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/workspace/problems/03_paged_attention
PROMPT.txt
shapes.py
sota.py
reference.py
check.py
benchmark.py
problem.yaml

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"""Naive PyTorch paged-attention decode reference (correctness oracle, not SOTA).

Single-query decode: each batch element has a query of shape (num_heads, head_dim)
and attends over a KV cache of `seq_len[b]` tokens stored as fixed-size pages in
a global pool. Pages for batch element b are listed in `block_table[b]`.

The reference performs the slow path:
  1. Gather pages -> contiguous (seq_len, num_kv_heads, head_dim) per batch element.
  2. Repeat KV heads for grouped-query (broadcast num_kv_heads -> num_heads).
  3. Manual softmax(QK^T / sqrt(d)) @ V in fp32, cast back to bf16.

This avoids torch.nn.functional.scaled_dot_product_attention (which is on the
forbidden list) so the agent cannot dispatch through SDPA either.
"""
import math

import torch
import torch.nn as nn

OP_TYPE = "attention"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]


# --- Shape knobs (overridden by check.py / benchmark.py from shapes.py) ----
BATCH = 8
NUM_HEADS = 32
NUM_KV_HEADS = 8
HEAD_DIM = 128
SEQ_LEN = 1024
PAGE_SIZE = 16


class Model(nn.Module):
    """Single-query paged attention decode.

    Forward inputs (all on device):
      query:       (batch, num_heads, head_dim)               bf16
      kv_cache:    (num_blocks, page_size, num_kv_heads, head_dim * 2)
                   Layout: last dim packs [K | V] so a single gather pulls both.
       

... (3756 more chars)

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"""Correctness runner for paged-attention decode.

Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
each, with per-dtype atol/rtol. Also rejects forbidden ops by grep.
"""
import re
import sys
from pathlib import Path

import torch
import yaml

REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))

from src.eval.correctness import check_correctness  # noqa: E402
from src.eval.numeric_stress import (  # noqa: E402
    numeric_stress_cases,
    numeric_stress_context,
    tolerance_for_case,
)


def _apply_shape(reference, shape: dict) -> None:
    reference.BATCH = shape["batch"]
    reference.NUM_HEADS = shape["num_heads"]
    reference.NUM_KV_HEADS = shape["num_kv_heads"]
    reference.HEAD_DIM = shape["head_dim"]
    reference.SEQ_LEN = shape["seq_len"]
    reference.PAGE_SIZE = shape["page_size"]


def main():
    try:
        import reference
        import shapes
        import solution
    except Exception as e:
        print(f"FAIL: import error: {e}")
        sys.exit(1)

    problem_yaml = Path("problem.yaml")
    meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}

    sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
    for forbidden in meta.get("forbidden", []):
        pat = re.escape(forbidden)
        if re.search(pat, sol_src):
           

... (2445 more chars)

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Output:
"""Roofline benchmark for paged-attention decode.

For each shape: times the agent's solution first. Optional eager, compiled,
and SOTA diagnostics are enabled with KBH_BENCHMARK_BASELINES=1. Reports achieved TFLOPS, GB/s, and peak_fraction.

Decode is memory-bound, so peak_fraction is computed from achieved GB/s vs
the GPU's peak DRAM bandwidth.
"""
import sys
from math import exp, log
from pathlib import Path

import torch
import yaml

REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))

from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction  # noqa: E402
from src.eval.timing import benchmark_baselines_enabled, time_variant  # noqa: E402
from src.hardware import get as get_hw  # noqa: E402


def _eval_formula(expr: str, vars: dict) -> float:
    return float(eval(expr, {"__builtins__": {}}, vars))


def _apply_shape(reference, shape: dict) -> None:
    reference.BATCH = shape["batch"]
    reference.NUM_HEADS = shape["num_heads"]
    reference.NUM_KV_HEADS = shape["num_kv_heads"]
    reference.HEAD_DIM = shape["head_dim"]
    reference.SEQ_LEN = shape["seq_len"]
    reference.PAGE_SIZE = shape["page_size"]


def main():
    import reference
    import shapes
    import solution

    meta = yaml.safe_load(Path("problem.yaml").read_text())
    hw = get_hw(meta["hardware"][0])
    peak_tflops = hw.peak_tflops_dense.get(meta["

... (3992 more chars)

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Output:
"""Shape sweep for paged attention decode.

Mix targets:
  - small batch / long context (Llama-3 8B-style decode)
  - large batch / medium context (server batched decode)
  - GQA wide ratio (Llama-3 70B: 64 heads / 8 kv-heads)
  - non-power-of-2 seq_len (forces predicated tail handling)
  - head_dim=64 small-head case
"""

SHAPES = [
    # (B, H, Hkv, D, L, P)
    {"batch": 8,  "num_heads": 32, "num_kv_heads": 8,  "head_dim": 128, "seq_len": 1024, "page_size": 16},
    {"batch": 32, "num_heads": 32, "num_kv_heads": 8,  "head_dim": 128, "seq_len": 2048, "page_size": 16},
    {"batch": 4,  "num_heads": 64, "num_kv_heads": 8,  "head_dim": 128, "seq_len": 4096, "page_size": 16},
    {"batch": 16, "num_heads": 32, "num_kv_heads": 8,  "head_dim": 128, "seq_len": 1535, "page_size": 16},  # non-pow2
    {"batch": 8,  "num_heads": 16, "num_kv_heads": 4,  "head_dim": 64,  "seq_len": 2000, "page_size": 16},  # small-D, non-pow2
]

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Output:
name: 03_paged_attention
display_name: "Paged Attention Decode"
precision: bf16
regime: memory  # decode is bandwidth-bound (KV cache streamed once per token)

# Effective FLOPs per call: QK^T + softmax*V across all batches/heads/seq.
# Sum-formula evaluated per-shape; the benchmark substitutes batch/num_heads/etc.
# 2 * B * H * L * D for QK^T plus 2 * B * H * L * D for AV  =>  4 * B * H * L * D
flops_formula: "4 * batch * num_heads * seq_len * head_dim"

# Bytes moved (the real bottleneck): the KV cache must be streamed end-to-end.
#   K and V each:  batch * seq_len * num_kv_heads * head_dim * 2 bytes (bf16)
#   Q: batch * num_heads * head_dim * 2  (negligible vs KV)
#   Out: same as Q
# So total ~ 2 * (B * L * Hkv * D * 2) + small.
bytes_formula: "2 * batch * seq_len * num_kv_heads * head_dim * 2 + batch * num_heads * head_dim * 2 * 2"

hardware: [RTX_PRO_6000]
peak_tflops_key: bf16          # informational; regime=memory uses bandwidth
peak_bandwidth_key: dram        # 1.8 TB/s GDDR7 on RTX PRO 6000 Blackwell

tolerance:
  bfloat16: 0.02
  float16: 0.02

# Forbidden ops -- using any of these in solution.py fails correctness post-hoc.
# vllm.attention / flashinfer.batch_decode_with_paged_kv_cache: dispatching to
# the SOTA library is the trivial cheat. SDPA is also banned because the agent
# could gather pages then call SDPA and inherit FlashAttention "for free".
forbidde

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Output:
"""SOTA reference for paged-attention decode.

Tries, in order:
  1. FlashInfer's BatchDecodeWithPagedKVCacheWrapper (preferred -- portable,
     supports SM120, GQA, arbitrary head_dim).
  2. vLLM's paged_attention_v2 CUDA op (requires its KV-cache layout, more
     finicky; we adapt the layout on the fly when possible).

If neither is importable, is_available() returns False and the benchmark just
reports eager + compiled + solution.

Agents are FORBIDDEN from importing these in solution.py (see problem.yaml).
This file is only for the benchmark's reference line.
"""
from __future__ import annotations

import torch


def _try_flashinfer(
    query: torch.Tensor,
    kv_cache: torch.Tensor,
    block_table: torch.Tensor,
    seq_lens: torch.Tensor,
    num_kv_heads: int,
    head_dim: int,
    page_size: int,
) -> torch.Tensor | None:
    try:
        import flashinfer  # noqa: F401
        from flashinfer.decode import BatchDecodeWithPagedKVCacheWrapper
    except Exception:
        return None

    B, H, D = query.shape
    # FlashInfer expects K and V as separate (num_blocks, page_size, num_kv_heads, head_dim) tensors.
    # Our reference packs [K|V] on the last dim -- split here.
    k_cache = kv_cache[..., :D].contiguous()
    v_cache = kv_cache[..., D:].contiguous()

    workspace = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=query.device)
    wrapper = 

... (1423 more chars)

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Output:
total 44
drwxrwxr-x 2 1002 1002 4096 Jun 13 10:45 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 10:45 ..
-rw-rw-r-- 1 1002 1002 2585 Apr 27 09:21 PROMPT.txt
-rw-rw-r-- 1 1002 1002 5387 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 3841 Jun  2 01:08 check.py
-rw-rw-r-- 1 1002 1002 2030 Apr 26 04:19 problem.yaml
-rw-rw-r-- 1 1002 1002 5151 Apr 26 04:19 reference.py
-rw-rw-r-- 1 1002 1002  933 Apr 26 04:19 shapes.py
-rw-rw-r-- 1 1002 1002 2819 Apr 26 04:24 sota.py

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Output:
/kbh/bin/nvcc

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Output:
/workspace/pyproject.toml
/workspace/uv.lock

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Output:
total 272
drwxrwxr-x 4 1002 1002   4096 Jun 13 10:45 .
drwxr-xr-x 1 root root   4096 Jun 13 10:45 ..
-rw-rw-r-- 1 1002 1002      5 Apr 24 06:30 .python-version
drwxrwxr-x 3 1002 1002   4096 Jun 13 10:45 problems
-rw-rw-r-- 1 1002 1002    756 Jun  7 09:39 pyproject.toml
drwxrwxr-x 9 1002 1002   4096 Jun 12 15:21 src
-rw-rw-r-- 1 1002 1002 253619 May 22 20:54 uv.lock
total 12
drwxrwxr-x 3 1002 1002 4096 Jun 13 10:45 .
drwxrwxr-x 4 1002 1002 4096 Jun 13 10:45 ..
drwxrwxr-x 2 1002 1002 4096 Jun 13 10:45 03_paged_attention

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Output:
    222      73 Ss         00:54 /bin/bash /kbh/bin/gpu-lock-exec nvidia-smi /usr/bin/nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv,noheader
    228      73 Ss         00:54 /usr/bin/bash -c uv run python - <<'PY' import torch print(torch.__version__) print(torch.version.cuda) print(torch.cuda.is_available()) if torch.cuda.is_available():     print(torch.cuda.get_device_name(0))     print(torch.cuda.get_device_capability(0)) PY
    229     228 S          00:54 /bin/bash /kbh/bin/gpu-lock-exec uv /usr/local/bin/uv run python -
    235      73 Ss         00:54 /usr/bin/bash -c which nvcc || true && nvcc --version || true
    237     235 S          00:54 /bin/bash /kbh/bin/gpu-lock-exec nvcc /usr/local/cuda-host/bin/nvcc --version
    251      73 Ss         00:00 /usr/bin/bash -c ps -eo pid,ppid,stat,etime,cmd | rg 'nvidia-smi|uv run python|nvcc --version|session|python - <<' || true
    253     251 S          00:00 rg nvidia-smi|uv run python|nvcc --version|session|python - <<

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Output:
"""Centralized GPU timing for benchmark.py files.

All problems' benchmark.py should call `time_fn` from here so we have one
implementation of warmup, L2 flush, and CUDA event capture to maintain.

Methodology:
  - 10 warmup calls absorb Triton autotune (typical ~7 configs) and
    torch.compile reduce-overhead CUDA-graph capture.
  - Between each timed call, `_l2_flush()` writes 128 MB to evict L2
    (Blackwell L2 is 96 MB; we want any prior L2 contents flushed so
    we measure HBM-load bandwidth, not L2-cached bandwidth).
  - GPU timing via cuda.Event with synchronize() AFTER record() but
    BEFORE elapsed_time() — the canonical NVIDIA pattern.
  - Reported value is the median of `iters` trials, robust to outliers.

Notes / known biases not addressed here:
  - torch.compile(mode="reduce-overhead") gets CUDA graphs which eliminate
    launch overhead. Custom Triton/CUDA kernels do NOT get this treatment.
    On small shapes where launch overhead matters, this gives compile an
    artificial advantage. We accept this as the cost of using the agreed
    "compiled" baseline policy.
  - cuBLAS / cuDNN allocate workspaces on first call. 10 warmup absorbs.
  - Median over a fairly small number of trials (default 30) is fine for
    headline numbers but won't catch bimodal distributions. Use --extra
    instrumentation if you ever care.
"""
from __future__ import annotations


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Output:
"""Per-dtype correctness tolerance.

Stanford's KernelBench uses 1e-4 atol/rtol for fp32 and 1e-2 for fp16/bf16.
We extend to fp8 (0.1, permissive) and int (exact).
"""
from __future__ import annotations

import torch

DEFAULT_TOLERANCE = {
    torch.float32: {"atol": 1e-4, "rtol": 1e-4},
    torch.float16: {"atol": 1e-2, "rtol": 1e-2},
    torch.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
    torch.float8_e4m3fn: {"atol": 1e-1, "rtol": 1e-1},
    torch.float8_e5m2: {"atol": 1e-1, "rtol": 1e-1},
    torch.int8: {"atol": 0, "rtol": 0},
    torch.int32: {"atol": 0, "rtol": 0},
    torch.int64: {"atol": 0, "rtol": 0},
}


def _coerce_float(x):
    """Accept int/float/str scientific-notation as float; pass through dicts."""
    if isinstance(x, (int, float)):
        return float(x)
    if isinstance(x, str):
        try:
            return float(x)
        except ValueError:
            return x
    return x


def tolerance_for_dtype(dtype: torch.dtype, override: dict | None = None) -> dict:
    """Lookup atol/rtol for a given dtype, with optional per-problem override.

    The override dict is keyed by str(dtype). Values may be:
      - a single number (or a string parseable as float, e.g. "5e-2") -> used
        as both atol and rtol
      - a dict {"atol": ..., "rtol": ...} -> used directly, with values coerced
    """
    # Accept several spellings of the same dtype key (PyYA

... (8749 more chars)

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Output:
    229       1 Z          01:08 [gpu-lock-exec] <defunct>
    237       1 Z          01:08 [gpu-lock-exec] <defunct>
    258      73 Ss         00:01 /usr/bin/bash -c kill 222 228 229 235 237 2>/dev/null || true && sleep 1 && ps -eo pid,ppid,stat,etime,cmd | rg 'gpu-lock-exec|nvidia-smi|uv run python|nvcc --version' || true
    261     258 S          00:00 rg gpu-lock-exec|nvidia-smi|uv run python|nvcc --version

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Output:

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Output:
      1       0 Ssl        03:32 node /opt/node/bin/codex exec -m gpt-5.5 -c model_reasoning_effort="xhigh" --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check -C /workspace/problems/03_paged_attention I need you to write a paged attention decode kernel for the RTX PRO 6000 (SM120 Blackwell, GDDR7, 1.8 TB/s). The reference implementation is in reference.py and your kernel goes in solution.py. You can make whatever mess you want in this directory — scratch files, intermediate kernels, build artifacts, profiling traces — but the final answer has to be in solution.py with the same Model, get_inputs, and get_init_inputs interface as reference.py.  This is single-query decode: each batch element has a query of shape (num_heads, head_dim) and attends over a KV cache of seq_len[b] tokens stored as fixed-size pages in a global pool, with block_table[b] listing which pages belong to batch element b. The KV cache is laid out as (num_blocks, page_size, num_kv_heads, head_dim * 2) where the last dim packs [K | V] so a single gather pulls both. You return the bf16 output of shape (batch, num_heads, head_dim). Correctness tolerance is 0.02 abs/rel on bf16/fp16. The shapes you have to handle are batch=8 num_heads=32 num_kv_heads=8 head_dim=128 seq_len=1024 page_size=16 (Llama-3 8B-style decode), batch=32 num_heads=32 num_kv_heads=8 head_dim=128 seq_len=2048 page_size=16 (ser

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Output:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2026 NVIDIA Corporation
Built on Mon_Mar_02_09:52:23_PM_PST_2026
Cuda compilation tools, release 13.2, V13.2.51
Build cuda_13.2.r13.2/compiler.37434383_0

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Output:
uv 0.11.3 (x86_64-unknown-linux-gnu)

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Output:
uv is /kbh/bin/uv
uv is /usr/local/bin/uv
python is /kbh/bin/python
python is /usr/bin/python
python is /bin/python
nvidia-smi is /kbh/bin/nvidia-smi
nvidia-smi is /usr/bin/nvidia-smi
nvidia-smi is /bin/nvidia-smi
nvcc is /kbh/bin/nvcc
nvcc is /usr/local/cuda-host/bin/nvcc

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Output:
CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0
CUDA_DRIVER_VERSION=575.57.08
CUDA_HOME=/usr/local/cuda-host
CUDA_MODULE_LOADING=LAZY
CUDA_VERSION=12.9.1.010
KBH_GPU_LOCK=/kbh/lock/gpu.lock
KBH_GPU_LOCK_LOG=/home/agent/gpu_lock_container.log
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/torch/lib:/usr/local/lib/python3.12/dist-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
LIBRARY_PATH=/usr/local/cuda/lib64/stubs:
NVIDIA_BUILD_ID=177567386
NVIDIA_CPU_ONLY=1
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=PyTorch
NVIDIA_PYTORCH_VERSION=25.06
NVIDIA_REQUIRE_CUDA=cuda>=9.0
NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=
NVIDIA_VISIBLE_DEVICES=all
PATH=/home/agent/.codex/tmp/arg0/codex-arg0qO6O8T:/opt/node/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/path:/kbh/bin:/usr/local/cuda-host/bin:/opt/node/bin:/usr/local/bin:/usr/bin:/bin
PYTORCH_BUILD_NUMBER=0
PYTORCH_BUILD_VERSION=2.8.0a0+5228986
PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.99999
PYTORCH_HOME=/opt/pytorch/pytorch
PYTORCH_VERSION=2.8.0a0+5228986
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1
TORCH_CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0+PTX
TORCH_NCCL_USE_COMM_NONBLOCKING=0
UV_CACHE_DIR=/uv-cache
UV_PYTHON_INSTALL_DIR=/uv-cache/python
_CUDA_COMPAT_PATH=/usr/local/cuda/compat

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Output:
/kbh/bin:
total 40
drwxrwxr-x 2 1002 1002 4096 Jun 13 10:45 .
drwxr-xr-x 4 root root 4096 Jun 13 10:45 ..
-rwxrwxr-x 1 1002 1002 2288 Jun 13 10:45 gpu-lock-exec
-rwxrwxr-x 1 1002 1002  243 Jun 13 10:45 ncu
-rwxrwxr-x 1 1002 1002  245 Jun 13 10:45 nsys
-rwxrwxr-x 1 1002 1002  245 Jun 13 10:45 nvcc
-rwxrwxr-x 1 1002 1002  257 Jun 13 10:45 nvidia-smi
-rwxrwxr-x 1 1002 1002  249 Jun 13 10:45 python
-rwxrwxr-x 1 1002 1002  251 Jun 13 10:45 python3
-rwxrwxr-x 1 1002 1002  241 Jun 13 10:45 uv

/usr/local/cuda-host/bin:
total 332608
drwxr-xr-x  3 root root     4096 May  5 19:55 .
drwxr-xr-x 14 root root     4096 Mar 19 04:50 ..
-rwxr-xr-x  1 root root    11032 Mar  3 07:10 __nvcc_device_query
-rwxr-xr-x  1 root root    92440 Mar  3 07:10 bin2c
-rwxr-xr-x  1 root root      112 Jan 16 03:42 compute-sanitizer
drwxr-xr-x  2 root root     4096 Mar 19 04:50 crt
-rwxr-xr-x  1 root root    79680 Mar  3 06:49 cu++filt
-rwxr-xr-x  1 root root     2088 Jan 10 05:28 cuda-gdb
-rwxr-xr-x  1 root root 15279840 Jan 10 05:28 cuda-gdb-minimal
-rwxr-xr-x  1 root root 16312816 Jan 10 05:28 cuda-gdb-python3.10-tui
-rwxr-xr-x  1 root root 16312744 Jan 10 05:28 cuda-gdb-python3.11-tui
-rwxr-xr-x  1 root root 16313680 Jan 10 05:28 cuda-gdb-python3.12-tui
-rwxr-xr-x  1 root root 16313424 Jan 10 05:28 cuda-gdb-python3.8-tui
-rwxr-xr-x  1 root root 16313464 Jan 10 05:28 cuda-gdb-python3.9-tui
-rwxr-xr-x  1 r

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Output:
Using CPython 3.11.15
Creating virtual environment at: /workspace/.venv
   Building kernelbench-hard @ file:///workspace
      Built kernelbench-hard @ file:///workspace
warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
         If the cache and target directories are on different filesystems, hardlinking may not be supported.
         If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.

Chunk ID: 43e6c8
Wall time: 3.1424 seconds
Process exited with code 0
Original token count: 28
Output:
Installed 56 packages in 3.54s
2.11.0+cu130
13.0
True
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
(12, 0)

output:
  Success. Updated the following files:
  A /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 6434da
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: c25690
Wall time: 1.0016 seconds
Process running with session ID 71194
Original token count: 0
Output:

Chunk ID: 58fe62
Wall time: 27.8476 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: ec2634
Wall time: 1.0016 seconds
Process running with session ID 48350
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:51:57.776211+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:51:57.826301+00:00 elapsed_s=0.050 ms=0.251728
shape=0 variant=solution tflops=0.533 gbps=133.817 ms=0.252

Chunk ID: 648198
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 336
Output:
shape=0 solution_peak_fraction=0.0743
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T10:51:59.467687+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T10:51:59.485551+00:00 elapsed_s=0.018 ms=0.400352
shape=1 variant=solution tflops=2.682 gbps=671.808 ms=0.400
shape=1 solution_peak_fraction=0.3732
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:51:59.894270+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:51:59.913169+00:00 elapsed_s=0.019 ms=0.430880
shape=2 variant=solution tflops=1.246 gbps=156.053 ms=0.431
shape=2 solution_peak_fraction=0.0867
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:52:00.525327+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:52:00.537991+00:00 elapsed_s=0.013 ms=0.277088
shape=3 variant=solution tflops=1.452 gbps=364.000 ms=0.277
shape=3 solution_peak_fraction=0.2022
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T10:52:00.640086+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T10:52:00.652006+00:00 elapsed_s=0.012 ms=0.243760
shape=4 variant=solution tflops=0.269 gbps=67.348 ms=0.244
shape=4 solution_peak_fraction=0.0374
peak_fraction: 0.1127
RESULT: OK

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 864f95
Wall time: 1.0010 seconds
Process running with session ID 53916
Original token count: 0
Output:

Chunk ID: aea39e
Wall time: 27.2354 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: be9a5d
Wall time: 1.0017 seconds
Process running with session ID 17235
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:54:15.890526+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:54:15.941265+00:00 elapsed_s=0.051 ms=0.181776
shape=0 variant=solution tflops=0.738 gbps=185.313 ms=0.182

Chunk ID: f68792
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 336
Output:
shape=0 solution_peak_fraction=0.1030
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T10:54:17.571968+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T10:54:17.589446+00:00 elapsed_s=0.017 ms=0.399456
shape=1 variant=solution tflops=2.688 gbps=673.315 ms=0.399
shape=1 solution_peak_fraction=0.3741
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:54:18.006504+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:54:18.017084+00:00 elapsed_s=0.011 ms=0.219920
shape=2 variant=solution tflops=2.441 gbps=305.747 ms=0.220
shape=2 solution_peak_fraction=0.1699
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:54:18.631460+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:54:18.640815+00:00 elapsed_s=0.009 ms=0.198832
shape=3 variant=solution tflops=2.024 gbps=507.262 ms=0.199
shape=3 solution_peak_fraction=0.2818
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T10:54:18.743402+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T10:54:18.752624+00:00 elapsed_s=0.009 ms=0.183024
shape=4 variant=solution tflops=0.358 gbps=89.697 ms=0.183
shape=4 solution_peak_fraction=0.0498
peak_fraction: 0.1558
RESULT: OK

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 335534
Wall time: 1.0005 seconds
Process running with session ID 54216
Original token count: 0
Output:

Chunk ID: 5b47b0
Wall time: 27.1805 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 3d1410
Wall time: 1.0015 seconds
Process running with session ID 70855
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:55:03.105616+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:55:03.151158+00:00 elapsed_s=0.046 ms=0.106384
shape=0 variant=solution tflops=1.262 gbps=316.641 ms=0.106

Chunk ID: 54740c
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 336
Output:
shape=0 solution_peak_fraction=0.1759
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T10:55:04.830045+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T10:55:04.845075+00:00 elapsed_s=0.015 ms=0.335520
shape=1 variant=solution tflops=3.200 gbps=801.621 ms=0.336
shape=1 solution_peak_fraction=0.4453
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:55:05.269507+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:55:05.278194+00:00 elapsed_s=0.009 ms=0.161776
shape=2 variant=solution tflops=3.319 gbps=415.636 ms=0.162
shape=2 solution_peak_fraction=0.2309
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:55:05.914553+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:55:05.921626+00:00 elapsed_s=0.007 ms=0.134864
shape=3 variant=solution tflops=2.984 gbps=747.864 ms=0.135
shape=3 solution_peak_fraction=0.4155
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T10:55:06.024826+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T10:55:06.031295+00:00 elapsed_s=0.006 ms=0.106736
shape=4 variant=solution tflops=0.614 gbps=153.807 ms=0.107
shape=4 solution_peak_fraction=0.0854
peak_fraction: 0.2299
RESULT: OK

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 843660
Wall time: 1.0010 seconds
Process running with session ID 46060
Original token count: 0
Output:

Chunk ID: 23a3e6
Wall time: 28.1505 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: a572c6
Wall time: 1.0005 seconds
Process running with session ID 13403
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:55:51.252911+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:55:51.297625+00:00 elapsed_s=0.045 ms=0.074208
shape=0 variant=solution tflops=1.809 gbps=453.934 ms=0.074

Chunk ID: 3958bd
Wall time: 0.2145 seconds
Process exited with code 0
Original token count: 336
Output:
shape=0 solution_peak_fraction=0.2522
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T10:55:52.967978+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T10:55:52.982366+00:00 elapsed_s=0.014 ms=0.316896
shape=1 variant=solution tflops=3.388 gbps=848.732 ms=0.317
shape=1 solution_peak_fraction=0.4715
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:55:53.400302+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:55:53.410481+00:00 elapsed_s=0.010 ms=0.202000
shape=2 variant=solution tflops=2.658 gbps=332.871 ms=0.202
shape=2 solution_peak_fraction=0.1849
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:55:54.034464+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:55:54.041703+00:00 elapsed_s=0.007 ms=0.140112
shape=3 variant=solution tflops=2.872 gbps=719.852 ms=0.140
shape=3 solution_peak_fraction=0.3999
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T10:55:54.143917+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T10:55:54.149123+00:00 elapsed_s=0.005 ms=0.073584
shape=4 variant=solution tflops=0.891 gbps=223.102 ms=0.074
shape=4 solution_peak_fraction=0.1239
peak_fraction: 0.2556
RESULT: OK

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 26a6c8
Wall time: 1.0011 seconds
Process running with session ID 47027
Original token count: 0
Output:

Chunk ID: 9dc7dc
Wall time: 26.5085 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: b6d711
Wall time: 1.0016 seconds
Process running with session ID 81028
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:56:40.219287+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:56:40.263295+00:00 elapsed_s=0.044 ms=0.081632
shape=0 variant=solution tflops=1.644 gbps=412.651 ms=0.082

Chunk ID: c44901
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 336
Output:
shape=0 solution_peak_fraction=0.2293
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T10:56:41.879713+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T10:56:41.895346+00:00 elapsed_s=0.016 ms=0.348000
shape=1 variant=solution tflops=3.085 gbps=772.873 ms=0.348
shape=1 solution_peak_fraction=0.4294
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:56:42.302204+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:56:42.314839+00:00 elapsed_s=0.013 ms=0.261280
shape=2 variant=solution tflops=2.055 gbps=257.348 ms=0.261
shape=2 solution_peak_fraction=0.1430
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:56:42.925587+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:56:42.933246+00:00 elapsed_s=0.008 ms=0.151216
shape=3 variant=solution tflops=2.661 gbps=666.992 ms=0.151
shape=3 solution_peak_fraction=0.3706
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T10:56:43.033482+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T10:56:43.039164+00:00 elapsed_s=0.006 ms=0.086192
shape=4 variant=solution tflops=0.760 gbps=190.467 ms=0.086
shape=4 solution_peak_fraction=0.1058
peak_fraction: 0.2230
RESULT: OK

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: ec8325
Wall time: 1.0011 seconds
Process running with session ID 82838
Original token count: 0
Output:

Chunk ID: 8bfeb8
Wall time: 27.9754 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: f450b1
Wall time: 1.0005 seconds
Process running with session ID 23783
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:57:48.728567+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:57:48.772680+00:00 elapsed_s=0.044 ms=0.072704
shape=0 variant=solution tflops=1.846 gbps=463.324 ms=0.073

Chunk ID: 1e2134
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 336
Output:
shape=0 solution_peak_fraction=0.2574
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T10:57:50.442581+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T10:57:50.457003+00:00 elapsed_s=0.014 ms=0.317536
shape=1 variant=solution tflops=3.381 gbps=847.021 ms=0.318
shape=1 solution_peak_fraction=0.4706
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:57:50.874740+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:57:50.884891+00:00 elapsed_s=0.010 ms=0.202000
shape=2 variant=solution tflops=2.658 gbps=332.871 ms=0.202
shape=2 solution_peak_fraction=0.1849
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:57:51.522889+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:57:51.530970+00:00 elapsed_s=0.008 ms=0.139664
shape=3 variant=solution tflops=2.881 gbps=722.161 ms=0.140
shape=3 solution_peak_fraction=0.4012
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T10:57:51.634000+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T10:57:51.639218+00:00 elapsed_s=0.005 ms=0.074000
shape=4 variant=solution tflops=0.886 gbps=221.848 ms=0.074
shape=4 solution_peak_fraction=0.1232
peak_fraction: 0.2564
RESULT: OK

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 84b5b0
Wall time: 1.0019 seconds
Process running with session ID 47376
Original token count: 0
Output:

Chunk ID: 4a17d2
Wall time: 29.8639 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: c6de81
Wall time: 1.0009 seconds
Process running with session ID 72158
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:59:10.264520+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:59:10.308155+00:00 elapsed_s=0.044 ms=0.072480
shape=0 variant=solution tflops=1.852 gbps=464.756 ms=0.072

Chunk ID: 89063a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 336
Output:
shape=0 solution_peak_fraction=0.2582
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T10:59:11.912458+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T10:59:11.926959+00:00 elapsed_s=0.014 ms=0.316896
shape=1 variant=solution tflops=3.388 gbps=848.732 ms=0.317
shape=1 solution_peak_fraction=0.4715
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:59:12.330330+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:59:12.338971+00:00 elapsed_s=0.009 ms=0.162624
shape=2 variant=solution tflops=3.301 gbps=413.469 ms=0.163
shape=2 solution_peak_fraction=0.2297
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:59:12.943568+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:59:12.950630+00:00 elapsed_s=0.007 ms=0.135008
shape=3 variant=solution tflops=2.980 gbps=747.066 ms=0.135
shape=3 solution_peak_fraction=0.4150
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T10:59:13.051249+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T10:59:13.056456+00:00 elapsed_s=0.005 ms=0.073824
shape=4 variant=solution tflops=0.888 gbps=222.377 ms=0.074
shape=4 solution_peak_fraction=0.1235
peak_fraction: 0.2700
RESULT: OK

Chunk ID: 42c1f0
Wall time: 1.0014 seconds
Process running with session ID 30416
Original token count: 124
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T10:59:18.946161+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T10:59:18.989597+00:00 elapsed_s=0.043 ms=0.073360
shape=0 variant=solution tflops=1.830 gbps=459.181 ms=0.073
benchmark_event event=variant_start shape=0 variant=eager ts=2026-06-13T10:59:18.989627+00:00
benchmark_event event=variant_end shape=0 variant=eager ts=2026-06-13T10:59:19.077687+00:00 elapsed_s=0.088 ms=0.602800

Chunk ID: 78c44f
Wall time: 3.3636 seconds
Process exited with code 0
Original token count: 1355
Output:
benchmark_event event=variant_start shape=0 variant=compiled ts=2026-06-13T10:59:21.749811+00:00
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break from `Tensor.item()`, consider setting:
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] or:
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] to include these operations in the captured graph.
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break: from user code at:
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]   File "/workspace/problems/03_paged_attention/reference.py", line 89, in forward
W0613 10:59:21.799000 1323 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     

... (4023 more chars)

Chunk ID: c8bfb8
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
3.6.0

output:
  Success. Updated the following files:
  A /workspace/problems/03_paged_attention/solution_triton.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: a3f200
Wall time: 1.0016 seconds
Process running with session ID 88730
Original token count: 0
Output:

Chunk ID: 9b6d6e
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 774
Output:
Traceback (most recent call last):
  File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 240, in __mul__
    return constexpr(self.value * _unwrap_if_constexpr(other))
                     ~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 41, in wrapper
    raise ValueError("Did you forget to add @triton.jit ? "
ValueError: Did you forget to add @triton.jit ? (`_semantic` argument must be provided outside of JIT functions.)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<stdin>", line 17, in <module>
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/problems/03_paged_attention/solution_triton.py", line 202, in forward
    _reduce_kernel[(self.batch, self.num_kv_heads)](
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kw

... (1699 more chars)

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution_triton.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: f5c531
Wall time: 1.0012 seconds
Process running with session ID 99811
Original token count: 0
Output:

Chunk ID: 802fbb
Wall time: 7.1881 seconds
Process exited with code 0
Original token count: 17
Output:
shape 0 ok
shape 1 ok
shape 2 ok
shape 3 ok
shape 4 ok
TRITON PASS

Chunk ID: 965243
Wall time: 1.0007 seconds
Process running with session ID 9091
Original token count: 75
Output:
benchmark_event event=variant_start shape=0 variant=triton_proto ts=2026-06-13T11:02:45.533654+00:00
benchmark_event event=variant_end shape=0 variant=triton_proto ts=2026-06-13T11:02:45.778091+00:00 elapsed_s=0.244 ms=0.040112
shape=0 triton_proto tflops=3.346 gbps=839.786 ms=0.040112 frac=0.4665

Chunk ID: 8cdae8
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 306
Output:
benchmark_event event=variant_start shape=1 variant=triton_proto ts=2026-06-13T11:02:47.407449+00:00
benchmark_event event=variant_end shape=1 variant=triton_proto ts=2026-06-13T11:02:47.419808+00:00 elapsed_s=0.012 ms=0.207632
shape=1 triton_proto tflops=5.171 gbps=1295.368 ms=0.207632 frac=0.7196
benchmark_event event=variant_start shape=2 variant=triton_proto ts=2026-06-13T11:02:47.827405+00:00
benchmark_event event=variant_end shape=2 variant=triton_proto ts=2026-06-13T11:02:47.834367+00:00 elapsed_s=0.007 ms=0.065632
shape=2 triton_proto tflops=8.180 gbps=1024.499 ms=0.065632 frac=0.5692
benchmark_event event=variant_start shape=3 variant=triton_proto ts=2026-06-13T11:02:48.448024+00:00
benchmark_event event=variant_end shape=3 variant=triton_proto ts=2026-06-13T11:02:48.455523+00:00 elapsed_s=0.008 ms=0.086752
shape=3 triton_proto tflops=4.638 gbps=1162.623 ms=0.086752 frac=0.6459
benchmark_event event=variant_start shape=4 variant=triton_proto ts=2026-06-13T11:02:48.555409+00:00
benchmark_event event=variant_end shape=4 variant=triton_proto ts=2026-06-13T11:02:48.560984+00:00 elapsed_s=0.006 ms=0.025408
shape=4 triton_proto tflops=2.579 gbps=646.126 ms=0.025408 frac=0.3590
gmean 0.5361587792900603

Chunk ID: 6741c3
Wall time: 1.0006 seconds
Process running with session ID 84591
Original token count: 27
Output:
shape 0 {'batch': 8, 'num_heads': 32, 'num_kv_heads': 8, 'head_dim': 128, 'seq_len': 1024, 'page_size': 16}

Chunk ID: 35db9b
Wall time: 30.0014 seconds
Process running with session ID 84591
Original token count: 125
Output:
  chunk=32 ms=0.042320 gbps=796.0
  chunk=64 ms=0.039920 gbps=843.8
  chunk=128 ms=0.038480 gbps=875.4
  chunk=256 ms=0.039200 gbps=859.3
shape 1 {'batch': 32, 'num_heads': 32, 'num_kv_heads': 8, 'head_dim': 128, 'seq_len': 2048, 'page_size': 16}
  chunk=32 ms=0.218784 gbps=1229.3
  chunk=64 ms=0.207056 gbps=1299.0
  chunk=128 ms=0.201568 gbps=1334.3
  chunk=256 ms=0.198656 gbps=1353.9
shape 2 {'batch': 4, 'num_heads': 64, 'num_kv_heads': 8, 'head_dim': 128, 'seq_len': 4096, 'page_size': 16}

Chunk ID: 5f9933
Wall time: 30.0006 seconds
Process running with session ID 84591
Original token count: 0
Output:

Chunk ID: 89e4a4
Wall time: 28.0094 seconds
Process exited with code 0
Original token count: 160
Output:
  chunk=32 ms=0.089152 gbps=754.2
  chunk=64 ms=0.061648 gbps=1090.7
  chunk=128 ms=0.055728 gbps=1206.6
  chunk=256 ms=0.053424 gbps=1258.6
shape 3 {'batch': 16, 'num_heads': 32, 'num_kv_heads': 8, 'head_dim': 128, 'seq_len': 1535, 'page_size': 16}
  chunk=32 ms=0.094240 gbps=1070.2
  chunk=64 ms=0.088976 gbps=1133.6
  chunk=128 ms=0.086400 gbps=1167.4
  chunk=256 ms=0.086432 gbps=1166.9
shape 4 {'batch': 8, 'num_heads': 16, 'num_kv_heads': 4, 'head_dim': 64, 'seq_len': 2000, 'page_size': 16}
  chunk=32 ms=0.027904 gbps=588.3
  chunk=64 ms=0.025920 gbps=633.4
  chunk=128 ms=0.024912 gbps=659.0
  chunk=256 ms=0.025552 gbps=642.5

Chunk ID: 962443
Wall time: 1.0007 seconds
Process running with session ID 94796
Original token count: 2
Output:
shape 0

Chunk ID: 156ae2
Wall time: 2.9896 seconds
Process exited with code 0
Original token count: 259
Output:
  chunk=128 ms=0.040224 gbps=837.4
  chunk=256 ms=0.040064 gbps=840.8
  chunk=512 ERR OutOfResources: out of resource: shared memory, Required: 135168, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
shape 1
  chunk=128 ms=0.202128 gbps=1330.6
  chunk=256 ms=0.198992 gbps=1351.6
  chunk=512 ERR OutOfResources: out of resource: shared memory, Required: 135168, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
shape 2
  chunk=128 ms=0.066864 gbps=1005.6
  chunk=256 ms=0.062800 gbps=1070.7
  chunk=512 ERR OutOfResources: out of resource: shared memory, Required: 139264, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
shape 3
  chunk=128 ms=0.086272 gbps=1169.1
  chunk=256 ms=0.086208 gbps=1170.0
  chunk=512 ERR OutOfResources: out of resource: shared memory, Required: 135168, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
shape 4
  chunk=128 ms=0.025744 gbps=637.7
  chunk=256 ms=0.024848 gbps=660.7
  chunk=512 ms=0.026720 gbps=614.4

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution_triton.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 6acf7f
Wall time: 1.0004 seconds
Process running with session ID 2266
Original token count: 12
Output:
shape 0
  chunk=128 pw=4 ms=0.038800 gbps=868.2

Chunk ID: d57a07
Wall time: 1.2862 seconds
Process exited with code 0
Original token count: 160
Output:
  chunk=128 pw=8 ms=0.038672 gbps=871.1
  chunk=256 pw=4 ms=0.039616 gbps=850.3
  chunk=256 pw=8 ms=0.038464 gbps=875.8
shape 1
  chunk=256 pw=4 ms=0.198736 gbps=1353.4
  chunk=256 pw=8 ms=0.197472 gbps=1362.0
shape 2
  chunk=256 pw=4 ms=0.062144 gbps=1082.0
  chunk=256 pw=8 ms=0.062256 gbps=1080.1
shape 3
  chunk=128 pw=4 ms=0.086368 gbps=1167.8
  chunk=128 pw=8 ms=0.086256 gbps=1169.3
  chunk=256 pw=4 ms=0.086016 gbps=1172.6
  chunk=256 pw=8 ms=0.086784 gbps=1162.2
shape 4
  chunk=128 pw=4 ms=0.025248 gbps=650.2
  chunk=128 pw=8 ms=0.025808 gbps=636.1
  chunk=256 pw=4 ms=0.025328 gbps=648.2
  chunk=256 pw=8 ms=0.025520 gbps=643.3

Chunk ID: 519048
Wall time: 1.0012 seconds
Process running with session ID 53485
Original token count: 6
Output:
shape 0 chunk 256 pw 8

Chunk ID: 6723f4
Wall time: 4.7608 seconds
Process exited with code 0
Original token count: 177
Output:
  rw=1 ms=0.030768 gbps=1094.8
  rw=2 ms=0.040048 gbps=841.1
  rw=4 ms=0.040608 gbps=829.5
  rw=8 ms=0.030736 gbps=1096.0
shape 1 chunk 256 pw 8
  rw=1 ms=0.178736 gbps=1504.8
  rw=2 ms=0.178368 gbps=1507.9
  rw=4 ms=0.197168 gbps=1364.1
  rw=8 ms=0.203312 gbps=1322.9
shape 2 chunk 256 pw 4
  rw=1 ms=0.068064 gbps=987.9
  rw=2 ms=0.053792 gbps=1250.0
  rw=4 ms=0.051488 gbps=1305.9
  rw=8 ms=0.062304 gbps=1079.2
shape 3 chunk 256 pw 4
  rw=1 ms=0.086848 gbps=1161.3
  rw=2 ms=0.073984 gbps=1363.3
  rw=4 ms=0.073952 gbps=1363.9
  rw=8 ms=0.073728 gbps=1368.0
shape 4 chunk 128 pw 4
  rw=1 ms=0.025616 gbps=640.9
  rw=2 ms=0.016704 gbps=982.8
  rw=4 ms=0.016544 gbps=992.3
  rw=8 ms=0.016736 gbps=980.9

output:
  Success. Updated the following files:
  M /workspace/problems/03_paged_attention/solution_triton.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: e480b7
Wall time: 1.0011 seconds
Process running with session ID 68555
Original token count: 0
Output:

Chunk ID: 1e6225
Wall time: 8.4273 seconds
Process exited with code 0
Original token count: 19
Output:
shape 0 ok
shape 1 ok
shape 2 ok
shape 3 ok
shape 4 ok
TRITON CONFIG PASS

Chunk ID: 28183e
Wall time: 1.0009 seconds
Process running with session ID 92787
Original token count: 26
Output:
benchmark_event event=variant_start shape=0 variant=triton_tuned ts=2026-06-13T11:06:52.295290+00:00

Chunk ID: 929cd6
Wall time: 0.9061 seconds
Process exited with code 0
Original token count: 356
Output:
benchmark_event event=variant_end shape=0 variant=triton_tuned ts=2026-06-13T11:06:52.697827+00:00 elapsed_s=0.403 ms=0.029184
shape=0 triton_tuned tflops=4.599 gbps=1154.246 ms=0.029184 frac=0.6412
benchmark_event event=variant_start shape=1 variant=triton_tuned ts=2026-06-13T11:06:54.382859+00:00
benchmark_event event=variant_end shape=1 variant=triton_tuned ts=2026-06-13T11:06:54.543506+00:00 elapsed_s=0.161 ms=0.188464
shape=1 triton_tuned tflops=5.697 gbps=1427.115 ms=0.188464 frac=0.7928
benchmark_event event=variant_start shape=2 variant=triton_tuned ts=2026-06-13T11:06:54.971049+00:00
benchmark_event event=variant_end shape=2 variant=triton_tuned ts=2026-06-13T11:06:54.986110+00:00 elapsed_s=0.015 ms=0.053744
shape=2 triton_tuned tflops=9.989 gbps=1251.115 ms=0.053744 frac=0.6951
benchmark_event event=variant_start shape=3 variant=triton_tuned ts=2026-06-13T11:06:55.622010+00:00
benchmark_event event=variant_end shape=3 variant=triton_tuned ts=2026-06-13T11:06:55.629309+00:00 elapsed_s=0.007 ms=0.086448
shape=3 triton_tuned tflops=4.655 gbps=1166.712 ms=0.086448 frac=0.6482
benchmark_event event=variant_start shape=4 variant=triton_tuned ts=2026-06-13T11:06:55.733598+00:00
benchmark_event event=variant_end shape=4 variant=triton_tuned ts=2026-06-13T11:06:55.738968+00:00 elapsed_s=0.005 ms=0.024816
shape=4 triton_tuned tflops=2.641 gbps=661.540 ms=0.024816 frac=0.367

... (27 more chars)

Chunk ID: d148c7
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1923
Output:
import math

import torch
import torch.nn as nn
import triton
import triton.language as tl


OP_TYPE = "attention"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]

BATCH = 8
NUM_HEADS = 32
NUM_KV_HEADS = 8
HEAD_DIM = 128
SEQ_LEN = 1024
PAGE_SIZE = 16


@triton.jit
def _partial_kernel(
    query,
    kv_cache,
    block_table,
    seq_lens,
    partial_m,
    partial_l,
    partial_acc,
    B: tl.constexpr,
    H: tl.constexpr,
    Hkv: tl.constexpr,
    D: tl.constexpr,
    P: tl.constexpr,
    MAX_BLOCKS: tl.constexpr,
    CHUNK: tl.constexpr,
    G: tl.constexpr,
    BG: tl.constexpr,
    SCALE: tl.constexpr,
):
    b = tl.program_id(0)
    kvh = tl.program_id(1)
    part = tl.program_id(2)

    offs_g = tl.arange(0, BG)
    offs_d = tl.arange(0, D)
    offs_m = part * CHUNK + tl.arange(0, CHUNK)
    seq_len = tl.load(seq_lens + b)
    valid_m = offs_m < seq_len

    q = tl.load(
        query + (b * H + kvh * G + offs_g[:, None]) * D + offs_d[None, :],
        mask=offs_g[:, None] < G,
        other=0.0,
    )

    page_idx = offs_m // P
    page_off = offs_m - page_idx * P
    phys = tl.load(block_table + b * MAX_BLOCKS + page_idx, mask=valid_m, other=0)
    token_base = ((phys * P + page_off) * Hkv + kvh) * (2 * D)

    k = tl.load(
        kv_cache + token_base[:, None] + offs_d[None, :],
        mask=valid_m[:, None],
        oth

... (6295 more chars)

output:
  Success. Updated the following files:
  D /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

output:
  Success. Updated the following files:
  A /workspace/problems/03_paged_attention/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: fb4f4a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: 9695a7
Wall time: 1.0011 seconds
Process running with session ID 7708
Original token count: 0
Output:

Chunk ID: 62ee66
Wall time: 8.4126 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 7f2bf8
Wall time: 1.0007 seconds
Process running with session ID 62001
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:08:25.389964+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:08:25.606087+00:00 elapsed_s=0.216 ms=0.040880
shape=0 variant=solution tflops=3.283 gbps=824.009 ms=0.041

Chunk ID: 422e71
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 337
Output:
shape=0 solution_peak_fraction=0.4578
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:08:27.271150+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:08:27.301056+00:00 elapsed_s=0.030 ms=0.186192
shape=1 variant=solution tflops=5.767 gbps=1444.529 ms=0.186
shape=1 solution_peak_fraction=0.8025
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:08:27.718141+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:08:27.761248+00:00 elapsed_s=0.043 ms=0.062512
shape=2 variant=solution tflops=8.588 gbps=1075.632 ms=0.063
shape=2 solution_peak_fraction=0.5976
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:08:28.384652+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:08:28.406454+00:00 elapsed_s=0.022 ms=0.077920
shape=3 variant=solution tflops=5.164 gbps=1294.403 ms=0.078
shape=3 solution_peak_fraction=0.7191
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:08:28.509017+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T11:08:28.525002+00:00 elapsed_s=0.016 ms=0.016736
shape=4 variant=solution tflops=3.916 gbps=980.925 ms=0.017
shape=4 solution_peak_fraction=0.5450
peak_fraction: 0.6123
RESULT: OK

Chunk ID: 7573ba
Wall time: 1.0010 seconds
Process running with session ID 51535
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:08:45.818924+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:08:46.032712+00:00 elapsed_s=0.214 ms=0.038864
shape=0 variant=solution tflops=3.454 gbps=866.753 ms=0.039

Chunk ID: 6fcc14
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 337
Output:
shape=0 solution_peak_fraction=0.4815
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:08:47.715425+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:08:47.727331+00:00 elapsed_s=0.012 ms=0.197664
shape=1 variant=solution tflops=5.432 gbps=1360.692 ms=0.198
shape=1 solution_peak_fraction=0.7559
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:08:48.142753+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:08:48.185434+00:00 elapsed_s=0.043 ms=0.061760
shape=2 variant=solution tflops=8.693 gbps=1088.730 ms=0.062
shape=2 solution_peak_fraction=0.6048
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:08:48.803671+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:08:48.811007+00:00 elapsed_s=0.007 ms=0.086320
shape=3 variant=solution tflops=4.662 gbps=1168.442 ms=0.086
shape=3 solution_peak_fraction=0.6491
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:08:48.911571+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T11:08:48.917001+00:00 elapsed_s=0.005 ms=0.025904
shape=4 variant=solution tflops=2.530 gbps=633.754 ms=0.026
shape=4 solution_peak_fraction=0.3521
peak_fraction: 0.5500
RESULT: OK

Chunk ID: 9359ac
Wall time: 1.0015 seconds
Process running with session ID 27028
Original token count: 11
Output:
shape 0 G 4
  BG=4 ms=0.039264 gbps=857.9

Chunk ID: 47298b
Wall time: 3.8864 seconds
Process exited with code 0
Original token count: 113
Output:
  BG=8 ms=0.039360 gbps=855.8
  BG=16 ms=0.039536 gbps=852.0
shape 1 G 4
  BG=4 ms=0.197520 gbps=1361.7
  BG=8 ms=0.197888 gbps=1359.2
  BG=16 ms=0.197792 gbps=1359.8
shape 2 G 8
  BG=8 ms=0.062688 gbps=1072.6
  BG=16 ms=0.062960 gbps=1068.0
shape 3 G 4
  BG=4 ms=0.086336 gbps=1168.2
  BG=8 ms=0.086128 gbps=1171.0
  BG=16 ms=0.086288 gbps=1168.9
shape 4 G 4
  BG=4 ms=0.025760 gbps=637.3
  BG=8 ms=0.026144 gbps=627.9
  BG=16 ms=0.027184 gbps=603.9

Chunk ID: 1df9ad
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1840
Output:
warning: Not a git repository. Use --no-index to compare two paths outside a working tree
usage: git diff --no-index [<options>] <path> <path>

Diff output format options
    -p, --patch           generate patch
    -s, --no-patch        suppress diff output
    -u                    generate patch
    -U, --unified[=<n>]   generate diffs with <n> lines context
    -W, --[no-]function-context
                          generate diffs with <n> lines context
    --raw                 generate the diff in raw format
    --patch-with-raw      synonym for '-p --raw'
    --patch-with-stat     synonym for '-p --stat'
    --numstat             machine friendly --stat
    --shortstat           output only the last line of --stat
    -X, --dirstat[=<param1,param2>...]
                          output the distribution of relative amount of changes for each sub-directory
    --cumulative          synonym for --dirstat=cumulative
    --dirstat-by-file[=<param1,param2>...]
                          synonym for --dirstat=files,param1,param2...
    --check               warn if changes introduce conflict markers or whitespace errors
    --summary             condensed summary such as creations, renames and mode changes
    --name-only           show only names of changed files
    --name-status         show only names and status of changed files
    --stat[=<width>[,<name-width>[,<count>]]

... (5962 more chars)

Chunk ID: 4d50ab
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 172
Output:
total 68
drwxrwxr-x 3 1002 1002 4096 Jun 13 11:08 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 10:45 ..
-rw-rw-r-- 1 1002 1002 2585 Apr 27 09:21 PROMPT.txt
drwxr-xr-x 2 1002 1002 4096 Jun 13 11:08 __pycache__
-rw-rw-r-- 1 1002 1002 5387 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 3841 Jun  2 01:08 check.py
-rw-r--r-- 1 1002 1002    7 Jun 13 11:08 framework.txt
-rw-rw-r-- 1 1002 1002 2030 Apr 26 04:19 problem.yaml
-rw-rw-r-- 1 1002 1002 5151 Apr 26 04:19 reference.py
-rw-rw-r-- 1 1002 1002  933 Apr 26 04:19 shapes.py
-rw-r--r-- 1 1002 1002 7690 Jun 13 11:08 solution.py
-rw-r--r-- 1 1002 1002 7690 Jun 13 11:06 solution_triton.py
-rw-rw-r-- 1 1002 1002 2819 Apr 26 04:24 sota.py
triton

Chunk ID: af7461
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 142
Output:
total 56
drwxrwxr-x 2 1002 1002 4096 Jun 13 11:09 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 10:45 ..
-rw-rw-r-- 1 1002 1002 2585 Apr 27 09:21 PROMPT.txt
-rw-rw-r-- 1 1002 1002 5387 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 3841 Jun  2 01:08 check.py
-rw-r--r-- 1 1002 1002    7 Jun 13 11:08 framework.txt
-rw-rw-r-- 1 1002 1002 2030 Apr 26 04:19 problem.yaml
-rw-rw-r-- 1 1002 1002 5151 Apr 26 04:19 reference.py
-rw-rw-r-- 1 1002 1002  933 Apr 26 04:19 shapes.py
-rw-r--r-- 1 1002 1002 7690 Jun 13 11:08 solution.py
-rw-rw-r-- 1 1002 1002 2819 Apr 26 04:24 sota.py

Chunk ID: bbc27f
Wall time: 1.0011 seconds
Process running with session ID 19639
Original token count: 0
Output:

Chunk ID: 361471
Wall time: 6.4973 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 58d18e
Wall time: 1.0013 seconds
Process running with session ID 90512
Original token count: 70
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:10:12.360244+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:10:12.573245+00:00 elapsed_s=0.213 ms=0.038992
shape=0 variant=solution tflops=3.442 gbps=863.908 ms=0.039

Chunk ID: 864f44
Wall time: 0.3320 seconds
Process exited with code 0
Original token count: 337
Output:
shape=0 solution_peak_fraction=0.4799
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:10:14.190404+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:10:14.202168+00:00 elapsed_s=0.012 ms=0.198064
shape=1 variant=solution tflops=5.421 gbps=1357.944 ms=0.198
shape=1 solution_peak_fraction=0.7544
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:10:14.608144+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:10:14.655439+00:00 elapsed_s=0.047 ms=0.062176
shape=2 variant=solution tflops=8.635 gbps=1081.445 ms=0.062
shape=2 solution_peak_fraction=0.6008
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:10:15.293438+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:10:15.301051+00:00 elapsed_s=0.008 ms=0.089392
shape=3 variant=solution tflops=4.501 gbps=1128.288 ms=0.089
shape=3 solution_peak_fraction=0.6268
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:10:15.422566+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T11:10:15.427958+00:00 elapsed_s=0.005 ms=0.025776
shape=4 variant=solution tflops=2.543 gbps=636.901 ms=0.026
shape=4 solution_peak_fraction=0.3538
peak_fraction: 0.5454
RESULT: OK

Chunk ID: 60662d
Wall time: 1.0010 seconds
Process running with session ID 7716
Original token count: 0
Output:

Chunk ID: 78d86a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 58
Output:
rw 1 ms 0.016608,0.016544,0.016784 gbps 988.5,992.3,978.1
rw 2 ms 0.016880,0.016544,0.016880 gbps 972.6,992.3,972.6
rw 4 ms 0.016544,0.016800,0.016704 gbps 992.3,977.2,982.8
rw 8 ms 0.016672,0.016656,0.016608 gbps 984.7,985.6,988.5

Chunk ID: 8b7ca3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1886
Output:
      1       0 Ssl        25:05 node /opt/node/bin/codex exec -m gpt-5.5 -c model_reasoning_effort="xhigh" --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check -C /workspace/problems/03_paged_attention I need you to write a paged attention decode kernel for the RTX PRO 6000 (SM120 Blackwell, GDDR7, 1.8 TB/s). The reference implementation is in reference.py and your kernel goes in solution.py. You can make whatever mess you want in this directory — scratch files, intermediate kernels, build artifacts, profiling traces — but the final answer has to be in solution.py with the same Model, get_inputs, and get_init_inputs interface as reference.py.  This is single-query decode: each batch element has a query of shape (num_heads, head_dim) and attends over a KV cache of seq_len[b] tokens stored as fixed-size pages in a global pool, with block_table[b] listing which pages belong to batch element b. The KV cache is laid out as (num_blocks, page_size, num_kv_heads, head_dim * 2) where the last dim packs [K | V] so a single gather pulls both. You return the bf16 output of shape (batch, num_heads, head_dim). Correctness tolerance is 0.02 abs/rel on bf16/fp16. The shapes you have to handle are batch=8 num_heads=32 num_kv_heads=8 head_dim=128 seq_len=1024 page_size=16 (Llama-3 8B-style decode), batch=32 num_heads=32 num_kv_heads=8 head_dim=128 seq_len=2048 page_size=16 (ser

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