{"type":"system","subtype":"init","cwd":"/workspace","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","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":"d647bfcb-8692-4e1d-8116-a98abfbcf13c","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_011Cg9ePVWnsNRyuegpGJeux","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":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"bcb0b338-46b3-4fe8-bbcc-553c7ba701b6","timestamp":"2026-10-18T11:44:57.038Z","request_id":"req_011Cg9ePVAi3vgCS97QJqhiJ","thinking_duration_ms":728,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9ePVWnsNRyuegpGJeux","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JovnWYzXU8J77ZNtjX4vZR","name":"Bash","input":{"command":"ls -la && cat task.py && cat starting_kernel.py && cat check.py && cat static_check.py","description":"List workspace and show task 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":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"ba5596a9-19b2-47ca-a3b8-d19662fc4a2a","timestamp":"2026-10-18T11:44:57.609Z","request_id":"req_011Cg9ePVAi3vgCS97QJqhiJ"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JovnWYzXU8J77ZNtjX4vZR","type":"tool_result","content":"<persisted-output>\nOutput too large (40.8KB). Full output saved to: /logs/agent/sessions/projects/-workspace/bdfe9e1b-6798-43cd-88fc-c22443aad0dd/tool-results/bay0jpbyu.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 11:44 ..\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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"53be64e2-1348-40db-84a0-ce8360619ec3","timestamp":"2026-10-18T11:44:57.729Z","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 11:44 ..\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/bdfe9e1b-6798-43cd-88fc-c22443aad0dd/tool-results/bay0jpbyu.txt","persistedOutputSize":41729},"tool_result_meta":[{"id":"toolu_01JovnWYzXU8J77ZNtjX4vZR","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9ePVWnsNRyuegpGJeux","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Lz1bWgDnqxF3GZMYRrUff4","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 dir, 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":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"f0d42077-b9dc-4867-ab89-bf27e93392ba","timestamp":"2026-10-18T11:44:59.072Z","request_id":"req_011Cg9ePVAi3vgCS97QJqhiJ"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Lz1bWgDnqxF3GZMYRrUff4","type":"tool_result","content":"Sun Oct 18 11:44:59 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  |   00000000:85:00.0 Off |                    0 |\n| N/A   37C    P0            186W / 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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"fc5bccec-bc75-4441-92c5-a762dd624c53","timestamp":"2026-10-18T11:45:03.702Z","tool_use_result":{"stdout":"Sun Oct 18 11:44:59 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  |   00000000:85:00.0 Off |                    0 |\n| N/A   37C    P0            186W / 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_01Lz1bWgDnqxF3GZMYRrUff4","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KNfm3wRaTeGrsuGQvfMXKK","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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"dcf5113e-b27f-4084-b8fb-706a3cb2dd7c","timestamp":"2026-10-18T11:45:07.261Z","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_01KNfm3wRaTeGrsuGQvfMXKK","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JXKnwpKPCbU6Fo4hnRjKxh","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())\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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"1847efea-01a4-4d32-af12-e7bd4d602a85","timestamp":"2026-10-18T11:45:08.964Z","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())\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_01JXKnwpKPCbU6Fo4hnRjKxh","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9eQUrsUywNzaQNkVGpq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YP1LDxeTm7HXDE8518xGfi","name":"Bash","input":{"command":"cat /workspace/static_check.py; cat /workspace/nodus_autosave.py | head -50","description":"Show static check and autosave"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6768,"cache_read_input_tokens":30407,"cache_creation":{"ephemeral_5m_input_tokens":6768,"ephemeral_1h_input_tokens":0},"output_tokens":16,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"88a1e878-a905-4eed-983c-f03010edae19","timestamp":"2026-10-18T11:45:10.848Z","request_id":"req_011Cg9eQUUoX9dJzKpDCwdCb"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YP1LDxeTm7HXDE8518xGfi","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                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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"1901e644-1d73-46d6-a93c-b95b2fcc5737","timestamp":"2026-10-18T11:45:10.894Z","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                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_01YP1LDxeTm7HXDE8518xGfi","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"b64d4687-ba0a-465c-a332-c5ec49728d01"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":200,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"6ce8787d-869f-41b1-bf10-bb6bce4a6944"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":400,"estimated_tokens_delta":200,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"bfc33883-08e9-43c1-b9d2-3bcf39a7d162"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":550,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"e803c315-e8d6-4f1a-89a5-06b21d96ce57"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":650,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"08c19380-191b-4e50-a959-84a8cf03a1d1"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":800,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"4b7e1808-fc63-4cd7-8699-2d92e70bc6db"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":900,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"c46d9e23-b2f2-4c9e-9e6f-c666a5be543f"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1050,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"c7b698a9-437b-4eb9-b4b0-5f0e071525a0"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1150,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"72600af3-1000-4747-91c6-978458af335b"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1300,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"0a0f2016-d774-4050-a749-d5dd7bef0616"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1450,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"033c3cf0-754b-495a-9f41-e81d823367c0"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1550,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"00e6f70b-1494-4080-aa07-d3093b112c94"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1700,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"f43f3209-4240-4ea3-b52e-2714706d06a0"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":1850,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"75b228c6-ece7-4ec6-b1de-1ed4c488891e"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2000,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"b765ee8f-e5c3-401b-b347-8f804c78f81e"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2050,"estimated_tokens_delta":50,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"84bec273-6683-48b5-9e33-b74783e1b48e"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2200,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"8e2cca51-a06a-4adb-ae86-3b835d714185"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2350,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"5ba696eb-6d3a-45df-862f-1b2915b394ac"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2450,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"fc86f1ba-5e73-4d29-ba7f-7dcdf8f76ce0"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2550,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"e920d811-d3b1-4253-b5d0-b75781939816"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2650,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"9203fdb9-d9dc-454b-88e9-fea1d6f545e2"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":2800,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"97acbcb2-50bb-4c5b-bb08-efc86af5be3c"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9eQd4zqFVXisGYVYHdW","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":13105,"cache_read_input_tokens":43742,"cache_creation":{"ephemeral_5m_input_tokens":13105,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"3903bab6-cfb5-4bd0-b356-995b122efae0","timestamp":"2026-10-18T11:50:07.603Z","request_id":"req_011Cg9eYxMaTPyY9Ko66Am9w","thinking_duration_ms":181539,"thinking_display":"updates"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9eYxiA3zgM2dSasPt2F","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HBkDbNZanC1vWJdQH4qxa1","name":"Bash","input":{"command":"cd /usr/local/lib/python3.12/dist-packages/triton; grep -rn \"two_ctas\\|cta_group\\|ctas_per_cga\\|num_ctas\" backends/nvidia/compiler.py | head -20; grep -rn \"def dot(\" -A 30 language/core.py | grep -n \"param\\|def dot\" | head -20","description":"Check Triton 2-CTA and dot options"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":13105,"cache_read_input_tokens":43742,"cache_creation":{"ephemeral_5m_input_tokens":13105,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"ddece394-a95c-4dfd-9ec4-f98289f8efaf","timestamp":"2026-10-18T11:50:08.800Z","request_id":"req_011Cg9eYxMaTPyY9Ko66Am9w"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HBkDbNZanC1vWJdQH4qxa1","type":"tool_result","content":"backends/nvidia/compiler.py:108:    num_ctas: int = 1\nbackends/nvidia/compiler.py:186:        if args.get(\"num_ctas\", 1) > 1 and capability < 90:\nbackends/nvidia/compiler.py:187:            raise ValueError((f\"num_ctas > 1 requires NVIDIA SM90+ (Hopper). \"\nbackends/nvidia/compiler.py:189:                              f\"Please set num_ctas=1 or target an SM90+ GPU.\"))\nbackends/nvidia/compiler.py:211:            metadata.num_ctas,\nbackends/nvidia/compiler.py:260:        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n1:language/core.py:2028:def dot(input, other, acc=None, input_precision=None, allow_tf32=None, max_num_imprecise_acc=None, out_dtype=float32,\n15:language/core.py-2042-    :param input: The first tensor to be multiplied.\n17:language/core.py-2044-    :param other: The second tensor to be multiplied.\n19:language/core.py-2046-    :param acc: The accumulator tensor. If not None, the result is added to this tensor.\n21:language/core.py-2048-    :param input_precision: How to exercise the Tensor Cores for f32 x f32. If\n26:language/core.py-2053-    :param allow_tf32: *Deprecated.* If true, input_precision is set to \"tf32\".\nShell cwd was reset to /workspace","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"55f59c0e-bc7d-4ea6-8dcf-7cd39ef0e2e0","timestamp":"2026-10-18T11:50:08.884Z","tool_use_result":{"stdout":"backends/nvidia/compiler.py:108:    num_ctas: int = 1\nbackends/nvidia/compiler.py:186:        if args.get(\"num_ctas\", 1) > 1 and capability < 90:\nbackends/nvidia/compiler.py:187:            raise ValueError((f\"num_ctas > 1 requires NVIDIA SM90+ (Hopper). \"\nbackends/nvidia/compiler.py:189:                              f\"Please set num_ctas=1 or target an SM90+ GPU.\"))\nbackends/nvidia/compiler.py:211:            metadata.num_ctas,\nbackends/nvidia/compiler.py:260:        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n1:language/core.py:2028:def dot(input, other, acc=None, input_precision=None, allow_tf32=None, max_num_imprecise_acc=None, out_dtype=float32,\n15:language/core.py-2042-    :param input: The first tensor to be multiplied.\n17:language/core.py-2044-    :param other: The second tensor to be multiplied.\n19:language/core.py-2046-    :param acc: The accumulator tensor. If not None, the result is added to this tensor.\n21:language/core.py-2048-    :param input_precision: How to exercise the Tensor Cores for f32 x f32. If\n26:language/core.py-2053-    :param allow_tf32: *Deprecated.* If true, input_precision is set to \"tf32\".","stderr":"\nShell cwd was reset to /workspace","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01HBkDbNZanC1vWJdQH4qxa1","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9enbEybse3umuLWWxc2","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":20182,"cache_read_input_tokens":56847,"cache_creation":{"ephemeral_5m_input_tokens":20182,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"5e3b9ca4-f8ab-482e-8217-45fd3aba3a52","timestamp":"2026-10-18T11:50:10.666Z","request_id":"req_011Cg9enakwtwLte73C8HSRE","thinking_duration_ms":512,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9enbEybse3umuLWWxc2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012hx1R4aPHEYYEj8u92LK4H","name":"Bash","input":{"command":"cd /usr/local/lib/python3.12/dist-packages/triton; sed -n 240,340p backends/nvidia/compiler.py","description":"Show Triton NVIDIA TTGIR pass pipeline"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":20182,"cache_read_input_tokens":56847,"cache_creation":{"ephemeral_5m_input_tokens":20182,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"5f10c966-a766-4d33-a9a1-aca2bc57737e","timestamp":"2026-10-18T11:50:11.119Z","request_id":"req_011Cg9enakwtwLte73C8HSRE"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012hx1R4aPHEYYEj8u92LK4H","type":"tool_result","content":"        if capability // 10 < 9:\n            passes.ttir.add_rewrite_tensor_descriptor_to_pointer(pm)\n        passes.common.add_canonicalizer(pm)\n        passes.ttir.add_combine(pm)\n        passes.ttir.add_reorder_broadcast(pm)\n        passes.common.add_cse(pm)\n        passes.common.add_symbol_dce(pm)\n        passes.ttir.add_loop_unroll(pm)\n        pm.run(mod, 'make_ttir')\n        return mod\n\n    @staticmethod\n    def make_ttgir(mod, metadata, opt, capability):\n        # Set maxnreg on all kernels, if it was provided.\n        if opt.maxnreg is not None:\n            mod.set_attr(\"ttg.maxnreg\", ir.builder(mod.context).get_int32_attr(opt.maxnreg))\n\n        pm = ir.pass_manager(mod.context)\n        dump_enabled = pm.enable_debug()\n        emuTF32 = (capability // 10 >= 8)\n        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n        # optimize TTGIR\n        passes.ttgpuir.add_coalesce(pm)\n        passes.ttgpuir.add_f32_dot_tc(pm, emuTF32)\n        # TODO(Qingyi): Move PlanCTAPass to the front of CoalescePass\n        nvidia.passes.ttnvgpuir.add_plan_cta(pm)\n        passes.ttgpuir.add_remove_layout_conversions(pm)\n        passes.ttgpuir.add_optimize_thread_locality(pm)\n        passes.ttgpuir.add_accelerate_matmul(pm)\n        passes.ttgpuir.add_remove_layout_conversions(pm)\n        passes.ttgpuir.add_optimize_dot_operands(pm, capability >= 80)\n        nvidia.passes.ttnvgpuir.add_optimize_descriptor_encoding(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        if capability // 10 in [8, 9]:\n            passes.ttgpuir.add_fuse_nested_loops(pm)\n            passes.common.add_canonicalizer(pm)\n            passes.ttir.add_triton_licm(pm)\n            passes.common.add_canonicalizer(pm)\n            passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n            nvidia.passes.hopper.add_hopper_warpspec(pm, opt.num_stages, dump_enabled)\n            passes.ttgpuir.add_assign_latencies(pm, opt.num_stages)\n            passes.ttgpuir.add_schedule_loops(pm)\n            passes.ttgpuir.add_pipeline(pm, opt.num_stages, dump_enabled)\n        elif capability // 10 >= 10:\n            passes.ttgpuir.add_fuse_nested_loops(pm)\n            passes.common.add_canonicalizer(pm)\n            passes.ttir.add_triton_licm(pm)\n            passes.ttgpuir.add_optimize_accumulator_init(pm)\n            passes.ttgpuir.add_hoist_tmem_alloc(pm, False)\n            nvidia.passes.ttnvgpuir.add_promote_lhs_to_tmem(pm)\n            passes.ttgpuir.add_assign_latencies(pm, opt.num_stages)\n            passes.ttgpuir.add_schedule_loops(pm)\n            passes.ttgpuir.add_warp_specialize(pm, opt.num_stages)\n            passes.ttgpuir.add_pipeline(pm, opt.num_stages, dump_enabled)\n            passes.ttgpuir.add_optimize_partition_warps(pm)\n            passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n            # hoist again and allow hoisting out of if statements\n            passes.ttgpuir.add_hoist_tmem_alloc(pm, True)\n            nvidia.passes.ttnvgpuir.add_remove_tmem_tokens(pm)\n        else:\n            passes.ttir.add_triton_licm(pm)\n        passes.common.add_canonicalizer(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        passes.ttgpuir.add_prefetch(pm)\n        passes.ttgpuir.add_optimize_dot_operands(pm, capability >= 80)\n        passes.ttgpuir.add_coalesce_async_copy(pm)\n        nvidia.passes.ttnvgpuir.add_optimize_tmem_layouts(pm)\n        if capability // 10 >= 9:\n            nvidia.passes.ttnvgpuir.add_tma_lowering(pm)\n        passes.ttgpuir.add_remove_layout_conversions(pm)\n        nvidia.passes.ttnvgpuir.add_interleave_tmem(pm)\n        passes.ttgpuir.add_reduce_data_duplication(pm)\n        passes.ttgpuir.add_reorder_instructions(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        passes.common.add_symbol_dce(pm)\n        nvidia.passes.ttnvgpuir.add_fence_insertion(pm, capability)\n        nvidia.passes.ttnvgpuir.add_lower_mma(pm)\n        passes.common.add_sccp(pm)\n        passes.common.add_cse(pm)\n        passes.common.add_canonicalizer(pm)\n\n        pm.run(mod, 'make_ttgir')\n        metadata[\"tensordesc_meta\"] = mod.get_tensordesc_metadata()\n        return mod\n\n    def gluon_to_ttgir(self, src, metadata, options, capability):\n        mod = src\n        pm = ir.pass_manager(mod.context)\n        pm.enable_debug()\n\n        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')\nShell cwd was reset to /workspace","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"84113efc-3927-4a64-b8d1-540a991fe91d","timestamp":"2026-10-18T11:50:11.164Z","tool_use_result":{"stdout":"        if capability // 10 < 9:\n            passes.ttir.add_rewrite_tensor_descriptor_to_pointer(pm)\n        passes.common.add_canonicalizer(pm)\n        passes.ttir.add_combine(pm)\n        passes.ttir.add_reorder_broadcast(pm)\n        passes.common.add_cse(pm)\n        passes.common.add_symbol_dce(pm)\n        passes.ttir.add_loop_unroll(pm)\n        pm.run(mod, 'make_ttir')\n        return mod\n\n    @staticmethod\n    def make_ttgir(mod, metadata, opt, capability):\n        # Set maxnreg on all kernels, if it was provided.\n        if opt.maxnreg is not None:\n            mod.set_attr(\"ttg.maxnreg\", ir.builder(mod.context).get_int32_attr(opt.maxnreg))\n\n        pm = ir.pass_manager(mod.context)\n        dump_enabled = pm.enable_debug()\n        emuTF32 = (capability // 10 >= 8)\n        passes.ttir.add_convert_to_ttgpuir(pm, f\"cuda:{capability}\", opt.num_warps, 32, opt.num_ctas)\n        # optimize TTGIR\n        passes.ttgpuir.add_coalesce(pm)\n        passes.ttgpuir.add_f32_dot_tc(pm, emuTF32)\n        # TODO(Qingyi): Move PlanCTAPass to the front of CoalescePass\n        nvidia.passes.ttnvgpuir.add_plan_cta(pm)\n        passes.ttgpuir.add_remove_layout_conversions(pm)\n        passes.ttgpuir.add_optimize_thread_locality(pm)\n        passes.ttgpuir.add_accelerate_matmul(pm)\n        passes.ttgpuir.add_remove_layout_conversions(pm)\n        passes.ttgpuir.add_optimize_dot_operands(pm, capability >= 80)\n        nvidia.passes.ttnvgpuir.add_optimize_descriptor_encoding(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        if capability // 10 in [8, 9]:\n            passes.ttgpuir.add_fuse_nested_loops(pm)\n            passes.common.add_canonicalizer(pm)\n            passes.ttir.add_triton_licm(pm)\n            passes.common.add_canonicalizer(pm)\n            passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n            nvidia.passes.hopper.add_hopper_warpspec(pm, opt.num_stages, dump_enabled)\n            passes.ttgpuir.add_assign_latencies(pm, opt.num_stages)\n            passes.ttgpuir.add_schedule_loops(pm)\n            passes.ttgpuir.add_pipeline(pm, opt.num_stages, dump_enabled)\n        elif capability // 10 >= 10:\n            passes.ttgpuir.add_fuse_nested_loops(pm)\n            passes.common.add_canonicalizer(pm)\n            passes.ttir.add_triton_licm(pm)\n            passes.ttgpuir.add_optimize_accumulator_init(pm)\n            passes.ttgpuir.add_hoist_tmem_alloc(pm, False)\n            nvidia.passes.ttnvgpuir.add_promote_lhs_to_tmem(pm)\n            passes.ttgpuir.add_assign_latencies(pm, opt.num_stages)\n            passes.ttgpuir.add_schedule_loops(pm)\n            passes.ttgpuir.add_warp_specialize(pm, opt.num_stages)\n            passes.ttgpuir.add_pipeline(pm, opt.num_stages, dump_enabled)\n            passes.ttgpuir.add_optimize_partition_warps(pm)\n            passes.ttgpuir.add_combine_tensor_select_and_if(pm)\n            # hoist again and allow hoisting out of if statements\n            passes.ttgpuir.add_hoist_tmem_alloc(pm, True)\n            nvidia.passes.ttnvgpuir.add_remove_tmem_tokens(pm)\n        else:\n            passes.ttir.add_triton_licm(pm)\n        passes.common.add_canonicalizer(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        passes.ttgpuir.add_prefetch(pm)\n        passes.ttgpuir.add_optimize_dot_operands(pm, capability >= 80)\n        passes.ttgpuir.add_coalesce_async_copy(pm)\n        nvidia.passes.ttnvgpuir.add_optimize_tmem_layouts(pm)\n        if capability // 10 >= 9:\n            nvidia.passes.ttnvgpuir.add_tma_lowering(pm)\n        passes.ttgpuir.add_remove_layout_conversions(pm)\n        nvidia.passes.ttnvgpuir.add_interleave_tmem(pm)\n        passes.ttgpuir.add_reduce_data_duplication(pm)\n        passes.ttgpuir.add_reorder_instructions(pm)\n        passes.ttir.add_loop_aware_cse(pm)\n        passes.common.add_symbol_dce(pm)\n        nvidia.passes.ttnvgpuir.add_fence_insertion(pm, capability)\n        nvidia.passes.ttnvgpuir.add_lower_mma(pm)\n        passes.common.add_sccp(pm)\n        passes.common.add_cse(pm)\n        passes.common.add_canonicalizer(pm)\n\n        pm.run(mod, 'make_ttgir')\n        metadata[\"tensordesc_meta\"] = mod.get_tensordesc_metadata()\n        return mod\n\n    def gluon_to_ttgir(self, src, metadata, options, capability):\n        mod = src\n        pm = ir.pass_manager(mod.context)\n        pm.enable_debug()\n\n        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')","stderr":"\nShell cwd was reset to /workspace","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_012hx1R4aPHEYYEj8u92LK4H","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9enmcYz6QMPeDhPAHf1","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me write a benchmark harness that caches reference results, then the first kernel version."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2575,"cache_read_input_tokens":77029,"cache_creation":{"ephemeral_5m_input_tokens":2575,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"3707b784-922b-4f59-a8a1-110eb44944f6","timestamp":"2026-10-18T11:50:14.338Z","request_id":"req_011Cg9enm1LjW7cLvhZtYKC3"}
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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1648,"cache_read_input_tokens":79604,"cache_creation":{"ephemeral_5m_input_tokens":1648,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"c9dac56d-072b-4bfb-a449-e888504acc34","timestamp":"2026-10-18T11:51:04.629Z","request_id":"req_011Cg9eojFTXT8sY6CVSBAnQ","thinking_duration_ms":39014,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9eojc32y68fb1eZat61","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014KxcMzngrRMTMH2XT51Mf5","name":"Write","input":{"file_path":"/workspace/dev/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    pid = tl.program_id(0)\n    h = tl.program_id(1)\n    rt = pid * BT + 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 _seq_block(CU, n, p, B: tl.constexpr, NP: tl.constexpr, REV: tl.constexpr):\n    # program p -> (valid, s0, s1, b0): the p-th block of B tokens over all sequences\n    rs = tl.arange(0, NP)\n    ms = rs < n\n    st = tl.load(CU + rs, mask=ms, other=0)\n    en = tl.load(CU + rs + 1, mask=ms, other=0)\n    nb = (en - st + B - 1) // B\n    cum = tl.cumsum(nb, 0)\n    s = tl.sum((cum <= p).to(tl.int32), 0)\n    sel = rs == s\n    s0 = tl.sum(tl.where(sel, st, 0), 0)\n    s1 = tl.sum(tl.where(sel, en, 0), 0)\n    c1 = tl.sum(tl.where(sel, cum, 0), 0)\n    nbs = tl.sum(tl.where(sel, nb, 0), 0)\n    b = p - (c1 - nbs)\n    if REV:\n        b = nbs - 1 - b\n    return s < n, s0, s1, s0 + b * B\n\n\n@triton.jit\ndef _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, i0, h, T, s1, j0, qk_scale, W,\n               BM: tl.constexpr, BN: tl.constexpr, D: tl.constexpr, MASK: tl.constexpr):\n    q = q_desc.load([i0, h * D])\n    do = do_desc.load([i0, h * D])\n    ri = i0 + tl.arange(0, BM)\n    hoff = h.to(tl.int64) * T\n    lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0)\n    dlt = tl.load(DELTA + hoff + ri, mask=ri < T, other=0.0)\n    s = tl.dot(q, tl.trans(k))\n    p = tl.math.exp2(s * qk_scale - lse2[:, None])\n    if MASK:\n        rj = j0 + tl.arange(0, BN)\n        vis = (ri[:, None] >= rj[None, :]) & (ri[:, None] < s1) & ((ri[:, None] - rj[None, :] <= W) | (W < 0))\n        p = tl.where(vis, p, 0.0)\n    dp = tl.dot(do, tl.trans(v))\n    ds = p * (dp - dlt[:, None])\n    dvT = tl.dot(tl.trans(do), p.to(tl.bfloat16), dvT)\n    dkT = tl.dot(tl.trans(q), ds.to(tl.bfloat16), dkT)\n    return dkT, dvT\n\n\n@triton.jit\ndef _dkdv(Q, K, V, DO, LSE2, DELTA, DK, DV, CU, n, qk_scale, sm_scale, T, W,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          NP: tl.constexpr):\n    hk = tl.program_id(0)\n    valid, s0, s1, j0 = _seq_block(CU, n, tl.program_id(1), BN, NP, False)\n    if valid:\n        G: tl.constexpr = H // HKV\n        q_desc = tl.make_tensor_descriptor(Q, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        do_desc = tl.make_tensor_descriptor(DO, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        k_desc = tl.make_tensor_descriptor(K, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        v_desc = tl.make_tensor_descriptor(V, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        k = k_desc.load([j0, hk * D])\n        v = v_desc.load([j0, hk * D])\n        i_end = s1\n        if W >= 0:\n            i_end = tl.minimum(s1, j0 + BN + W)\n        nq = tl.cdiv(i_end - j0, BM)\n        ka = tl.minimum(tl.cdiv(BN - 1, BM), nq)\n        kb = (s1 - j0) // BM\n        if W >= 0:\n            kb = tl.minimum(kb, (W + 1) // BM)\n        kb = tl.maximum(tl.minimum(kb, nq), ka)\n        dkT = tl.zeros([D, BN], dtype=tl.float32)\n        dvT = tl.zeros([D, BN], dtype=tl.float32)\n        for g in range(G):\n            h = hk * G + g\n            for kk in range(0, ka):\n                dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, h, T, s1, j0,\n                                      qk_scale, W, BM, BN, D, True)\n            for kk in range(ka, kb):\n                dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, h, T, s1, j0,\n                                      qk_scale, W, BM, BN, D, False)\n            for kk in range(kb, nq):\n                dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, h, T, s1, j0,\n                                      qk_scale, W, BM, BN, D, True)\n        rj = j0 + tl.arange(0, BN)\n        rd = tl.arange(0, D)\n        off = (rj.to(tl.int64) * HKV + hk) * D\n        msk = (rj < s1)[None, :]\n        tl.store(DK + off[None, :] + rd[:, None], (dkT * sm_scale).to(tl.bfloat16), mask=msk)\n        tl.store(DV + off[None, :] + rd[:, None], dvT.to(tl.bfloat16), mask=msk)\n\n\n@triton.jit\ndef _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, j0, hk, s0, qk_scale, W,\n             BM: tl.constexpr, BN: tl.constexpr, D: tl.constexpr, MASK: tl.constexpr):\n    k = k_desc.load([j0, hk * D])\n    v = v_desc.load([j0, hk * D])\n    s = tl.dot(q, tl.trans(k))\n    p = tl.math.exp2(s * qk_scale - lse2[:, None])\n    if MASK:\n        rj = j0 + tl.arange(0, BN)\n        vis = (ri[:, None] >= rj[None, :]) & (rj[None, :] >= s0) & ((ri[:, None] - rj[None, :] <= W) | (W < 0))\n        p = tl.where(vis, p, 0.0)\n    dp = tl.dot(do, tl.trans(v))\n    ds = p * (dp - dlt[:, None])\n    dq = tl.dot(ds.to(tl.bfloat16), k, dq)\n    return dq\n\n\n@triton.jit\ndef _dq(Q, K, V, DO, LSE2, DELTA, DQ, CU, n, qk_scale, sm_scale, T, W,\n        H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n        NP: tl.constexpr):\n    h = tl.program_id(0)\n    valid, s0, s1, i0 = _seq_block(CU, n, tl.program_id(1), BM, NP, True)\n    if valid:\n        G: tl.constexpr = H // HKV\n        hk = h // G\n        q_desc = tl.make_tensor_descriptor(Q, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        do_desc = tl.make_tensor_descriptor(DO, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        k_desc = tl.make_tensor_descriptor(K, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        v_desc = tl.make_tensor_descriptor(V, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        q = q_desc.load([i0, h * D])\n        do = do_desc.load([i0, h * D])\n        ri = i0 + tl.arange(0, BM)\n        hoff = h.to(tl.int64) * T\n        lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0)\n        dlt = tl.load(DELTA + hoff + ri, mask=ri < T, other=0.0)\n        lo = s0\n        if W >= 0:\n            lo = tl.maximum(s0, i0 - W)\n        hi = tl.minimum(i0 + BM, s1)\n        nk = tl.cdiv(hi - lo, BN)\n        ka = 0\n        if W >= 0:\n            ka = tl.minimum(tl.maximum(tl.cdiv(i0 + BM - 1 - W - lo, BN), 0), nk)\n        kb = tl.maximum(tl.minimum((i0 + 1 - lo) // BN, nk), ka)\n        dq = tl.zeros([BM, D], dtype=tl.float32)\n        for kk in range(0, ka):\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D, True)\n        for kk in range(ka, kb):\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D,\n                          False)\n        for kk in range(kb, nk):\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D, True)\n        rd = tl.arange(0, D)\n        off = (ri.to(tl.int64) * H + h) * D\n        tl.store(DQ + off[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=(ri < s1)[:, 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, Q_BM=128, Q_BN=64, Q_W=8, Q_S=1)\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\n    triton.set_allocator(_alloc)\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    c = CFG\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    NP = triton.next_power_of_2(n + 1)\n    nbk = T // c['KV_BN'] + n\n    _dkdv[(HKV, nbk)](q, k, v, do, lse2, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                      H=H, HKV=HKV, D=D, BM=c['KV_BM'], BN=c['KV_BN'], NP=NP,\n                      num_warps=c['KV_W'], num_stages=c['KV_S'])\n    nbq = T // c['Q_BM'] + n\n    _dq[(H, nbq)](q, k, v, do, lse2, delta, dq, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                  H=H, HKV=HKV, D=D, BM=c['Q_BM'], BN=c['Q_BN'], NP=NP,\n                  num_warps=c['Q_W'], num_stages=c['Q_S'])\n"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1648,"cache_read_input_tokens":79604,"cache_creation":{"ephemeral_5m_input_tokens":1648,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"d95b92aa-b41e-4dbc-a637-26963c71d3cc","timestamp":"2026-10-18T11:51:36.384Z","request_id":"req_011Cg9eojFTXT8sY6CVSBAnQ"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014KxcMzngrRMTMH2XT51Mf5","type":"tool_result","content":"File created successfully at: /workspace/dev/v1.py (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"2d763b35-fd3c-45ee-b25f-9a91549c69bc","timestamp":"2026-10-18T11:51:36.422Z","tool_use_result":{"type":"create","filePath":"/workspace/dev/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    pid = tl.program_id(0)\n    h = tl.program_id(1)\n    rt = pid * BT + 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 _seq_block(CU, n, p, B: tl.constexpr, NP: tl.constexpr, REV: tl.constexpr):\n    # program p -> (valid, s0, s1, b0): the p-th block of B tokens over all sequences\n    rs = tl.arange(0, NP)\n    ms = rs < n\n    st = tl.load(CU + rs, mask=ms, other=0)\n    en = tl.load(CU + rs + 1, mask=ms, other=0)\n    nb = (en - st + B - 1) // B\n    cum = tl.cumsum(nb, 0)\n    s = tl.sum((cum <= p).to(tl.int32), 0)\n    sel = rs == s\n    s0 = tl.sum(tl.where(sel, st, 0), 0)\n    s1 = tl.sum(tl.where(sel, en, 0), 0)\n    c1 = tl.sum(tl.where(sel, cum, 0), 0)\n    nbs = tl.sum(tl.where(sel, nb, 0), 0)\n    b = p - (c1 - nbs)\n    if REV:\n        b = nbs - 1 - b\n    return s < n, s0, s1, s0 + b * B\n\n\n@triton.jit\ndef _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, i0, h, T, s1, j0, qk_scale, W,\n               BM: tl.constexpr, BN: tl.constexpr, D: tl.constexpr, MASK: tl.constexpr):\n    q = q_desc.load([i0, h * D])\n    do = do_desc.load([i0, h * D])\n    ri = i0 + tl.arange(0, BM)\n    hoff = h.to(tl.int64) * T\n    lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0)\n    dlt = tl.load(DELTA + hoff + ri, mask=ri < T, other=0.0)\n    s = tl.dot(q, tl.trans(k))\n    p = tl.math.exp2(s * qk_scale - lse2[:, None])\n    if MASK:\n        rj = j0 + tl.arange(0, BN)\n        vis = (ri[:, None] >= rj[None, :]) & (ri[:, None] < s1) & ((ri[:, None] - rj[None, :] <= W) | (W < 0))\n        p = tl.where(vis, p, 0.0)\n    dp = tl.dot(do, tl.trans(v))\n    ds = p * (dp - dlt[:, None])\n    dvT = tl.dot(tl.trans(do), p.to(tl.bfloat16), dvT)\n    dkT = tl.dot(tl.trans(q), ds.to(tl.bfloat16), dkT)\n    return dkT, dvT\n\n\n@triton.jit\ndef _dkdv(Q, K, V, DO, LSE2, DELTA, DK, DV, CU, n, qk_scale, sm_scale, T, W,\n          H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n          NP: tl.constexpr):\n    hk = tl.program_id(0)\n    valid, s0, s1, j0 = _seq_block(CU, n, tl.program_id(1), BN, NP, False)\n    if valid:\n        G: tl.constexpr = H // HKV\n        q_desc = tl.make_tensor_descriptor(Q, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        do_desc = tl.make_tensor_descriptor(DO, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        k_desc = tl.make_tensor_descriptor(K, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        v_desc = tl.make_tensor_descriptor(V, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        k = k_desc.load([j0, hk * D])\n        v = v_desc.load([j0, hk * D])\n        i_end = s1\n        if W >= 0:\n            i_end = tl.minimum(s1, j0 + BN + W)\n        nq = tl.cdiv(i_end - j0, BM)\n        ka = tl.minimum(tl.cdiv(BN - 1, BM), nq)\n        kb = (s1 - j0) // BM\n        if W >= 0:\n            kb = tl.minimum(kb, (W + 1) // BM)\n        kb = tl.maximum(tl.minimum(kb, nq), ka)\n        dkT = tl.zeros([D, BN], dtype=tl.float32)\n        dvT = tl.zeros([D, BN], dtype=tl.float32)\n        for g in range(G):\n            h = hk * G + g\n            for kk in range(0, ka):\n                dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, h, T, s1, j0,\n                                      qk_scale, W, BM, BN, D, True)\n            for kk in range(ka, kb):\n                dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, h, T, s1, j0,\n                                      qk_scale, W, BM, BN, D, False)\n            for kk in range(kb, nq):\n                dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, h, T, s1, j0,\n                                      qk_scale, W, BM, BN, D, True)\n        rj = j0 + tl.arange(0, BN)\n        rd = tl.arange(0, D)\n        off = (rj.to(tl.int64) * HKV + hk) * D\n        msk = (rj < s1)[None, :]\n        tl.store(DK + off[None, :] + rd[:, None], (dkT * sm_scale).to(tl.bfloat16), mask=msk)\n        tl.store(DV + off[None, :] + rd[:, None], dvT.to(tl.bfloat16), mask=msk)\n\n\n@triton.jit\ndef _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, j0, hk, s0, qk_scale, W,\n             BM: tl.constexpr, BN: tl.constexpr, D: tl.constexpr, MASK: tl.constexpr):\n    k = k_desc.load([j0, hk * D])\n    v = v_desc.load([j0, hk * D])\n    s = tl.dot(q, tl.trans(k))\n    p = tl.math.exp2(s * qk_scale - lse2[:, None])\n    if MASK:\n        rj = j0 + tl.arange(0, BN)\n        vis = (ri[:, None] >= rj[None, :]) & (rj[None, :] >= s0) & ((ri[:, None] - rj[None, :] <= W) | (W < 0))\n        p = tl.where(vis, p, 0.0)\n    dp = tl.dot(do, tl.trans(v))\n    ds = p * (dp - dlt[:, None])\n    dq = tl.dot(ds.to(tl.bfloat16), k, dq)\n    return dq\n\n\n@triton.jit\ndef _dq(Q, K, V, DO, LSE2, DELTA, DQ, CU, n, qk_scale, sm_scale, T, W,\n        H: tl.constexpr, HKV: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr,\n        NP: tl.constexpr):\n    h = tl.program_id(0)\n    valid, s0, s1, i0 = _seq_block(CU, n, tl.program_id(1), BM, NP, True)\n    if valid:\n        G: tl.constexpr = H // HKV\n        hk = h // G\n        q_desc = tl.make_tensor_descriptor(Q, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        do_desc = tl.make_tensor_descriptor(DO, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n        k_desc = tl.make_tensor_descriptor(K, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        v_desc = tl.make_tensor_descriptor(V, shape=[T, HKV * D], strides=[HKV * D, 1], block_shape=[BN, D])\n        q = q_desc.load([i0, h * D])\n        do = do_desc.load([i0, h * D])\n        ri = i0 + tl.arange(0, BM)\n        hoff = h.to(tl.int64) * T\n        lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0)\n        dlt = tl.load(DELTA + hoff + ri, mask=ri < T, other=0.0)\n        lo = s0\n        if W >= 0:\n            lo = tl.maximum(s0, i0 - W)\n        hi = tl.minimum(i0 + BM, s1)\n        nk = tl.cdiv(hi - lo, BN)\n        ka = 0\n        if W >= 0:\n            ka = tl.minimum(tl.maximum(tl.cdiv(i0 + BM - 1 - W - lo, BN), 0), nk)\n        kb = tl.maximum(tl.minimum((i0 + 1 - lo) // BN, nk), ka)\n        dq = tl.zeros([BM, D], dtype=tl.float32)\n        for kk in range(0, ka):\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D, True)\n        for kk in range(ka, kb):\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D,\n                          False)\n        for kk in range(kb, nk):\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D, True)\n        rd = tl.arange(0, D)\n        off = (ri.to(tl.int64) * H + h) * D\n        tl.store(DQ + off[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=(ri < s1)[:, 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, Q_BM=128, Q_BN=64, Q_W=8, Q_S=1)\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\n    triton.set_allocator(_alloc)\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    c = CFG\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    NP = triton.next_power_of_2(n + 1)\n    nbk = T // c['KV_BN'] + n\n    _dkdv[(HKV, nbk)](q, k, v, do, lse2, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                      H=H, HKV=HKV, D=D, BM=c['KV_BM'], BN=c['KV_BN'], NP=NP,\n                      num_warps=c['KV_W'], num_stages=c['KV_S'])\n    nbq = T // c['Q_BM'] + n\n    _dq[(H, nbq)](q, k, v, do, lse2, delta, dq, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                  H=H, HKV=HKV, D=D, BM=c['Q_BM'], BN=c['Q_BN'], NP=NP,\n                  num_warps=c['Q_W'], num_stages=c['Q_S'])\n","structuredPatch":[],"originalFile":null,"userModified":false},"tool_result_meta":[{"id":"toolu_014KxcMzngrRMTMH2XT51Mf5","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9eu3NFrY1kQ7J6qmULi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015rygvtJSpSaDvABqQ64G3x","name":"Bash","input":{"command":"timeout 900 python dev/bench.py dev/v1.py 2>&1 | tail -20","description":"Run v1 correctness and timing","timeout":900000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9400,"cache_read_input_tokens":81252,"cache_creation":{"ephemeral_5m_input_tokens":9400,"ephemeral_1h_input_tokens":0},"output_tokens":16,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"a12ba691-a1fe-49c0-a166-911d24fa68aa","timestamp":"2026-10-18T11:51:38.984Z","request_id":"req_011Cg9eu33B4zJpo8cjFBEzK"}
{"type":"system","subtype":"task_started","task_id":"b5o9bfktj","run_id":"0mvdrm2er-aff74855","tool_use_id":"toolu_015rygvtJSpSaDvABqQ64G3x","description":"Run v1 correctness and timing","is_backgrounded":false,"task_type":"local_bash","uuid":"f1d97cc4-f399-4f0d-800d-7ddc53ce8f66","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"tool_progress","tool_use_id":"toolu_015rygvtJSpSaDvABqQ64G3x-heartbeat-0","tool_name":"Bash","parent_tool_use_id":"toolu_015rygvtJSpSaDvABqQ64G3x","elapsed_time_seconds":30,"heartbeat":true,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"fffe74ec-aca7-4a66-b995-6b55988508f7"}
{"type":"system","subtype":"task_notification","task_id":"b5o9bfktj","run_id":"0mvdrm2er-aff74855","tool_use_id":"toolu_015rygvtJSpSaDvABqQ64G3x","status":"completed","output_file":"","summary":"Run v1 correctness and timing","uuid":"4aaadbfa-e6fb-4900-b498-7de4af297953","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015rygvtJSpSaDvABqQ64G3x","type":"tool_result","content":"Traceback (most recent call last):\n  File \"/workspace/dev/bench.py\", line 77, in <module>\n    main()\n  File \"/workspace/dev/bench.py\", line 57, in main\n    sol.kernel_fn(*args)\n  File \"/workspace/dev/v1.py\", line 189, in kernel_fn\n    _dkdv[(HKV, nbk)](q, k, v, do, lse2, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\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: 279552, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"330259ea-23c7-4911-9667-f9d8755efcc3","timestamp":"2026-10-18T11:52:22.251Z","tool_use_result":{"stdout":"Traceback (most recent call last):\n  File \"/workspace/dev/bench.py\", line 77, in <module>\n    main()\n  File \"/workspace/dev/bench.py\", line 57, in main\n    sol.kernel_fn(*args)\n  File \"/workspace/dev/v1.py\", line 189, in kernel_fn\n    _dkdv[(HKV, nbk)](q, k, v, do, lse2, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\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: 279552, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_015rygvtJSpSaDvABqQ64G3x","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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"05e6eb8b-9c70-4109-a450-9bd29a428db2"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":250,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"ca0103b2-e0af-44ed-8eae-86aaa84f37ab"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":390,"estimated_tokens_delta":140,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"a82967ea-0598-4342-bfbd-e224c0a0e627"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9exRHhsQQToeStu7gNQ","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":890,"cache_read_input_tokens":90652,"cache_creation":{"ephemeral_5m_input_tokens":890,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"31cc30fe-8519-482d-b761-cd3d287e26d5","timestamp":"2026-10-18T11:52:26.020Z","request_id":"req_011Cg9exQutYGXjksC4BaLNE","thinking_duration_ms":2650,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9exRHhsQQToeStu7gNQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Qq2Xivu4Rk4CDCR4qGXYxw","name":"Bash","input":{"command":"sed -i \"s/CFG = dict(KV_BM=64, KV_BN=64, KV_W=4, KV_S=2, Q_BM=128, Q_BN=64, Q_W=8, Q_S=1)/CFG = dict(KV_BM=64, KV_BN=64, KV_W=4, KV_S=1, Q_BM=128, Q_BN=64, Q_W=8, Q_S=1)/\" dev/v1.py && timeout 900 python dev/bench.py dev/v1.py 2>&1 | tail -20","description":"Run v1 with 1 stage","timeout":900000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":890,"cache_read_input_tokens":90652,"cache_creation":{"ephemeral_5m_input_tokens":890,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"0004ce3f-23f3-4e0c-beac-ce8c33c2584b","timestamp":"2026-10-18T11:52:27.849Z","request_id":"req_011Cg9exQutYGXjksC4BaLNE"}
{"type":"system","subtype":"task_started","task_id":"bxh7wmslm","run_id":"0mvdrn44h-16f86545","tool_use_id":"toolu_01Qq2Xivu4Rk4CDCR4qGXYxw","description":"Run v1 with 1 stage","is_backgrounded":false,"task_type":"local_bash","uuid":"6743a9b9-a649-47ae-9585-4cbf8a5e1d64","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"system","subtype":"task_notification","task_id":"bxh7wmslm","run_id":"0mvdrn44h-16f86545","tool_use_id":"toolu_01Qq2Xivu4Rk4CDCR4qGXYxw","status":"completed","output_file":"","summary":"Run v1 with 1 stage","uuid":"e5ce650a-11a5-4f54-aedc-26184fa3faa4","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Qq2Xivu4Rk4CDCR4qGXYxw","type":"tool_result","content":"Traceback (most recent call last):\n  File \"/workspace/dev/bench.py\", line 77, in <module>\n    main()\n  File \"/workspace/dev/bench.py\", line 66, in main\n    print(f\"shape {i} {T.shape_key(shape)}: {'OK' if ok else 'WRONG'} {stats} {us:.1f} us  expert {EXPERT[i]}  ratio {EXPERT[i]/us:.3f}\", flush=True)\n                       ^^^^^^^^^^^\nAttributeError: module 'task' has no attribute 'shape_key'","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"e60e125d-38dd-4d27-b625-de1a841d855a","timestamp":"2026-10-18T11:52:39.440Z","tool_use_result":{"stdout":"Traceback (most recent call last):\n  File \"/workspace/dev/bench.py\", line 77, in <module>\n    main()\n  File \"/workspace/dev/bench.py\", line 66, in main\n    print(f\"shape {i} {T.shape_key(shape)}: {'OK' if ok else 'WRONG'} {stats} {us:.1f} us  expert {EXPERT[i]}  ratio {EXPERT[i]/us:.3f}\", flush=True)\n                       ^^^^^^^^^^^\nAttributeError: module 'task' has no attribute 'shape_key'","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01Qq2Xivu4Rk4CDCR4qGXYxw","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9eygp8f9cV8rMrhBhha","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013XMhssotJMccxqu7UMbXx9","name":"Bash","input":{"command":"sed -i \"s/T.shape_key(shape)/{k: v for k, v in shape.items() if k != 'model'}/\" dev/bench.py && timeout 900 python dev/bench.py dev/v1.py 2>&1 | tail -20","description":"Fix harness and rerun v1","timeout":900000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":955,"cache_read_input_tokens":91542,"cache_creation":{"ephemeral_5m_input_tokens":955,"ephemeral_1h_input_tokens":0},"output_tokens":16,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"0ff9b359-2f95-4e6c-8f79-ff118b7b715f","timestamp":"2026-10-18T11:52:41.352Z","request_id":"req_011Cg9eygQaLZVftYcCbXkRd"}
{"type":"system","subtype":"task_started","task_id":"blrng8zpw","run_id":"0mvdrneji-06ef50c7","tool_use_id":"toolu_013XMhssotJMccxqu7UMbXx9","description":"Fix harness and rerun v1","is_backgrounded":false,"task_type":"local_bash","uuid":"6e7f7f69-2c67-4b19-9d75-69453539f816","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013XMhssotJMccxqu7UMbXx9","type":"tool_result","content":"shape 0 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 4779.0 us  expert 1518  ratio 0.318\nshape 1 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1433.6 us  expert 510  ratio 0.356\nshape 2 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 2439.2 us  expert 980  ratio 0.402\nshape 3 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 3123.2 us  expert 1165  ratio 0.373\nshape 4 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 754.8 us  expert 302  ratio 0.400\nshape 5 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 1433.6 us  expert 520  ratio 0.363\ngeomean ratio vs expert: 0.36735454963244796","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"5b2ea078-f5a9-41be-889b-0948ec3952e8","timestamp":"2026-10-18T11:53:00.201Z","tool_use_result":{"stdout":"shape 0 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 4779.0 us  expert 1518  ratio 0.318\nshape 1 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1433.6 us  expert 510  ratio 0.356\nshape 2 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 2439.2 us  expert 980  ratio 0.402\nshape 3 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 3123.2 us  expert 1165  ratio 0.373\nshape 4 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 754.8 us  expert 302  ratio 0.400\nshape 5 {k: v for k, v in shape.items() if k != 'model'}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 1433.6 us  expert 520  ratio 0.363\ngeomean ratio vs expert: 0.36735454963244796","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_013XMhssotJMccxqu7UMbXx9","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9f1DX7ZA1DHtMxjg66L","type":"message","role":"assistant","content":[{"type":"text","text":"Correct on all shapes at 0.37x the expert. Saving it as the baseline solution, then profiling per-kernel time."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":808,"cache_read_input_tokens":92497,"cache_creation":{"ephemeral_5m_input_tokens":808,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"1cfaca86-db06-4f56-805e-16289af74f60","timestamp":"2026-10-18T11:53:02.649Z","request_id":"req_011Cg9f1DAnA9JVPDgon1wTM"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9f1DX7ZA1DHtMxjg66L","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U7oezUfj3nUimmu6qyRmqX","name":"Bash","input":{"command":"cp dev/v1.py solution.py && sed -i \"s/{k: v for k, v in shape.items() if k != 'model'}/{i}/\" dev/bench.py && cat > dev/prof.py << 'EOF'\nimport sys, torch, importlib.util\nsys.path.insert(0, '/workspace'); sys.path.insert(0, '/workspace/dev')\nimport task as T\nfrom bench import load\nsol = load(sys.argv[1], 'sol')\nshapes = [int(x) for x in sys.argv[2].split(',')] if len(sys.argv) > 2 else range(6)\nfor i in shapes:\n    case = T.TASK.make_case(T.TASK.shapes[i], 1, 'cuda')\n    args = case.args\n    sol.kernel_fn(*args); torch.cuda.synchronize()\n    with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CUDA]) as prof:\n        for _ in range(5):\n            sol.kernel_fn(*args)\n        torch.cuda.synchronize()\n    tot = {}\n    for e in prof.events():\n        if e.device_type == torch.autograd.DeviceType.CUDA:\n            tot[e.name] = tot.get(e.name, 0) + e.device_time / 5\n    print(i, {k[:20]: round(v, 1) for k, v in tot.items()})\nEOF\npython dev/prof.py dev/v1.py 2>&1 | tail","description":"Save baseline and profile per-kernel times","timeout":600000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":808,"cache_read_input_tokens":92497,"cache_creation":{"ephemeral_5m_input_tokens":808,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"c3860d56-dec0-4f54-880d-d4a6ad7adbad","timestamp":"2026-10-18T11:53:06.685Z","request_id":"req_011Cg9f1DAnA9JVPDgon1wTM"}
{"type":"system","subtype":"task_started","task_id":"btsrqepxw","run_id":"0mvdrny2u-1cd4875d","tool_use_id":"toolu_01U7oezUfj3nUimmu6qyRmqX","description":"Save baseline and profile per-kernel times","is_backgrounded":false,"task_type":"local_bash","uuid":"2ea70e9e-a014-45ff-b6b1-df45fa692b63","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"system","subtype":"task_notification","task_id":"btsrqepxw","run_id":"0mvdrny2u-1cd4875d","tool_use_id":"toolu_01U7oezUfj3nUimmu6qyRmqX","status":"completed","output_file":"","summary":"Save baseline and profile per-kernel times","uuid":"83821f57-3f8e-43cb-a1fc-4d68e795db4b","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U7oezUfj3nUimmu6qyRmqX","type":"tool_result","content":"USDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:52] profiler_start\nUSDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:59] profiler_stop\nUSDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:52] profiler_start\nUSDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:59] profiler_stop\n0 {'_prep': 30.4, '_dkdv': 3277.6, '_dq': 1461.4}\n1 {'_prep': 29.1, '_dkdv': 957.0, '_dq': 427.4}\n2 {'_prep': 30.1, '_dkdv': 1651.1, '_dq': 748.7}\n3 {'_prep': 49.8, '_dkdv': 2101.4, '_dq': 962.5}\n4 {'_prep': 15.3, '_dkdv': 486.6, '_dq': 244.1}\n5 {'_prep': 28.9, '_dkdv': 969.8, '_dq': 424.3}","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"c07a81e0-725f-44f6-89a5-c3e72f95d67a","timestamp":"2026-10-18T11:53:16.996Z","tool_use_result":{"stdout":"USDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:52] profiler_start\nUSDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:59] profiler_stop\nUSDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:52] profiler_start\nUSDT:2026-10-18 11:53:16 5323:5323 SyncActivityProfilerHandler.cpp:59] profiler_stop\n0 {'_prep': 30.4, '_dkdv': 3277.6, '_dq': 1461.4}\n1 {'_prep': 29.1, '_dkdv': 957.0, '_dq': 427.4}\n2 {'_prep': 30.1, '_dkdv': 1651.1, '_dq': 748.7}\n3 {'_prep': 49.8, '_dkdv': 2101.4, '_dq': 962.5}\n4 {'_prep': 15.3, '_dkdv': 486.6, '_dq': 244.1}\n5 {'_prep': 28.9, '_dkdv': 969.8, '_dq': 424.3}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01U7oezUfj3nUimmu6qyRmqX","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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gknk4TpwwmM0iAJp49YbUxJTwyfce+7vg9ysb/RM43U/GGtHukMJpOHFuXTA5dQCP87pyXmmiqqAdv2K4gMDpSNZ3cbc5NzOTh0qsUNcwYMQFMWVfjncg3ZUMUPx685pwwH6po72MX8xbHTMmwO0GdNlBZ4/31BLYGl57Zl4CBW6+uFtU9/BWuN5IGwqeImx40fmdv3isNGVwbUaYdUuLOo1D31ltUikfaVMgffxhM83t1dkBIkBT+7/IcevscxZYcZzpAHmlVptXkVUoGUtfwy7wYUufzdafSyID0iQBUjJnWbjHn53R90/S+SyIz7TLZwH2nYsPYW8at9ZlSOhBsJU2uqxhDrYA6LpmyDX5AKWsE2itx5s5TgiqxlSstTKQ1lh1UFD4SAlTZhRJVKtYUPxHe6O/LRjgIc6oSenBUmvYoZ9zb4o+cupYntnqzZj2j2s3+xe7ujK8t5dMC1KkYAQ=="}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1602,"cache_read_input_tokens":94583,"cache_creation":{"ephemeral_5m_input_tokens":1602,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"cd463ffd-9b3c-435a-a09d-4de33eb7d90f","timestamp":"2026-10-18T11:54:51.787Z","request_id":"req_011Cg9f8MBw44MAWAJuebDKM","thinking_duration_ms":13010,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9f8MZFy1Q2nkUb6KYus","type":"message","role":"assistant","content":[{"type":"text","text":"Both kernels are far below tensor-core peak (dkdv ~500 TFLOPS, dq ~630). Let me try flattening the GQA/head loop and Triton's automatic warp specialization."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1602,"cache_read_input_tokens":94583,"cache_creation":{"ephemeral_5m_input_tokens":1602,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"31530e88-3bd1-45e7-89cb-9b1c08c0514c","timestamp":"2026-10-18T11:54:51.790Z","request_id":"req_011Cg9f8MBw44MAWAJuebDKM"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9f8MZFy1Q2nkUb6KYus","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01S1Bjy7jFgV3ynJuyF7F8WR","name":"Bash","input":{"command":"python - << 'EOF'\nsrc = open('v1cfg.py').read()\nold_kv = src[src.index(\"        dkT = tl.zeros([D, BN], dtype=tl.float32)\"):src.index(\"        rj = j0 + tl.arange(0, BN)\\n        rd = tl.arange(0, D)\\n        off = (rj\")]\nnew_kv = '''        dkT = tl.zeros([D, BN], dtype=tl.float32)\n        dvT = tl.zeros([D, BN], dtype=tl.float32)\n        nu = kb - ka\n        for t in tl.range(0, G * nu, warp_specialize=WS):\n            g = t // nu\n            kk = ka + t - g * nu\n            dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, hk * G + g, T, s1, j0,\n                                  qk_scale, W, BM, BN, D, False)\n        nm = nq - nu\n        for t in range(0, G * nm):\n            g = t // nm\n            kk = t - g * nm\n            kk = tl.where(kk < ka, kk, kk + nu)\n            dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT, j0 + kk * BM, hk * G + g, T, s1, j0,\n                                  qk_scale, W, BM, BN, D, True)\n'''\nsrc = src.replace(old_kv, new_kv)\nold_q = src[src.index(\"        for kk in range(0, ka):\\n            dq = \"):src.index(\"        rd = tl.arange(0, D)\\n        off = (ri.to(tl.int64) * H + h) * D\")]\nnew_q = '''        for kk in tl.range(ka, kb, warp_specialize=WS):\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D,\n                          False)\n        nm = nk - (kb - ka)\n        for t in range(0, nm):\n            kk = tl.where(t < ka, t, t + kb - ka)\n            dq = _dq_step(k_desc, v_desc, q, do, lse2, dlt, dq, ri, lo + kk * BN, hk, s0, qk_scale, W, BM, BN, D, True)\n'''\nsrc = src.replace(old_q, new_q)\nsrc = src.replace(\"          NP: tl.constexpr):\\n    hk = tl.program_id(0)\", \"          NP: tl.constexpr, WS: tl.constexpr):\\n    hk = tl.program_id(0)\")\nsrc = src.replace(\"        NP: tl.constexpr):\\n    h = tl.program_id(0)\", \"        NP: tl.constexpr, WS: tl.constexpr):\\n    h = tl.program_id(0)\")\nsrc = src.replace(\"BM=c['KV_BM'], BN=c['KV_BN'], NP=NP,\", \"BM=c['KV_BM'], BN=c['KV_BN'], NP=NP, WS=c['KV_WS'],\")\nsrc = src.replace(\"BM=c['Q_BM'], BN=c['Q_BN'], NP=NP,\", \"BM=c['Q_BM'], BN=c['Q_BN'], NP=NP, WS=c['Q_WS'],\")\nsrc = src.replace(\"CFG = dict(KV_BM=64, KV_BN=64, KV_W=4, KV_S=1, Q_BM=128, Q_BN=64, Q_W=8, Q_S=1)\",\n                  \"CFG = dict(KV_BM=128, KV_BN=64, KV_W=8, KV_S=1, Q_BM=128, Q_BN=32, Q_W=4, Q_S=2, KV_WS=False, Q_WS=False)\")\nopen('v2cfg.py', 'w').write(src)\nEOF\n./sweep.sh v2cfg.py 0 '{}' '{\"KV_WS\":true,\"Q_WS\":true}' '{\"KV_WS\":true,\"Q_WS\":true,\"KV_W\":4,\"Q_W\":4}' 2>&1","description":"Flatten loops and test warp specialization","timeout":1800000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1602,"cache_read_input_tokens":94583,"cache_creation":{"ephemeral_5m_input_tokens":1602,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"0f73823a-bddf-4b93-8d09-d32a960b6632","timestamp":"2026-10-18T11:55:01.789Z","request_id":"req_011Cg9f8MBw44MAWAJuebDKM"}
{"type":"system","subtype":"task_started","task_id":"b7sxhtypn","run_id":"0mvdrqewa-a6d1236e","tool_use_id":"toolu_01S1Bjy7jFgV3ynJuyF7F8WR","description":"Flatten loops and test warp specialization","is_backgrounded":false,"task_type":"local_bash","uuid":"28e9a3ee-dabe-4f98-806e-8e412b1ed4fe","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01S1Bjy7jFgV3ynJuyF7F8WR","type":"tool_result","content":"== {}\n0 {'_prep': 29.8, '_dkdv': 2201.3, '_dq': 1271.8}\n== {\"KV_WS\":true,\"Q_WS\":true}\n  File \"/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py\", line 321, in make_ttgir\n    pm.run(mod, 'make_ttgir')\nRuntimeError: PassManager::run failed\n== {\"KV_WS\":true,\"Q_WS\":true,\"KV_W\":4,\"Q_W\":4}\n  File \"/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py\", line 321, in make_ttgir\n    pm.run(mod, 'make_ttgir')\nRuntimeError: PassManager::run failed","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"99b4a97a-58e7-4f28-9934-31b085c19fe9","timestamp":"2026-10-18T11:55:26.388Z","tool_use_result":{"stdout":"== {}\n0 {'_prep': 29.8, '_dkdv': 2201.3, '_dq': 1271.8}\n== {\"KV_WS\":true,\"Q_WS\":true}\n  File \"/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py\", line 321, in make_ttgir\n    pm.run(mod, 'make_ttgir')\nRuntimeError: PassManager::run failed\n== {\"KV_WS\":true,\"Q_WS\":true,\"KV_W\":4,\"Q_W\":4}\n  File \"/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py\", line 321, in make_ttgir\n    pm.run(mod, 'make_ttgir')\nRuntimeError: PassManager::run failed","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01S1Bjy7jFgV3ynJuyF7F8WR","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fBzdHACR48hDdcneZv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018eRC8JeU1jf4Y7xKdN18pm","name":"Bash","input":{"command":"CFG='{\"KV_WS\":true}' timeout 300 python prof.py v2cfg.py 0 2>&1 | grep -v USDT | grep -i \"error\\|fail\\|_dkdv\\|_dq\" | head -10; CFG='{\"Q_WS\":true}' timeout 300 python prof.py v2cfg.py 0 2>&1 | grep -v USDT | grep -i \"error\\|fail\\|_dkdv\\|_dq\" | head -10","description":"Isolate which kernel fails with warp specialization","timeout":900000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3299,"cache_read_input_tokens":96185,"cache_creation":{"ephemeral_5m_input_tokens":3299,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"b722c625-cec2-4483-a607-eab20989f95f","timestamp":"2026-10-18T11:55:29.039Z","request_id":"req_011Cg9fBzDytX4T5daZvsthg"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018eRC8JeU1jf4Y7xKdN18pm","type":"tool_result","content":"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.\n  tt.func public @_dkdv(%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<bf16> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg8: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg9: f32, %arg10: f32, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32) attributes {noinline = false} {\n/workspace/dev/v2cfg.py:68:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/dev/v2cfg.py:68:0: 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    _dkdv[(HKV, nbk)](q, k, v, do, lse2, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\nRuntimeError: PassManager::run failed\npython: /root/.triton/llvm/llvm-1f126a6d-almalinux-x64-1/include/llvm/Support/Casting.h:560: decltype(auto) llvm::cast(const From &) [To = mlir::detail::TypedValue<mlir::triton::gpu::AsyncTokenType>, From = mlir::Value]: Assertion `isa<To>(Val) && \"cast<Ty>() argument of incompatible type!\"' failed.\n  tt.func public @_dq(%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<bf16> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg8: f32, %arg9: f32, %arg10: i32 {tt.divisibility = 16 : i32}, %arg11: i32) attributes {noinline = false} {\n/workspace/dev/v2cfg.py:131:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/dev/v2cfg.py:131:0: note: Pipeline failed while executing [`TritonGPUHoistTMEMAlloc` on 'builtin.module' operation]: reproducer generated at `std::errs, please share the reproducer above with Triton project.`\n    _dq[(H, nbq)](q, k, v, do, lse2, delta, dq, cu_seqlens, n, qk_scale, sm_scale, T, window,\nRuntimeError: PassManager::run failed","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"56057960-9431-4799-9305-4f5092b70d74","timestamp":"2026-10-18T11:55:41.477Z","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.\n  tt.func public @_dkdv(%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<bf16> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg8: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg9: f32, %arg10: f32, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32) attributes {noinline = false} {\n/workspace/dev/v2cfg.py:68:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/dev/v2cfg.py:68:0: 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    _dkdv[(HKV, nbk)](q, k, v, do, lse2, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\nRuntimeError: PassManager::run failed\npython: /root/.triton/llvm/llvm-1f126a6d-almalinux-x64-1/include/llvm/Support/Casting.h:560: decltype(auto) llvm::cast(const From &) [To = mlir::detail::TypedValue<mlir::triton::gpu::AsyncTokenType>, From = mlir::Value]: Assertion `isa<To>(Val) && \"cast<Ty>() argument of incompatible type!\"' failed.\n  tt.func public @_dq(%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<bf16> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg8: f32, %arg9: f32, %arg10: i32 {tt.divisibility = 16 : i32}, %arg11: i32) attributes {noinline = false} {\n/workspace/dev/v2cfg.py:131:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/dev/v2cfg.py:131:0: note: Pipeline failed while executing [`TritonGPUHoistTMEMAlloc` on 'builtin.module' operation]: reproducer generated at `std::errs, please share the reproducer above with Triton project.`\n    _dq[(H, nbq)](q, k, v, do, lse2, delta, dq, cu_seqlens, n, qk_scale, sm_scale, T, window,\nRuntimeError: PassManager::run failed","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_018eRC8JeU1jf4Y7xKdN18pm","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fD74G4ioeibeVRHMrU","type":"message","role":"assistant","content":[{"type":"text","text":"Warp specialization crashes the compiler with the multi-loop structure. Let me try a single loop with always-on masking, which is the simplest structure for the WS pass."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1773,"cache_read_input_tokens":99484,"cache_creation":{"ephemeral_5m_input_tokens":1773,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"a7509366-e218-4d28-a5f8-cf6714c4c27e","timestamp":"2026-10-18T11:55:47.714Z","request_id":"req_011Cg9fD6igCt1wAsKYVnhUp"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NPn5jdzjr1WGFB9E8pFXgb","type":"tool_result","content":"== {}\n0 {'_prep': 30.2, '_dkdv': 2301.1, '_dq': 1348.7}\n== {\"KV_WS\":true}\nRuntimeError: PassManager::run failed\n== {\"Q_WS\":true}\n0 {'_prep': 30.2, '_dkdv': 2299.4, '_dq': 1529.0}","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"61d337ce-3e36-48e3-b234-64ee151580a0","timestamp":"2026-10-18T11:56:20.806Z","tool_use_result":{"stdout":"== {}\n0 {'_prep': 30.2, '_dkdv': 2301.1, '_dq': 1348.7}\n== {\"KV_WS\":true}\nRuntimeError: PassManager::run failed\n== {\"Q_WS\":true}\n0 {'_prep': 30.2, '_dkdv': 2299.4, '_dq': 1529.0}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01NPn5jdzjr1WGFB9E8pFXgb","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fVb8QqLi686NtTVGYU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012MBMEtQRTzBNnMZfxdGdvW","name":"Bash","input":{"command":"cat > /workspace/dev/ir.py << 'EOF'\nimport sys, os, json, torch\nsys.path.insert(0, '/workspace'); sys.path.insert(0, '/workspace/dev')\nimport task as T\nfrom bench import load\nsol = load(sys.argv[1], 'sol')\nkname = sys.argv[2]\ncase = T.TASK.make_case(T.TASK.shapes[int(sys.argv[3]) if len(sys.argv) > 3 else 4], 1, 'cuda')\ntry:\n    sol.kernel_fn(*case.args)\nexcept Exception as e:\n    print('ERR', str(e)[:200])\nk = getattr(sol, kname)\nfor key, ck in k.device_caches[0][0].items():\n    print('shared', ck.metadata.shared, 'tmem', getattr(ck.metadata, 'tmem_size', None), 'nregs', getattr(ck, 'n_regs', None), 'spills', getattr(ck, 'n_spills', None))\n    open(f'/workspace/dev/{kname}.ttgir', 'w').write(ck.asm['ttgir'])\n    open(f'/workspace/dev/{kname}.ptx', 'w').write(ck.asm.get('ptx', ''))\nEOF\ncd /workspace/dev && CFG='{\"KV_BM\":64,\"KV_BN\":64,\"KV_W\":4,\"KV_S\":1}' python ir.py v3cfg.py _dkdv 2>&1 | tail -3; grep -o \"ttg.local_alloc.*\" _dkdv.ttgir | grep -o \"memdesc<[^>]*>\" | sort | uniq -c; grep -o \"tmem_alloc.*\" _dkdv.ttgir | grep -o \"memdesc<[^>]*>\" | sort | uniq -c; grep -c \"ttg.warp_specialize\" _dkdv.ttgir; grep \"ttg.shared\\|tensor_memory_size\" _dkdv.ttgir | head -3","description":"Inspect dkdv TTGIR SMEM/TMEM allocations","timeout":600000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2784,"cache_read_input_tokens":111739,"cache_creation":{"ephemeral_5m_input_tokens":2784,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"00dbe35d-f277-4e79-a505-333efd1347a9","timestamp":"2026-10-18T11:59:35.673Z","request_id":"req_011Cg9fVakLycmPeM8eD69PU"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012MBMEtQRTzBNnMZfxdGdvW","type":"tool_result","content":"shared 139272 tmem 512 nregs 255 spills 34\n      8 memdesc<1xi64, #shared2, #smem, mutable>\n      4 memdesc<64x256xbf16, #shared, #smem, mutable>\n      2 memdesc<64x64xbf16, #shared, #smem>\n      2 memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable>\n      2 memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable>\n0\n#smem = #ttg.shared_memory","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"13e000eb-a8af-46c0-bb6b-c57af6ebfcee","timestamp":"2026-10-18T11:59:43.562Z","tool_use_result":{"stdout":"shared 139272 tmem 512 nregs 255 spills 34\n      8 memdesc<1xi64, #shared2, #smem, mutable>\n      4 memdesc<64x256xbf16, #shared, #smem, mutable>\n      2 memdesc<64x64xbf16, #shared, #smem>\n      2 memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable>\n      2 memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable>\n0\n#smem = #ttg.shared_memory","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_012MBMEtQRTzBNnMZfxdGdvW","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fWx8VkhKnSgiXQYexb","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":1442,"cache_read_input_tokens":114523,"cache_creation":{"ephemeral_5m_input_tokens":1442,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"a0c84612-f82e-489c-aa26-721b836a5910","timestamp":"2026-10-18T11:59:47.638Z","request_id":"req_011Cg9fWwjghBzS9b4V2wYbW","thinking_duration_ms":2737,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fWx8VkhKnSgiXQYexb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EVwDduykoCCoWCWYKEMch8","name":"Bash","input":{"command":"grep -n \"scf.for\\|tc_gen5_mma\\|tmem_load\\|tmem_store\\|async_tma_copy\\|wait_barrier\\|scf.yield\\|local_store\\|local_alloc\\|tmem_alloc\\|ttg.async_wait\\|tt.descriptor_load\" _dkdv.ttgir | cut -c1-220 | head -60","description":"Show loop structure of dkdv TTGIR"},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1442,"cache_read_input_tokens":114523,"cache_creation":{"ephemeral_5m_input_tokens":1442,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"88e8c305-05da-40ad-84c0-b8845e99c92a","timestamp":"2026-10-18T11:59:49.323Z","request_id":"req_011Cg9fWwjghBzS9b4V2wYbW"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EVwDduykoCCoWCWYKEMch8","type":"tool_result","content":"151:      %k_29 = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc173)\n153:      %k_31 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc173)\n156:      ttng.async_tma_copy_global_to_local %k_desc_27[%i_end_24, %k] %k_29, %k_31, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shar\n157:      ttng.wait_barrier %k_31, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc173)\n159:      %v = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc175)\n161:      %v_32 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc175)\n164:      ttng.async_tma_copy_global_to_local %v_desc_28[%i_end_24, %k] %v, %v_32, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shared,\n165:      ttng.wait_barrier %v_32, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc175)\n172:        scf.yield %i_end_59 : i32 loc(#loc265)\n174:        scf.yield %s1_19 : i32 loc(#loc48)\n194:      %dvT = ttng.tmem_alloc : () -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc279)\n195:      ttng.tmem_store %cst_0, %dvT, %true : tensor<256x64xf32, #linear1> -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc279)\n196:      %dkT = ttng.tmem_alloc : () -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc280)\n197:      ttng.tmem_store %cst_0, %dkT, %true : tensor<256x64xf32, #linear1> -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc280)\n198:      %s_42 = ttng.tmem_alloc : () -> !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> loc(#loc281)\n199:      %dp_43 = ttng.tmem_alloc : () -> !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> loc(#loc282)\n200:      %dvT_44 = scf.for %dvT_57 = %c0_i32 to %3 step %c1_i32 iter_args(%dvT_58 = %false) -> (i1)  : i32 {\n208:        %q_60 = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc285)\n210:        %q_62 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc285)\n213:        ttng.async_tma_copy_global_to_local %q_desc_25[%25, %q] %q_60, %q_62, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shared, \n214:        ttng.wait_barrier %q_62, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc285)\n216:        %do = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc287)\n218:        %do_64 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc287)\n221:        ttng.async_tma_copy_global_to_local %do_desc_26[%25, %q] %do, %do_64, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shared, \n222:        ttng.wait_barrier %do_64, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc287)\n237:        %s_77 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc281)\n239:        ttng.tc_gen5_mma %q_60, %s_30, %s_42, %false, %true, %s_77[%true] {is_async} : !ttg.memdesc<64x256xbf16, #shared, #smem, mutable>, !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xf32, \n240:        ttng.wait_barrier %s_77, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc281)\n254:        %dp_90 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc282)\n256:        ttng.tc_gen5_mma %do, %dp, %dp_43, %false, %true, %dp_90[%true] {is_async} : !ttg.memdesc<64x256xbf16, #shared, #smem, mutable>, !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xf32, #t\n257:        ttng.wait_barrier %dp_90, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc282)\n261:        %s_92 = ttng.tmem_load %s_42 : !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> -> tensor<64x64xf32, #linear> loc(#loc281)\n267:        %dvT_98 = ttg.local_alloc %dvT_97 : (tensor<64x64xbf16, #linear>) -> !ttg.memdesc<64x64xbf16, #shared, #smem> loc(#loc307)\n269:        %dvT_99 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc279)\n271:        ttng.tc_gen5_mma %dvT_63, %dvT_98, %dvT, %dvT_58, %true, %dvT_99[%true] {is_async} : !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xbf16, #shared, #smem>, !ttg.memdesc<256x64xf32, #tm\n272:        ttng.wait_barrier %dvT_99, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc279)\n274:        %dp_100 = ttng.tmem_load %dp_43 : !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> -> tensor<64x64xf32, #linear> loc(#loc282)\n278:        %dkT_104 = ttg.local_alloc %dkT_103 : (tensor<64x64xbf16, #linear>) -> !ttg.memdesc<64x64xbf16, #shared, #smem> loc(#loc309)\n280:        %dkT_105 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc280)\n282:        ttng.tc_gen5_mma %dkT_61, %dkT_104, %dkT, %dvT_58, %true, %dkT_105[%true] {is_async} : !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xbf16, #shared, #smem>, !ttg.memdesc<256x64xf32, #\n283:        ttng.wait_barrier %dkT_105, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc280)\n285:        scf.yield %true : i1 loc(#loc106)\n309:      %dkT_55 = ttng.tmem_load %dkT : !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> -> tensor<256x64xf32, #linear1> loc(#loc280)\n318:      %dvT_56 = ttng.tmem_load %dvT : !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> -> tensor<256x64xf32, #linear1> loc(#loc279)","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"697ea53c-5834-4510-a114-34a0b7d3f46f","timestamp":"2026-10-18T11:59:49.376Z","tool_use_result":{"stdout":"151:      %k_29 = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc173)\n153:      %k_31 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc173)\n156:      ttng.async_tma_copy_global_to_local %k_desc_27[%i_end_24, %k] %k_29, %k_31, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shar\n157:      ttng.wait_barrier %k_31, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc173)\n159:      %v = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc175)\n161:      %v_32 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc175)\n164:      ttng.async_tma_copy_global_to_local %v_desc_28[%i_end_24, %k] %v, %v_32, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shared,\n165:      ttng.wait_barrier %v_32, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc175)\n172:        scf.yield %i_end_59 : i32 loc(#loc265)\n174:        scf.yield %s1_19 : i32 loc(#loc48)\n194:      %dvT = ttng.tmem_alloc : () -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc279)\n195:      ttng.tmem_store %cst_0, %dvT, %true : tensor<256x64xf32, #linear1> -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc279)\n196:      %dkT = ttng.tmem_alloc : () -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc280)\n197:      ttng.tmem_store %cst_0, %dkT, %true : tensor<256x64xf32, #linear1> -> !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> loc(#loc280)\n198:      %s_42 = ttng.tmem_alloc : () -> !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> loc(#loc281)\n199:      %dp_43 = ttng.tmem_alloc : () -> !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> loc(#loc282)\n200:      %dvT_44 = scf.for %dvT_57 = %c0_i32 to %3 step %c1_i32 iter_args(%dvT_58 = %false) -> (i1)  : i32 {\n208:        %q_60 = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc285)\n210:        %q_62 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc285)\n213:        ttng.async_tma_copy_global_to_local %q_desc_25[%25, %q] %q_60, %q_62, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shared, \n214:        ttng.wait_barrier %q_62, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc285)\n216:        %do = ttg.local_alloc : () -> !ttg.memdesc<64x256xbf16, #shared, #smem, mutable> loc(#loc287)\n218:        %do_64 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc287)\n221:        ttng.async_tma_copy_global_to_local %do_desc_26[%25, %q] %do, %do_64, %true : !tt.tensordesc<tensor<64x256xbf16, #shared>>, !ttg.memdesc<1xi64, #shared2, #smem, mutable> -> !ttg.memdesc<64x256xbf16, #shared, \n222:        ttng.wait_barrier %do_64, %c0_i32 : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc287)\n237:        %s_77 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc281)\n239:        ttng.tc_gen5_mma %q_60, %s_30, %s_42, %false, %true, %s_77[%true] {is_async} : !ttg.memdesc<64x256xbf16, #shared, #smem, mutable>, !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xf32, \n240:        ttng.wait_barrier %s_77, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc281)\n254:        %dp_90 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc282)\n256:        ttng.tc_gen5_mma %do, %dp, %dp_43, %false, %true, %dp_90[%true] {is_async} : !ttg.memdesc<64x256xbf16, #shared, #smem, mutable>, !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xf32, #t\n257:        ttng.wait_barrier %dp_90, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc282)\n261:        %s_92 = ttng.tmem_load %s_42 : !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> -> tensor<64x64xf32, #linear> loc(#loc281)\n267:        %dvT_98 = ttg.local_alloc %dvT_97 : (tensor<64x64xbf16, #linear>) -> !ttg.memdesc<64x64xbf16, #shared, #smem> loc(#loc307)\n269:        %dvT_99 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc279)\n271:        ttng.tc_gen5_mma %dvT_63, %dvT_98, %dvT, %dvT_58, %true, %dvT_99[%true] {is_async} : !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xbf16, #shared, #smem>, !ttg.memdesc<256x64xf32, #tm\n272:        ttng.wait_barrier %dvT_99, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc279)\n274:        %dp_100 = ttng.tmem_load %dp_43 : !ttg.memdesc<64x64xf32, #tmem1, #ttng.tensor_memory, mutable> -> tensor<64x64xf32, #linear> loc(#loc282)\n278:        %dkT_104 = ttg.local_alloc %dkT_103 : (tensor<64x64xbf16, #linear>) -> !ttg.memdesc<64x64xbf16, #shared, #smem> loc(#loc309)\n280:        %dkT_105 = ttg.local_alloc : () -> !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc280)\n282:        ttng.tc_gen5_mma %dkT_61, %dkT_104, %dkT, %dvT_58, %true, %dkT_105[%true] {is_async} : !ttg.memdesc<256x64xbf16, #shared1, #smem, mutable>, !ttg.memdesc<64x64xbf16, #shared, #smem>, !ttg.memdesc<256x64xf32, #\n283:        ttng.wait_barrier %dkT_105, %c0_i32, %true : !ttg.memdesc<1xi64, #shared2, #smem, mutable> loc(#loc280)\n285:        scf.yield %true : i1 loc(#loc106)\n309:      %dkT_55 = ttng.tmem_load %dkT : !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> -> tensor<256x64xf32, #linear1> loc(#loc280)\n318:      %dvT_56 = ttng.tmem_load %dvT : !ttg.memdesc<256x64xf32, #tmem, #ttng.tensor_memory, mutable> -> tensor<256x64xf32, #linear1> loc(#loc279)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01EVwDduykoCCoWCWYKEMch8","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"1cf2169a-d5de-461a-bf72-365d64697c0d"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":200,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"f0c3697a-835b-4a96-b28d-998fd0908cb1"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":300,"estimated_tokens_delta":100,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"f47660de-a84f-42e6-b1a5-af6ca1491479"}
{"type":"system","subtype":"thinking_tokens","estimated_tokens":450,"estimated_tokens_delta":150,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"89305523-d129-48f6-819b-2c7e879ecdc6"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019YX6j1WMSqb1tFtR4GkcMw","type":"tool_result","content":"ERR out of resource: shared memory, Required: 279136, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\nshared 279136 tmem 512 nregs None spills None\n      2 memdesc<1x64xf32, #shared2, #smem, mutable>\n      2 memdesc<1xi64, #shared2, #smem, mutable>\n      4 memdesc<2x1xi64, #shared2, #smem, mutable>\n      1 memdesc<3x1xi64, #shared2, #smem, mutable>\n      2 memdesc<3x64x256xbf16, #shared, #smem, mutable>\n      2 memdesc<64x256xbf16, #shared, #smem, mutable>\n      2 memdesc<64x64xbf16, #shared, #smem, mutable>\n205:      %dvT_46 = ttg.local_alloc : () -> !ttg.memdesc<2x1xi64, #shared2, #smem, mutable> loc(#loc280)\n210:      %dvT_49 = ttg.local_alloc : () -> !ttg.memdesc<64x64xbf16, #shared, #smem, mutable> loc(#loc281)\n211:      %dvT_50 = ttg.local_alloc : () -> !ttg.memdesc<2x1xi64, #shared2, #smem, mutable> loc(#loc280)\n216:      %dkT_53 = ttg.local_alloc : () -> !ttg.memdesc<64x64xbf16, #shared, #smem, mutable> loc(#loc282)\n217:      %dvT_54 = ttg.local_alloc : () -> !ttg.memdesc<2x1xi64, #shared2, #smem, mutable> loc(#loc280)\n222:      %q = ttg.local_alloc : () -> !ttg.memdesc<3x64x256xbf16, #shared, #smem, mutable> loc(#loc283)\n223:      %do = ttg.local_alloc : () -> !ttg.memdesc<3x64x256xbf16, #shared, #smem, mutable> loc(#loc284)\n224:      %lse2_57 = ttg.local_alloc : () -> !ttg.memdesc<1x64xf32, #shared2, #smem, mutable> loc(#loc285)\n225:      %dlt = ttg.local_alloc : () -> !ttg.memdesc<1x64xf32, #shared2, #smem, mutable> loc(#loc286)\n226:      %dvT_58 = ttg.local_alloc : () -> !ttg.memdesc<3x1xi64, #shared2, #smem, mutable> loc(#loc280)\n517:      %26 = ttg.convert_layout %25 : tensor<256x64xbf16, #linear1> -> tensor<256x64xbf16, #blocked1> loc(#loc119)\n525:      %32 = ttg.convert_layout %31 : tensor<256x64xbf16, #linear1> -> tensor<256x64xbf16, #blocked1> loc(#loc124)","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"b2b9ea5d-2d1f-4ab9-81d6-b17825d79e70","timestamp":"2026-10-18T12:00:05.678Z","tool_use_result":{"stdout":"ERR out of resource: shared memory, Required: 279136, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\nshared 279136 tmem 512 nregs None spills None\n      2 memdesc<1x64xf32, #shared2, #smem, mutable>\n      2 memdesc<1xi64, #shared2, #smem, mutable>\n      4 memdesc<2x1xi64, #shared2, #smem, mutable>\n      1 memdesc<3x1xi64, #shared2, #smem, mutable>\n      2 memdesc<3x64x256xbf16, #shared, #smem, mutable>\n      2 memdesc<64x256xbf16, #shared, #smem, mutable>\n      2 memdesc<64x64xbf16, #shared, #smem, mutable>\n205:      %dvT_46 = ttg.local_alloc : () -> !ttg.memdesc<2x1xi64, #shared2, #smem, mutable> loc(#loc280)\n210:      %dvT_49 = ttg.local_alloc : () -> !ttg.memdesc<64x64xbf16, #shared, #smem, mutable> loc(#loc281)\n211:      %dvT_50 = ttg.local_alloc : () -> !ttg.memdesc<2x1xi64, #shared2, #smem, mutable> loc(#loc280)\n216:      %dkT_53 = ttg.local_alloc : () -> !ttg.memdesc<64x64xbf16, #shared, #smem, mutable> loc(#loc282)\n217:      %dvT_54 = ttg.local_alloc : () -> !ttg.memdesc<2x1xi64, #shared2, #smem, mutable> loc(#loc280)\n222:      %q = ttg.local_alloc : () -> !ttg.memdesc<3x64x256xbf16, #shared, #smem, mutable> loc(#loc283)\n223:      %do = ttg.local_alloc : () -> !ttg.memdesc<3x64x256xbf16, #shared, #smem, mutable> loc(#loc284)\n224:      %lse2_57 = ttg.local_alloc : () -> !ttg.memdesc<1x64xf32, #shared2, #smem, mutable> loc(#loc285)\n225:      %dlt = ttg.local_alloc : () -> !ttg.memdesc<1x64xf32, #shared2, #smem, mutable> loc(#loc286)\n226:      %dvT_58 = ttg.local_alloc : () -> !ttg.memdesc<3x1xi64, #shared2, #smem, mutable> loc(#loc280)\n517:      %26 = ttg.convert_layout %25 : tensor<256x64xbf16, #linear1> -> tensor<256x64xbf16, #blocked1> loc(#loc119)\n525:      %32 = ttg.convert_layout %31 : tensor<256x64xbf16, #linear1> -> tensor<256x64xbf16, #blocked1> loc(#loc124)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_019YX6j1WMSqb1tFtR4GkcMw","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Uod5pAUdLdVy4MKtMsL4u8","type":"tool_result","content":"1:import torch\n2:import triton\n3:import triton.language as tl\n175:CFG = dict(KV_BM=128, KV_BN=64, KV_W=8, KV_S=1, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2, KV_WS=False, Q_WS=False)\nshape 0 {i}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 3512.3 us  expert 1518  ratio 0.432\nshape 1 {i}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1106.9 us  expert 510  ratio 0.461\nshape 2 {i}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 1880.1 us  expert 980  ratio 0.521\nshape 3 {i}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 2391.1 us  expert 1165  ratio 0.487\nshape 4 {i}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 573.4 us  expert 302  ratio 0.527\nshape 5 {i}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 1082.4 us  expert 520  ratio 0.480\ngeomean ratio vs expert: 0.48362803510026886","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"6ae4b4bd-f94f-47a6-9143-cc1baf691d2a","timestamp":"2026-10-18T12:01:21.737Z","tool_use_result":{"stdout":"1:import torch\n2:import triton\n3:import triton.language as tl\n175:CFG = dict(KV_BM=128, KV_BN=64, KV_W=8, KV_S=1, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2, KV_WS=False, Q_WS=False)\nshape 0 {i}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 3512.3 us  expert 1518  ratio 0.432\nshape 1 {i}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1106.9 us  expert 510  ratio 0.461\nshape 2 {i}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 1880.1 us  expert 980  ratio 0.521\nshape 3 {i}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 2391.1 us  expert 1165  ratio 0.487\nshape 4 {i}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 573.4 us  expert 302  ratio 0.527\nshape 5 {i}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 1082.4 us  expert 520  ratio 0.480\ngeomean ratio vs expert: 0.48362803510026886","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01Uod5pAUdLdVy4MKtMsL4u8","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BxjmnhnCHGhWDnGFks5Li2","type":"tool_result","content":"#tmem = #ttng.tensor_memory_encoding<blockM = 128, blockN = 64, colStride = 1>\n#tmem1 = #ttng.tensor_memory_encoding<blockM = 64, blockN = 64, colStride = 1>\n815","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"bbf6dd6d-07da-4878-a2d5-eb3b5e1302b9","timestamp":"2026-10-18T12:01:25.217Z","tool_use_result":{"stdout":"#tmem = #ttng.tensor_memory_encoding<blockM = 128, blockN = 64, colStride = 1>\n#tmem1 = #ttng.tensor_memory_encoding<blockM = 64, blockN = 64, colStride = 1>\n815","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01BxjmnhnCHGhWDnGFks5Li2","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"system","subtype":"task_started","task_id":"b9bt9yhy4","run_id":"0mvdryzsw-f7230dd5","tool_use_id":"toolu_01PnRRkKhF1XGAncSWGdy3ma","description":"Try formulation 1 for dkdv (keys as M)","is_backgrounded":false,"task_type":"local_bash","uuid":"5a2da6d3-89b6-4384-844f-99f7d02137dd","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PnRRkKhF1XGAncSWGdy3ma","type":"tool_result","content":"== {\"KV_BM\":64,\"KV_W\":4,\"KV_S\":1}\n0 {'_prep': 29.7, '_dkdv': 3423.3, '_dq': 1339.4}\n== {\"KV_BM\":128,\"KV_W\":8,\"KV_S\":1}\n0 {'_prep': 30.0, '_dkdv': 2712.0, '_dq': 1348.4}\n== {\"KV_BM\":64,\"KV_W\":4,\"KV_S\":2}\ntriton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 262752, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n== {\"KV_BM\":64,\"KV_W\":4,\"KV_S\":2,\"KREG\":true}\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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"21130cce-c8d1-4411-8b96-72d010fbd328","timestamp":"2026-10-18T12:02:19.492Z","tool_use_result":{"stdout":"== {\"KV_BM\":64,\"KV_W\":4,\"KV_S\":1}\n0 {'_prep': 29.7, '_dkdv': 3423.3, '_dq': 1339.4}\n== {\"KV_BM\":128,\"KV_W\":8,\"KV_S\":1}\n0 {'_prep': 30.0, '_dkdv': 2712.0, '_dq': 1348.4}\n== {\"KV_BM\":64,\"KV_W\":4,\"KV_S\":2}\ntriton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 262752, Hardware limit: 232448. Reducing block sizes or `num_stages` may help.\n== {\"KV_BM\":64,\"KV_W\":4,\"KV_S\":2,\"KREG\":true}\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_01PnRRkKhF1XGAncSWGdy3ma","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fiSrSsfXnqpYVzvoME","type":"message","role":"assistant","content":[{"type":"text","text":"Formulation 1 is worse. The real blocker is SMEM for pipelining. Let me test whether Triton's warp specialization can compile a minimal dkdv loop at all (it would give exactly `num_stages` buffers)."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2689,"cache_read_input_tokens":129401,"cache_creation":{"ephemeral_5m_input_tokens":2689,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"df62ad4f-f489-4057-be48-02d569ea9ffa","timestamp":"2026-10-18T12:02:31.773Z","request_id":"req_011Cg9fiSPRdtA9AsUtpZ9t8"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fiSrSsfXnqpYVzvoME","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01A3HGNTYwKyfaR6hzWh2jvy","name":"Bash","input":{"command":"cat > ws_test.py << 'EOF'\nimport torch, triton, triton.language as tl, sys, os\n@triton.jit\ndef _k(Q, K, V, DO, LSE2, DELTA, DK, DV, T, qk_scale, nq,\n       H: tl.constexpr, D: tl.constexpr, BM: tl.constexpr, BN: tl.constexpr, VAR: tl.constexpr):\n    j0 = tl.program_id(0) * BN\n    q_desc = tl.make_tensor_descriptor(Q, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n    do_desc = tl.make_tensor_descriptor(DO, shape=[T, H * D], strides=[H * D, 1], block_shape=[BM, D])\n    k_desc = tl.make_tensor_descriptor(K, shape=[T, H * D], strides=[H * D, 1], block_shape=[BN, D])\n    v_desc = tl.make_tensor_descriptor(V, shape=[T, H * D], strides=[H * D, 1], block_shape=[BN, D])\n    k = k_desc.load([j0, 0])\n    v = v_desc.load([j0, 0])\n    dkT = tl.zeros([D, BN], dtype=tl.float32)\n    dvT = tl.zeros([D, BN], dtype=tl.float32)\n    for t in tl.range(0, nq, warp_specialize=True):\n        i0 = t * BM\n        q = q_desc.load([i0, 0])\n        do = do_desc.load([i0, 0])\n        ri = i0 + tl.arange(0, BM)\n        if VAR >= 1:\n            lse2 = tl.load(LSE2 + ri)\n            dlt = tl.load(DELTA + ri)\n        else:\n            lse2 = tl.zeros([BM], tl.float32)\n            dlt = tl.zeros([BM], tl.float32)\n        s = tl.dot(q, tl.trans(k))\n        p = tl.math.exp2(s * qk_scale - lse2[:, None])\n        dp = tl.dot(do, tl.trans(v))\n        ds = p * (dp - dlt[:, None])\n        dvT = tl.dot(tl.trans(do), p.to(tl.bfloat16), dvT)\n        dkT = tl.dot(tl.trans(q), ds.to(tl.bfloat16), dkT)\n    rj = j0 + tl.arange(0, BN)\n    rd = tl.arange(0, D)\n    off = rj.to(tl.int64) * H * D\n    tl.store(DK + off[None, :] + rd[:, None], dkT.to(tl.bfloat16))\n    tl.store(DV + off[None, :] + rd[:, None], dvT.to(tl.bfloat16))\n\ntriton.set_allocator(lambda size, align, stream: torch.empty(size, dtype=torch.int8, device='cuda'))\nT, H, D = 8192, 1, 256\nBM, BN, VAR, W, S = [int(x) for x in sys.argv[1:6]]\nq = torch.randn(T, H, D, device='cuda', dtype=torch.bfloat16)\nk, v, do = torch.randn_like(q), torch.randn_like(q), torch.randn_like(q)\nlse2 = torch.zeros(T, device='cuda'); dl = torch.zeros(T, device='cuda')\ndk, dv = torch.empty_like(q), torch.empty_like(q)\nnblk = 148 * 4\nnq = T // BM\nf = lambda: _k[(nblk,)](q, k, v, do, lse2, dl, dk, dv, T, 0.1, nq, H=H, D=D, BM=BM, BN=BN, VAR=VAR, num_warps=W, num_stages=S)\nf(); torch.cuda.synchronize()\ns, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\ns.record(); f(); e.record(); torch.cuda.synchronize()\nus = s.elapsed_time(e) * 1000\nfl = 4 * 2 * BM * BN * D * nq * nblk\nck = list(_k.device_caches[0][0].values())[-1]\nprint(f'BM={BM} BN={BN} VAR={VAR} W={W} S={S}: {us:.0f} us, {fl/us/1e6:.0f} TFLOPS, smem {ck.metadata.shared}, regs {ck.n_regs} spills {ck.n_spills}')\nEOF\nfor a in \"64 64 0 4 2\" \"64 64 1 4 2\" \"128 64 0 4 2\" \"128 64 1 4 1\" \"64 64 1 4 3\"; do timeout 300 python ws_test.py $a 2>&1 | grep -v \"^  \\|tt.func\\|Assert\" | tail -2; done","description":"Minimal warp-specialized dkdv loop test","timeout":1200000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2689,"cache_read_input_tokens":129401,"cache_creation":{"ephemeral_5m_input_tokens":2689,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"4cd529f4-7435-40cf-9bac-38eec36623a9","timestamp":"2026-10-18T12:02:43.965Z","request_id":"req_011Cg9fiSPRdtA9AsUtpZ9t8"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01A3HGNTYwKyfaR6hzWh2jvy","type":"tool_result","content":"Traceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"bc44177d-f138-4701-b222-3cb92580b70d","timestamp":"2026-10-18T12:03:09.273Z","tool_use_result":{"stdout":"Traceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed\nTraceback (most recent call last):\nRuntimeError: PassManager::run failed","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01A3HGNTYwKyfaR6hzWh2jvy","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SsQjDgG9KjcE8oGA3kgUmX","type":"tool_result","content":"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.\n/workspace/dev/ws_test.py:3:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/dev/ws_test.py:3:0: 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.`\nRuntimeError: PassManager::run failed\ntriton.runtime.errors.OutOfResources: 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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"1f55fc95-23cc-41ed-bd29-ac4d78c37af9","timestamp":"2026-10-18T12:03:44.150Z","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.\n/workspace/dev/ws_test.py:3:0: error: Failures have been detected while processing an MLIR pass pipeline\n/workspace/dev/ws_test.py:3:0: 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.`\nRuntimeError: PassManager::run failed\ntriton.runtime.errors.OutOfResources: 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_01SsQjDgG9KjcE8oGA3kgUmX","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2349,"cache_read_input_tokens":136990,"cache_creation":{"ephemeral_5m_input_tokens":2349,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"70b1b7a1-7d19-476f-9ec2-4de0f6b5a3b6","timestamp":"2026-10-18T12:06:03.013Z","request_id":"req_011Cg9fwxwp6Z6YBofWbkeRC","thinking_duration_ms":38517,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fwyJsuA8JmYpcEerj1","type":"message","role":"assistant","content":[{"type":"text","text":"Formulation 1 with 32-query tiles and 3 stages is marginally better (2121 vs 2166 us). Let me combine it with the separate unmasked fast-path loop from v2."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2349,"cache_read_input_tokens":136990,"cache_creation":{"ephemeral_5m_input_tokens":2349,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"538f0cd7-b16b-40c3-a8f7-5218bffdb742","timestamp":"2026-10-18T12:06:03.016Z","request_id":"req_011Cg9fwxwp6Z6YBofWbkeRC"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9fwyJsuA8JmYpcEerj1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UfgC5be1uwxNDuk8TqrC3J","name":"Bash","input":{"command":"python - << 'EOF'\nsrc = open('v2cfg.py').read()\ns4 = open('v4cfg.py').read()\na = s4.index(\"@triton.jit\\ndef _dkdv_step1(\")\nb = s4.index(\"@triton.jit\\ndef _dkdv(\")\nsrc = src.replace(\"\\n@triton.jit\\ndef _dkdv(\", \"\\n\" + s4[a:b] + \"@triton.jit\\ndef _dkdv(\")\nsrc = src.replace(\"dkT, dvT = _dkdv_step(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT,\", \"dkT, dvT = _dkdv_step1(q_desc, do_desc, LSE2, DELTA, k, v, dkT, dvT,\")\nsrc = src.replace(\"        dkT = tl.zeros([D, BN], dtype=tl.float32)\\n        dvT = tl.zeros([D, BN], dtype=tl.float32)\\n\", \"        dkT = tl.zeros([BN, D], dtype=tl.float32)\\n        dvT = tl.zeros([BN, D], dtype=tl.float32)\\n\")\nsrc = src.replace(\"\"\"        msk = (rj < s1)[None, :]\n        tl.store(DK + off[None, :] + rd[:, None], (dkT * sm_scale).to(tl.bfloat16), mask=msk)\n        tl.store(DV + off[None, :] + rd[:, None], dvT.to(tl.bfloat16), mask=msk)\"\"\", \"\"\"        msk = (rj < s1)[:, None]\n        tl.store(DK + off[:, None] + rd[None, :], (dkT * sm_scale).to(tl.bfloat16), mask=msk)\n        tl.store(DV + off[:, None] + rd[None, :], dvT.to(tl.bfloat16), mask=msk)\"\"\")\nsrc = src.replace(\"CFG = dict(KV_BM=128, KV_BN=64, KV_W=8, KV_S=1, Q_BM=128, Q_BN=32, Q_W=4, Q_S=2,\", \"CFG = dict(KV_BM=32, KV_BN=64, KV_W=4, KV_S=3, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2,\")\nopen('v5cfg.py', 'w').write(src)\nEOF\ngrep -n \"^CFG\" v5cfg.py; ./sweep.sh v5cfg.py 0 '{}' '{\"KV_S\":2}' '{\"KV_BM\":64,\"KV_S\":1,\"KV_W\":4}' 2>&1 | grep -v \"^  \\|tt.func\\|Assert\"","description":"Formulation 1 with unmasked fast path","timeout":1800000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2349,"cache_read_input_tokens":136990,"cache_creation":{"ephemeral_5m_input_tokens":2349,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"afc5cd49-5e7f-4807-a7a5-cc4e4b36f5a7","timestamp":"2026-10-18T12:06:09.453Z","request_id":"req_011Cg9fwxwp6Z6YBofWbkeRC"}
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"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1384,"cache_read_input_tokens":145648,"cache_creation":{"ephemeral_5m_input_tokens":1384,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"8a9d25aa-d5a5-40d4-a624-76ad74909160","timestamp":"2026-10-18T12:08:43.263Z","request_id":"req_011Cg9gAAebALXtZ3hGXidMD","thinking_duration_ms":33469,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gAB3Ajz16hF8maauht","type":"message","role":"assistant","content":[{"type":"text","text":"Current: 0.5125x geomean (saved). Before more speed work, correctness on edge shapes is critical (a failure scores -1). Let me test odd shapes."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1384,"cache_read_input_tokens":145648,"cache_creation":{"ephemeral_5m_input_tokens":1384,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"2e6ef81a-b43e-4914-a5a4-9dc63c22b993","timestamp":"2026-10-18T12:08:43.266Z","request_id":"req_011Cg9gAAebALXtZ3hGXidMD"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gAB3Ajz16hF8maauht","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DoeTtgapahrtsczDKoNw2u","name":"Bash","input":{"command":"cat > /workspace/dev/edge.py << 'EOF'\nimport sys, torch\nsys.path.insert(0, '/workspace'); sys.path.insert(0, '/workspace/dev')\nimport task as T\nfrom bench import load, exp_full\nsol = load(sys.argv[1], 'sol')\nshapes = [\n    dict(n=1, L=1, Hq=2, Hkv=1, window=-1),\n    dict(n=3, L=100, Hq=4, Hkv=1, window=-1),\n    dict(n=2, L=37, Hq=2, Hkv=2, window=0),\n    dict(n=1, L=300, Hq=4, Hkv=2, window=5),\n    dict(n=5, L=129, Hq=8, Hkv=4, window=64, ragged=True),\n    dict(n=4, L=513, Hq=16, Hkv=2, window=-1, ragged=True),\n    dict(n=1, L=1000, Hq=6, Hkv=3, window=2000),\n    dict(n=7, L=200, Hq=4, Hkv=4, window=63, ragged=True),\n    dict(n=2, L=2048, Hq=4, Hkv=1, window=-1, long=3000),\n    dict(n=16, L=65, Hq=2, Hkv=1, window=-1, ragged=True),\n]\nfor sh in shapes:\n    case = T.TASK.make_case(sh, 3, 'cuda')\n    exp = T.TASK.reference([a.clone() if torch.is_tensor(a) else a for a in case.args])\n    for j in (9, 10, 11):\n        case.args[j].fill_(float('nan'))\n    sol.kernel_fn(*case.args)\n    torch.cuda.synchronize()\n    ok, st = T.TASK.check({j: case.args[j] for j in (9, 10, 11)}, exp)\n    print('OK ' if ok else 'BAD', sh, {k: (v['bad'], v['nonfinite'], round(v['max_over_tol'], 3)) for k, v in st.items()} if 'error' not in st else st, flush=True)\nEOF\ncd /workspace && timeout 900 python dev/edge.py solution.py 2>&1 | tail -12","description":"Run edge-shape correctness tests","timeout":900000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1384,"cache_read_input_tokens":145648,"cache_creation":{"ephemeral_5m_input_tokens":1384,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"88e9f889-0dda-4a04-899a-cdece263228c","timestamp":"2026-10-18T12:08:48.905Z","request_id":"req_011Cg9gAAebALXtZ3hGXidMD"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DoeTtgapahrtsczDKoNw2u","type":"tool_result","content":"OK  {'n': 1, 'L': 1, 'Hq': 2, 'Hkv': 1, 'window': -1} {'dq': (0, 0, 0.0), 'dk': (0, 0, 0.0), 'dv': (0, 0, 0.244)}\nOK  {'n': 3, 'L': 100, 'Hq': 4, 'Hkv': 1, 'window': -1} {'dq': (0, 0, 0.41), 'dk': (0, 0, 0.32), 'dv': (0, 0, 0.362)}\nOK  {'n': 2, 'L': 37, 'Hq': 2, 'Hkv': 2, 'window': 0} {'dq': (0, 0, 0.001), 'dk': (0, 0, 0.001), 'dv': (0, 0, 0.0)}\nOK  {'n': 1, 'L': 300, 'Hq': 4, 'Hkv': 2, 'window': 5} {'dq': (0, 0, 0.41), 'dk': (0, 0, 0.36), 'dv': (0, 0, 0.364)}\nOK  {'n': 5, 'L': 129, 'Hq': 8, 'Hkv': 4, 'window': 64, 'ragged': True} {'dq': (0, 0, 0.387), 'dk': (0, 0, 0.404), 'dv': (0, 0, 0.408)}\nOK  {'n': 4, 'L': 513, 'Hq': 16, 'Hkv': 2, 'window': -1, 'ragged': True} {'dq': (0, 0, 0.395), 'dk': (0, 0, 0.332), 'dv': (0, 0, 0.305)}\nOK  {'n': 1, 'L': 1000, 'Hq': 6, 'Hkv': 3, 'window': 2000} {'dq': (0, 0, 0.386), 'dk': (0, 0, 0.402), 'dv': (0, 0, 0.369)}\nOK  {'n': 7, 'L': 200, 'Hq': 4, 'Hkv': 4, 'window': 63, 'ragged': True} {'dq': (0, 0, 0.395), 'dk': (0, 0, 0.413), 'dv': (0, 0, 0.43)}\nOK  {'n': 2, 'L': 2048, 'Hq': 4, 'Hkv': 1, 'window': -1, 'long': 3000} {'dq': (0, 0, 0.414), 'dk': (0, 0, 0.331), 'dv': (0, 0, 0.31)}\nOK  {'n': 16, 'L': 65, 'Hq': 2, 'Hkv': 1, 'window': -1, 'ragged': True} {'dq': (0, 0, 0.392), 'dk': (0, 0, 0.399), 'dv': (0, 0, 0.397)}","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"db6dc99e-28af-459f-9140-79cffb6e6403","timestamp":"2026-10-18T12:09:21.577Z","tool_use_result":{"stdout":"OK  {'n': 1, 'L': 1, 'Hq': 2, 'Hkv': 1, 'window': -1} {'dq': (0, 0, 0.0), 'dk': (0, 0, 0.0), 'dv': (0, 0, 0.244)}\nOK  {'n': 3, 'L': 100, 'Hq': 4, 'Hkv': 1, 'window': -1} {'dq': (0, 0, 0.41), 'dk': (0, 0, 0.32), 'dv': (0, 0, 0.362)}\nOK  {'n': 2, 'L': 37, 'Hq': 2, 'Hkv': 2, 'window': 0} {'dq': (0, 0, 0.001), 'dk': (0, 0, 0.001), 'dv': (0, 0, 0.0)}\nOK  {'n': 1, 'L': 300, 'Hq': 4, 'Hkv': 2, 'window': 5} {'dq': (0, 0, 0.41), 'dk': (0, 0, 0.36), 'dv': (0, 0, 0.364)}\nOK  {'n': 5, 'L': 129, 'Hq': 8, 'Hkv': 4, 'window': 64, 'ragged': True} {'dq': (0, 0, 0.387), 'dk': (0, 0, 0.404), 'dv': (0, 0, 0.408)}\nOK  {'n': 4, 'L': 513, 'Hq': 16, 'Hkv': 2, 'window': -1, 'ragged': True} {'dq': (0, 0, 0.395), 'dk': (0, 0, 0.332), 'dv': (0, 0, 0.305)}\nOK  {'n': 1, 'L': 1000, 'Hq': 6, 'Hkv': 3, 'window': 2000} {'dq': (0, 0, 0.386), 'dk': (0, 0, 0.402), 'dv': (0, 0, 0.369)}\nOK  {'n': 7, 'L': 200, 'Hq': 4, 'Hkv': 4, 'window': 63, 'ragged': True} {'dq': (0, 0, 0.395), 'dk': (0, 0, 0.413), 'dv': (0, 0, 0.43)}\nOK  {'n': 2, 'L': 2048, 'Hq': 4, 'Hkv': 1, 'window': -1, 'long': 3000} {'dq': (0, 0, 0.414), 'dk': (0, 0, 0.331), 'dv': (0, 0, 0.31)}\nOK  {'n': 16, 'L': 65, 'Hq': 2, 'Hkv': 1, 'window': -1, 'ragged': True} {'dq': (0, 0, 0.392), 'dk': (0, 0, 0.399), 'dv': (0, 0, 0.397)}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01DoeTtgapahrtsczDKoNw2u","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gFZNjB27nHigVZkXEf","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":5331,"cache_read_input_tokens":147032,"cache_creation":{"ephemeral_5m_input_tokens":5331,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"4a80c62f-149b-448b-a636-3da4b628ffe1","timestamp":"2026-10-18T12:09:26.618Z","request_id":"req_011Cg9gFZ1eD1RPaiNE3hHzU","thinking_duration_ms":3513,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gFZNjB27nHigVZkXEf","type":"message","role":"assistant","content":[{"type":"text","text":"All edge shapes pass. Now hidden-distribution style inputs (outliers, huge/tiny scales, zero rows, mixed row scales):"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5331,"cache_read_input_tokens":147032,"cache_creation":{"ephemeral_5m_input_tokens":5331,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"c40c1ebd-9266-4a01-9eb5-21b5be7cc04c","timestamp":"2026-10-18T12:09:26.618Z","request_id":"req_011Cg9gFZ1eD1RPaiNE3hHzU"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gFZNjB27nHigVZkXEf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01B3iQmuCuGbin3wjMBwo1Jo","name":"Bash","input":{"command":"cat > /workspace/dev/dist.py << 'EOF'\nimport sys, torch\nsys.path.insert(0, '/workspace'); sys.path.insert(0, '/workspace/dev')\nimport task as T\nfrom task_api import Case\nfrom bench import load\nsol = load(sys.argv[1], 'sol')\ndef mk(shape, kind, seed=5):\n    torch.manual_seed(seed)\n    D = 256; Hq, Hkv, W = shape['Hq'], shape['Hkv'], shape['window']\n    g = torch.Generator(device='cuda'); g.manual_seed(seed)\n    lens = T.seq_lens(shape, g, 'cuda'); Tt = int(lens.sum())\n    cu = torch.zeros(lens.numel() + 1, device='cuda', dtype=torch.int32); cu[1:] = torch.cumsum(lens, 0)\n    def r(H):\n        x = torch.randn(Tt, H * D, device='cuda')\n        if kind == 'outlier':\n            m = torch.rand_like(x) < 0.001; x = torch.where(m, x * 80, x)\n        elif kind == 'large':\n            x = x * 30\n        elif kind == 'small':\n            x = x * 1e-4\n        elif kind == 'zero_rows':\n            x[torch.rand(Tt, device='cuda') < 0.1] = 0\n        elif kind == 'rowscale':\n            x = x * torch.exp(torch.randn(Tt, 1, device='cuda') * 3)\n        elif kind == 'shift':\n            x = x + 3.0\n        return x.view(Tt, H, D).to(torch.bfloat16)\n    q, k, v, do = r(Hq), r(Hkv), r(Hkv), r(Hq)\n    o = torch.empty(Tt, Hq, D, device='cuda', dtype=torch.bfloat16)\n    lse = torch.empty(Hq, Tt, device='cuda')\n    ml = int(lens.max())\n    T._tf32(T._forward, [q, k, v, o, do, lse, cu, ml, W])\n    args = [q, k, v, o, do, lse, cu, ml, W, torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)]\n    return args\nfor kind in ['outlier', 'large', 'small', 'zero_rows', 'rowscale', 'shift']:\n    for sh in [dict(n=2, L=700, Hq=4, Hkv=2, window=-1, ragged=True), dict(n=1, L=1500, Hq=2, Hkv=1, window=300)]:\n        args = mk(sh, kind)\n        exp = T.TASK.reference([a.clone() if torch.is_tensor(a) else a for a in args])\n        for j in (9, 10, 11): args[j].fill_(float('nan'))\n        sol.kernel_fn(*args); torch.cuda.synchronize()\n        ok, st = T.TASK.check({j: args[j] for j in (9, 10, 11)}, exp)\n        print('OK ' if ok else 'BAD', kind, sh['window'], {k: (v['bad'], v['nonfinite'], round(v['max_over_tol'], 3)) for k, v in st.items()}, flush=True)\nEOF\ntimeout 900 python dev/dist.py solution.py 2>&1 | tail -12","description":"Test hidden-like input distributions","timeout":900000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5331,"cache_read_input_tokens":147032,"cache_creation":{"ephemeral_5m_input_tokens":5331,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"31373038-e941-4f7c-847f-e5fd2f17935c","timestamp":"2026-10-18T12:09:35.026Z","request_id":"req_011Cg9gFZ1eD1RPaiNE3hHzU"}
{"type":"system","subtype":"task_started","task_id":"b7ii63hnl","run_id":"0mvds94op-4a52447b","tool_use_id":"toolu_01B3iQmuCuGbin3wjMBwo1Jo","description":"Test hidden-like input distributions","is_backgrounded":false,"task_type":"local_bash","uuid":"b818c0e8-17ab-47ff-95f5-d1984f013234","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01B3iQmuCuGbin3wjMBwo1Jo","type":"tool_result","content":"OK  outlier -1 {'dq': (0, 0, 0.345), 'dk': (0, 0, 0.328), 'dv': (0, 0, 0.351)}\nOK  outlier 300 {'dq': (0, 0, 0.281), 'dk': (0, 0, 0.28), 'dv': (0, 0, 0.281)}\nOK  large -1 {'dq': (0, 0, 0.021), 'dk': (0, 0, 0.023), 'dv': (0, 0, 0.023)}\nOK  large 300 {'dq': (0, 0, 0.02), 'dk': (0, 0, 0.022), 'dv': (0, 0, 0.022)}\nOK  small -1 {'dq': (0, 0, 0.402), 'dk': (0, 0, 0.34), 'dv': (0, 0, 0.319)}\nOK  small 300 {'dq': (0, 0, 0.294), 'dk': (0, 0, 0.305), 'dv': (0, 0, 0.3)}\nOK  zero_rows -1 {'dq': (0, 0, 0.389), 'dk': (0, 0, 0.382), 'dv': (0, 0, 0.391)}\nOK  zero_rows 300 {'dq': (0, 0, 0.317), 'dk': (0, 0, 0.316), 'dv': (0, 0, 0.303)}\nOK  rowscale -1 {'dq': (0, 0, nan), 'dk': (0, 0, nan), 'dv': (0, 0, nan)}\nOK  rowscale 300 {'dq': (0, 0, nan), 'dk': (0, 0, nan), 'dv': (0, 0, nan)}\nOK  shift -1 {'dq': (0, 0, 0.264), 'dk': (0, 0, 0.27), 'dv': (0, 0, 0.302)}\nOK  shift 300 {'dq': (0, 0, 0.265), 'dk': (0, 0, 0.244), 'dv': (0, 0, 0.29)}","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"2a988d34-3803-4309-b724-cf18d1c6caed","timestamp":"2026-10-18T12:09:45.893Z","tool_use_result":{"stdout":"OK  outlier -1 {'dq': (0, 0, 0.345), 'dk': (0, 0, 0.328), 'dv': (0, 0, 0.351)}\nOK  outlier 300 {'dq': (0, 0, 0.281), 'dk': (0, 0, 0.28), 'dv': (0, 0, 0.281)}\nOK  large -1 {'dq': (0, 0, 0.021), 'dk': (0, 0, 0.023), 'dv': (0, 0, 0.023)}\nOK  large 300 {'dq': (0, 0, 0.02), 'dk': (0, 0, 0.022), 'dv': (0, 0, 0.022)}\nOK  small -1 {'dq': (0, 0, 0.402), 'dk': (0, 0, 0.34), 'dv': (0, 0, 0.319)}\nOK  small 300 {'dq': (0, 0, 0.294), 'dk': (0, 0, 0.305), 'dv': (0, 0, 0.3)}\nOK  zero_rows -1 {'dq': (0, 0, 0.389), 'dk': (0, 0, 0.382), 'dv': (0, 0, 0.391)}\nOK  zero_rows 300 {'dq': (0, 0, 0.317), 'dk': (0, 0, 0.316), 'dv': (0, 0, 0.303)}\nOK  rowscale -1 {'dq': (0, 0, nan), 'dk': (0, 0, nan), 'dv': (0, 0, nan)}\nOK  rowscale 300 {'dq': (0, 0, nan), 'dk': (0, 0, nan), 'dv': (0, 0, nan)}\nOK  shift -1 {'dq': (0, 0, 0.264), 'dk': (0, 0, 0.27), 'dv': (0, 0, 0.302)}\nOK  shift 300 {'dq': (0, 0, 0.265), 'dk': (0, 0, 0.244), 'dv': (0, 0, 0.29)}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01B3iQmuCuGbin3wjMBwo1Jo","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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"5700b16f-d24e-4d3f-8a9d-2bf2bcc17152"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gHMHkFkTW5sH41Kz8M","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":2370,"cache_read_input_tokens":152363,"cache_creation":{"ephemeral_5m_input_tokens":2370,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"2f47a804-5112-4ef0-8750-f47447642318","timestamp":"2026-10-18T12:09:53.262Z","request_id":"req_011Cg9gHLwfEwPBTdhjemzAQ","thinking_duration_ms":5637,"thinking_display":"updates"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gHMHkFkTW5sH41Kz8M","type":"message","role":"assistant","content":[{"type":"text","text":"All distributions pass. Next: fold the delta/lse preprocessing into the dq kernel (run dq first, it writes delta for dkdv), removing a 15-50 µs bandwidth-bound pass."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2370,"cache_read_input_tokens":152363,"cache_creation":{"ephemeral_5m_input_tokens":2370,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"798c6cfb-3074-4b7b-b5f4-25e106c897e0","timestamp":"2026-10-18T12:09:53.263Z","request_id":"req_011Cg9gHLwfEwPBTdhjemzAQ"}
{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gHMHkFkTW5sH41Kz8M","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FYw511SJ5iRAso5BPs9gXM","name":"Bash","input":{"command":"cd /workspace/dev && python - << 'EOF'\nsrc = open('v5cfg.py').read()\n# dq: compute delta from o and do, write it; lse2 from lse\nold = \"\"\"        lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0)\n        dlt = tl.load(DELTA + hoff + ri, mask=ri < T, other=0.0)\n        lo = s0\"\"\"\nnew = \"\"\"        lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0) * LOG2E\n        rd = tl.arange(0, D)\n        off = (ri.to(tl.int64) * H + h) * D\n        rmask = (ri < s1)[:, None]\n        o_r = tl.load(O + off[:, None] + rd[None, :], mask=rmask, other=0.0).to(tl.float32)\n        do_r = tl.load(DO + off[:, None] + rd[None, :], mask=rmask, other=0.0).to(tl.float32)\n        dlt = tl.sum(o_r * do_r, 1)\n        tl.store(DELTA + hoff + ri, dlt, mask=ri < s1)\n        lo = s0\"\"\"\nassert old in src\nsrc = src.replace(old, new)\nsrc = src.replace(\"\"\"        rd = tl.arange(0, D)\n        off = (ri.to(tl.int64) * H + h) * D\n        tl.store(DQ + off[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=(ri < s1)[:, None])\"\"\",\n\"\"\"        tl.store(DQ + off[:, None] + rd[None, :], (dq * sm_scale).to(tl.bfloat16), mask=rmask)\"\"\")\nsrc = src.replace(\"def _dq(Q, K, V, DO, LSE2, DELTA, DQ,\", \"def _dq(Q, K, V, DO, O, LSE2, DELTA, DQ,\")\n# dkdv: lse2 from lse\nold1 = \"\"\"    lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0)\n    dlt = tl.load(DELTA + hoff + ri, mask=ri < T, other=0.0)\n    sT = tl.dot(k, tl.trans(q))\"\"\"\nassert old1 in src\nsrc = src.replace(old1, \"\"\"    lse2 = tl.load(LSE2 + hoff + ri, mask=ri < T, other=0.0) * LOG2E\n    dlt = tl.load(DELTA + hoff + ri, mask=ri < T, other=0.0)\n    sT = tl.dot(k, tl.trans(q))\"\"\")\n# host\na = src.index(\"    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\")\nb = src.index(\"    sm_scale = D ** -0.5\")\nsrc = src[:a] + \"    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\\n\" + src[b:]\na = src.index(\"    nbk = T // c['KV_BN'] + n\")\nb = src.index(\"    nbq = T // c['Q_BM'] + n\")\nkv = src[a:b]\nqq = src[b:]\nqq = qq.replace(\"_dq[(H, nbq)](q, k, v, do, lse2, delta, dq,\", \"_dq[(H, nbq)](q, k, v, do, o, lse, delta, dq,\")\nkv = kv.replace(\"_dkdv[(HKV, nbk)](q, k, v, do, lse2, delta, dk, dv,\", \"_dkdv[(HKV, nbk)](q, k, v, do, lse, delta, dk, dv,\")\nsrc = src[:a] + qq.rstrip('\\n') + '\\n' + kv\nopen('v6cfg.py', 'w').write(src)\nEOF\ntail -22 v6cfg.py; sed \"s/^CFG.update.*//; s/^import torch, os, json/import torch/\" v6cfg.py > v6.py; cd /workspace && timeout 900 python dev/bench.py dev/v6.py 2>&1 | tail -7","description":"Fuse delta prep into dq kernel and benchmark","timeout":900000},"caller":{"type":"direct"}}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2370,"cache_read_input_tokens":152363,"cache_creation":{"ephemeral_5m_input_tokens":2370,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"156f946a-43d5-488a-a93e-e5509c958abf","timestamp":"2026-10-18T12:10:03.129Z","request_id":"req_011Cg9gHLwfEwPBTdhjemzAQ"}
{"type":"system","subtype":"task_started","task_id":"bgialuvkv","run_id":"0mvds9qdx-4be038e9","tool_use_id":"toolu_01FYw511SJ5iRAso5BPs9gXM","description":"Fuse delta prep into dq kernel and benchmark","is_backgrounded":false,"task_type":"local_bash","uuid":"a7aa6ba1-f9da-4c77-bde2-2680e02df3a4","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"system","subtype":"task_notification","task_id":"bgialuvkv","run_id":"0mvds9qdx-4be038e9","tool_use_id":"toolu_01FYw511SJ5iRAso5BPs9gXM","status":"completed","output_file":"","summary":"Fuse delta prep into dq kernel and benchmark","uuid":"39ef49a6-61e0-4b0c-a131-0438d549b49b","session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FYw511SJ5iRAso5BPs9gXM","type":"tool_result","content":"CFG = dict(KV_BM=32, KV_BN=64, KV_W=4, KV_S=3, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2, KV_WS=False, Q_WS=False)\nCFG.update(json.loads(os.environ.get('CFG', '{}')))\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\n    triton.set_allocator(_alloc)\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    c = CFG\n    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    sm_scale = D ** -0.5\n    qk_scale = sm_scale * 1.4426950408889634\n    NP = triton.next_power_of_2(n + 1)\n    nbq = T // c['Q_BM'] + n\n    _dq[(H, nbq)](q, k, v, do, o, lse, delta, dq, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                  H=H, HKV=HKV, D=D, BM=c['Q_BM'], BN=c['Q_BN'], NP=NP, WS=c['Q_WS'],\n                  num_warps=c['Q_W'], num_stages=c['Q_S'])\n    nbk = T // c['KV_BN'] + n\n    _dkdv[(HKV, nbk)](q, k, v, do, lse, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                      H=H, HKV=HKV, D=D, BM=c['KV_BM'], BN=c['KV_BN'], NP=NP, WS=c['KV_WS'],\n                      num_warps=c['KV_W'], num_stages=c['KV_S'])\nshape 0 {i}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 3342.3 us  expert 1518  ratio 0.454\nshape 1 {i}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1056.8 us  expert 510  ratio 0.483\nshape 2 {i}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 1831.9 us  expert 980  ratio 0.535\nshape 3 {i}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 2243.5 us  expert 1165  ratio 0.519\nshape 4 {i}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 547.7 us  expert 302  ratio 0.551\nshape 5 {i}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 1026.1 us  expert 520  ratio 0.507\ngeomean ratio vs expert: 0.5071584564124365","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"59fb018d-c24b-49cf-8018-5f50bf6408b7","timestamp":"2026-10-18T12:10:22.947Z","tool_use_result":{"stdout":"CFG = dict(KV_BM=32, KV_BN=64, KV_W=4, KV_S=3, Q_BM=64, Q_BN=64, Q_W=4, Q_S=2, KV_WS=False, Q_WS=False)\nCFG.update(json.loads(os.environ.get('CFG', '{}')))\n\n\ndef kernel_fn(q, k, v, o, do, lse, cu_seqlens, max_seqlen, window, dq, dk, dv):\n    triton.set_allocator(_alloc)\n    T, H, D = q.shape\n    HKV = k.shape[1]\n    n = cu_seqlens.shape[0] - 1\n    c = CFG\n    delta = torch.empty((H, T), device=q.device, dtype=torch.float32)\n    sm_scale = D ** -0.5\n    qk_scale = sm_scale * 1.4426950408889634\n    NP = triton.next_power_of_2(n + 1)\n    nbq = T // c['Q_BM'] + n\n    _dq[(H, nbq)](q, k, v, do, o, lse, delta, dq, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                  H=H, HKV=HKV, D=D, BM=c['Q_BM'], BN=c['Q_BN'], NP=NP, WS=c['Q_WS'],\n                  num_warps=c['Q_W'], num_stages=c['Q_S'])\n    nbk = T // c['KV_BN'] + n\n    _dkdv[(HKV, nbk)](q, k, v, do, lse, delta, dk, dv, cu_seqlens, n, qk_scale, sm_scale, T, window,\n                      H=H, HKV=HKV, D=D, BM=c['KV_BM'], BN=c['KV_BN'], NP=NP, WS=c['KV_WS'],\n                      num_warps=c['KV_W'], num_stages=c['KV_S'])\nshape 0 {i}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 3342.3 us  expert 1518  ratio 0.454\nshape 1 {i}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1056.8 us  expert 510  ratio 0.483\nshape 2 {i}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 1831.9 us  expert 980  ratio 0.535\nshape 3 {i}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 2243.5 us  expert 1165  ratio 0.519\nshape 4 {i}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 547.7 us  expert 302  ratio 0.551\nshape 5 {i}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 1026.1 us  expert 520  ratio 0.507\ngeomean ratio vs expert: 0.5071584564124365","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01FYw511SJ5iRAso5BPs9gXM","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gR8qd3MsMxoeS6Y4rk","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gWi14DhmZDvBWeDpjw","type":"message","role":"assistant","content":[{"type":"text","text":"Block-fastest ordering helps the windowed shape (5: 540+397 vs 566+410) but hurts causal ones. Let me check shape 1 and the dq alternative under that ordering."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6240,"cache_read_input_tokens":161156,"cache_creation":{"ephemeral_5m_input_tokens":6240,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"968e160c-64cc-42c7-9f93-0e548e15d3dc","timestamp":"2026-10-18T12:12:51.629Z","request_id":"req_011Cg9gWheyLbFFf7TEZuYqE"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gZQUgsQjwTpL2doDEP","type":"message","role":"assistant","content":[{"type":"text","text":"dq with 128-row tiles is clearly better for windowed shapes (394 vs 429, 364 vs 410). Let me make the dq config depend on the window and validate."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":925,"cache_read_input_tokens":167396,"cache_creation":{"ephemeral_5m_input_tokens":925,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global","speed":"standard"},"input_transformations":[],"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"f7c3f9de-503a-455a-8f6a-2f0ae2942ee2","timestamp":"2026-10-18T12:13:29.228Z","request_id":"req_011Cg9gZQ6NLcxoEUPgsXkRX"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01341gF1HWMGKXAADVm3NeSy","type":"tool_result","content":"shape 0 {i}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 3316.9 us  expert 1518  ratio 0.458\nshape 1 {i}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1014.5 us  expert 510  ratio 0.503\nshape 2 {i}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 1820.7 us  expert 980  ratio 0.538\nshape 3 {i}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 2222.1 us  expert 1165  ratio 0.524\nshape 4 {i}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 542.7 us  expert 302  ratio 0.556\nshape 5 {i}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 992.2 us  expert 520  ratio 0.524\ngeomean ratio vs expert: 0.5162660083531532","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"8f2435c0-9807-4db2-86a9-651a63dc42ad","timestamp":"2026-10-18T12:13:40.655Z","tool_use_result":{"stdout":"shape 0 {i}: OK {'dq': (0, 0.398), 'dk': (0, 0.388), 'dv': (0, 0.36)} 3316.9 us  expert 1518  ratio 0.458\nshape 1 {i}: OK {'dq': (0, 0.397), 'dk': (0, 0.401), 'dv': (0, 0.375)} 1014.5 us  expert 510  ratio 0.503\nshape 2 {i}: OK {'dq': (0, 0.38), 'dk': (0, 0.304), 'dv': (0, 0.314)} 1820.7 us  expert 980  ratio 0.538\nshape 3 {i}: OK {'dq': (0, 0.428), 'dk': (0, 0.416), 'dv': (0, 0.427)} 2222.1 us  expert 1165  ratio 0.524\nshape 4 {i}: OK {'dq': (0, 0.386), 'dk': (0, 0.441), 'dv': (0, 0.404)} 542.7 us  expert 302  ratio 0.556\nshape 5 {i}: OK {'dq': (0, 0.422), 'dk': (0, 0.416), 'dv': (0, 0.409)} 992.2 us  expert 520  ratio 0.524\ngeomean ratio vs expert: 0.5162660083531532","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01341gF1HWMGKXAADVm3NeSy","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gaf4ZakG7yFRms48E9","type":"message","role":"assistant","content":[{"type":"text","text":"Small gain (0.5163). Validating v8 on edge shapes before promoting it:"}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1664,"cache_read_input_tokens":168321,"cache_creation":{"ephemeral_5m_input_tokens":1664,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"d4ab6d3a-f4cd-4de0-8f07-07ce9ebaf915","timestamp":"2026-10-18T12:13:42.741Z","request_id":"req_011Cg9gaegEqYZEZSqP4buzc"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DUVoSvYxAcC5HN1hcrm6Zk","type":"tool_result","content":"OK  {'n': 1, 'L': 1, 'Hq': 2, 'Hkv': 1, 'window': -1} {'dq':\nOK  {'n': 3, 'L': 100, 'Hq': 4, 'Hkv': 1, 'window': -1} {'dq\nOK  {'n': 2, 'L': 37, 'Hq': 2, 'Hkv': 2, 'window': 0} {'dq':\nOK  {'n': 1, 'L': 300, 'Hq': 4, 'Hkv': 2, 'window': 5} {'dq'\nOK  {'n': 5, 'L': 129, 'Hq': 8, 'Hkv': 4, 'window': 64, 'rag\nOK  {'n': 4, 'L': 513, 'Hq': 16, 'Hkv': 2, 'window': -1, 'ra\nOK  {'n': 1, 'L': 1000, 'Hq': 6, 'Hkv': 3, 'window': 2000} {\nOK  {'n': 7, 'L': 200, 'Hq': 4, 'Hkv': 4, 'window': 63, 'rag\nOK  {'n': 2, 'L': 2048, 'Hq': 4, 'Hkv': 1, 'window': -1, 'lo\nOK  {'n': 16, 'L': 65, 'Hq': 2, 'Hkv': 1, 'window': -1, 'rag","is_error":false}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"403e442b-9079-46d2-a6b5-4bb63a4d70de","timestamp":"2026-10-18T12:13:53.644Z","tool_use_result":{"stdout":"OK  {'n': 1, 'L': 1, 'Hq': 2, 'Hkv': 1, 'window': -1} {'dq':\nOK  {'n': 3, 'L': 100, 'Hq': 4, 'Hkv': 1, 'window': -1} {'dq\nOK  {'n': 2, 'L': 37, 'Hq': 2, 'Hkv': 2, 'window': 0} {'dq':\nOK  {'n': 1, 'L': 300, 'Hq': 4, 'Hkv': 2, 'window': 5} {'dq'\nOK  {'n': 5, 'L': 129, 'Hq': 8, 'Hkv': 4, 'window': 64, 'rag\nOK  {'n': 4, 'L': 513, 'Hq': 16, 'Hkv': 2, 'window': -1, 'ra\nOK  {'n': 1, 'L': 1000, 'Hq': 6, 'Hkv': 3, 'window': 2000} {\nOK  {'n': 7, 'L': 200, 'Hq': 4, 'Hkv': 4, 'window': 63, 'rag\nOK  {'n': 2, 'L': 2048, 'Hq': 4, 'Hkv': 1, 'window': -1, 'lo\nOK  {'n': 16, 'L': 65, 'Hq': 2, 'Hkv': 1, 'window': -1, 'rag","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"tool_result_meta":[{"id":"toolu_01DUVoSvYxAcC5HN1hcrm6Zk","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gcbdzBTPhy8Cp3rbzX","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Saved and validated—5.5x faster than the starting kernel, about 0.52x of expert performance. With ~15 minutes left, I'll try one cheap optimization: using larger tiles for the bandwidth-bound prep pass.\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":997,"cache_read_input_tokens":170763,"cache_creation":{"ephemeral_5m_input_tokens":997,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"cdab5eb8-f057-4795-9cd3-bdee7803d855","timestamp":"2026-10-18T12:14:16.819Z","request_id":"req_011Cg9gcbH9jQYn1xMrUvuuK","narration_block_indexes":[0],"thinking_duration_ms":1,"thinking_display":"updates"}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gen75RT5JuhiKCtoKF","type":"message","role":"assistant","content":[{"type":"text","text":"Negligible gain, so I'll keep v8. Let me record the key learnings in memory for future sessions."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1276,"cache_read_input_tokens":171760,"cache_creation":{"ephemeral_5m_input_tokens":1276,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"4fad4164-4332-406a-a824-2a76514f5d1e","timestamp":"2026-10-18T12:14:40.524Z","request_id":"req_011Cg9gemc4fHXafNdMD4G14"}
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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P5U116T7rxarg7gm2bE8Zk","type":"tool_result","content":"File created successfully at: /logs/agent/sessions/projects/-workspace/memory/hd256-attn-bwd-triton-b300.md"}]},"parent_tool_use_id":null,"session_id":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"b47fd516-5509-42f6-a0a3-1dca7582db21","timestamp":"2026-10-18T12:14:47.807Z","tool_use_result":{"type":"create","filePath":"/logs/agent/sessions/projects/-workspace/memory/hd256-attn-bwd-triton-b300.md","content":"---\nname: hd256-attn-bwd-triton-b300\ndescription: Learnings from writing a Triton hd256 causal attention backward (GQA/window/varlen) on B300 with Triton 3.7.1\nmetadata:\n  node_type: memory\n  type: project\n  originSessionId: bdfe9e1b-6798-43cd-88fc-c22443aad0dd\n  modified: 2026-10-18T12:14:47.787Z\n---\n\nTask (2026-10-18): beat FA4 hd256 backward on B300 with Triton; reached ~0.52x expert geomean (5.5x starting kernel).\nFinal design: prep (delta, lse*log2e) -> dkdv kernel (keys as MMA M: BN=64 keys, BM=32 query tile, 4 warps,\nnum_stages=3, unmasked fast-path loop + masked loop, GQA heads flattened into the loop) -> dq kernel (BM=64/BN=64,\n4 warps, 2 stages; BM=128/BN=32/8 warps for windowed shapes). In-kernel varlen block mapping via tl.cumsum over cu_seqlens.\n\n**Why:** hd256 makes SMEM/TMEM the binding constraint; Triton's pipeliner gives 3 buffers per TMA load at\nnum_stages=2 when the consumer is an async accumulate MMA, so Q/dO tiles of 64x256 overflow 227 KB.\n\n**How to apply:**\n- Triton 3.7.1 automatic warp_specialize crashes (NVWSInsertAref / HoistTMEMAlloc asserts) on attention-bwd loops; don't spend time on it.\n- num_stages=1 fully serializes load->MMA->wait; prefer smaller tiles that allow >=2 stages.\n- Fusing delta into the dq kernel was slower than a separate prep pass.\n- Heads-fastest grid order is better for causal (load balance); block-fastest only helps windowed shapes slightly.\n- Untried ideas with most upside: 2-CTA MMA (Triton has TCGen5MMAOp two_ctas + CheckMatmulTwoCTA pass), TMEM-resident K/V A-operands.\n","structuredPatch":[],"originalFile":null,"userModified":false,"memdirStamped":true},"tool_result_meta":[{"id":"toolu_01P5U116T7rxarg7gm2bE8Zk","permission_decision":{"decision":"accept","source":"config","reason_type":"mode"}}]}
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{"type":"assistant","message":{"model":"claude-opus-5-5","id":"msg_011Cg9gfiiDWvBuybp4x9yyE","type":"message","role":"assistant","content":[{"type":"text","text":"`/workspace/solution.py` is correct and runs about **5.5× faster than the starting kernel**, but it reaches only **about 0.52× the expert's speed** (geometric mean over the timed shapes). That is well short of the 1.0× bar, so it won't pass; the expected reward is roughly log2(0.52) ≈ −0.95.\n\n| Shape | Mine (µs) | Expert (µs) | Speed vs expert |\n|---|---|---|---|\n| 1×8K global, 16/8 heads | 3317 | 1518 | 0.46 |\n| 1×8K, window 1024 | 1015 | 510 | 0.50 |\n| 2×4K, 16/2 heads | 1821 | 980 | 0.54 |\n| 8 packed documents, 16K tokens | 2222 | ~1165 | 0.52 |\n| 4×2K, 8/4 heads | 543 | 302 | 0.56 |\n| Packed 16K, window 1024 | 992 | 520 | 0.52 |\n\nThe expert times come from the notes in `task.py`, since check.py can't run the expert kernel here.\n\n**Correctness checks:** it passes check.py and the static check, and the version is also saved in `/workspace/.best/`. I also tested 10 odd shapes and 6 input distributions, all within tolerance:\n- **Shapes:** a single token, lengths that aren't multiples of the tile size, window 0, a window longer than the sequence, equal query and KV head counts, one long sequence plus short ones, and 16 ragged documents.\n- **Distributions:** outliers 80× larger, very large and very small values, all-zero rows, rows whose scales differ by orders of magnitude, and shifted means.\n\n**Design:** three kernels.\n1. A short pass computes the per-row term delta = rowsum(dO·o) and converts the log-sum-exp to base 2.\n2. One kernel computes dk and dv: each program owns 64 keys and walks every query head that shares those keys, so there are no atomic adds. Blocks fully inside the causal or window region skip all masking.\n3. A second kernel computes dq. Windowed shapes use a different tile size for it, which gave about 10% on that kernel.\n\nSequence boundaries for packed documents are worked out inside the kernels from `cu_seqlens`, and the heaviest causal blocks are scheduled first.\n\n**What stopped it getting faster:**\n- **Memory for staging tiles:** with head dimension 256, one 64-row tile is 32 KB. Asking the Triton compiler to prefetch the next tile makes it keep three copies of each, which overflows the 227 KB of on-chip memory. Without prefetch, the dk/dv loop waits on every load.\n- **Overlapping loads, matrix multiplies and the softmax work:** Triton 3.7.1's automatic way to run these in parallel crashes the compiler on this loop, even in a stripped-down version.\n- **Ideas that didn't help:** computing delta inside the dq kernel, larger tiles for the delta pass, and changing the block order for better cache reuse. Each was neutral or slower overall.\n\nThe approaches most likely to close the gap are pairing two thread blocks per matrix multiply (which the FlashAttention-4 kernel does) and keeping the key/value tiles in tensor memory. Neither fit in the time available."}],"container":null,"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1667,"cache_read_input_tokens":173036,"cache_creation":{"ephemeral_5m_input_tokens":1667,"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":"bdfe9e1b-6798-43cd-88fc-c22443aad0dd","uuid":"fb14463a-bdaf-4162-bfcf-91c673d6eccd","timestamp":"2026-10-18T12:15:02.184Z","request_id":"req_011Cg9gfiLQ99XLTaZFbtv3b"}
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