total 44
drwxrwxr-x 2 1002 1002 4096 Jun 13 22:39 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 22:39 ..
-rw-rw-r-- 1 1002 1002 2084 Apr 27 09:21 PROMPT.txt
-rw-rw-r-- 1 1002 1002 5078 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 6288 Jun  2 01:09 check.py
-rw-rw-r-- 1 1002 1002 2199 Apr 26 04:20 problem.yaml
-rw-rw-r-- 1 1002 1002 1561 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  945 Apr 26 04:20 sota.py
"""Naive top-k reference: torch.topk over the last dim.

This is the correctness oracle. The agent's solution must produce the same
top-k values (and equivalent indices modulo ties) within the tolerance
declared in problem.yaml. Note that solution.py is FORBIDDEN from calling
torch.topk / torch.sort / torch.kthvalue (see problem.yaml).
"""
import torch
import torch.nn as nn

OP_TYPE = "topk"
SUPPORTED_PRECISIONS = ["fp32"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]


class Model(nn.Module):
    """Top-k over the last dim of a 2D tensor.

    Input:
        x: (batch, n) fp32
    Output:
        values:  (batch, k) fp32, sorted descending
        indices: (batch, k) int64, into the last dim of x
    """

    def __init__(self, batch: int, n: int, k: int):
        super().__init__()
        self.batch, self.n, self.k = batch, n, k
        # No learned parameters, but declare a dummy buffer so state_dict
        # is non-empty and load_state_dict(strict=True) is meaningful.
        self.register_buffer("_dummy", torch

... (520 more chars)

"""Correctness runner for TopK.

Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
each. Top-k correctness has two parts:

  1. VALUES: sol_values must match ref_values within fp32 tol. Both are
     returned sorted descending, so positional comparison is well-defined.
  2. INDICES: lenient — we do NOT require sol_indices == ref_indices because
     ties in x can yield multiple valid index sets. Instead we gather x at
     sol_indices and check those values match ref_values within tol. This
     catches "wrong indices" without false-failing on legitimate tie-breaks.

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 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 {}

    # --- Forbidden-op check ------------------------------------------------
    sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
    for forbidden in meta.get

... (4785 more chars)

"""Roofline benchmark for TopK.

For each shape: times eager reference (torch.topk), compiled reference, SOTA
(also torch.topk — see sota.py), and the agent's solution. Reports achieved
TFLOPS, GB/s, and peak_fraction (vs DRAM bandwidth, since this is memory-bound).

Output lines the harness picks up:
  shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
  peak_fraction: <N>  (geomean over shapes of solution's peak_fraction)
"""
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 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["peak_tflops_key"], 0.0)
    peak_gbps = hw.peak_bandwidth_gb_s
    regime = meta.get("regime", "memory")
    flops_formula = meta["flops_formula"]
    bytes_formula = meta["bytes_formula"]
    num_perf_trials = int(meta.get("num_perf_trials", 50))

    device = torch.device("cuda:0")
    include_baselines = benchmark_baselines_enabled(

... (3575 more chars)

"""Canonical shape sweep for TopK.

Mix of:
  - decoder vocab top-k (single sequence, very large n, moderate k) — pure
    bandwidth test; the input read dominates everything.
  - prefill / batched attention top-k (many rows, moderate n, small k) — tests
    per-row parallelism and shared-memory bitonic networks.
  - non-power-of-2 n stress case — bitonic sort networks naturally want
    powers of two; this forces the agent to handle padding or partial sorts.
  - small-k limit — k=1 (argmax) is a degenerate but useful sanity case.
"""

SHAPES = [
    {"batch": 1,   "n": 131072, "k": 64},   # decoder vocab top-k (Llama vocab ~128k)
    {"batch": 64,  "n": 8192,   "k": 8},    # prefill / attention top-k
    {"batch": 32,  "n": 16384,  "k": 32},   # mid-size batched
    {"batch": 16,  "n": 12000,  "k": 16},   # non-power-of-2 n stress
    {"batch": 128, "n": 4096,   "k": 1},    # batched argmax (k=1 corner case)
]
---
"""SOTA reference for TopK: torch.topk itself.

torch.topk dispatches to a CUB-backed kernel that uses radix-select for
moderate k and a tuned bitonic sort for small n. It is the bar the agent's
hand-rolled bitonic kernel must beat. There is no obvious vendor library that
does better on the (batch, n, k) shape mix we evaluate — Faiss BlockSelect is
specialized for k>=32 with much larger n, and CUB's DeviceSegmentedRadixSort
sorts the full row (overkill for top-k).

This file is INTENTIONALLY allowed to call torch.topk because it is the SOTA
oracle, not the agent's 

... (2572 more chars)

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.
Installed 56 packages in 857ms
2.11.0+cu130
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
12 0

3.6.0

Exit code 1
Traceback (most recent call last):
  File "/tmp/profile_topk.py", line 3, in <module>
    from shapes import SHAPES
ModuleNotFoundError: No module named 'shapes'

Exit code 1
Traceback (most recent call last):
  File "/tmp/profile_topk.py", line 3, in <module>
    from shapes import SHAPES
ModuleNotFoundError: No module named 'shapes'

shape=(1,131072,64) ms=0.041 gbps=12.7
shape=(64,8192,8) ms=0.037 gbps=56.8
shape=(32,16384,32) ms=0.037 gbps=56.6
shape=(16,12000,16) ms=0.034 gbps=22.5
shape=(128,4096,1) ms=0.013 gbps=156.6

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T22:40:41.954765+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T22:40:41.985092+00:00 elapsed_s=0.030 ms=0.049584
shape=0 variant=solution tflops=0.011 gbps=10.589 ms=0.050
shape=0 solution_peak_fraction=0.0059
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T22:40:41.987049+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T22:40:41.994642+00:00 elapsed_s=0.008 ms=0.048160
shape=1 variant=solution tflops=0.044 gbps=43.673 ms=0.048
shape=1 solution_peak_fraction=0.0243
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T22:40:41.996490+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T22:40:42.003882+00:00 elapsed_s=0.007 ms=0.047856
shape=2 variant=solution tflops=0.044 gbps=44.079 ms=0.048
shape=2 solution_peak_fraction=0.0245
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T22:40:42.004813+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T22:40:42.013985+00:00 elapsed_s=0.009 ms=0.045840
shape=3 variant=solution tflops=0.017 gbps=16.821 ms=0.046
shape=3 solution_peak_fraction=0.0093
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T22:40:42.015287+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T22:40:42.021019+00:00 elapsed_s=0.006 ms=0.021680
shape=4 vari

... (118 more chars)

"""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

import os
import re
import statistics
import time
from datetime import UTC, datetime

import torch

# Bl

... (2813 more chars)

'sort'

Help on JITFunction in module triton.language.standard object:

sort = class JITFunction(JITCallable, KernelInterface)
 |  sort(fn, version=None, do_not_specialize=None, do_not_specialize_on_alignment=None, debug=None, noinline=None, repr=None, launch_metadata=None)
 |  
 |  Method resolution order:
 |      JITFunction
 |      JITCallable
 |      KernelInterface
 |      typing.Generic
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __call__(self, *args, **kwargs)
 |      Call self as a function.
 |  
 |  __init__(self, fn, version=None, do_not_specialize=None, do_not_specialize_on_alignment=None, debug=None, noinline=None, repr=None, launch_metadata=None)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  __repr__(self)
 |      Return repr(self).
 |  
 |  add_pre_run_hook(self, hook)
 |      Add a hook that will be executed prior to the execution of run
 |      function with args and kwargs passed into the kernel
 |  
 |  create_binder(self)
 |      Precompute as much as possible.
 |  
 |  is_gluon(self)
 |  
 |  preload(self, specialization_data)
 |  
 |  repr(self, _)
 |  
 |  run(self, *args, grid, warmup, **kwargs)
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  __annotations__ = {}
 |  
 |  __orig_bases__ = (<class 'triton.runtime.jit.JITCallable'>, triton.run...
 |  
 |  __parameters__ = (~T,)
 |  
 |  ----------------------------------------

... (1959 more chars)

def sort(x, dim: core.constexpr = None, descending: core.constexpr = core.CONSTEXPR_0):
    return sort_impl(x, dim=dim, descending=descending)

bitonic descending sort OK

File created successfully at: /workspace/problems/05_topk_bitonic/solution.py (file state is current in your context — no need to Read it back)

FAIL: import error: Error building extension 'topk_bitonic': [1/3] /usr/local/cuda-host/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=topk_bitonic -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_100,code=sm_100 -gencode=arch=compute_120,code=compute_120 -gencode=arch=compute_120,code=sm_120 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic/cuda.cu -o cuda.cuda.o 
FAILED: cuda.cuda.o 
/usr/local/cuda-host/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=topk_bitonic -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__

... (2130 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

FAIL: import error: Error building extension 'topk_bitonic': [1/3] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=topk_bitonic -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -fPIC -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic/main.cpp -o main.o 
FAILED: main.o 
c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=topk_bitonic -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -fPIC -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic/main.cpp -o main.o 
/home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic/main.cpp: In function ‘void pybind11_init_topk_bitonic(pybind11::module_&)’:
/home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic/main.cpp:4:51: error: ‘topk_forward’ was not declared in this scope
    4 | m.def("topk_forward", torch::wrap_pybind_function(topk_forward), "topk_forward");
      |                                                   ^~~~~~~~~~~~
[2/3] /usr/local/cuda-host/bin/nvcc -MD -MF cuda.cuda

... (949 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

Task #1 created successfully: Implement custom top-k kernel

Task #2 created successfully: Profile and optimize kernel performance

Updated task #1 status

FAIL: shape 0 {'batch': 1, 'n': 131072, 'k': 64} seed 42 case nominal values: tolerance exceeded: max_abs_diff=1.83222 max_rel_diff=0.564788 bad=63/64 worst_idx=(0, 63) (atol=0.0001, rtol=0.0001)

Traceback (most recent call last):
  File "<string>", line 23, in <module>
RuntimeError: gather(): Expected dtype int32/int64 for index
ref values: tensor([2.1228, 1.9269, 1.8446, 1.7174, 1.6806, 1.6487, 1.6423, 1.4873],
       device='cuda:0')
sol values: tensor([2.1228, 1.9269, 1.8446, 1.7174, 1.6806, 1.6487, 1.6423, 1.4873],
       device='cuda:0')
ref indices: tensor([45,  0, 61, 35, 23,  9, 16,  1], device='cuda:0')
sol indices: tensor([45.,  0., 61., 35., 23.,  9., 16.,  1.], device='cuda:0')
max abs diff values: tensor(0., device='cuda:0')

(Bash completed with no output)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated. All occurrences were successfully replaced. (file state is current in your context — no need to Read it back)

FAIL: import error: Error building extension 'topk_bitonic': [1/3] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=topk_bitonic -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -fPIC -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic/main.cpp -o main.o 
[2/3] /usr/local/cuda-host/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=topk_bitonic -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_100,code=sm_100 -gencode=arch=compute_120,code=compute_120 -gencode=arch=compute_120,code=sm_120 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic/cuda.cu -o cuda.cuda.o 
FAI

... (1374 more chars)

212:    return torch::stack({out_val, out_idx}, 0);
240:        stacked = _topk_cuda.topk_forward(x, self.k)
241:        values = stacked[0]
242:        indices = stacked[1]

205	                x.data_ptr<float>(), out_val.data_ptr<float>(),
206	                out_idx.data_ptr<int64_t>(), B, N);
207	            break;
208	        default:
209	            TORCH_CHECK(false, "unsupported k for fast path");
210	    }
211	
212	    return torch::stack({out_val, out_idx}, 0);
213	}
214	"""
215	
216	
217	_CPP_SOURCE = r"""
218	#include <torch/extension.h>
219	#include <tuple>

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

FAIL: shape 0 {'batch': 1, 'n': 131072, 'k': 64} seed 42 case nominal values: tolerance exceeded: max_abs_diff=1.83222 max_rel_diff=0.564788 bad=63/64 worst_idx=(0, 63) (atol=0.0001, rtol=0.0001)

values match: False
max abs diff: tensor(0.6803, device='cuda:0')
ref last 5: tensor([1.8890, 1.8868, 1.8778, 1.8754, 1.8697], device='cuda:0')
sol last 5: tensor([1.2888, 1.2780, 1.2732, 1.1960, 1.1895], device='cuda:0')
gathered match: False

values match: False
max abs diff: tensor(0.4904, device='cuda:0')

n=64 k=64 match: True maxdiff: 0.0
n=128 k=64 match: True maxdiff: 0.0
n=256 k=64 match: True maxdiff: 0.0
n=512 k=64 match: True maxdiff: 0.0
n=1024 k=64 match: False maxdiff: 0.4903531074523926

match: True

match: False
max diff: 2.8960518836975098
out first 10: tensor([2.9411, 2.6006, 2.6036, 2.3647, 2.5517, 2.3056, 2.3269, 2.3042, 2.4969,
        2.2182], device='cuda:0')
ref first 10: tensor([2.9411, 2.6036, 2.6006, 2.5517, 2.4969, 2.4849, 2.3647, 2.3452, 2.3269,
        2.3056], device='cuda:0')

match no-unroll: False
max diff: 2.0985679626464844

match i<N: True
max diff: 0.0

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

FAIL: shape 4 {'batch': 128, 'n': 4096, 'k': 1} seed 42 case nominal values: tolerance exceeded: max_abs_diff=2.19344 max_rel_diff=0.553012 bad=123/128 worst_idx=(67, 0) (atol=0.0001, rtol=0.0001)

38	                        }
39	                    } else {
40	                        // ascending
41	                        if (val[lo] > val[hi]) {
42	                            float tv = val[lo]; val[lo] = val[hi]; val[hi] = tv;
43	                            int ti = idx[lo]; idx[lo] = idx[hi]; idx[hi] = ti;
44	                        }
45	                    }
46	                }
47	            }
48	            __syncthreads();
49	        }
50	    }
51	}
52	
53	// ----------------------------------------------------------------------------
54	// k = 1  (argmax)
55	// ----------------------------------------------------------------------------
56	__global__ void topk_k1_kernel(const float* __restrict__ x,
57	                               float* __restrict__ out_val,
58	                               int64_t* __restrict__ out_idx,
59	                               int B, int N) {
60	    int row = blockIdx.x;
61	    if (row >= B) return;
62	    const float* row_x = x + (int64_t)row * N;
63	
64	    float best_val = -FLT_MAX;
65	    int best_idx = 0;
66	
67	    int tid = threadIdx.x;
68	    int nt = blockDim.x;
69	    for (int i = tid; i < N; i += nt) {
70	        float v = row_x[i];
71	        if (v > best_val) {
72	            best_val = v;
73	            best_idx = i;
74	        }
75	    }
76	
77	    #pragma unroll
78	    for (int offset = 16; offset > 0; offset >>= 1) {
79	        float other_v = __shfl_down_sync(0xFFFFFFFF, best_val, offset);
80	        int other_

... (593 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

PASS

Updated task #1 status

Updated task #2 status

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T22:59:04.466995+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T22:59:04.625529+00:00 elapsed_s=0.159 ms=2.504320
shape=0 variant=solution tflops=0.000 gbps=0.210 ms=2.504
shape=0 solution_peak_fraction=0.0001
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T22:59:04.627645+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T22:59:04.640809+00:00 elapsed_s=0.013 ms=0.144256
shape=1 variant=solution tflops=0.015 gbps=14.580 ms=0.144
shape=1 solution_peak_fraction=0.0081
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T22:59:04.642541+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T22:59:04.665265+00:00 elapsed_s=0.023 ms=0.305344
shape=2 variant=solution tflops=0.007 gbps=6.908 ms=0.305
shape=2 solution_peak_fraction=0.0038
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T22:59:04.665905+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T22:59:04.683549+00:00 elapsed_s=0.018 ms=0.219568
shape=3 variant=solution tflops=0.003 gbps=3.512 ms=0.220
shape=3 solution_peak_fraction=0.0020
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T22:59:04.684895+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T22:59:04.689942+00:00 elapsed_s=0.005 ms=0.009024
shape=4 variant

... (116 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

170	    // Each warp scans a contiguous chunk; lane t reads every WARP_SIZE-th
171	    // element starting at warp_begin + t for perfect coalescing.
172	    int elements_per_warp = (N + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK;
173	    int warp_begin = wid * elements_per_warp;
174	    int warp_end = min(warp_begin + elements_per_warp, N);
175	
176	    float vals[V_PER_THREAD];
177	    int   idxs[V_PER_THREAD];
178	    #pragma unroll
179	    for (int p = 0; p < V_PER_THREAD; ++p) {
180	        vals[p] = -FLT_MAX;
181	        idxs[p] = 0;
182	    }
183	
184	    for (int i = warp_begin + lane; i < warp_end; i += WARP_SIZE) {
185	        warp_topk_update<K, V_PER_THREAD>(vals, idxs, row_x[i], i);
186	    }
187	
188	    // ------------------------------------------------------------------
189	    // Collect all warp-local heaps into shared memory and sort them.
190	    // ------------------------------------------------------------------
191	    extern __shared__ char smem[];
192	    float* s_val = (float*)smem;
193	    int*   s_idx = (int*)(s_val + THREADS_PER_BLOCK * V_PER_THREAD);
194	
195	    int heap_size = HEAP_PER_WARP * WARPS_PER_BLOCK;  // = THREADS_PER_BLOCK * V_PER_THREAD
196	    int my_smem_base = tid * V_PER_THREAD;
197	    #pragma unroll
198	    for (int p = 0; p < V_PER_THREAD; ++p) {
199	        s_val[my_smem_base + p] = vals[p];
200	        s_idx[my_smem_base + p] = idxs[p];
201	    }
202	    __syncthreads();
203	
204	    bitonic_sort_desc<heap_size>(s_val, s_idx);


... (989 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

FAIL: import error: Error building extension 'topk_bitonic_v2': [1/3] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=topk_bitonic_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -fPIC -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic_v2/main.cpp -o main.o 
[2/3] /usr/local/cuda-host/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=topk_bitonic_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_100,code=sm_100 -gencode=arch=compute_120,code=compute_120 -gencode=arch=compute_120,code=sm_120 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic_v2/cuda.cu -o c

... (1740 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

argument types are: (float *, int *)
      bitonic_sort_desc<heap_size>(s_val, s_idx);
      ^
/home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic_v2/cuda.cu(15): note #3323-D: substituting explicit template arguments "<<expression>>" for function template "bitonic_sort_desc" failed
                            void bitonic_sort_desc(float* __restrict__ val,
                                 ^
          detected during instantiation of "void topk_kernel<K,V_PER_THREAD,HEAP_PER_WARP>(const float *, float *, int64_t *, int, int) [with K=32, V_PER_THREAD=1, HEAP_PER_WARP=32]" at line 262

/home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic_v2/cuda.cu(214): error: no instance of function template "bitonic_sort_desc" matches the argument list
            argument types are: (float *, int *)
      bitonic_sort_desc<heap_size>(s_val, s_idx);
      ^
/home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic_v2/cuda.cu(15): note #3323-D: substituting explicit template arguments "<<expression>>" for function template "bitonic_sort_desc" failed
                            void bitonic_sort_desc(float* __restrict__ val,
                                 ^
          detected during instantiation of "void topk_kernel<K,V_PER_THREAD,HEAP_PER_WARP>(const float *, float *, int64_t *, int, int) [with K=64, V_PER_THREAD=2, HEAP_PER_WARP=64]" at line 267

5 errors detected in the compilation of "/home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic_v2/cuda.cu".
ninja: bu

... (31 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

Traceback (most recent call last):
  File "/workspace/problems/05_topk_bitonic/check.py", line 157, in <module>
    main()
  File "/workspace/problems/05_topk_bitonic/check.py", line 79, in main
    with numeric_stress_context(ref_model, sol_model, base_inputs, case) as inputs:
  File "/uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/contextlib.py", line 144, in __exit__
    next(self.gen)
  File "/workspace/src/eval/numeric_stress.py", line 129, in numeric_stress_context
    torch.cuda.synchronize()
  File "/workspace/.venv/lib/python3.11/site-packages/torch/cuda/__init__.py", line 1162, in synchronize
    return torch._C._cuda_synchronize()
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: an illegal instruction was encountered
Search for `cudaErrorIllegalInstruction' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

100	        int num_warps = nt >> 5;
101	        float v = (lane < num_warps) ? warp_val[lane] : -FLT_MAX;
102	        int i = (lane < num_warps) ? warp_idx[lane] : 0;
103	        #pragma unroll
104	        for (int offset = 16; offset > 0; offset >>= 1) {
105	            float other_v = __shfl_down_sync(0xFFFFFFFF, v, offset);
106	            int other_i = __shfl_down_sync(0xFFFFFFFF, i, offset);
107	            if (other_v > v) {
108	                v = other_v;
109	                i = other_i;
110	            }
111	        }
112	        if (lane == 0) {
113	            out_val[row] = v;
114	            out_idx[row] = (int64_t)i;
115	        }
116	    }
117	}
118	
119	// ----------------------------------------------------------------------------
120	// General k > 1 : warp-local heap + shared-memory merge
121	// ----------------------------------------------------------------------------
122	__device__ __forceinline__ float warp_reduce_min(float v) {
123	    #pragma unroll
124	    for (int offset = 16; offset > 0; offset >>= 1) {
125	        float other = __shfl_down_sync(0xFFFFFFFF, v, offset);
126	        if (other < v) v = other;
127	    }
128	    return v;
129	}
130	
131	__device__ __forceinline__ int warp_reduce_argmin(float v, int pos) {
132	    #pragma unroll
133	    for (int offset = 16; offset > 0; offset >>= 1) {
134	        float other_v = __shfl_down_sync(0xFFFFFFFF, v, offset);
135	        int other_p = __shfl_down_sync(0xFFFFFFFF, pos, offset);
136	        if

... (4750 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

PASS

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T23:09:58.138888+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T23:09:58.188901+00:00 elapsed_s=0.050 ms=0.684240
shape=0 variant=solution tflops=0.001 gbps=0.767 ms=0.684
shape=0 solution_peak_fraction=0.0004
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:09:58.190825+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:09:58.199083+00:00 elapsed_s=0.008 ms=0.062784
shape=1 variant=solution tflops=0.033 gbps=33.501 ms=0.063
shape=1 solution_peak_fraction=0.0186
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:09:58.200692+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:09:58.211320+00:00 elapsed_s=0.011 ms=0.102256
shape=2 variant=solution tflops=0.021 gbps=20.629 ms=0.102
shape=2 solution_peak_fraction=0.0115
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:09:58.211926+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:09:58.221329+00:00 elapsed_s=0.009 ms=0.082000
shape=3 variant=solution tflops=0.009 gbps=9.403 ms=0.082
shape=3 solution_peak_fraction=0.0052
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:09:58.222587+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:09:58.227587+00:00 elapsed_s=0.005 ms=0.008704
shape=4 varian

... (117 more chars)

match 64: False
max diff: 0.5928475856781006

match 64: True
max diff: 0.0

118	
119	// ----------------------------------------------------------------------------
120	// General k > 1 : warp-local heap + shared-memory merge
121	// ----------------------------------------------------------------------------
122	__device__ __forceinline__ float warp_reduce_min(float v) {
123	    #pragma unroll
124	    for (int offset = 16; offset > 0; offset >>= 1) {
125	        float other = __shfl_down_sync(0xFFFFFFFF, v, offset);
126	        if (other < v) v = other;
127	    }
128	    return v;
129	}
130	
131	__device__ __forceinline__ int warp_reduce_argmin(float v, int pos) {
132	    #pragma unroll
133	    for (int offset = 16; offset > 0; offset >>= 1) {
134	        float other_v = __shfl_down_sync(0xFFFFFFFF, v, offset);
135	        int other_p = __shfl_down_sync(0xFFFFFFFF, pos, offset);
136	        if (other_v < v) {
137	            v = other_v;
138	            pos = other_p;
139	        }
140	    }
141	    return pos;
142	}
143	
144	template <int V_PER_THREAD>
145	__device__ __forceinline__ void warp_heap_insert(float vals[V_PER_THREAD],
146	                                                  int   idxs[V_PER_THREAD],
147	                                                  float new_val, int new_idx) {
148	    int local_min_pos = 0;
149	    float local_min = vals[0];
150	    #pragma unroll
151	    for (int p = 1; p < V_PER_THREAD; ++p) {
152	        if (vals[p] < local_min) {
153	            local_min = vals[p];
154	            local_min_pos = p;
155	        }


... (3204 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

Traceback (most recent call last):
  File "/workspace/problems/05_topk_bitonic/check.py", line 157, in <module>
    main()
  File "/workspace/problems/05_topk_bitonic/check.py", line 102, in main
    ok, msg = check_correctness(
              ^^^^^^^^^^^^^^^^^^
  File "/workspace/src/eval/correctness.py", line 71, in check_correctness
    if torch.isnan(solution_out).any():
       ^^^^^^^^^^^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

torch.Size([1, 64])

torch.Size([1, 64])

Traceback (most recent call last):
  File "/workspace/problems/05_topk_bitonic/check.py", line 157, in <module>
    main()
  File "/workspace/problems/05_topk_bitonic/check.py", line 102, in main
    ok, msg = check_correctness(
              ^^^^^^^^^^^^^^^^^^
  File "/workspace/src/eval/correctness.py", line 71, in check_correctness
    if torch.isnan(solution_out).any():
       ^^^^^^^^^^^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Traceback (most recent call last):
  File "<string>", line 14, in <module>
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

shape (1,131072,64) OK

torch.Size([64, 8])

(32,16384,32) OK torch.Size([32, 32])
(16,12000,16) OK torch.Size([16, 16])
(128,4096,1) OK torch.Size([128, 1])

"""Numeric distribution stress cases for correctness checks.

These cases are not hidden shapes. They rerun the same shape/seed validation
under a few scale regimes that catch kernels that only work for the nominal
N(0, 1)-ish inputs.
"""
from __future__ import annotations

import os
from collections.abc import Iterator, Mapping, Sequence
from contextlib import contextmanager
from dataclasses import dataclass, field

import torch

ToleranceOverride = Mapping[str, float | Mapping[str, float]]


@dataclass(frozen=True)
class NumericStressCase:
    name: str
    input_scales: Mapping[int, float] = field(default_factory=dict)
    state_scales: Mapping[str, float] = field(default_factory=dict)
    tolerance: ToleranceOverride | None = None


NOMINAL_CASE = NumericStressCase("nominal")

_SMALL_BF16 = {"bfloat16": {"atol": 1e-4, "rtol": 5e-2}}
_MED_BF16 = {"bfloat16": {"atol": 5e-4, "rtol": 5e-2}}
_LARGE_BF16 = {"bfloat16": {"atol": 2e-1, "rtol": 5e-2}}
_TINY_FP32 = {"float32": {"atol": 1e-7, "rtol": 1e-4}}
_LARGE_FP32 = {"float32": {"atol": 1e-1, "rtol": 1e-4}}

_CASES: dict[str, tuple[NumericStressCase, ...]] = {
    "01_fp8_gemm": (
        NumericStressCase("small_input", input_scales={0: 1e-3}, tolerance=_SMALL_BF16),
        NumericStressCase("large_input", input_scales={0: 64.0}, tolerance=_LARGE_BF16),
        NumericStressCase(
            "small_weight",
            state_scales={"weight": 1e-2},
            tolerance=_SMALL_BF16,
        ),
    ),
    "02_kda_cutlass": (


... (3574 more chars)

def _scale_inputs(inputs: Sequence[object], scales: Mapping[int, float]) -> list[object]:
    out = list(inputs)
    for idx, scale in scales.items():
        if idx >= len(out):
            raise IndexError(f"input scale index {idx} out of range for {len(out)} inputs")
        value = out[idx]
        if not isinstance(value, torch.Tensor):
            raise TypeError(f"input {idx} is not a tensor and cannot be scaled")
        out[idx] = _scale_tensor(value, scale)
    return out


def _scale_states(
    models: Sequence[torch.nn.Module],
    scales: Mapping[str, float],
) -> list[tuple[torch.Tensor, torch.Tensor]]:
    backups: list[tuple[torch.Tensor, torch.Tensor]] = []
    if not scales:
        return backups
    for model in models:
        state = dict(model.named_parameters())
        state.update(dict(model.named_buffers()))
        for name, scale in scales.items():
            if name not in state:
                raise KeyError(f"state scale target {name!r} not found")
            tensor = state[name]
            if not torch.is_floating_point(tensor):
                raise TypeError(f"state scale target {name!r} is not floating point")
            backups.append((tensor, tensor.detach().clone()))
            with torch.no_grad():
                tensor.copy_(_scale_tensor(tensor, scale))
    return backups


def _scale_tensor(tensor: torch.Tensor, scale: float) -> torch.Tensor:
    if not torch.is_floating_point(tensor):
        raise TypeError(f"cannot scale n

... (122 more chars)

Traceback (most recent call last):
  File "<string>", line 17, in <module>
  File "<string>", line 17, in <listcomp>
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

shape 0 seed 42 case nominal OK
shape 0 seed 42 case tiny_values OK
shape 0 seed 42 case large_values OK
shape 0 seed 123 case nominal OK
shape 0 seed 123 case tiny_values OK
shape 0 seed 123 case large_values OK
shape 0 seed 456 case nominal OK
shape 0 seed 456 case tiny_values OK
shape 0 seed 456 case large_values OK

119	// ----------------------------------------------------------------------------
120	// General k > 1 : warp-local bitonic merge + shared-memory final sort
121	// ----------------------------------------------------------------------------
122	
123	// Sort N elements distributed as VPT per thread (consecutive layout) within a
124	// single warp.  Logical index of vals[pos] is lane * VPT + pos.
125	template <int N, int VPT>
126	__device__ __forceinline__ void warp_bitonic_sort_desc(float val[VPT],
127	                                                        int   idx[VPT]) {
128	    int lane = threadIdx.x & 31;
129	    float old_val[VPT];
130	    int   old_idx[VPT];
131	
132	    #pragma unroll
133	    for (int k = 2; k <= N; k <<= 1) {
134	        #pragma unroll
135	        for (int j = k >> 1; j > 0; j >>= 1) {
136	            #pragma unroll
137	            for (int p = 0; p < VPT; ++p) {
138	                old_val[p] = val[p];
139	                old_idx[p] = idx[p];
140	            }
141	            __syncwarp();
142	
143	            #pragma unroll
144	            for (int pos = 0; pos < VPT; ++pos) {
145	                int i = lane * VPT + pos;
146	                int l = i ^ j;
147	                float other_val;
148	                int other_idx;
149	                int partner_thread = l / VPT;
150	                int partner_pos = l % VPT;
151	                if (partner_thread == lane) {
152	                    other_val = old_val[partner_pos];
153	              

... (3938 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

280	        warp_bitonic_sort_desc<128, 4>(mval, midx);
281	        vals[0] = mval[0];
282	        vals[1] = mval[1];
283	        idxs[0] = midx[0];
284	        idxs[1] = midx[1];
285	    }
286	
287	    extern __shared__ char smem[];
288	    float* s_val = (float*)smem;
289	    int*   s_idx = (int*)(s_val + THREADS_PER_BLOCK * 2);
290	    int base = tid * 2;
291	    s_val[base + 0] = vals[0];
292	    s_val[base + 1] = vals[1];
293	    s_idx[base + 0] = idxs[0];
294	    s_idx[base + 1] = idxs[1];
295	    __syncthreads();
296	
297	    topk_row_finalize<K, HEAP_PER_WARP>(s_val, s_idx, row_out_v, row_out_i, tid);
298	}
299	
300	// ----------------------------------------------------------------------------
301	// Dispatch wrapper
302	// ----------------------------------------------------------------------------
303	std::tuple<torch::Tensor, torch::Tensor> topk_forward(torch::Tensor x, int64_t k) {
304	    int B = x.size(0);
305	    int N = x.size(1);
306	    auto out_val = torch::empty({B, k}, x.options());
307	    auto out_idx = torch::empty({B, k}, torch::dtype(torch::kLong).device(x.device()));
308	
309	    if (k == 1) {
310	        int threads = 1024;
311	        int blocks = B;
312	        topk_k1_kernel<<<blocks, threads>>>(
313	            x.data_ptr<float>(),
314	            out_val.data_ptr<float>(),
315	            out_idx.data_ptr<int64_t>(),
316	            B, N);
317	        return std::make_tuple(out_val, out_idx);
318	    }
319	
320	    // V_PER_THREAD = ceil(k / 

... (1534 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

Traceback (most recent call last):
  File "/workspace/problems/05_topk_bitonic/check.py", line 157, in <module>
    main()
  File "/workspace/problems/05_topk_bitonic/check.py", line 102, in main
    ok, msg = check_correctness(
              ^^^^^^^^^^^^^^^^^^
  File "/workspace/src/eval/correctness.py", line 71, in check_correctness
    if torch.isnan(solution_out).any():
       ^^^^^^^^^^^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Traceback (most recent call last):
  File "/workspace/problems/05_topk_bitonic/check.py", line 157, in <module>
    main()
  File "/workspace/problems/05_topk_bitonic/check.py", line 102, in main
    ok, msg = check_correctness(
              ^^^^^^^^^^^^^^^^^^
  File "/workspace/src/eval/correctness.py", line 71, in check_correctness
    if torch.isnan(solution_out).any():
       ^^^^^^^^^^^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Traceback (most recent call last):
  File "<string>", line 21, in <module>
  File "/uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/contextlib.py", line 137, in __enter__
    return next(self.gen)
           ^^^^^^^^^^^^^^
  File "/workspace/src/eval/numeric_stress.py", line 126, in numeric_stress_context
    yield _scale_inputs(inputs, case.input_scales)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/src/eval/numeric_stress.py", line 143, in _scale_inputs
    out[idx] = _scale_tensor(value, scale)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/src/eval/numeric_stress.py", line 172, in _scale_tensor
    return (tensor.float() * scale).to(dtype=tensor.dtype, device=tensor.device)
            ~~~~~~~~~~~~~~~^~~~~~~
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

shape 0 seed 42 case nominal forward done

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

(Bash completed with no output)

(Bash completed with no output)

total 8408
drwxr-xr-x 2 1002 1002    4096 Jun 13 23:25 .
drwxr-xr-x 9 1002 1002    4096 Jun 13 23:24 ..
-rw-r--r-- 1 1002 1002  589576 Jun 13 23:25 .ninja_deps
-rw-r--r-- 1 1002 1002     190 Jun 13 23:25 .ninja_log
-rw-r--r-- 1 1002 1002    2150 Jun 13 23:24 build.ninja
-rw-r--r-- 1 1002 1002   12353 Jun 13 23:24 cuda.cu
-rw-r--r-- 1 1002 1002 2434728 Jun 13 23:25 cuda.cuda.o
-rw-r--r-- 1 1002 1002     286 Jun 13 23:24 main.cpp
-rw-r--r-- 1 1002 1002 1985440 Jun 13 23:24 main.o
-rwxr-xr-x 1 1002 1002 3555360 Jun 13 23:25 topk_bitonic_v2.so
ninja_required_version = 1.3
cxx = c++
nvcc = /usr/local/cuda-host/bin/nvcc

cflags = -DTORCH_EXTENSION_NAME=topk_bitonic_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -fPIC -std=c++17
post_cflags = 
cuda_cflags = -DTORCH_EXTENSION_NAME=topk_bitonic_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -

... (1254 more chars)

Shell cwd was reset to /workspace/problems/05_topk_bitonic

/home/agent/.cache/torch_extensions/py311_cu130/topk_bitonic_v2/cuda.cu:1:10: fatal error: torch/types.h: No such file or directory
    1 | #include <torch/types.h>
      |          ^~~~~~~~~~~~~~~
compilation terminated.

cuobjdump fatal   : Unknown option 'dump-resource-usage'

--dump-resource-usage                       (-res-usage)  
        Dump resource usage for each ELF. Useful in getting all the resource usage

Fatbin elf code:
================
arch = sm_100
code version = [1,8]
host = linux
compile_size = 64bit

Resource usage:
 Common:
  GLOBAL:46 CONSTANT[4]:368
 Function _ZN3cub38_V_300200_SM_750_800_860_900_1000_12006detail11EmptyKernelIvEEvv:
  REG:4 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:896 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_kernel_v2ILi64ELi64EEvPKfPfPlii:
  REG:76 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_kernel_v1ILi32ELi32EEvPKfPfPlii:
  REG:40 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_kernel_v1ILi16ELi32EEvPKfPfPlii:
  REG:40 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_kernel_v1ILi8ELi32EEvPKfPfPlii:
  REG:40 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_k1_kernelPKfPfPlii:
  REG:25 STACK:0 SHARED:1280 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0

Fatbin elf code:
================
arch = sm_120
code version = [1,8]
host = linux
compile_size = 64bit

Resource usage:
 Common:
  GLOBAL:46 CONSTANT[4]:368
 Function _ZN3cub38_V_300200_SM_750_800_860_900_1000_12006detail11EmptyKernelIvEEvv:
  REG:4 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:896 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_kernel_v2ILi64ELi64EEvPKfPfPlii:
  REG:76 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_kernel_v1ILi32ELi32EEvPKfPfPlii:
  R

... (674 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated. All occurrences were successfully replaced. (file state is current in your context — no need to Read it back)

185:    constexpr int THREADS_PER_BLOCK = 1024;
208:    constexpr int THREADS_PER_BLOCK = WARP_SIZE * WARPS_PER_BLOCK;
259:    constexpr int THREADS_PER_BLOCK = WARP_SIZE * WARPS_PER_BLOCK;
310:        int threads = 1024;
312:        topk_k1_kernel<<<blocks, threads>>>(

150	                int partner_pos = l % VPT;
151	                if (partner_thread == lane) {
152	                    other_val = old_val[partner_pos];
153	                    other_idx = old_idx[partner_pos];
154	                } else {
155	                    other_val = __shfl_sync(0xFFFFFFFF, old_val[partner_pos], partner_thread);
156	                    other_idx = __shfl_sync(0xFFFFFFFF, old_idx[partner_pos], partner_thread);
157	                }
158	                bool is_low = i < l;
159	                bool desc = ((i & k) == 0);
160	                bool take_other;
161	                if (desc) {
162	                    take_other = is_low ? (old_val[pos] < other_val)
163	                                        : (old_val[pos] > other_val);
164	                } else {
165	                    take_other = is_low ? (old_val[pos] > other_val)
166	                                        : (old_val[pos] < other_val);
167	                }
168	                if (take_other) {
169	                    val[pos] = other_val;
170	                    idx[pos] = other_idx;
171	                }
172	            }
173	        }
174	    }
175	}
176	
177	// Common setup / finalization for a row scan.  The per-warp merge step differs
178	// between V_PER_THREAD == 1 (k <= 32) and V_PER_THREAD == 2 (k == 64), so we
179	// expose it as a device function the two kernels can override inline.
180	
181	template <int K, int HEAP_PER_WARP>
182	__device__ __forceinline__ void topk_row

... (887 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated. All occurrences were successfully replaced. (file state is current in your context — no need to Read it back)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

FAIL: shape 0 {'batch': 1, 'n': 131072, 'k': 64} seed 42 case nominal values: tolerance exceeded: max_abs_diff=1.87328 max_rel_diff=0.577446 bad=51/64 worst_idx=(0, 63) (atol=0.0001, rtol=0.0001)

n=128 match=False maxdiff=1.3257
n=256 match=False maxdiff=0.4659
n=512 match=False maxdiff=0.3593
n=1024 match=False maxdiff=0.4682
n=2048 match=False maxdiff=0.6043
n=4096 match=False maxdiff=0.7853
n=8192 match=False maxdiff=1.1166
n=131072 match=False maxdiff=1.8733

match 128: True
max diff: 0.0
out first 10: tensor([2.4412, 2.1701, 2.1385, 1.7733, 1.6268, 1.5793, 1.5280, 1.5094, 1.4944,
        1.4247], device='cuda:0')
ref first 10: tensor([2.4412, 2.1701, 2.1385, 1.7733, 1.6268, 1.5793, 1.5280, 1.5094, 1.4944,
        1.4247], device='cuda:0')

match 1024 block512: True
max diff: 0.0

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T23:40:55.112262+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T23:40:55.274510+00:00 elapsed_s=0.162 ms=2.559744
shape=0 variant=solution tflops=0.000 gbps=0.205 ms=2.560
shape=0 solution_peak_fraction=0.0001
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:40:55.276502+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:40:55.286802+00:00 elapsed_s=0.010 ms=0.097024
shape=1 variant=solution tflops=0.022 gbps=21.678 ms=0.097
shape=1 solution_peak_fraction=0.0120
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:40:55.288463+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:40:55.303251+00:00 elapsed_s=0.015 ms=0.172592
shape=2 variant=solution tflops=0.012 gbps=12.222 ms=0.173
shape=2 solution_peak_fraction=0.0068
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:40:55.303899+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:40:55.316539+00:00 elapsed_s=0.013 ms=0.135888
shape=3 variant=solution tflops=0.006 gbps=5.674 ms=0.136
shape=3 solution_peak_fraction=0.0032
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:40:55.317815+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:40:55.322839+00:00 elapsed_s=0.005 ms=0.008912
shape=4 varian

... (117 more chars)

1	"""Custom CUDA top-k kernel using warp-local register heaps + shared merge.
2	
3	One block per row, 32 warps per block.  Each warp keeps the largest K values
4	seen so far in registers (distributed across the 32 lanes).  New values update
5	the heap via a single warp-min reduction + ballot.  After the scan, warps
6	write their local top-K into shared memory, a bitonic sort produces the global
7	top-K, and the first K entries are written out.
8	"""
9	import torch
10	import torch.nn as nn
11	from torch.utils.cpp_extension import load_inline
12	
13	
14	_CUDA_SOURCE = r"""
15	#include <cuda_runtime.h>
16	#include <float.h>
17	#include <stdint.h>
18	#include <torch/extension.h>
19	#include <tuple>
20	
21	// ----------------------------------------------------------------------------
22	// Bitonic sort helpers (descending by value; indices carried along)
23	// ----------------------------------------------------------------------------
24	template <int N>
25	__device__ __forceinline__ void bitonic_sort_desc(float* __restrict__ val,
26	                                                  int*   __restrict__ idx) {
27	    #pragma unroll
28	    for (int k = 2; k <= N; k <<= 1) {
29	        #pragma unroll
30	        for (int j = k >> 1; j > 0; j >>= 1) {
31	            int tid = threadIdx.x;
32	            #pragma unroll
33	            for (int i = tid; i < N; i += blockDim.x) {
34	                int lo = i;
35	                int hi = i ^ j;
36	                if (hi > lo) {
37	      

... (10464 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

Function _Z17topk_tiled_kernelILi64ELi2048EEvPKfPfPlii:
  REG:15 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_tiled_kernelILi32ELi2048EEvPKfPfPlii:
  REG:72 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_tiled_kernelILi16ELi2048EEvPKfPfPlii:
  REG:252 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_tiled_kernelILi8ELi2048EEvPKfPfPlii:
  REG:126 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_k1_kernelPKfPfPlii:
  REG:25 STACK:0 SHARED:1280 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0

--
 Function _Z17topk_tiled_kernelILi64ELi2048EEvPKfPfPlii:
  REG:15 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_tiled_kernelILi32ELi2048EEvPKfPfPlii:
  REG:72 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_tiled_kernelILi16ELi2048EEvPKfPfPlii:
  REG:252 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_tiled_kernelILi8ELi2048EEvPKfPfPlii:
  REG:126 STACK:0 SHARED:1024 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_k1_kernelPKfPfPlii:
  REG:26 STACK:0 SHARED:1280 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0

--
 Function _Z17topk_tiled_kernelILi64ELi2048EEvPKfPfPlii:
  REG:56 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:384 TEXTURE:0 SURFACE:0 

... (342 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

PASS

message: 'Successfully stopped task: bsiw9nwv9 (uv run python benchmark.py 2>&1 | grep -E "shape=|peak_fraction|RESULT")'
task_id: 'bsiw9nwv9'
task_type: 'local_bash'
command: 'uv run python benchmark.py 2>&1 | grep -E "shape=|peak_fraction|RESULT"'

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T23:57:45.266671+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T23:57:45.411771+00:00 elapsed_s=0.145 ms=2.280448
shape=0 variant=solution tflops=0.000 gbps=0.230 ms=2.280
shape=0 solution_peak_fraction=0.0001
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:57:45.413695+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:57:45.426582+00:00 elapsed_s=0.013 ms=0.139984
shape=1 variant=solution tflops=0.015 gbps=15.025 ms=0.140
shape=1 solution_peak_fraction=0.0083
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:57:45.428198+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:57:45.449710+00:00 elapsed_s=0.022 ms=0.285024
shape=2 variant=solution tflops=0.007 gbps=7.401 ms=0.285
shape=2 solution_peak_fraction=0.0041
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:57:45.450347+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:57:45.467394+00:00 elapsed_s=0.017 ms=0.209584
shape=3 variant=solution tflops=0.004 gbps=3.679 ms=0.210
shape=3 solution_peak_fraction=0.0020
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:57:45.468696+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:57:45.473721+00:00 elapsed_s=0.005 ms=0.008976
shape=4 variant

... (116 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

PASS

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T00:01:58.953904+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T00:01:58.968917+00:00 elapsed_s=0.015 ms=0.104272
shape=0 variant=solution tflops=0.005 gbps=5.035 ms=0.104
shape=0 solution_peak_fraction=0.0028
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T00:01:58.970804+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T00:01:58.979911+00:00 elapsed_s=0.009 ms=0.076864
shape=1 variant=solution tflops=0.027 gbps=27.364 ms=0.077
shape=1 solution_peak_fraction=0.0152
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T00:01:58.981568+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T00:01:58.990817+00:00 elapsed_s=0.009 ms=0.079568
shape=2 variant=solution tflops=0.026 gbps=26.511 ms=0.080
shape=2 solution_peak_fraction=0.0147
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T00:01:58.991427+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T00:01:59.000567+00:00 elapsed_s=0.009 ms=0.077088
shape=3 variant=solution tflops=0.010 gbps=10.002 ms=0.077
shape=3 solution_peak_fraction=0.0056
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-14T00:01:59.001827+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-14T00:01:59.006852+00:00 elapsed_s=0.005 ms=0.008848
shape=4 varia

... (118 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

FAIL: shape 0 {'batch': 1, 'n': 131072, 'k': 64} seed 42 case nominal values: tolerance exceeded: max_abs_diff=0.889789 max_rel_diff=0.192217 bad=64/64 worst_idx=(0, 0) (atol=0.0001, rtol=0.0001)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

<tool_use_error>String to replace not found in file.
String: // ----------------------------------------------------------------------------
// Pass 1: sort each chunk and write its top-K candidates to global memory
// ----------------------------------------------------------------------------
template <int K, int CHUNK>
__global__ void topk_pass1_kernel(const float* __restrict__ x,
                                  float* __restrict__ chunk_val,
                                  int*   __restrict__ chunk_idx,
                                  int B, int N, int chunks_per_row) {
    int block_id = blockIdx.x;
    int total_blocks = B * chunks_per_row;
    if (block_id >= total_blocks) return;

    int row = block_id / chunks_per_row;
    int chunk = block_id - row * chunks_per_row;
    const float* row_x = x + (int64_t)row * N;
    int base = chunk * CHUNK;
    int len = min(CHUNK, N - base);

    extern __shared__ char smem[];
    float* s_val = (float*)smem;
    int*   s_idx = (int*)(s_val + CHUNK);

    int tid = threadIdx.x;
    int nt = blockDim.x;
    for (int i = tid; i < CHUNK; i += nt) {
        if (i < len) {
            s_val[i] = row_x[base + i];
            s_idx[i] = base + i;
        } else {
            s_val[i] = -FLT_MAX;
            s_idx[i] = 0;
        }
    }
    __syncthreads();

    bitonic_sort_desc<CHUNK>(s_val, s_idx);

    // Write top K of this chunk to global memory.
    int out_base = (row * chunks_per_row + chunk) * K;
    for (int i = tid; i 

... (128 more chars)

115	
116	// Partial bitonic sort: after completion, the first K entries contain the K
117	// largest values in descending order.  Comparisons entirely within the tail
118	// (both indices >= K) are skipped.
119	template <int N, int K>
120	__device__ __forceinline__ void bitonic_partial_sort_desc(float* __restrict__ val,
121	                                                          int*   __restrict__ idx) {
122	    for (int k = 2; k <= N; k <<= 1) {
123	        for (int j = k >> 1; j > 0; j >>= 1) {
124	            int tid = threadIdx.x;
125	            for (int i = tid; i < N; i += blockDim.x) {
126	                int lo = i;
127	                int hi = i ^ j;
128	                if (hi > lo && (lo < K || hi < K)) {
129	                    if ((lo & k) == 0) {
130	                        if (val[lo] < val[hi]) {
131	                            float tv = val[lo]; val[lo] = val[hi]; val[hi] = tv;
132	                            int ti = idx[lo]; idx[lo] = idx[hi]; idx[hi] = ti;
133	                        }
134	                    } else {
135	                        if (val[lo] > val[hi]) {
136	                            float tv = val[lo]; val[lo] = val[hi]; val[hi] = tv;
137	                            int ti = idx[lo]; idx[lo] = idx[hi]; idx[hi] = ti;
138	                        }
139	                    }
140	                }
141	            }
142	            __syncthreads();
143	        }
144	    }
145	}
146	template <int K, int CHUNK>
147	__global__ void topk_pass1

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The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T00:13:56.364869+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T00:13:56.396271+00:00 elapsed_s=0.031 ms=0.375296
shape=0 variant=solution tflops=0.001 gbps=1.399 ms=0.375
shape=0 solution_peak_fraction=0.0008
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T00:13:56.398168+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T00:13:56.415283+00:00 elapsed_s=0.017 ms=0.209456
shape=1 variant=solution tflops=0.010 gbps=10.042 ms=0.209
shape=1 solution_peak_fraction=0.0056
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T00:13:56.416891+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T00:13:56.437367+00:00 elapsed_s=0.020 ms=0.265056
shape=2 variant=solution tflops=0.008 gbps=7.958 ms=0.265
shape=2 solution_peak_fraction=0.0044
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T00:13:56.437970+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T00:13:56.456046+00:00 elapsed_s=0.018 ms=0.225600
shape=3 variant=solution tflops=0.003 gbps=3.418 ms=0.226
shape=3 solution_peak_fraction=0.0019
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-14T00:13:56.457295+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-14T00:13:56.462311+00:00 elapsed_s=0.005 ms=0.008816
shape=4 variant

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The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

260	    int pass2_blocks = B;
261	    int pass2_threads = 1024;
262	    int pass2_smem = max_candidates * (sizeof(float) + sizeof(int));
263	
264	    switch (max_candidates) {
265	        case 2048:
266	            topk_pass2_kernel<64, 2048><<<pass2_blocks, pass2_threads, pass2_smem>>>(
267	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
268	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
269	                B, chunks_per_row);
270	            break;
271	        case 128:
272	            topk_pass2_kernel<32, 128><<<pass2_blocks, pass2_threads, pass2_smem>>>(
273	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
274	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
275	                B, chunks_per_row);
276	            break;
277	        case 64:
278	            topk_pass2_kernel<16, 64><<<pass2_blocks, pass2_threads, pass2_smem>>>(
279	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
280	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
281	                B, chunks_per_row);
282	            break;
283	        case 32:
284	            topk_pass2_kernel<8, 32><<<pass2_blocks, pass2_threads, pass2_smem>>>(
285	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
286	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
287	                B, chunks_per_row);
288	            break;
289	        case 16:
29

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The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

match: False
maxdiff: 0.5318460464477539
ref top 10: tensor([4.6291, 4.2209, 4.1319, 4.1205, 4.1100, 4.0710, 3.7811, 3.7393, 3.7376,
        3.7348], device='cuda:0')
sol top 10: tensor([4.2209, 4.1205, 3.7376, 3.7215, 3.6106, 3.5391, 3.5067, 3.4501, 3.4456,
        3.4443], device='cuda:0')

match 4096 block1024: True
max diff: 0.0

119:template <int K, int CHUNK>
131:    int base = chunk * CHUNK;
132:    int len = min(CHUNK, N - base);
136:    int*   s_idx = (int*)(s_val + CHUNK);
140:    for (int i = tid; i < CHUNK; i += nt) {
151:    bitonic_sort_desc<CHUNK>(s_val, s_idx);
220:    constexpr int CHUNK = 2048;
221:    int chunks_per_row = (N + CHUNK - 1) / CHUNK;
223:    int max_candidates = 1;
224:    while (max_candidates < total_candidates) max_candidates <<= 1;
226:    if (max_candidates > 2048) max_candidates = 2048;
233:    int pass1_smem = CHUNK * (sizeof(float) + sizeof(int));
237:            topk_pass1_kernel<8, CHUNK><<<pass1_blocks, pass1_threads, pass1_smem>>>(
242:            topk_pass1_kernel<16, CHUNK><<<pass1_blocks, pass1_threads, pass1_smem>>>(
247:            topk_pass1_kernel<32, CHUNK><<<pass1_blocks, pass1_threads, pass1_smem>>>(
252:            topk_pass1_kernel<64, CHUNK><<<pass1_blocks, pass1_threads, pass1_smem>>>(
262:    int pass2_smem = max_candidates * (sizeof(float) + sizeof(int));
264:    switch (max_candidates) {
266:            topk_pass2_kernel<64, 4096><<<pass2_blocks, pass2_threads, pass2_smem>>>(
272:            topk_pass2_kernel<64, 2048><<<pass2_blocks, pass2_threads, pass2_smem>>>(
278:            topk_pass2_kernel<32, 256><<<pass2_blocks, pass2_threads, pass2_smem>>>(
284:            topk_pass2_kernel<32, 128><<<pass2_blocks, pass2_threads, pass2_smem>>>(
290:            topk_pass2_kernel<16, 64><<<pass2_blocks, pass2_threads, pass2_smem>>>(
296:            topk

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The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

FAIL: shape 3 {'batch': 16, 'n': 12000, 'k': 16} seed 42 case nominal values: tolerance exceeded: max_abs_diff=1.48235 max_rel_diff=0.320226 bad=227/256 worst_idx=(7, 0) (atol=0.0001, rtol=0.0001)
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T00:22:45.262438+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T00:22:45.277957+00:00 elapsed_s=0.016 ms=0.103184
shape=0 variant=solution tflops=0.005 gbps=5.089 ms=0.103
shape=0 solution_peak_fraction=0.0028
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T00:22:45.279823+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T00:22:45.288859+00:00 elapsed_s=0.009 ms=0.076400
shape=1 variant=solution tflops=0.027 gbps=27.530 ms=0.076
shape=1 solution_peak_fraction=0.0153
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T00:22:45.290450+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T00:22:45.299681+00:00 elapsed_s=0.009 ms=0.080128
shape=2 variant=solution tflops=0.026 gbps=26.326 ms=0.080
shape=2 solution_peak_fraction=0.0146
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T00:22:45.300274+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T00:22:45.307480+00:00 elapsed_s=0.007 ms=0.045424
shape=3 variant=solution tflops=0.017 gbps=16.975 ms=0.045
shape=3 solution_peak_fraction=0.0094
benchmark_event event=variant_start 

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match: False
maxdiff: 1.4823498725891113
ref row7 top 5: tensor([4.6291, 3.7393, 3.5927, 3.5668, 3.3915], device='cuda:0')
sol row7 top 5: tensor([3.1467, 3.1213, 3.1137, 3.1125, 3.0949], device='cuda:0')

match 128 block1024: True
max diff: 0.0

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

/kbh/bin/ncu

Traceback (most recent call last):
  File "/tmp/profile_topk.py", line 2, in <module>
    import solution
ModuleNotFoundError: No module named 'solution'
==ERROR== The application returned an error code (1).

FAIL: shape 0 {'batch': 1, 'n': 131072, 'k': 64} seed 42 case nominal values: tolerance exceeded: max_abs_diff=0.0379953 max_rel_diff=0.0112673 bad=42/64 worst_idx=(0, 39) (atol=0.0001, rtol=0.0001)

benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T00:59:50.229982+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T00:59:50.241871+00:00 elapsed_s=0.012 ms=0.053920
shape=0 variant=solution tflops=0.010 gbps=9.738 ms=0.054
shape=0 solution_peak_fraction=0.0054
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T00:59:50.243712+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T00:59:50.249825+00:00 elapsed_s=0.006 ms=0.026992
shape=1 variant=solution tflops=0.078 gbps=77.923 ms=0.027
shape=1 solution_peak_fraction=0.0433
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T00:59:50.251411+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T00:59:50.257640+00:00 elapsed_s=0.006 ms=0.029376
shape=2 variant=solution tflops=0.071 gbps=71.808 ms=0.029
shape=2 solution_peak_fraction=0.0399
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T00:59:50.258243+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T00:59:50.264367+00:00 elapsed_s=0.006 ms=0.027264
shape=3 variant=solution tflops=0.028 gbps=28.282 ms=0.027
shape=3 solution_peak_fraction=0.0157
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-14T00:59:50.265644+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-14T00:59:50.270642+00:00 elapsed_s=0.005 ms=0.008624
shape=4 varia

... (118 more chars)

match 8192 CUB: True
max diff: 0.0

Function _Z4testPKfPf:
  REG:64 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:912 TEXTURE:0 SURFACE:0 SAMPLER:0

Fatbin elf code:
--
 Function _Z4testPKfPf:
  REG:64 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:912 TEXTURE:0 SURFACE:0 SAMPLER:0

Fatbin ptx code:
--
Shell cwd was reset to /workspace/problems/05_topk_bitonic

Function _Z17topk_pass1_kernelILi64ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_pass1_kernelILi32ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_pass1_kernelILi16ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_pass1_kernelILi8ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_k1_kernelPKfPfPlii:
  REG:25 STACK:0 SHARED:1280 LOCAL:0 CONSTANT[0]:928 TEXTURE:0 SURFACE:0 SAMPLER:0
--
 Function _Z17topk_pass1_kernelILi64ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_pass1_kernelILi32ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_pass1_kernelILi16ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z17topk_pass1_kernelILi8ELi4096EEvPKfPfPiiii:
  REG:48 STACK:0 SHARED:38080 LOCAL:0 CONSTANT[0]:932 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z14topk_k1_kernelPKfPfPlii:
Shell cwd was reset to /workspace/problems/05_topk_bitonic

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

285	            break;
286	        case 128:
287	            topk_pass2_kernel<32, 128><<<pass2_blocks, pass2_threads, pass2_smem>>>(
288	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
289	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
290	                B, chunks_per_row);
291	            break;
292	        case 64:
293	            topk_pass2_kernel<16, 64><<<pass2_blocks, pass2_threads, pass2_smem>>>(
294	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
295	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
296	                B, chunks_per_row);
297	            break;
298	        case 32:
299	            topk_pass2_kernel<8, 32><<<pass2_blocks, pass2_threads, pass2_smem>>>(
300	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
301	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
302	                B, chunks_per_row);
303	            break;
304	        case 16:
305	            topk_pass2_kernel<8, 16><<<pass2_blocks, pass2_threads, pass2_smem>>>(
306	                chunk_val.data_ptr<float>(), chunk_idx.data_ptr<int>(),
307	                out_val.data_ptr<float>(), out_idx.data_ptr<int64_t>(),
308	                B, chunks_per_row);
309	            break;
310	        default:
311	            TORCH_CHECK(false, "unsupported max_candidates");
312	    }
313	
314	    return std::make_tuple(out_val, out_idx);
315	}
316	"""
317	
318	_CPP_S

... (808 more chars)

The file /workspace/problems/05_topk_bitonic/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

Traceback (most recent call last):
  File "/workspace/problems/05_topk_bitonic/check.py", line 157, in <module>
    main()
  File "/workspace/problems/05_topk_bitonic/check.py", line 102, in main
    ok, msg = check_correctness(
              ^^^^^^^^^^^^^^^^^^
  File "/workspace/src/eval/correctness.py", line 71, in check_correctness
    if torch.isnan(solution_out).any():
       ^^^^^^^^^^^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: too many resources requested for launch
Search for `cudaErrorLaunchOutOfResources' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.