc.load([s0 + i0, h * D])\n        do = do_desc.load([s0 + i0, h * D])\n        ri = i0 + rm\n        mi = ri < Ls\n        off = h.to(tl.int64) * T + s0 + ri\n        lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n        delta = tl.load(DELTA + off, mask=mi, other=0.0)\n        sT = tl.dot(k, tl.trans(q))\n        vis = (rj[:, None] <= ri[None, :]) & mi[None, :]\n        if W >= 0:\n            vis = vis & (ri[None, :] - rj[:, None] <= W)\n        pT = tl.where(vis, tl.exp2(sT * qk_scale - lse[None, :]), 0.0)\n'''\nnew = '''    for it in tl.range(0, nq * G, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + g\n        q = q_desc.load([s0 + i0, h * D])\n        do = do_desc.load([s0 + i0, h * D])\n        if VEC_TMA:\n            lse = tl.reshape(l_desc.load([h, s0 + i0]), [BM])\n            delta = tl.reshape(d_desc.load([h, s0 + i0]), [BM])\n        else:\n            off = h.to(tl.int64) * TP + s0 + i0 + rm\n            lse = tl.load(LSE2 + off)\n            delta = tl.load(DELTA + off)\n        sT = tl.dot(k, tl.trans(q))\n        pT = tl.exp2(sT * qk_scale - lse[None, :])\n        full = (i0 >= j0 + BN - 1) & (i0 + BM <= Ls)\n        if W >= 0:\n            full = full & (i0 + BM - 1 - j0 <= W)\n        if not full:\n            ri = i0 + rm\n            vis = (rj[:, None] <= ri[None, :]) & (ri[None, :] < Ls)\n            if W >= 0:\n                vis = vis & (ri[None, :] - rj[:, None] <= W)\n            pT = tl.where(vis, pT, 0.0)\n'''\nassert old in s\ns = s.replace(old, new)\ns = s.replace('''def _dkdv(Q, K, V, DO, LSE2, DELTA, CU, DK, DV, qk_scale, sm_scale, T, W,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          STAGES: tl.constexpr, WS: tl.constexpr):\n    G: tl.constexpr = H // HKV''', '''def _dkdv(Q, K, V, DO, LSE2, DELTA, CU, DK, DV, qk_scale, sm_scale, T, W, TP,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          STAGES: tl.constexpr, WS: tl.constexpr, VEC_TMA: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    l_desc = tl.make_tensor_descriptor(LSE2, [H, TP], [TP, 1], [1, BM])\n    d_desc = tl.make_tensor_descriptor(DELTA, [H, TP], [TP, 1], [1, BM])''')\n# dq: conditional mask + padded stride\nold_dq = '''        s = tl.dot(q, tl.trans(k))\n        vis = (rj[None, :] <= ri[:, None]) & mi[:, None]\n        if W >= 0:\n            vis = vis & (ri[:, None] - rj[None, :] <= W)\n        p = tl.where(vis, tl.exp2(s * qk_scale - lse[:, None]), 0.0)\n'''\nnew_dq = '''        s = tl.dot(q, tl.trans(k))\n        p = tl.exp2(s * qk_scale - lse[:, None])\n        full = (j0 + BN - 1 <= i0) & (i0 + BM <= Ls)\n        if W >= 0:\n            full = full & (i0 + BM - 1 - j0 <= W)\n        if not full:\n            vis = (rj[None, :] <= ri[:, None]) & mi[:, None]\n            if W >= 0:\n                vis = vis & (ri[:, None] - rj[None, :] <= W)\n            p = tl.where(vis, p, 0.0)\n'''\nassert old_dq in s\ns = s.replace(old_dq, new_dq)\ns = s.replace('''def _dq(Q, K, V, DO, LSE2, DELTA, CU, DQ, qk_scale, sm_scale, T, W, NQB,''', '''def _dq(Q, K, V, DO, LSE2, DELTA, CU, DQ, qk_scale, sm_scale, T, W, NQB, TP,''')\ns = s.replace('''    off = h.to(tl.int64) * T + s0 + ri\n    lse = tl.load(LSE2 + off, mask=mi, other=0.0)\n    delta = tl.load(DELTA + off, mask=mi, other=0.0)\n    k_lo = 0''', '''    off = h.to(tl.int64) * TP + s0 + ri\n    lse = tl.load(LSE2 + off)\n    delta = tl.load(DELTA + off)\n    k_lo = 0''')\n# prep: padded buffers, write zeros in pad\ns = s.replace('''def _prep(O, DO, LSE, DELTA, LSE2, T, H, D: tl.constexpr, BT: tl.constexpr):''', '''def _prep(O, DO, LSE, DELTA, LSE2, T, TP, H, D: tl.constexpr, BT: tl.constexpr):''')\ns = s.replace('''    off = h.to(tl.int64) * T + rt\n    tl.store(DELTA + off, tl.sum(o * do, 1), mask=m)\n    lse = tl.load(LSE + off, mask=m, other=0.0)\n    tl.store(LSE2 + off, lse * LOG2E, mask=m)''', '''    off = h.to(tl.int64) * TP + rt\n    mp = rt < TP\n    tl.store(DELTA + off, tl.sum(o * do, 1), mask=mp)\n    lse = tl.load(LSE + h.to(tl.int64) * T + rt, mask=m, other=0.0)\n    tl.store(LSE2 + off, lse * LOG2E, mask=mp)''')\ns = s.replace('''    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    lse2 = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    _prep[(triton.cdiv(T, 32), H)](o, do, lse, delta, lse2, T, H, D=D, BT=32, num_warps=4)''', '''    TP = triton.cdiv(T, 128) * 128 + 128\n    delta = torch.empty((H, TP), device=q.device, dtype=torch.float32)\n    lse2 = torch.empty((H, TP), device=q.device, dtype=torch.float32)\n    _prep[(TP // 32, H)](o, do, lse, delta, lse2, T, TP, H, D=D, BT=32, num_warps=4)''')\ns = s.replace(\"q, k, v, do, lse2, delta, cu_seqlens, dk, dv, qk_scale, sm_scale, T, window,\", \"q, k, v, do, lse2, delta, cu_seqlens, dk, dv, qk_scale, sm_scale, T, window, TP, VEC_TMA=cfg.get('VT', 1),\")\ns = s.replace(\"q, k, v, do, lse2, delta, cu_seqlens, dq, qk_scale, sm_scale, T, window, nqb,\", \"q, k, v, do, lse2, delta, cu_seqlens, dq, qk_scale, sm_scale, T, window, nqb, TP,\")\nopen('sol_v3.py', 'w').write(s)\nEOF\ngrep -n \"_dkdv2\\[\\|kern = \\|kern\\[\" sol_v3.py | head; tools/msweep.sh sol_v3.py 0,1 '{}' '{\"VT\":0}' '{\"KV_W\":8}' 2>&1 | grep -v \"^{-#\"","description":"Add TMA lse/delta loads and conditional masking"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1211,"cache_read_input_tokens":118199,"cache_creation":{"ephemeral_5m_input_tokens":1211,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"f98804f3-38c4-449c-88ce-cc502bbf4875","timestamp":"2026-10-18T14:04:09.030Z","request_id":"req_011Cg9pwjCs67xTMG2voXyxR"}
{"type":"system","subtype":"task_started","task_id":"b0gjfm6nj","run_id":"0mvdwcgpt-2c0d67c5","tool_use_id":"toolu_01LoLrZKkC4KYpPDVQAgocCY","description":"Add TMA lse/delta loads and conditional masking","is_backgrounded":false,"task_type":"local_bash","uuid":"00f83621-155c-4395-b80c-b292e1107a84","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LoLrZKkC4KYpPDVQAgocCY","type":"tool_result","content":"226:    kern = _dkdv2 if cfg.get('KV_T', 0) else _dkdv\n227:    kern[(n * HKV, triton.cdiv(max_seqlen, BN))](\n{} s0: _dkdv=1610 _dq=948 _prep=30 | s1: _dkdv=507 _dq=326 _prep=29\n{\"VT\": 0} s0: _dkdv=1593 _dq=948 _prep=29 | s1: _dkdv=492 _dq=327 _prep=29\n{\"KV_W\": 8} s0: _dkdv=1549 _dq=948 _prep=29 | s1: _dkdv=476 _dq=325 _prep=29","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"ea21ace0-50ae-475e-835c-508cb94d7b6c","timestamp":"2026-10-18T14:04:38.175Z","tool_use_result":{"stdout":"226:    kern = _dkdv2 if cfg.get('KV_T', 0) else _dkdv\n227:    kern[(n * HKV, triton.cdiv(max_seqlen, BN))](\n{} s0: _dkdv=1610 _dq=948 _prep=30 | s1: _dkdv=507 _dq=326 _prep=29\n{\"VT\": 0} s0: _dkdv=1593 _dq=948 _prep=29 | s1: _dkdv=492 _dq=327 _prep=29\n{\"KV_W\": 8} s0: _dkdv=1549 _dq=948 _prep=29 | s1: _dkdv=476 _dq=325 _prep=29","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01LoLrZKkC4KYpPDVQAgocCY","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9q3QozthbC7i6SCTqbG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017JA6dNzqkG9GaaAUn9aBDy","name":"Bash","input":{"command":"python - <<'EOF'\ns = open('sol_v3.py').read()\nadd = '''\n\n@triton.jit\ndef _dkdvq(Q, K, V, DO, LSE2, DELTA, CU, DK, DV, DQA, qk_scale, sm_scale, T, W, TP,\n           H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n           STAGES: tl.constexpr, WS: tl.constexpr, ATMA: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    q_desc = tl.make_tensor_descriptor(Q, [T, H * D], [H * D, 1], [BM, D])\n    do_desc = tl.make_tensor_descriptor(DO, [T, H * D], [H * D, 1], [BM, D])\n    k_desc = tl.make_tensor_descriptor(K, [T, HKV * D], [HKV * D, 1], [BN, D])\n    v_desc = tl.make_tensor_descriptor(V, [T, HKV * D], [HKV * D, 1], [BN, D])\n    dq_desc = tl.make_tensor_descriptor(DQA, [T, H * D], [H * D, 1], [BM, D])\n    pid_h = tl.program_id(0)\n    hk = pid_h % HKV\n    sid = pid_h // HKV\n    kb = tl.program_id(1)\n    s0 = tl.load(CU + sid)\n    Ls = tl.load(CU + sid + 1) - s0\n    j0 = kb * BN\n    if j0 >= Ls:\n        return\n    k = k_desc.load([s0 + j0, hk * D])\n    v = v_desc.load([s0 + j0, hk * D])\n    rj = j0 + tl.arange(0, BN)\n    q_lo = (j0 // BM) * BM\n    q_hi = Ls\n    if W >= 0:\n        q_hi = tl.minimum(Ls, j0 + BN + W)\n    nq = tl.cdiv(q_hi - q_lo, BM)\n    dk = tl.zeros((BN, D), tl.float32)\n    dv = tl.zeros((BN, D), tl.float32)\n    rm = tl.arange(0, BM)\n    rd = tl.arange(0, D)\n    for it in tl.range(0, nq * G, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + g\n        q = q_desc.load([s0 + i0, h * D])\n        do = do_desc.load([s0 + i0, h * D])\n        off = h.to(tl.int64) * TP + s0 + i0 + rm\n        lse = tl.load(LSE2 + off)\n        delta = tl.load(DELTA + off)\n        sT = tl.dot(k, tl.trans(q))\n        pT = tl.exp2(sT * qk_scale - lse[None, :])\n        ri = i0 + rm\n        vis = (rj[:, None] <= ri[None, :]) & (ri[None, :] < Ls)\n        if W >= 0:\n            vis = vis & (ri[None, :] - rj[:, None] <= W)\n        pT = tl.where(vis, pT, 0.0)\n        dpT = tl.dot(v, tl.trans(do))\n        dv = tl.dot(pT.to(tl.bfloat16), do, dv)\n        dsT = (pT * (dpT - delta[None, :])).to(tl.bfloat16)\n        dk = tl.dot(dsT, q, dk)\n        dqp = tl.dot(tl.trans(dsT), k)\n        if ATMA:\n            dq_desc.atomic_add([s0 + i0, h * D], dqp)\n        else:\n            qrow = ((s0 + ri).to(tl.int64) * H + h) * D\n            tl.atomic_add(DQA + qrow[:, None] + rd[None, :], dqp, mask=(ri < Ls)[:, None], sem='relaxed')\n    mj = rj < Ls\n    krow = ((s0 + rj).to(tl.int64) * HKV + hk) * D\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n\n\n@triton.jit\ndef _dqconv(DQA, DQ, N, sm_scale, BLOCK: tl.constexpr):\n    o = tl.program_id(0).to(tl.int64) * BLOCK + tl.arange(0, BLOCK)\n    m = o < N\n    x = tl.load(DQA + o, mask=m, other=0.0)\n    tl.store(DQ + o, (x * sm_scale).to(tl.bfloat16), mask=m)\n'''\ns = s.replace(\"\\n\\n@triton.jit\\ndef _dq(\", add + \"\\n\\n@triton.jit\\ndef _dq(\")\ns = s.replace(\"\"\"    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n    kern =\"\"\", \"\"\"    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n    if cfg.get('FUSED', 0):\n        dqa = torch.zeros((T, H * D), device=q.device, dtype=torch.float32)\n        _dkdvq[(n * HKV, triton.cdiv(max_seqlen, BN))](\n            q, k, v, do, lse2, delta, cu_seqlens, dk, dv, dqa, qk_scale, sm_scale, T, window, TP,\n            H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], ATMA=cfg.get('ATMA', 1),\n            num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))\n        _dqconv[(triton.cdiv(T * H * D, 4096),)](dqa, dq, T * H * D, sm_scale, BLOCK=4096, num_warps=4)\n        return\n    kern =\"\"\")\nopen('sol_v4.py', 'w').write(s)\nEOF\ntools/msweep.sh sol_v4.py 0 '{\"FUSED\":1,\"KV_BM\":64,\"KV_BN\":64,\"KV_S\":2,\"KV_NS\":2,\"KV_W\":8,\"KV_WS\":true}' '{\"FUSED\":1,\"KV_BM\":64,\"KV_BN\":64,\"KV_S\":2,\"KV_NS\":2,\"KV_W\":8,\"KV_WS\":false}' '{\"FUSED\":1,\"KV_BM\":64,\"KV_BN\":64,\"KV_S\":2,\"KV_NS\":2,\"KV_W\":8,\"KV_WS\":true,\"ATMA\":0}' 2>&1 | grep -v \"^{-#\" | cut -c1-400","description":"Implement fused dkdv+atomic dQ kernel and test"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6069,"cache_read_input_tokens":119410,"cache_creation":{"ephemeral_5m_input_tokens":6069,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"c790ba34-86e7-4a84-bd4c-0ce9f04cba9f","timestamp":"2026-10-18T14:05:24.096Z","request_id":"req_011Cg9q3QTes4ymTnpR4KXiU"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017JA6dNzqkG9GaaAUn9aBDy","type":"tool_result","content":"{\"FUSED\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_S\": 2, \"KV_NS\": 2, \"KV_W\": 8, \"KV_WS\": true} s0: ERR PassManager::run failed\n{\"FUSED\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_S\": 2, \"KV_NS\": 2, \"KV_W\": 8, \"KV_WS\": false} s0: ERR out of resource: shared memory, Required: 336488, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{\"FUSED\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_S\": 2, \"KV_NS\": 2, \"KV_W\": 8, \"KV_WS\": true, \"ATMA\": 0} s0: ERR PassManager::run failed","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"f92ce3be-e273-41c2-aa5d-5a0600ca8ad8","timestamp":"2026-10-18T14:05:40.765Z","tool_use_result":{"stdout":"{\"FUSED\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_S\": 2, \"KV_NS\": 2, \"KV_W\": 8, \"KV_WS\": true} s0: ERR PassManager::run failed\n{\"FUSED\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_S\": 2, \"KV_NS\": 2, \"KV_W\": 8, \"KV_WS\": false} s0: ERR out of resource: shared memory, Required: 336488, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n{\"FUSED\": 1, \"KV_BM\": 64, \"KV_BN\": 64, \"KV_S\": 2, \"KV_NS\": 2, \"KV_W\": 8, \"KV_WS\": true, \"ATMA\": 0} s0: ERR PassManager::run failed","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_017JA6dNzqkG9GaaAUn9aBDy","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qMS78GpCxHDns15f9R","type":"message","role":"assistant","content":[{"type":"text","text":"Best config so far: 8 warps in both kernels, giving 1549 + 934 µs on the 8K shape. I'm writing it into solution.py without the experimental code, then testing edge shapes."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":967,"cache_read_input_tokens":137262,"cache_creation":{"ephemeral_5m_input_tokens":967,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"7ac4750b-bee1-4c72-bd03-4d464b643f8a","timestamp":"2026-10-18T14:09:14.751Z","request_id":"req_011Cg9qMRdMJiQqZsaAgPmdg"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qMS78GpCxHDns15f9R","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CTKAiNgN1ybV6QXtAY5AKN","name":"Bash","input":{"command":"python - <<'EOF'\nimport re\ns = open('sol_v3.py').read()\n# drop _dkdv2\na = s.index(\"\\n\\n@triton.jit\\ndef _dkdv2(\")\nb = s.index(\"\\n\\n@triton.jit\\ndef _dq(\")\ns = s[:a] + s[b:]\ns = s.replace(\"CFG = dict(KV_BM=32, KV_BN=64, KV_W=4, KV_S=2, KV_WS=True, Q_BM=128, Q_BN=32, Q_W=4, Q_S=2, Q_WS=True)\",\n              \"CFG = dict(KV_BM=32, KV_BN=64, KV_W=8, KV_S=2, KV_WS=True, Q_BM=128, Q_BN=32, Q_W=8, Q_S=2, Q_WS=True, VT=1)\")\ns = s.replace(\"def kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, cfg=CFG):\",\n              \"def kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\\n    _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, CFG)\\n\\n\\ndef _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, cfg):\")\ns = s.replace(\"    kern = _dkdv2 if cfg.get('KV_T', 0) else _dkdv\\n    kern[(n * HKV\", \"    _dkdv[(n * HKV\")\nopen('sol_v5.py', 'w').write(s)\nEOF\npython -c \"\nimport static_check; print(static_check.check(open('/workspace/sol_v5.py').read()))\"; wc -c sol_v5.py; grep -n \"cfg\" sol_v5.py | head -20","description":"Create clean solution candidate v5"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":967,"cache_read_input_tokens":137262,"cache_creation":{"ephemeral_5m_input_tokens":967,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"46f3ea90-4730-4847-9647-fb12d961c5a4","timestamp":"2026-10-18T14:09:19.544Z","request_id":"req_011Cg9qMRdMJiQqZsaAgPmdg"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CTKAiNgN1ybV6QXtAY5AKN","type":"tool_result","content":"(True, 'ok')\n7187 sol_v5.py\n157:def _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, cfg):\n168:    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n170:        q, k, v, do, lse2, delta, cu_seqlens, dk, dv, qk_scale, sm_scale, T, window, TP, VEC_TMA=cfg.get('VT', 1),\n171:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))\n172:    BM, BN = cfg['Q_BM'], cfg['Q_BN']\n176:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['Q_S'], WS=cfg['Q_WS'], num_warps=cfg['Q_W'], num_stages=cfg.get('Q_NS', 3))","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"cace7a9c-7947-43e6-b847-d3435a6fa671","timestamp":"2026-10-18T14:09:19.664Z","tool_use_result":{"stdout":"(True, 'ok')\n7187 sol_v5.py\n157:def _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, cfg):\n168:    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n170:        q, k, v, do, lse2, delta, cu_seqlens, dk, dv, qk_scale, sm_scale, T, window, TP, VEC_TMA=cfg.get('VT', 1),\n171:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))\n172:    BM, BN = cfg['Q_BM'], cfg['Q_BN']\n176:        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['Q_S'], WS=cfg['Q_WS'], num_warps=cfg['Q_W'], num_stages=cfg.get('Q_NS', 3))","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01CTKAiNgN1ybV6QXtAY5AKN","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qQAFEBnJqKut6FATMC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L31Vyuv8WHM1AgFQu5yp6W","name":"Bash","input":{"command":"cat > /workspace/tools/edge.py <<'EOF'\nimport sys, importlib.util, torch\nsys.path.insert(0, '/workspace')\nimport task as T\nspec = importlib.util.spec_from_file_location('sol', sys.argv[1]); m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m)\nshapes = [\n dict(n=1, L=1, Hq=2, Hkv=1, window=-1),\n dict(n=1, L=7, Hq=4, Hkv=4, window=-1),\n dict(n=3, L=100, Hq=4, Hkv=1, window=5, ragged=True),\n dict(n=2, L=333, Hq=6, Hkv=2, window=-1, ragged=True),\n dict(n=5, L=130, Hq=8, Hkv=2, window=0, ragged=True),\n dict(n=1, L=1000, Hq=2, Hkv=1, window=63),\n dict(n=2, L=257, Hq=3, Hkv=1, window=-1),\n dict(n=1, L=700, Hq=4, Hkv=2, window=1023, long=129),\n]\nfor sh in shapes:\n    c = T.make_case(sh, 3, 'cuda')\n    exp = T.reference([a.clone() if torch.is_tensor(a) else a for a in c.args])\n    for i in (9, 10, 11): c.args[i].fill_(float('nan'))\n    m.kernel_fn(*c.args); torch.cuda.synchronize()\n    ok, st = T.check({i: c.args[i] for i in (9, 10, 11)}, exp)\n    print(sh, 'ok' if ok else f'WRONG {st}')\nEOF\ncd /workspace && timeout 900 python tools/edge.py sol_v5.py 2>&1 | grep -v USDT","description":"Run edge-shape correctness tests"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4491,"cache_read_input_tokens":138229,"cache_creation":{"ephemeral_5m_input_tokens":4491,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"92178bfa-c0ee-43db-a077-d38879721049","timestamp":"2026-10-18T14:09:25.295Z","request_id":"req_011Cg9qQ9ue4t3gSvFVSC1D3"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L31Vyuv8WHM1AgFQu5yp6W","type":"tool_result","content":"{'n': 1, 'L': 1, 'Hq': 2, 'Hkv': 1, 'window': -1} ok\n{'n': 1, 'L': 7, 'Hq': 4, 'Hkv': 4, 'window': -1} ok\nTraceback (most recent call last):\n  File \"/workspace/tools/edge.py\", line 19, in <module>\n    m.kernel_fn(*c.args); torch.cuda.synchronize()\n    ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/sol_v5.py\", line 154, in kernel_fn\n    _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, CFG)\n  File \"/workspace/sol_v5.py\", line 174, in _run\n    _dq[(n * H, nqb)](\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 370, in <lambda>\n    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)\n                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 760, in run\n    launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values())\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 485, in launch_metadata\n    self._init_handles()\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 468, in _init_handles\n    self.module, self.function, self.n_regs, self.n_spills, self.n_max_threads = driver.active.utils.load_binary(\n                                                                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Triton Error [CUDA]: an illegal instruction was encountered","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"76bef140-4215-4571-a78b-badc765da74f","timestamp":"2026-10-18T14:09:36.693Z","tool_use_result":{"stdout":"{'n': 1, 'L': 1, 'Hq': 2, 'Hkv': 1, 'window': -1} ok\n{'n': 1, 'L': 7, 'Hq': 4, 'Hkv': 4, 'window': -1} ok\nTraceback (most recent call last):\n  File \"/workspace/tools/edge.py\", line 19, in <module>\n    m.kernel_fn(*c.args); torch.cuda.synchronize()\n    ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/sol_v5.py\", line 154, in kernel_fn\n    _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, CFG)\n  File \"/workspace/sol_v5.py\", line 174, in _run\n    _dq[(n * H, nqb)](\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 370, in <lambda>\n    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)\n                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py\", line 760, in run\n    launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values())\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 485, in launch_metadata\n    self._init_handles()\n  File \"/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py\", line 468, in _init_handles\n    self.module, self.function, self.n_regs, self.n_spills, self.n_max_threads = driver.active.utils.load_binary(\n                                                                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Triton Error [CUDA]: an illegal instruction was encountered","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01L31Vyuv8WHM1AgFQu5yp6W","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qRR5UeLY74eMRHdaE5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01275vSZ6EoqgzxuwEYV65RX","name":"Bash","input":{"command":"cat > /workspace/tools/edge1.py <<'EOF'\nimport sys, importlib.util, torch, json\nsys.path.insert(0, '/workspace')\nimport task as T\nspec = importlib.util.spec_from_file_location('sol', sys.argv[1]); m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m)\nsh = json.loads(sys.argv[2])\nc = T.make_case(sh, 3, 'cuda')\nexp = T.reference([a.clone() if torch.is_tensor(a) else a for a in c.args])\nfor i in (9, 10, 11): c.args[i].fill_(float('nan'))\nm.kernel_fn(*c.args); torch.cuda.synchronize()\nok, st = T.check({i: c.args[i] for i in (9, 10, 11)}, exp)\nprint(sh, 'ok' if ok else f'WRONG {st}')\nEOF\ncd /workspace; for s in '{\"n\":3,\"L\":100,\"Hq\":4,\"Hkv\":1,\"window\":-1,\"ragged\":true}' '{\"n\":3,\"L\":100,\"Hq\":2,\"Hkv\":2,\"window\":5,\"ragged\":true}' '{\"n\":1,\"L\":100,\"Hq\":4,\"Hkv\":1,\"window\":-1}' '{\"n\":1,\"L\":200,\"Hq\":2,\"Hkv\":1,\"window\":-1}'; do CUDA_LAUNCH_BLOCKING=1 timeout 300 python tools/edge1.py sol_v5.py \"$s\" 2>&1 | grep -v USDT | tail -1; done","description":"Isolate illegal instruction edge case"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1553,"cache_read_input_tokens":142720,"cache_creation":{"ephemeral_5m_input_tokens":1553,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"d1a964fd-3382-4515-8070-12ab82dde1b0","timestamp":"2026-10-18T14:09:44.428Z","request_id":"req_011Cg9qRQiem3J1edxcrJ5R7"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01275vSZ6EoqgzxuwEYV65RX","type":"tool_result","content":"RuntimeError: Triton Error [CUDA]: an illegal instruction was encountered\nRuntimeError: Triton Error [CUDA]: an illegal instruction was encountered\n{'n': 1, 'L': 100, 'Hq': 4, 'Hkv': 1, 'window': -1} ok\n{'n': 1, 'L': 200, 'Hq': 2, 'Hkv': 1, 'window': -1} ok","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"1a8a4973-72d0-45b9-9d39-8587727cfb98","timestamp":"2026-10-18T14:10:08.516Z","tool_use_result":{"stdout":"RuntimeError: Triton Error [CUDA]: an illegal instruction was encountered\nRuntimeError: Triton Error [CUDA]: an illegal instruction was encountered\n{'n': 1, 'L': 100, 'Hq': 4, 'Hkv': 1, 'window': -1} ok\n{'n': 1, 'L': 200, 'Hq': 2, 'Hkv': 1, 'window': -1} ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01275vSZ6EoqgzxuwEYV65RX","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HTjq6g2saVXoNkyUEYeL96","type":"tool_result","content":"    m.kernel_fn(*c.args); torch.cuda.synchronize()\n    ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/sol_v5.py\", line 154, in kernel_fn\n    _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, CFG)\n  File \"/workspace/sol_v5.py\", line 169, in _run\n[0, 150, 200, 300] 150","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"ea975109-d7c3-4286-b50a-e57cfacea215","timestamp":"2026-10-18T14:10:27.003Z","tool_use_result":{"stdout":"    m.kernel_fn(*c.args); torch.cuda.synchronize()\n    ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/sol_v5.py\", line 154, in kernel_fn\n    _run(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv, CFG)\n  File \"/workspace/sol_v5.py\", line 169, in _run\n[0, 150, 200, 300] 150","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01HTjq6g2saVXoNkyUEYeL96","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"system","subtype":"task_started","task_id":"bu09gcc27","run_id":"0mvdwkpy9-9f8500e1","tool_use_id":"toolu_01K7Ztd3JjDBCpinouiAybpn","description":"Test with pointer loads for lse/delta","is_backgrounded":false,"task_type":"local_bash","uuid":"feb5038a-d62f-4696-a22d-567b9a7d9640","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01K7Ztd3JjDBCpinouiAybpn","type":"tool_result","content":"150:CFG = dict(KV_BM=32, KV_BN=64, KV_W=8, KV_S=2, KV_WS=True, Q_BM=128, Q_BN=32, Q_W=8, Q_S=2, Q_WS=True, VT=0)\n{'n': 3, 'L': 100, 'Hq': 2, 'Hkv': 2, 'window': 5, 'ragged': True} ok","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"db9c8b1d-8799-40cf-bb88-b2c313e73be5","timestamp":"2026-10-18T14:10:41.131Z","tool_use_result":{"stdout":"150:CFG = dict(KV_BM=32, KV_BN=64, KV_W=8, KV_S=2, KV_WS=True, Q_BM=128, Q_BN=32, Q_W=8, Q_S=2, Q_WS=True, VT=0)\n{'n': 3, 'L': 100, 'Hq': 2, 'Hkv': 2, 'window': 5, 'ragged': True} ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01K7Ztd3JjDBCpinouiAybpn","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UqhuYs7TzUwYFfTxSgMAfG","type":"tool_result","content":"Gemma-3-12B-style global layer (16 query / 8 KV heads): correct, 2586.85 us, starting kernel 20786.02 us\nGemma-3-12B-style local layer: correct, 848.48 us, starting kernel 5683.81 us\nQwen3-Next-style full-attention layer (16 query / 2 KV heads): correct, 1426.46 us, starting kernel 11413.28 us\npacked documents (16 / 8 heads): 8 documents of 1K-3K tokens: correct, 1964.45 us, starting kernel 11158.08 us\nGemma-3-4B-style global layer (8 query / 4 KV heads): correct, 452.96 us, starting kernel 3030.85 us\nGemma-3-4B-style local layer (8 / 4 heads): correct, 838.94 us, starting kernel 5167.49 us\nautosave: kept this version in /workspace/.best/solution.py (6.822x the starting kernel on the public shapes)","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"17f064c1-ac94-424e-8fa5-40ada05f2751","timestamp":"2026-10-18T14:11:19.803Z","tool_use_result":{"stdout":"Gemma-3-12B-style global layer (16 query / 8 KV heads): correct, 2586.85 us, starting kernel 20786.02 us\nGemma-3-12B-style local layer: correct, 848.48 us, starting kernel 5683.81 us\nQwen3-Next-style full-attention layer (16 query / 2 KV heads): correct, 1426.46 us, starting kernel 11413.28 us\npacked documents (16 / 8 heads): 8 documents of 1K-3K tokens: correct, 1964.45 us, starting kernel 11158.08 us\nGemma-3-4B-style global layer (8 query / 4 KV heads): correct, 452.96 us, starting kernel 3030.85 us\nGemma-3-4B-style local layer (8 / 4 heads): correct, 838.94 us, starting kernel 5167.49 us\nautosave: kept this version in /workspace/.best/solution.py (6.822x the starting kernel on the public shapes)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01UqhuYs7TzUwYFfTxSgMAfG","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WYTco7CPqBrEfNMPaQ4Fcs","type":"tool_result","content":"{} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'\n{\"KV_W\": 4} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'\n{\"Q_BM\": 64, \"Q_BN\": 64, \"Q_W\": 4} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'\n{\"Q_BM\": 64, \"Q_BN\": 32, \"Q_W\": 4} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"1691d514-0a63-4dba-a7b9-9301c1c78595","timestamp":"2026-10-18T14:12:15.247Z","tool_use_result":{"stdout":"{} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'\n{\"KV_W\": 4} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'\n{\"Q_BM\": 64, \"Q_BN\": 64, \"Q_W\": 4} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'\n{\"Q_BM\": 64, \"Q_BN\": 32, \"Q_W\": 4} s1: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s4: ERR kernel_fn() got an unexpected keyword argument 'cfg' | s5: ERR kernel_fn() got an unexpected keyword argument 'cfg'","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01WYTco7CPqBrEfNMPaQ4Fcs","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qoaJDiHmu2YSg3fMA9","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"KV_W=8 remains the best setting, but the 16/2-head shape still has a bottleneck: the heaviest dK/dV CTA dominates runtime. I'll split the query-head group across 2 CTAs with fp32 partials plus a small reduce kernel, applied only when the group size is ≥ 4.\n\n","signature":"CAQSrAgKEQgSGAI4AUIJbmFycmF0aW9uEgwe8PbuOVLu4Y31CuAaDCpQlRBfwRq+r6bCDiIwMjjitR91iazBeVrizDaXK1+4hTsRVJpSO6tK2X+vyReTR3Oi86QtYNu1wAxCVUZzKsgHFf7CROpcwV24Lz44KVy0fRRGuUVYP4/A0zMnnFiymr7vNj+sReLqgwNwRav4uEo1eHVO1PkTeHPCXYf7C2QCvzQxaYBj40oy/foJe6MR6bMP2Z8FpmjRKdnLwTDtTAXfUfDxGneSTwE6TZc7gHzxelDtK5aMA0I9vHyvEfzVxxxJUKXCdMezNvQq4StAD5thIs1nWHk1MoEU+9FlDOuVhDDmUcx8kc7ENOykKGfDorymCTFvQ/cwbPLNTbdNhiqKsWFnCRCdebPMdcvMRtFCi+7gDi/hctqUm7bOJwS0ZwIJINdC2tWhQNqquM5o+7GeWsycZjcgaIsM7sldBwyoeYBoMRNNGTp+zBHXha86p7nFkIoLJ+4IwbzQ3ZllkaZszeZKh8CKb/GxbKbrvrPHPoSP3guhdTE0ld8cg/pIR/VPCeC/oEoxEU8sSljQZdaBx9VZVEcYEsbBTReeyD32a4opNCsKtfynh3IzhgrS7PhSQy67oSKItKkn82m6YzhGjPn1AOXWd5Y/BzQCgIZAQ79FvvUsMxF2Q02rr7k0qL3HJC+U5qPUd62M1WEiEg27NJ/37Q8PK6gFHEcW7CcH4eh4ndTkc70P2jXCSRLQ+XwBl1+3bpg3RdU2ihgGmVkFzI9vw0Lq+JlGWWb9ONbAaqA9ogTERVJko/+5/RvQm2Lad63lLVQtFUrG11dLlMdR262TbPwXC9C2/lwdlS/WEeS69789CVeDC6uOAYneqgoJcs8J4hCsitD4urBu8ITrY9kYinw7IBt50XqBC+h1UmR6x6XUsh2nfEkIGodw1G+BWHw3MUPa7IBvZI1jRjE919ihdB9NDOMZlmrDZxj7g+RXEKSKsyhaBGRdZ335e9K8FwNVTet8Go9vmBgCIncDJ00vqBx0w6QAn8I8xw/BG4WTv0BCtM0TpLj6lBPMAIY7/fnSp5WD0VWrbvl29YJf/f8HiFz0OCfzwOLIvTsVQ0fE0Po2v09trlKweR+b2Xueh5/aZ28+xl21LuEU9DIfRJDgWMoPbrO4B7z27j5Gnk4jmPs4zkF42W/80qM+ZQybyGD9huKR0tfP58rHhDYOEPi9vrmIOtBSgYORUJn9/R6QczclJ6HIEEKOtfC/pY+COeVg1Ob4RI40XMLlf1a1GKm0e+qlBA1mczZsRLnhSPXvZAaRjlGzhaSmljK1C8BSr2xgYE4O2eY5VU8gONZIQNTHxkZHi8NH2qtjxO+cbAzzgDl6VQwrJHr3xcOlmz/bM3QQKXYjRFQkQI8qmTDa62fwBV9Dr10YAQ=="}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":825,"cache_read_input_tokens":158181,"cache_creation":{"ephemeral_5m_input_tokens":825,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"9b3b9d7f-826c-48c8-aede-c8a6f50108c4","timestamp":"2026-10-18T14:14:54.439Z","request_id":"req_011Cg9qoZy89mA7pBauT1HQY","narration_block_indexes":[0],"thinking_duration_ms":1,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qoaJDiHmu2YSg3fMA9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019HkXUbMP96DYtb2k9jq1m4","name":"Bash","input":{"command":"cd /workspace; python - <<'EOF'\ns = open('sol_v6.py').read()\nold_sig = '''def _dkdv(Q, K, V, DO, LSE2, DELTA, CU, DK, DV, qk_scale, sm_scale, T, W, TP,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          STAGES: tl.constexpr, WS: tl.constexpr):\n    G: tl.constexpr = H // HKV'''\nnew_sig = '''def _dkdv(Q, K, V, DO, LSE2, DELTA, CU, DK, DV, qk_scale, sm_scale, T, W, TP,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          STAGES: tl.constexpr, WS: tl.constexpr, SPLIT: tl.constexpr):\n    G: tl.constexpr = H // HKV\n    GS: tl.constexpr = G // SPLIT'''\nassert old_sig in s; s = s.replace(old_sig, new_sig)\nold = '''    pid_h = tl.program_id(0)\n    hk = pid_h % HKV\n    sid = pid_h // HKV\n    kb = tl.program_id(1)'''\nnew = '''    pid_h = tl.program_id(0)\n    sp = pid_h % SPLIT\n    hk = (pid_h // SPLIT) % HKV\n    sid = pid_h // (SPLIT * HKV)\n    kb = tl.program_id(1)'''\nassert old in s; s = s.replace(old, new, 1)\nold = '''    for it in tl.range(0, nq * G, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + g'''\nnew = '''    for it in tl.range(0, nq * GS, num_stages=STAGES, warp_specialize=WS):\n        g = it // nq\n        i0 = q_lo + (it - g * nq) * BM\n        h = hk * G + sp * GS + g'''\nassert old in s; s = s.replace(old, new)\nold = '''    mj = rj < Ls\n    krow = ((s0 + rj).to(tl.int64) * HKV + hk) * D\n    rd = tl.arange(0, D)\n    tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n    tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n\n\n@triton.jit\ndef _dq('''\nnew = '''    mj = rj < Ls\n    krow = ((s0 + rj).to(tl.int64) * HKV + hk) * D\n    rd = tl.arange(0, D)\n    if SPLIT == 1:\n        tl.store(DK + krow[:, None] + rd[None, :], (dk * sm_scale).to(tl.bfloat16), mask=mj[:, None])\n        tl.store(DV + krow[:, None] + rd[None, :], dv.to(tl.bfloat16), mask=mj[:, None])\n    else:\n        po = sp.to(tl.int64) * T * HKV * D + krow\n        tl.store(DK + po[:, None] + rd[None, :], dk * sm_scale, mask=mj[:, None])\n        tl.store(DV + po[:, None] + rd[None, :], dv, mask=mj[:, None])\n\n\n@triton.jit\ndef _reduce(PK, PV, DK, DV, N, SPLIT: tl.constexpr, BLOCK: tl.constexpr):\n    o = tl.program_id(0).to(tl.int64) * BLOCK + tl.arange(0, BLOCK)\n    m = o < N\n    ak = tl.zeros((BLOCK,), tl.float32)\n    av = tl.zeros((BLOCK,), tl.float32)\n    for i in tl.static_range(SPLIT):\n        ak += tl.load(PK + i * N + o, mask=m, other=0.0)\n        av += tl.load(PV + i * N + o, mask=m, other=0.0)\n    tl.store(DK + o, ak.to(tl.bfloat16), mask=m)\n    tl.store(DV + o, av.to(tl.bfloat16), mask=m)\n\n\n@triton.jit\ndef _dq('''\nassert old in s; s = s.replace(old, new)\nold = '''    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n    _dkdv[(n * HKV, triton.cdiv(max_seqlen, BN))](\n        q, k, v, do, lse2, delta, cu_seqlens, dk, dv, qk_scale, sm_scale, T, window, TP,\n        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))'''\nnew = '''    BM, BN = cfg['KV_BM'], cfg['KV_BN']\n    G = H // HKV\n    SPLIT = 2 if G >= cfg.get('SPLIT_G', 4) and G % 2 == 0 else 1\n    ok, ov = dk, dv\n    if SPLIT > 1:\n        ok = torch.empty((SPLIT, T, HKV, D), device=q.device, dtype=torch.float32)\n        ov = torch.empty((SPLIT, T, HKV, D), device=q.device, dtype=torch.float32)\n    _dkdv[(n * HKV * SPLIT, triton.cdiv(max_seqlen, BN))](\n        q, k, v, do, lse2, delta, cu_seqlens, ok, ov, qk_scale, sm_scale, T, window, TP,\n        H=H, HKV=HKV, D=D, BM=BM, BN=BN, STAGES=cfg['KV_S'], WS=cfg['KV_WS'], SPLIT=SPLIT,\n        num_warps=cfg['KV_W'], num_stages=cfg.get('KV_NS', 3))\n    if SPLIT > 1:\n        N = T * HKV * D\n        _reduce[(triton.cdiv(N, 2048),)](ok, ov, dk, dv, N, SPLIT=SPLIT, BLOCK=2048, num_warps=4)'''\nassert old in s; s = s.replace(old, new)\nopen('sol_v7.py', 'w').write(s)\nEOF\ntools/msweep.sh sol_v7.py 2 '{}' '{\"SPLIT_G\":100}' 2>&1 | grep -v \"^{-#\" | cut -c1-500; timeout 300 python tools/bench.py sol_v7.py '{}' 2 2>&1 | grep -v USDT | tail -2","description":"Implement GQA head split for dkdv and test"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":825,"cache_read_input_tokens":158181,"cache_creation":{"ephemeral_5m_input_tokens":825,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"f9a1aa8b-178f-44d7-a39c-1bc0a0cf3b95","timestamp":"2026-10-18T14:15:09.098Z","request_id":"req_011Cg9qoZy89mA7pBauT1HQY"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019HkXUbMP96DYtb2k9jq1m4","type":"tool_result","content":"{} s2: _dkdv=822 _dq=518 _prep=30 _reduc=11\n{\"SPLIT_G\": 100} s2: _dkdv=853 _dq=516 _prep=30\n    m.kernel_fn(*args, cfg=full)\nTypeError: kernel_fn() got an unexpected keyword argument 'cfg'","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"fc69a88d-dca7-4312-b119-261333e76bac","timestamp":"2026-10-18T14:15:33.196Z","tool_use_result":{"stdout":"{} s2: _dkdv=822 _dq=518 _prep=30 _reduc=11\n{\"SPLIT_G\": 100} s2: _dkdv=853 _dq=516 _prep=30\n    m.kernel_fn(*args, cfg=full)\nTypeError: kernel_fn() got an unexpected keyword argument 'cfg'","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_019HkXUbMP96DYtb2k9jq1m4","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qshwDL92j4WGuNMkbL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KDv4kePF9Avq1gcWCm6Kir","name":"Bash","input":{"command":"cd /workspace; sed -i \"s/    SPLIT = 2 if G >= cfg.get('SPLIT_G', 4) and G % 2 == 0 else 1/    SPLIT = cfg.get('SPLIT', 2) if G >= cfg.get('SPLIT_G', 4) and G % 2 == 0 else 1/\" sol_v7.py; tools/msweep.sh sol_v7.py 2 '{\"SPLIT\":4}' '{\"SPLIT\":2}' 2>&1 | grep -v \"^{-#\" | cut -c1-500","description":"Try 4-way head split"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4188,"cache_read_input_tokens":159006,"cache_creation":{"ephemeral_5m_input_tokens":4188,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"6c00a96e-ce35-4f66-a1ac-06c03c8fd02e","timestamp":"2026-10-18T14:15:37.979Z","request_id":"req_011Cg9qsgxg9mnzsnbEseDuZ"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KDv4kePF9Avq1gcWCm6Kir","type":"tool_result","content":"{\"SPLIT\": 4} s2: _dkdv=858 _dq=517 _prep=30 _reduc=24\n{\"SPLIT\": 2} s2: _dkdv=822 _dq=518 _prep=30 _reduc=11","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"661f6ea8-c7f6-4d6d-885f-2fe3190c97cf","timestamp":"2026-10-18T14:15:55.716Z","tool_use_result":{"stdout":"{\"SPLIT\": 4} s2: _dkdv=858 _dq=517 _prep=30 _reduc=24\n{\"SPLIT\": 2} s2: _dkdv=822 _dq=518 _prep=30 _reduc=11","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01KDv4kePF9Avq1gcWCm6Kir","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"system","subtype":"task_notification","task_id":"bs892152d","run_id":"0mvdws5y4-b2159f46","tool_use_id":"toolu_01W2gHhiksMv5FUhGsXCGi7J","status":"completed","output_file":"","summary":"Test G=6 and G=16 cases and static check","uuid":"1f801a47-ad23-4033-934c-abdf50cdd813","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01W2gHhiksMv5FUhGsXCGi7J","type":"tool_result","content":"{'n': 2, 'L': 300, 'Hq': 6, 'Hkv': 1, 'window': -1, 'ragged': True} ok\n{'n': 1, 'L': 513, 'Hq': 16, 'Hkv': 1, 'window': 100} ok\n(True, 'ok')\n8180 sol_v7.py","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"c4034ed3-df08-479e-ba09-79b5042b619b","timestamp":"2026-10-18T14:16:35.175Z","tool_use_result":{"stdout":"{'n': 2, 'L': 300, 'Hq': 6, 'Hkv': 1, 'window': -1, 'ragged': True} ok\n{'n': 1, 'L': 513, 'Hq': 16, 'Hkv': 1, 'window': 100} ok\n(True, 'ok')\n8180 sol_v7.py","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01W2gHhiksMv5FUhGsXCGi7J","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SoYxZJxhqqeqE3edbVBeap","type":"tool_result","content":"Gemma-3-12B-style global layer (16 query / 8 KV heads): correct, 2586.85 us, starting kernel 20825.34 us\nGemma-3-12B-style local layer: correct, 848.61 us, starting kernel 5680.70 us\nQwen3-Next-style full-attention layer (16 query / 2 KV heads): correct, 1414.82 us, starting kernel 11423.30 us\npacked documents (16 / 8 heads): 8 documents of 1K-3K tokens: correct, 1963.39 us, starting kernel 11159.42 us\nGemma-3-4B-style global layer (8 query / 4 KV heads): correct, 454.14 us, starting kernel 3026.72 us\nGemma-3-4B-style local layer (8 / 4 heads): correct, 839.97 us, starting kernel 5168.35 us\nautosave: kept this version in /workspace/.best/solution.py (6.829x the starting kernel on the public shapes)","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"80defcd6-8429-483e-be4c-c936cd37e933","timestamp":"2026-10-18T14:16:49.507Z","tool_use_result":{"stdout":"Gemma-3-12B-style global layer (16 query / 8 KV heads): correct, 2586.85 us, starting kernel 20825.34 us\nGemma-3-12B-style local layer: correct, 848.61 us, starting kernel 5680.70 us\nQwen3-Next-style full-attention layer (16 query / 2 KV heads): correct, 1414.82 us, starting kernel 11423.30 us\npacked documents (16 / 8 heads): 8 documents of 1K-3K tokens: correct, 1963.39 us, starting kernel 11159.42 us\nGemma-3-4B-style global layer (8 query / 4 KV heads): correct, 454.14 us, starting kernel 3026.72 us\nGemma-3-4B-style local layer (8 / 4 heads): correct, 839.97 us, starting kernel 5168.35 us\nautosave: kept this version in /workspace/.best/solution.py (6.829x the starting kernel on the public shapes)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01SoYxZJxhqqeqE3edbVBeap","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9qyKZyMcb89boojCFW2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xjc8TjvdHL73rP4G7JZ6Ly","name":"Bash","input":{"command":"cd /workspace; cat > /tmp/robust.py <<'EOF'\nimport sys, importlib.util, torch\nsys.path.insert(0, '/workspace')\nimport task as T, task_api\nspec = importlib.util.spec_from_file_location('sol', '/workspace/solution.py'); m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m)\norig = task_api.randn\ndef mk(scale_fn):\n    def r(shape, g, device, dist='normal', scale=1.0):\n        x = orig(shape, g, device, dist, scale)\n        return scale_fn(x, g)\n    return r\ndists = {\n 'big': lambda x, g: x * 300.0,\n 'small': lambda x, g: x * 1e-3,\n 'outlier': lambda x, g: torch.where(torch.rand(x.shape, generator=g, device=x.device) < 0.01, x * 80, x),\n 'rowscale': lambda x, g: x * (10.0 ** torch.randint(-3, 3, (x.shape[0], 1), generator=g, device=x.device).float()),\n 'zero_rows': lambda x, g: x * (torch.rand((x.shape[0], 1), generator=g, device=x.device) > 0.2).float(),\n}\nshapes = [T.TASK.shapes[i] for i in (1, 3)] + [dict(n=3, L=300, Hq=8, Hkv=2, window=-1, ragged=True)]\nfor name, fn in dists.items():\n    T.randn = mk(fn)\n    for sh in shapes:\n        c = T.make_case(sh, 7, 'cuda')\n        exp = T.reference([a.clone() if torch.is_tensor(a) else a for a in c.args])\n        for i in (9, 10, 11): c.args[i].fill_(float('nan'))\n        m.kernel_fn(*c.args); torch.cuda.synchronize()\n        ok, st = T.check({i: c.args[i] for i in (9, 10, 11)}, exp)\n        print(name, {k: sh[k] for k in ('n', 'L', 'Hq', 'Hkv', 'window')}, 'ok' if ok else f'WRONG {st}', flush=True)\nEOF\ngrep -n \"from task_api import\\|randn(\" task.py | head -5; timeout 900 python /tmp/robust.py 2>&1 | grep -v USDT","description":"Test hidden-like input distributions"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":548,"cache_read_input_tokens":165414,"cache_creation":{"ephemeral_5m_input_tokens":548,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"d04983c7-29df-4d5b-a1f2-6a67f7fb844c","timestamp":"2026-10-18T14:17:21.427Z","request_id":"req_011Cg9qyKBuNQY9q8EZFHrMq"}
{"type":"system","subtype":"task_started","task_id":"bmgyaa5mo","run_id":"0mvdwtg4a-858b17a9","tool_use_id":"toolu_01Xjc8TjvdHL73rP4G7JZ6Ly","description":"Test hidden-like input distributions","is_backgrounded":false,"task_type":"local_bash","uuid":"1ecf4ff0-2fac-4c6d-a9f9-ed838ace5e3b","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Xjc8TjvdHL73rP4G7JZ6Ly","type":"tool_result","content":"20:from task_api import Case, Task\n110:    from task_api import gen, randn\n118:    q = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n119:    k = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n120:    v = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\nbig {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nbig {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nbig {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nsmall {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nsmall {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nsmall {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\noutlier {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\noutlier {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\noutlier {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nrowscale {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nrowscale {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nrowscale {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nzero_rows {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nzero_rows {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nzero_rows {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"96aa8f86-ac11-4f58-8ad8-c7c3e73c0a07","timestamp":"2026-10-18T14:17:28.878Z","tool_use_result":{"stdout":"20:from task_api import Case, Task\n110:    from task_api import gen, randn\n118:    q = randn((T, Hq * D), g, device, dist).view(T, Hq, D).to(bf)\n119:    k = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\n120:    v = randn((T, Hkv * D), g, device, dist).view(T, Hkv, D).to(bf)\nbig {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nbig {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nbig {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nsmall {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nsmall {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nsmall {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\noutlier {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\noutlier {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\noutlier {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nrowscale {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nrowscale {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nrowscale {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nzero_rows {'n': 1, 'L': 8192, 'Hq': 16, 'Hkv': 8, 'window': 1023} ok\nzero_rows {'n': 8, 'L': 2048, 'Hq': 16, 'Hkv': 8, 'window': -1} ok\nzero_rows {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01Xjc8TjvdHL73rP4G7JZ6Ly","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01R1aCfmrHjp13FwbekBaVLU","type":"tool_result","content":"big {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nbig {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nsmall {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nsmall {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\noutlier {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\noutlier {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nrowscale {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nrowscale {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nzero_rows {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nzero_rows {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"e50498ce-4c64-493b-8689-080c1cd36ab4","timestamp":"2026-10-18T14:17:38.225Z","tool_use_result":{"stdout":"big {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nbig {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nsmall {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nsmall {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\noutlier {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\noutlier {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nrowscale {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nrowscale {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok\nzero_rows {'n': 4, 'L': 2048, 'Hq': 8, 'Hkv': 4, 'window': -1} ok\nzero_rows {'n': 3, 'L': 300, 'Hq': 8, 'Hkv': 2, 'window': -1} ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01R1aCfmrHjp13FwbekBaVLU","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9r2ut4Any2Ftt6Rzgcc","type":"message","role":"assistant","content":[{"type":"text","text":"All hidden-style distributions pass (×300, ×1e-3, 1% outliers ×80, per-row scales spanning 5 orders of magnitude, zero rows). I'll save the hardware/compiler findings to memory, then wrap up."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":905,"cache_read_input_tokens":170397,"cache_creation":{"ephemeral_5m_input_tokens":905,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"ddc3592b-be43-4e27-9940-efb41570baf8","timestamp":"2026-10-18T14:17:42.277Z","request_id":"req_011Cg9r2uVkNyRk9uB5DVcEo"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9r2ut4Any2Ftt6Rzgcc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U22ra5xpMFJFL9D2DwDx8f","name":"Write","input":{"file_path":"/logs/agent/sessions/projects/-workspace/memory/triton-b300-attn-bwd-findings.md","content":"---\nname: triton-b300-attn-bwd-findings\ndescription: Measured Triton 3.7.1 / B300 (sm_103) limits for hd256 attention-backward kernels: MMA rates by tile, warp-spec quirks, compile failures\nmetadata:\n  type: project\n---\nFindings from the hd256 attention-backward task (2026-10-18), Triton 3.7.1, torch 2.13, B300:\n\n- tcgen05 via tl.dot, SMEM operands, measured: M=128,N=256 ~2150 TFLOPS; any M=64 tile caps at ~1145 (half rate);\n  M=128,N=32,K=256 ~916 and M=64,N=32 ~759 (SMEM-bandwidth bound: A re-read per small-N MMA).\n- tl.range(warp_specialize=True) was the biggest win (dkdv 2123->1616 us, dq 1300->943 us). Keep the LAUNCH\n  num_stages at the default 3: num_stages=2 at launch made WS kernels ~1.6x slower.\n- hd256 dK+dV fp32 for 128 keys = 512 TMEM cols, so a 1-CTA dkdv kernel must use BN=64 (M=64 MMAs).\n- Fused dkdv + atomic dQ (dS^T as A operand / tl.trans) hits \"PassManager::run failed\" (TritonGPUPipeline) with\n  WS + stages>=2, and TMEM/SMEM overflow otherwise. The query-major dkdv (tl.trans(p) as A) also fails under WS.\n- TMA descriptor loads of fp32 [1, BM] lse/delta vectors at unaligned token offsets -> \"illegal instruction\"\n  on ragged batches. Use plain pointer loads with padded buffers instead.\n- Final design: prep(delta, lse*log2e) + dkdv(BN=64 keys, BM=32 q, 8 warps, WS) + dq(BM=128, BN=32, 8 warps,\n  WS), and a 2-way query-head split with fp32 partials when G>=4. Result ~0.62x of FA4 expert.\n"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":905,"cache_read_input_tokens":170397,"cache_creation":{"ephemeral_5m_input_tokens":905,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"066d0136-8f98-4a4b-932b-de3a60e6bd81","timestamp":"2026-10-18T14:17:49.583Z","request_id":"req_011Cg9r2uVkNyRk9uB5DVcEo"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U22ra5xpMFJFL9D2DwDx8f","type":"tool_result","content":"File created successfully at: /logs/agent/sessions/projects/-workspace/memory/triton-b300-attn-bwd-findings.md"}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"89b53cc3-7bd2-4734-b4d9-347fca905c5d","timestamp":"2026-10-18T14:17:49.613Z","tool_use_result":{"type":"create","filePath":"/logs/agent/sessions/projects/-workspace/memory/triton-b300-attn-bwd-findings.md","content":"---\nname: triton-b300-attn-bwd-findings\ndescription: \"Measured Triton 3.7.1 / B300 (sm_103) limits for hd256 attention-backward kernels: MMA rates by tile, warp-spec quirks, compile failures\"\nmetadata:\n  node_type: memory\n  type: project\n  originSessionId: 711e70e5-71bc-4971-97a3-904cce35aad5\n  modified: 2026-10-18T14:17:49.592Z\n---\n\nFindings from the hd256 attention-backward task (2026-10-18), Triton 3.7.1, torch 2.13, B300:\n\n- tcgen05 via tl.dot, SMEM operands, measured: M=128,N=256 ~2150 TFLOPS; any M=64 tile caps at ~1145 (half rate);\n  M=128,N=32,K=256 ~916 and M=64,N=32 ~759 (SMEM-bandwidth bound: A re-read per small-N MMA).\n- tl.range(warp_specialize=True) was the biggest win (dkdv 2123->1616 us, dq 1300->943 us). Keep the LAUNCH\n  num_stages at the default 3: num_stages=2 at launch made WS kernels ~1.6x slower.\n- hd256 dK+dV fp32 for 128 keys = 512 TMEM cols, so a 1-CTA dkdv kernel must use BN=64 (M=64 MMAs).\n- Fused dkdv + atomic dQ (dS^T as A operand / tl.trans) hits \"PassManager::run failed\" (TritonGPUPipeline) with\n  WS + stages>=2, and TMEM/SMEM overflow otherwise. The query-major dkdv (tl.trans(p) as A) also fails under WS.\n- TMA descriptor loads of fp32 [1, BM] lse/delta vectors at unaligned token offsets -> \"illegal instruction\"\n  on ragged batches. Use plain pointer loads with padded buffers instead.\n- Final design: prep(delta, lse*log2e) + dkdv(BN=64 keys, BM=32 q, 8 warps, WS) + dq(BM=128, BN=32, 8 warps,\n  WS), and a 2-way query-head split with fp32 partials when G>=4. Result ~0.62x of FA4 expert.\n","structuredPatch":[],"originalFile":null,"userModified":false,"memdirStamped":true},"tool_result_meta":[{"id":"toolu_01U22ra5xpMFJFL9D2DwDx8f","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9r2ut4Any2Ftt6Rzgcc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015EMFPCCKW2KLksBd62omew","name":"Bash","input":{"command":"cd /workspace; echo \"- [Triton B300 attn-bwd findings](triton-b300-attn-bwd-findings.md) — MMA rates by tile, WS quirks, compile failures for hd256 bwd\" >> /logs/agent/sessions/projects/-workspace/memory/MEMORY.md; cmp solution.py sol_v7.py && echo \"solution.py == v7\"; python -c \"\nimport static_check; print(static_check.check(open('/workspace/solution.py').read()))\"; grep -n \"^def kernel_fn\" solution.py","description":"Update memory index and verify final solution"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":905,"cache_read_input_tokens":170397,"cache_creation":{"ephemeral_5m_input_tokens":905,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"58bc8ec1-b69d-452d-97d0-d858b24909a9","timestamp":"2026-10-18T14:17:51.316Z","request_id":"req_011Cg9r2uVkNyRk9uB5DVcEo"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015EMFPCCKW2KLksBd62omew","type":"tool_result","content":"solution.py == v7\n(True, 'ok')\n167:def kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):","is_error":false}]},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"998306ca-49ef-42a6-9f29-094a3d557e03","timestamp":"2026-10-18T14:17:51.431Z","tool_use_result":{"stdout":"solution.py == v7\n(True, 'ok')\n167:def kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_015EMFPCCKW2KLksBd62omew","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9r3tPo25WAENKhM9pDp","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1624,"cache_read_input_tokens":171302,"cache_creation":{"ephemeral_5m_input_tokens":1624,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"dae60397-7db9-418a-aade-410965c9f768","timestamp":"2026-10-18T14:18:00.445Z","request_id":"req_011Cg9r3swWJrugnc27NCeJS","thinking_duration_ms":7814,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9r3tPo25WAENKhM9pDp","type":"message","role":"assistant","content":[{"type":"text","text":"`/workspace/solution.py` is correct on every shape I tested, but it does not reach the bar: on the public shapes it runs at about **0.62x the expert's speed** (geometric mean). Passing needs at least 1.0x. The starting kernel was about 0.08x, so this is 6.8x faster than that. With reward = log2(speedup), that is roughly −0.69, which beats the −1 for an incorrect answer.\n\n| Shape | Mine (µs) | Expert (µs, from task notes) | Ratio |\n|---|---|---|---|\n| 1×8K, 16/8 heads, global | 2587 | 1518 | 0.59 |\n| 1×8K, sliding window 1024 | 849 | 510 | 0.60 |\n| 2×4K, 16/2 heads | 1415 | 980 | 0.69 |\n| 8 packed docs, 16K tokens | 1963 | ~1165 | 0.59 |\n| 4×2K, 8/4 heads | 454 | 302 | 0.67 |\n| packed 16K, window 1024 | 840 | 520 | 0.62 |\n\nThe expert kernel isn't installed here, so the ratios use the expert times from `task.py`, not a same-run comparison.\n\n**Design:**\n- **Preprocess:** one small kernel computes delta (the row sum of dO·o) and pre-scales lse.\n- **dK/dV kernel:** one program per 64-key block and KV head loops over all of that head's query heads, so there are no atomics across heads. It uses warp specialization and 8 warps.\n- **dQ kernel:** one program per 128-query block, using warp specialization.\n- **Large GQA groups:** when 4 or more query heads share a KV head, the dK/dV work is split across two programs and their fp32 partial sums are added by a small reduce kernel.\n- **Loads and masks:** tiles load through TMA, the causal/window/sequence mask is applied only on edge blocks, and work starts with the heaviest blocks.\n\n**Why it stops at ~0.6x:**\n- **Half-rate tiles:** measured on this B300, Triton's 64-row tensor-core instructions run at half rate (~1145 vs ~2150 TFLOPS). For head dim 256, fp32 dK and dV for 128 keys fill all of the tensor memory, so a single-CTA dK/dV kernel is stuck with 64-row tiles. The expert gets around this with paired-CTA instructions, which plain Triton doesn't expose to me.\n- **dQ kernel:** it is limited by shared-memory bandwidth with its 32-key tiles, and shared-memory capacity rules out larger ones.\n- **Extra recompute:** two kernels mean 7 matmuls instead of the minimum 5. I tried fusing dQ into the dK/dV kernel with fp32 atomic adds, but Triton either failed to compile it or ran out of tensor/shared memory in every useful configuration.\n\n**Checks:**\n- `check.py` passes on all six timed shapes.\n- My own edge shapes pass: sequence lengths of 1 and 7, odd sizes, windows of 0 and 5, group sizes of 3, 6 and 16, and an extra long sequence.\n- Scaled inputs pass: ×300, ×1e-3, 1% outliers at ×80, per-row scales spanning 5 orders of magnitude, and all-zero rows.\n- I fixed one real bug on the way: TMA loads of lse/delta at unaligned sequence starts caused an illegal-instruction crash on small packed batches, so those now use ordinary loads.\n\nThe file passes the static check and is about 8 KB. The same version is saved in `/workspace/.best/`. I also saved the measured hardware and compiler limits to memory for future runs."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1624,"cache_read_input_tokens":171302,"cache_creation":{"ephemeral_5m_input_tokens":1624,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","uuid":"7b6f6e18-624b-42fd-8894-246339195120","timestamp":"2026-10-18T14:18:03.861Z","request_id":"req_011Cg9r3swWJrugnc27NCeJS"}
{"duration_api_ms":929223,"stop_reason":"end_turn","session_id":"711e70e5-71bc-4971-97a3-904cce35aad5","total_cost_usd":4.089822200000001,"usage":{"input_tokens":108,"cache_creation_input_tokens":160870,"cache_read_input_tokens":6022701,"output_tokens":104025,"output_tokens_details":{"thinking_tokens":66960},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":160870},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"modelUsage":{"claude-opus-5-5":{"inputTokens":108,"outputTokens":104025,"cacheReadInputTokens":6022701,"cacheCreationInputTokens":160870,"webSearchRequests":0,"costUSD":4.089822200000001,"contextWindow":1000000,"maxOutputTokens":128000,"thinkingTokens":66960,"canonicalModel":"claude-opus-5-5","provider":"firstParty","costBasis":"list"}},"permission_denials":[],"terminal_reason":"completed","fast_mode_state":"off","fast_mode_disabled_reason":"sdk_opt_in_required","subagent_stats":{"spawned":0,"requested":{"background":0,"foreground":0,"unset":0},"started_in_background":0,"max_depth":0,"spawned_by_subagents":0,"completed":0,"failed":0,"killed":{"parent":0,"user":0,"system":0},"refused":{"depth_limit":0,"concurrency_limit":0,"budget":0},"by_type":{}},"safety_stops":0,"is_error":false,"num_turns":57,"subtype":"success","api_error_status":null,"result":"`/workspace/solution.py` is correct on every shape I tested, but it does not reach the bar: on the public shapes it runs at about **0.62x the expert's speed** (geometric mean). Passing needs at least 1.0x. The starting kernel was about 0.08x, so this is 6.8x faster than that. With reward = log2(speedup), that is roughly −0.69, which beats the −1 for an incorrect answer.\n\n| Shape | Mine (µs) | Expert (µs, from task notes) | Ratio |\n|---|---|---|---|\n| 1×8K, 16/8 heads, global | 2587 | 1518 | 0.59 |\n| 1×8K, sliding window 1024 | 849 | 510 | 0.60 |\n| 2×4K, 16/2 heads | 1415 | 980 | 0.69 |\n| 8 packed docs, 16K tokens | 1963 | ~1165 | 0.59 |\n| 4×2K, 8/4 heads | 454 | 302 | 0.67 |\n| packed 16K, window 1024 | 840 | 520 | 0.62 |\n\nThe expert kernel isn't installed here, so the ratios use the expert times from `task.py`, not a same-run comparison.\n\n**Design:**\n- **Preprocess:** one small kernel computes delta (the row sum of dO·o) and pre-scales lse.\n- **dK/dV kernel:** one program per 64-key block and KV head loops over all of that head's query heads, so there are no atomics across heads. It uses warp specialization and 8 warps.\n- **dQ kernel:** one program per 128-query block, using warp specialization.\n- **Large GQA groups:** when 4 or more query heads share a KV head, the dK/dV work is split across two programs and their fp32 partial sums are added by a small reduce kernel.\n- **Loads and masks:** tiles load through TMA, the causal/window/sequence mask is applied only on edge blocks, and work starts with the heaviest blocks.\n\n**Why it stops at ~0.6x:**\n- **Half-rate tiles:** measured on this B300, Triton's 64-row tensor-core instructions run at half rate (~1145 vs ~2150 TFLOPS). For head dim 256, fp32 dK and dV for 128 keys fill all of the tensor memory, so a single-CTA dK/dV kernel is stuck with 64-row tiles. The expert gets around this with paired-CTA instructions, which plain Triton doesn't expose to me.\n- **dQ kernel:** it is limited by shared-memory bandwidth with its 32-key tiles, and shared-memory capacity rules out larger ones.\n- **Extra recompute:** two kernels mean 7 matmuls instead of the minimum 5. I tried fusing dQ into the dK/dV kernel with fp32 atomic adds, but Triton either failed to compile it or ran out of tensor/shared memory in every useful configuration.\n\n**Checks:**\n- `check.py` passes on all six timed shapes.\n- My own edge shapes pass: sequence lengths of 1 and 7, odd sizes, windows of 0 and 5, group sizes of 3, 6 and 16, and an extra long sequence.\n- Scaled inputs pass: ×300, ×1e-3, 1% outliers at ×80, per-row scales spanning 5 orders of magnitude, and all-zero rows.\n- I fixed one real bug on the way: TMA loads of lse/delta at unaligned sequence starts caused an illegal-instruction crash on small packed batches, so those now use ordinary loads.\n\nThe file passes the static check and is about 8 KB. The same version is saved in `/workspace/.best/`. I also saved the measured hardware and compiler limits to memory for future runs.","ttft_ms":1729,"type":"result","duration_ms":1688314,"uuid":"f206a87f-086d-461d-b4ed-d7dc6a82cca4","ttft_stream_ms":1003,"time_to_request_ms":75,"first_content_frame_ms":1003,"queued_turn_count":0,"result_index":0}
