solution.py
final answer
check.log
result.json
"""Fast top-k CUDA kernel for RTX PRO 6000 Blackwell (SM120).
Strategy: per-row block reduction. Each block handles one row. Threads find
local top-k in registers, then one thread merges all local top-k via shared mem.
"""
import torch
import torch.nn as nn
import torch.utils.cpp_extension
CPP_SOURCE = """
#include <torch/extension.h>
void topk_launch(
const float* input,
float* values,
int64_t* indices,
int batch,
int n,
int k,
int threads_per_block
);
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("topk_launch", &topk_launch, "topk_launch");
}
"""
CUDA_SOURCE = r"""
#include <cuda_runtime.h>
#include <c10/cuda/CUDAStream.h>
#include <torch/extension.h>
#define INF_NEG -3.4028235e+38f
template <int K>
__global__ void topk_kernel(
const float* __restrict__ input,
float* __restrict__ values,
int64_t* __restrict__ indices,
int batch,
int n
)
{
extern __shared__ char smem[];
float* s_vals = (float*)smem;
int* s_idxs = (int*)(s_vals + blockDim.x * K);
int row = blockIdx.x;
if (row >= batch) return;
const float* row_ptr = input + row * n;
int tid = threadIdx.x;
int nthreads = blockDim.x;
// Thread-local top-k in registers
float t_vals[K];
int t_idxs[K];
#pragma unroll
for (int i = 0; i < K; i++) {
t_vals[i] = INF_NEG;
t_idxs[i] = -1;
}
// Grid-stride loop over elements
for (int idx = tid; idx < n; idx += nthreads) {
float val = row_ptr[idx];
#pragma unroll
for (int j = 0; j < K; j++) {
if (val > t_vals[j]) {
#pragma unroll
for (int l = K-1; l > j; l--) {
t_vals[l] = t_vals[l-1];
t_idxs[l] = t_idxs[l-1];
}
t_vals[j] = val;
t_idxs[j] = idx;
break;
}
}
}
// Write thread's top-k to shared memory
#pragma unroll
for (int i = 0; i < K; i++) {
s_vals[tid * K + i] = t_vals[i];
s_idxs[tid * K + i] = t_idxs[i];
}
__syncthreads();
// Thread 0 merges all threads' top-k into final answer
if (tid == 0) {
float g_vals[K];
int g_idxs[K];
#pragma unroll
for (int i = 0; i < K; i++) {
g_vals[i] = INF_NEG;
g_idxs[i] = -1;
}
for (int t = 0; t < nthreads; t++) {
#pragma unroll
for (int i = 0; i < K; i++) {
float val = s_vals[t * K + i];
int idx = s_idxs[t * K + i];
#pragma unroll
for (int j = 0; j < K; j++) {
if (val > g_vals[j]) {
#pragma unroll
for (int l = K-1; l > j; l--) {
g_vals[l] = g_vals[l-1];
g_idxs[l] = g_idxs[l-1];
}
g_vals[j] = val;
g_idxs[j] = idx;
break;
}
}
}
}
for (int i = 0; i < K; i++) {
values[row * K + i] = g_vals[i];
indices[row * K + i] = (int64_t)g_idxs[i];
}
}
}
// Launcher with K dispatch
void topk_launch(
const float* input,
float* values,
int64_t* indices,
int batch,
int n,
int k,
int threads_per_block
)
{
// Round k up to next power of 2
int k_pow2 = 1;
while (k_pow2 < k) k_pow2 *= 2;
size_t smem_sz = k_pow2 * (sizeof(float) + sizeof(int)) * threads_per_block;
auto stream = c10::cuda::getCurrentCUDAStream();
switch (k_pow2) {
case 1:
topk_kernel<1><<<batch, threads_per_block, smem_sz, stream>>>(
input, values, indices, batch, n);
break;
case 2:
topk_kernel<2><<<batch, threads_per_block, smem_sz, stream>>>(
input, values, indices, batch, n);
break;
case 4:
topk_kernel<4><<<batch, threads_per_block, smem_sz, stream>>>(
input, values, indices, batch, n);
break;
case 8:
topk_kernel<8><<<batch, threads_per_block, smem_sz, stream>>>(
input, values, indices, batch, n);
break;
case 16:
topk_kernel<16><<<batch, threads_per_block, smem_sz, stream>>>(
input, values, indices, batch, n);
break;
case 32:
topk_kernel<32><<<batch, threads_per_block, smem_sz, stream>>>(
input, values, indices, batch, n);
break;
case 64:
topk_kernel<64><<<batch, threads_per_block, smem_sz, stream>>>(
input, values, indices, batch, n);
break;
default:
break;
}
}
"""
_cache = {}
def get_module(threads_per_block):
if threads_per_block in _cache:
return _cache[threads_per_block]
import shutil
cache_dir = f"/tmp/topk_bitonic_ext_{threads_per_block}"
# Force fresh compile every time
if os.path.exists(cache_dir):
shutil.rmtree(cache_dir, ignore_errors=True)
mod = torch.utils.cpp_extension.load_inline(
name=f"topk_bitonic_{threads_per_block}",
cpp_sources=CPP_SOURCE,
cuda_sources=CUDA_SOURCE,
functions=["topk_launch"],
extra_cuda_cflags=[
"-O3",
"-use_fast_math",
"-arch=sm_120",
],
build_directory=cache_dir,
verbose=False,
)
_cache[threads_per_block] = mod
return mod
class Model(nn.Module):
"""Top-k over the last dim of a 2D tensor using custom CUDA kernel."""
def __init__(self, batch: int, n: int, k: int):
super().__init__()
self.batch = batch
self.n = n
self.k = k
self.register_buffer("_dummy", torch.zeros(1))
if n >= 65536:
self.threads = 512
elif n >= 16384:
self.threads = 256
else:
self.threads = 128
self._module = get_module(self.threads)
self._values = torch.empty(batch, k, dtype=torch.float32, device="cuda")
self._indices = torch.empty(batch, k, dtype=torch.int64, device="cuda")
def forward(self, x: torch.Tensor):
if x.shape != (self.batch, self.n):
raise ValueError(f"Expected ({self.batch}, {self.n}), got {x.shape}")
self._module.topk_launch(
x,
self._values,
self._indices,
self.batch,
self.n,
self.k,
self.threads,
)
return self._values, self._indices
batch = 64
n = 8192
k = 8
def get_inputs():
torch.manual_seed(42)
x = torch.randn(batch, n, dtype=torch.float32)
return [x]
def get_init_inputs():
return [batch, n, k]
Traceback (most recent call last):
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/check.py", line 149, in <module>
main()
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/check.py", line 59, in main
sol_model = solution.Model(*init_args).to(device).eval()
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py", line 229, in __init__
self._module = get_module(self.threads)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py", line 192, in get_module
if os.path.exists(cache_dir):
^^
NameError: name 'os' is not defined
{
"problem": "05_topk_bitonic",
"harness": "opencode",
"model": "openrouter-pinned/qwen/qwen3.6-plus",
"reasoning_effort": "",
"has_solution": true,
"correct": false,
"peak_fraction": null,
"elapsed_seconds": 392,
"harness_exit_code": 0,
"session_complete": true,
"usage": {"input_tokens": 827203, "output_tokens": 13920, "cache_read_tokens": 0, "cache_creation_tokens": 0, "reasoning_tokens": 1311, "total_cost_usd": null}
}
timeline (105 events)
tool
stdout (209 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic</path>
<type>directory</type>
<entries>
benchmark.py
check.py
problem.yaml
PROMPT.txt
reference.py
shapes.py
sota.py
(7 entries)
</entries>
tool
stdout (1919 chars) — TRUNCATED
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/reference.py</path>
<type>file</type>
<content>
1: """Naive top-k reference: torch.topk over the last dim.
2:
3: This is the correctness oracle. The agent's solution must produce the same
4: top-k values (and equivalent indices modulo ties) within the tolerance
5: declared in problem.yaml. Note that solution.py is FORBIDDEN from calling
6: torch.topk / torch.sort / torch.kthvalue (see problem.yaml).
7: """
8: import torch
9: import torch.nn as nn
10:
11: OP_TYPE = "topk"
12: SUPPORTED_PRECISIONS = ["fp32"]
13: HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
14:
15:
16: class Model(nn.Module):
17: """Top-k over the last dim of a 2D tensor.
18:
19: Input:
20: x: (batch, n) fp32
21: Output:
22: values: (batch, k) fp32, sorted descending
23: indices: (batch, k) int64, into the last dim of x
24: """
25:
26: def __init__(self, batch: int, n: int, k: int):
27: super().__init__()
28: self.batch, self.n, self.k = batch, n, k
29: # No learned parameters, but declare a dummy buffer so state_dict
30: # is non-empty and load_state_dict(strict=True) is meaningful.
31: self.register_buffer("_dummy", torch.zeros(1))
32:
33: def forward(self, x: torch.Tensor):
34: values, indices = torch.topk(x, k=self.k, dim=-1, largest=True, sorted=True)
35: return values, indices
36:
37:
38: # Module-level shims rebuilt b
... (419 more chars)
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stdout (1188 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/sota.py</path>
<type>file</type>
<content>
1: """SOTA reference for TopK: torch.topk itself.
2:
3: torch.topk dispatches to a CUB-backed kernel that uses radix-select for
4: moderate k and a tuned bitonic sort for small n. It is the bar the agent's
5: hand-rolled bitonic kernel must beat. There is no obvious vendor library that
6: does better on the (batch, n, k) shape mix we evaluate — Faiss BlockSelect is
7: specialized for k>=32 with much larger n, and CUB's DeviceSegmentedRadixSort
8: sorts the full row (overkill for top-k).
9:
10: This file is INTENTIONALLY allowed to call torch.topk because it is the SOTA
11: oracle, not the agent's submission. The agent's solution.py is forbidden from
12: using torch.topk (see problem.yaml.forbidden).
13: """
14: from __future__ import annotations
15:
16: import torch
17:
18:
19: def sota_forward(x: torch.Tensor, k: int):
20: """Best-available top-k reference. x: (batch, n) fp32."""
21: return torch.topk(x, k=k, dim=-1, largest=True, sorted=True)
22:
23:
24: def is_available() -> bool:
25: return True
(End of file - total 25 lines)
</content>
tool
stdout (6402 chars) — TRUNCATED
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/check.py</path>
<type>file</type>
<content>
1: """Correctness runner for TopK.
2:
3: Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
4: each. Top-k correctness has two parts:
5:
6: 1. VALUES: sol_values must match ref_values within fp32 tol. Both are
7: returned sorted descending, so positional comparison is well-defined.
8: 2. INDICES: lenient — we do NOT require sol_indices == ref_indices because
9: ties in x can yield multiple valid index sets. Instead we gather x at
10: sol_indices and check those values match ref_values within tol. This
11: catches "wrong indices" without false-failing on legitimate tie-breaks.
12:
13: Also rejects forbidden ops by grep.
14: """
15: import re
16: import sys
17: from pathlib import Path
18:
19: import torch
20: import yaml
21:
22: REPO_ROOT = Path(__file__).resolve().parents[2]
23: sys.path.insert(0, str(REPO_ROOT))
24:
25: from src.eval.correctness import check_correctness # noqa: E402
26:
27:
28: def main():
29: try:
30: import reference
31: import shapes
32: import solution
33: except Exception as e:
34: print(f"FAIL: import error: {e}")
35: sys.exit(1)
36:
37: problem_yaml = Path("problem.yaml")
38: meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}
39:
40: # --- Forbidden-op check --------------------------------
... (4902 more chars)
tool
stdout (1148 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/shapes.py</path>
<type>file</type>
<content>
1: """Canonical shape sweep for TopK.
2:
3: Mix of:
4: - decoder vocab top-k (single sequence, very large n, moderate k) — pure
5: bandwidth test; the input read dominates everything.
6: - prefill / batched attention top-k (many rows, moderate n, small k) — tests
7: per-row parallelism and shared-memory bitonic networks.
8: - non-power-of-2 n stress case — bitonic sort networks naturally want
9: powers of two; this forces the agent to handle padding or partial sorts.
10: - small-k limit — k=1 (argmax) is a degenerate but useful sanity case.
11: """
12:
13: SHAPES = [
14: {"batch": 1, "n": 131072, "k": 64}, # decoder vocab top-k (Llama vocab ~128k)
15: {"batch": 64, "n": 8192, "k": 8}, # prefill / attention top-k
16: {"batch": 32, "n": 16384, "k": 32}, # mid-size batched
17: {"batch": 16, "n": 12000, "k": 16}, # non-power-of-2 n stress
18: {"batch": 128, "n": 4096, "k": 1}, # batched argmax (k=1 corner case)
19: ]
(End of file - total 19 lines)
</content>
tool
stdout (2571 chars) — TRUNCATED
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/problem.yaml</path>
<type>file</type>
<content>
1: name: 05_topk_bitonic
2: display_name: "TopK via Bitonic Sort"
3: precision: fp32
4: regime: memory
5:
6: # Top-k is dominated by the input read (small output, no reduction over k).
7: # Comparator-network FLOPs are not the bottleneck on real hardware, so we
8: # track them but score on bandwidth.
9: flops_formula: "batch * n * 4" # ~O(n log^2 n) compares total, but the
10: # bitonic network is so cheap relative
11: # to memory that a coarse 4*n estimate
12: # is fine for telemetry only.
13: bytes_formula: "batch * n * 4 + batch * k * (4 + 8)" # fp32 input read + (fp32 value + int64 idx) output
14:
15: hardware: [RTX_PRO_6000]
16: peak_tflops_key: fp32
17: peak_bandwidth_key: dram
18:
19: # Top-k correctness:
20: # - VALUES must match within fp32 atol/rtol (the kth largest value is
21: # well-defined modulo float-equal ties, so we use a loose-ish tol).
22: # - INDICES are checked leniently: for each row, the multiset of returned
23: # indices must select values that match ref values within tol. Direct
24: # index equality is NOT required (ties in x can yield different valid
25: # index sets).
26: tolerance:
27: float32: 1.0e-4
28:
29: # Forbidden ops — using any of these in solution.py fails corre
... (1071 more chars)
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stdout (4750 chars) — TRUNCATED
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/benchmark.py</path>
<type>file</type>
<content>
1: """Roofline benchmark for TopK.
2:
3: For each shape: times eager reference (torch.topk), compiled reference, SOTA
4: (also torch.topk — see sota.py), and the agent's solution. Reports achieved
5: TFLOPS, GB/s, and peak_fraction (vs DRAM bandwidth, since this is memory-bound).
6:
7: Output lines the harness picks up:
8: shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
9: peak_fraction: <N> (geomean over shapes of solution's peak_fraction)
10: """
11: import sys
12: from math import exp, log
13: from pathlib import Path
14:
15: import torch
16: import yaml
17:
18: REPO_ROOT = Path(__file__).resolve().parents[2]
19: sys.path.insert(0, str(REPO_ROOT))
20:
21: from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction # noqa: E402
22: from src.eval.timing import time_fn # noqa: E402
23: from src.hardware import get as get_hw # noqa: E402
24:
25:
26: def _eval_formula(expr: str, vars: dict) -> float:
27: return float(eval(expr, {"__builtins__": {}}, vars))
28:
29:
30: def main():
31: import reference
32: import shapes
33: import solution
34:
35: meta = yaml.safe_load(Path("problem.yaml").read_text())
36: hw = get_hw(meta["hardware"][0])
37: peak_tflops = hw.peak_tflops_dense.get(meta["peak_tflops_key"], 0.0)
38: peak_gbps = hw.peak_bandwidth_gb_s
39: regime = meta.get("regime", "memory")
40
... (3250 more chars)
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stdout (75 chars)
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 627 MiB, 97887 MiB, 0 %
assistant
Now let me check what's currently on the GPU and understand the environment:
assistant
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stdout (119 chars)
AGENTS.md
CLAUDE.md
DEVLOG.md
docs
LICENSE
outputs
problems
pyproject.toml
README.md
scripts
SPEC.md
src
tests
uv.lock
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stdout (914 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/pyproject.toml</path>
<type>file</type>
<content>
1: [project]
2: name = "kernelbench-hard"
3: version = "0.1.0"
4: description = "Surgical GPU kernel benchmark. Small deck, frontier agents, roofline metric, link-don't-spoil problem briefs."
5: requires-python = ">=3.11"
6: dependencies = [
7: "torch>=2.11",
8: "numpy>=1.26",
9: "pyyaml>=6.0",
10: "rich>=13.7",
11: "matplotlib>=3.9",
12: "pytest>=8.0",
13: "ruff>=0.6",
14: "einops>=0.8.2",
15: "requests<2.33",
16: ]
17:
18: [build-system]
19: requires = ["hatchling"]
20: build-backend = "hatchling.build"
21:
22: [tool.hatch.build.targets.wheel]
23: packages = ["src"]
24:
25: [tool.ruff]
26: line-length = 100
27: target-version = "py311"
28:
29: [tool.ruff.lint]
30: select = ["E", "F", "W", "I", "B", "UP"]
31: ignore = ["E501"]
(End of file - total 31 lines)
</content>
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stdout (178 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/src</path>
<type>directory</type>
<entries>
__init__.py
__pycache__/
eval/
hardware/
harness/
sandbox/
viewer/
(7 entries)
</entries>
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stdout (198 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/src/eval</path>
<type>directory</type>
<entries>
__init__.py
__pycache__/
correctness.py
report.py
roofline.py
shapes.py
timing.py
(7 entries)
</entries>
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stdout (4080 chars) — TRUNCATED
<path>/home/infatoshi/cuda/KernelBench-Hard/src/eval/correctness.py</path>
<type>file</type>
<content>
1: """Per-dtype correctness tolerance.
2:
3: Stanford's KernelBench uses 1e-4 atol/rtol for fp32 and 1e-2 for fp16/bf16.
4: We extend to fp8 (0.1, permissive) and int (exact).
5: """
6: from __future__ import annotations
7:
8: import torch
9:
10: DEFAULT_TOLERANCE = {
11: torch.float32: {"atol": 1e-4, "rtol": 1e-4},
12: torch.float16: {"atol": 1e-2, "rtol": 1e-2},
13: torch.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
14: torch.float8_e4m3fn: {"atol": 1e-1, "rtol": 1e-1},
15: torch.float8_e5m2: {"atol": 1e-1, "rtol": 1e-1},
16: torch.int8: {"atol": 0, "rtol": 0},
17: torch.int32: {"atol": 0, "rtol": 0},
18: torch.int64: {"atol": 0, "rtol": 0},
19: }
20:
21:
22: def _coerce_float(x):
23: """Accept int/float/str scientific-notation as float; pass through dicts."""
24: if isinstance(x, (int, float)):
25: return float(x)
26: if isinstance(x, str):
27: try:
28: return float(x)
29: except ValueError:
30: return x
31: return x
32:
33:
34: def tolerance_for_dtype(dtype: torch.dtype, override: dict | None = None) -> dict:
35: """Lookup atol/rtol for a given dtype, with optional per-problem override.
36:
37: The override dict is keyed by str(dtype). Values may be:
38: - a single number (or a string parseable as float, e.g. "5e-2") -> used
39: as both atol and rtol
4
... (2580 more chars)
tool
stdout (1180 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/src/eval/roofline.py</path>
<type>file</type>
<content>
1: """Roofline math: achieved TFLOPS / GB/s, peak fraction.
2:
3: FLOPS and bytes formulas come from the problem's `problem.yaml`. They are the
4: dense-equivalent algorithmic work; kernels that skip work (sparsity, early
5: exit) are still measured against the dense cost.
6: """
7: from __future__ import annotations
8:
9:
10: def compute_tflops(flops: float, time_ms: float) -> float:
11: """flops per invocation and wall time in ms -> TFLOPS."""
12: if time_ms <= 0:
13: return 0.0
14: return flops / (time_ms * 1e-3) / 1e12
15:
16:
17: def compute_gbps(bytes_moved: float, time_ms: float) -> float:
18: """bytes per invocation and wall time in ms -> GB/s."""
19: if time_ms <= 0:
20: return 0.0
21: return bytes_moved / (time_ms * 1e-3) / 1e9
22:
23:
24: def peak_fraction(
25: achieved: float,
26: peak: float,
27: ) -> float:
28: """Return achieved / peak, clamped to [0, infinity)."""
29: if peak <= 0:
30: return 0.0
31: return max(0.0, achieved / peak)
(End of file - total 31 lines)
</content>
tool
stdout (2978 chars) — TRUNCATED
<path>/home/infatoshi/cuda/KernelBench-Hard/src/eval/timing.py</path>
<type>file</type>
<content>
1: """Centralized GPU timing for benchmark.py files.
2:
3: All problems' benchmark.py should call `time_fn` from here so we have one
4: implementation of warmup, L2 flush, and CUDA event capture to maintain.
5:
6: Methodology:
7: - 10 warmup calls absorb Triton autotune (typical ~7 configs) and
8: torch.compile reduce-overhead CUDA-graph capture.
9: - Between each timed call, `_l2_flush()` writes 128 MB to evict L2
10: (Blackwell L2 is 96 MB; we want any prior L2 contents flushed so
11: we measure HBM-load bandwidth, not L2-cached bandwidth).
12: - GPU timing via cuda.Event with synchronize() AFTER record() but
13: BEFORE elapsed_time() — the canonical NVIDIA pattern.
14: - Reported value is the median of `iters` trials, robust to outliers.
15:
16: Notes / known biases not addressed here:
17: - torch.compile(mode="reduce-overhead") gets CUDA graphs which eliminate
18: launch overhead. Custom Triton/CUDA kernels do NOT get this treatment.
19: On small shapes where launch overhead matters, this gives compile an
20: artificial advantage. We accept this as the cost of using the agreed
21: "compiled" baseline policy.
22: - cuBLAS / cuDNN allocate workspaces on first call. 10 warmup absorbs.
23: - Median over a fairly small number of trials (default 30) is fine for
24: headline numbers but won't catch bimodal distributions. Use --ext
... (1478 more chars)
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Now I have a good understanding of the problem. Let me also check if there's an existing solution in a similar problem directory to understand the patterns used:
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stdout (418 chars)
content: 'Implement initial top-k CUDA kernel (bitonic sort based) in solution.py'
status: 'in_progress'
priority: 'high'
content: 'Run check.py to verify correctness across all shapes'
status: 'pending'
priority: 'high'
content: 'Profile with benchmark.py and iterate for performance'
status: 'pending'
priority: 'high'
content: 'Optimize for Blackwell RTX PRO 6000 - maximize GB/s'
status: 'pending'
priority: 'high'
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Let me understand the problem constraints better and design the kernel:
Key observations:
1. **Memory-bound problem**: `batch * n * 4` bytes of input read dominates. The output is tiny (`batch * k * (4+8)` bytes).
2. **Shapes**: Large N (up to 131072), small K (1-64). One shape has non-power-of-2 N (12000).
3. **Target**: RTX PRO 6000 Blackwell, 1.8 TB/s GDDR7 peak.
The strategy:
- For large N / small K, use a simple **reduction-based approach**: each thread block processes one row and keeps top-K in shared memory using a simple tournament/insert strategy
- For very large N, use **radix-based selection** or **grid-stride loop with top-K register files**
Actually, for memory-bound top-K with small K, the best approach is:
1. Each block processes one or more rows
2. Within each row, use a grid-stride loop to find top-K
3. Keep K best candidates in registers/shared memory
Let me write a fast CUDA kernel:
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stdout (273 chars)
2.11.0+cu130
_CudaDeviceProperties(name='NVIDIA RTX PRO 6000 Blackwell Workstation Edition', major=12, minor=0, total_memory=97227MB, multi_processor_count=188, uuid=d594fb7a-abcb-bad5-1fc4-675b478a47df, pci_bus_id=1, pci_device_id=0, pci_domain_id=0, L2_cache_size=128MB)
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I'll implement a high-performance top-k CUDA kernel for the RTX PRO 6000 Blackwell. Let me start with a solid implementation.
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stdout (119 chars)
FAIL: import error: closing parenthesis ']' does not match opening parenthesis '(' on line 345 (solution.py, line 352)
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stdout (1155 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py</path>
<type>file</type>
<content>
340: """ + "".join([rf"""
341: {{
342: int idx = base + {i};
343: if (idx < n) {{
344: float val = row_ptr[idx];
345: """ + ("".join([rf"""
346: if (val > t_vals[{j}]) {{
347: """ + ("".join([f" t_vals[{l}] = t_vals[{l-1}]; t_idxs[{l}] = t_idxs[{l-1}];\n" for l in range(k-1, j, -1)])) + f"""
348: t_vals[{j}] = val;
349: t_idxs[{j}] = idx;
350: {'goto next_' + str(i) if j > 0 else ''}
351: }
352: """ for j in range(k)]) + (f" next_{i}:\n" if k > 1 else "") + " }\n }\n" for i in range(4)])) + rf"""
353: }}
354:
355: // Write to shared memory
356: """ + "".join([f" s_vals[tid * {k} + {i}] = t_vals[{i}];\n s_idxs[tid * {k} + {i}] = t_idxs[{i}];\n" for i in range(k)]) + rf"""
357: __syncthreads();
358:
359: // Sequential merge by thread 0
(Showing lines 340-359 of 664. Use offset=360 to continue.)
</content>
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Let me rewrite the solution.py cleanly from scratch with a complete, working implementation.
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stdout (4466 chars) — TRUNCATED
Traceback (most recent call last):
File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2774, in _run_ninja_build
subprocess.run(
File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/subprocess.py", line 571, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/check.py", line 149, in <module>
main()
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/check.py", line 59, in main
sol_model = solution.Model(*init_args).to(device).eval()
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py", line 204, in __init__
self._module = get_module(self.threads)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py", line 171, in get_module
mod = torch.utils.cpp_extension.load_inline(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2174, in load_inline
return _jit_compile(
^^^^^^^^^^^^^
File "/home/infa
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stdout (5527 chars) — TRUNCATED
Traceback (most recent call last):
File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2774, in _run_ninja_build
subprocess.run(
File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/subprocess.py", line 571, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/check.py", line 149, in <module>
main()
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/check.py", line 59, in main
sol_model = solution.Model(*init_args).to(device).eval()
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py", line 222, in __init__
self._module = get_module(self.threads)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py", line 189, in get_module
mod = torch.utils.cpp_extension.load_inline(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2174, in load_inline
return _jit_compile(
^^^^^^^^^^^^^
File "/home/infa
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stdout (7875 chars) — TRUNCATED
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py</path>
<type>file</type>
<content>
1: """Fast top-k CUDA kernel for RTX PRO 6000 Blackwell (SM120).
2:
3: Strategy: per-row block reduction. Each block handles one row. Threads find
4: local top-k in registers, then one thread merges all local top-k via shared mem.
5: """
6: import torch
7: import torch.nn as nn
8: import torch.utils.cpp_extension
9:
10: CPP_SOURCE = """
11: #include <torch/extension.h>
12:
13: void topk_launch(
14: const float* input,
15: float* values,
16: int64_t* indices,
17: int batch,
18: int n,
19: int k,
20: int threads_per_block
21: );
22:
23: PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
24: m.def("topk_launch", &topk_launch, "topk_launch");
25: }
26: """
27:
28: CUDA_SOURCE = r"""
29: #include <cuda_runtime.h>
30: #include <c10/cuda/CUDAStream.h>
31: #include <torch/extension.h>
32:
33: #define INF_NEG -3.4028235e+38f
34:
35: template <int K>
36: __global__ void topk_kernel(
37: const float* __restrict__ input,
38: float* __restrict__ values,
39: int64_t* __restrict__ indices,
40: int batch,
41: int n
42: )
43: {
44: extern __shared__ char smem[];
45: float* s_vals = (float*)smem;
46: int* s_idxs = (int*)(s_vals + blockDim.x * K);
47:
48: int row = blockIdx.x;
49: if (row >= batch) return;
50:
51: const float* row_ptr = input + row * n;
52: int tid = threadIdx.x;
53: int nthreads
... (6375 more chars)
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The issue is the old cached torch extension. The cache directory has an old `main.cpp` from the first attempt. Let me use a unique name to avoid the stale cache.
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stdout (26 chars)
Edit applied successfully.
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The stale cache has an old auto-generated PYBIND11_MODULE. Let me use unique names and delete the old cache.
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stdout (1400 chars)
<path>/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/solution.py</path>
<type>file</type>
<content>
180: """
181:
182: _cache = {}
183:
184:
185: def get_module(threads_per_block):
186: if threads_per_block in _cache:
187: return _cache[threads_per_block]
188:
189: import shutil
190: cache_dir = f"/tmp/topk_bitonic_ext_{threads_per_block}"
191: # Force fresh compile every time
192: if os.path.exists(cache_dir):
193: shutil.rmtree(cache_dir, ignore_errors=True)
194:
195: mod = torch.utils.cpp_extension.load_inline(
196: name=f"topk_bitonic_{threads_per_block}",
197: cpp_sources=CPP_SOURCE,
198: cuda_sources=CUDA_SOURCE,
199: functions=["topk_launch"],
200: extra_cuda_cflags=[
201: "-O3",
202: "-use_fast_math",
203: "-arch=sm_120",
204: ],
205: build_directory=cache_dir,
206: verbose=False,
207: )
208: _cache[threads_per_block] = mod
209: return mod
210:
211:
212: class Model(nn.Module):
213: """Top-k over the last dim of a 2D tensor using custom CUDA kernel."""
214:
215: def __init__(self, batch: int, n: int, k: int):
216: super().__init__()
217: self.batch = batch
218: self.n = n
219: self.k = k
(Showing lines 180-219 of 261. Use offset=220 to continue.)
</content>
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Missing `os` import.
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