L1 P11 · popcorn

compile_fail 11_DegreeNormalizedAggregation.py

model

gpt-5.5-priority

turns / max

1 / 1

tool calls

tokens (total)

wall clock

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truncated

backend

cuda fp32

variant

popcorn

runtime

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ref runtime

-1.00 μs

speedup

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started

2026-05-05T00:21:53Z

compile error: Error building extension 'gcn_norm_agg_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=gcn_norm_agg_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_a

timingkernelbench.timing

kernel runtime

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ref runtime

—

speedup

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kernel mean / median / std

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ref mean / median / std

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reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Degree-normalized neighbor aggregation.

    Computes a GCN-style sparse aggregation where each incoming message is
    scaled by 1 / sqrt(deg(dst) * deg(src)). This captures a common spectral
    graph normalization pattern over CSR adjacency.
    """

    def __init__(self):
        super().__init__()

    def forward(
        self,
        row_ptr: torch.Tensor,
        col_idx: torch.Tensor,
        node_feat: torch.Tensor,
        degrees: torch.Tensor,
    ) -> torch.Tensor:
        num_nodes = row_ptr.numel() - 1
        feat_dim = node_feat.shape[1]
        out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)

        for dst in range(num_nodes):
            start = int(row_ptr[dst].item())
            end = int(row_ptr[dst + 1].item())
            if end <= start:
                continue
            src = col_idx[start:end].long()
            norm = 1.0 / torch.sqrt(degrees[dst] * degrees[src]).unsqueeze(-1)
            out[dst] = (node_feat[src] * norm).sum(dim=0)

        return out


num_nodes = 640
avg_degree = 16
feat_dim = 80




def get_inputs():
    return list(popcorn_pri.make_csr_degree_normalized_graph(num_nodes, avg_degree, feat_dim))



def get_init_inputs():
    return []

turn 0 · FINAL · 1 tool call(s) · 13.9s turn · 13.9s LLM · 7,369 chars (click to expand)

turn 013.9s turn · 13.9s LLM · 0.0s tools · FINAL

call → submit_kernel

callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void gcn_norm_agg_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim)\n{\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n if (dst >= num_nodes) return;\n\n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n float deg_dst = degrees[dst];\n\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float acc = 0.0f;\n #pragma unroll 1\n for (int e = start; e < end; ++e) {\n int src = col_idx[e];\n float norm = 1.0f / sqrtf(deg_dst * degrees[src]);\n acc += node_feat[src * feat_dim + f] * norm;\n }\n out[dst * feat_dim + f] = acc;\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat,\n torch::Tensor degrees) {\n const int num_nodes = (int)row_ptr.numel() - 1;\n const int feat_dim = (int)node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n\n const int threads = 128;\n dim3 grid(num_nodes);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n gcn_norm_agg_kernel<<<grid, threads, 0, stream>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim);\n return out;\n}\n'''\n\n_ext = load_inline(\n name='gcn_norm_agg_ext_v1',\n cpp_sources='',\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n extra_cuda_cflags=['-O3'],\n with_cuda=True,\n verbose=False,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }

result ← submit_kernel (fail)

failsubmit_kernel submit_kernel FAILED: kernel did not compile. Error building extension 'gcn_norm_agg_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=gcn_norm_agg_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/main.cpp -o main.o FAILED: [code=1] main.o /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=gcn_norm_agg_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/main.cpp -o main.o /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/main.cpp: In function 'void pybind11_init_gcn_norm_agg_ext_v1(pybind11::module_&)': /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/main.cpp:4:50: error: 'gcn_forward' was not declared in this scope 4 | m.def("gcn_forward", torch::wrap_pybind_function(gcn_forward), "gcn_forward"); | ^~~~~~~~~~~ [2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=gcn_norm_agg_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -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_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/cuda.cu -o cuda.cuda.o FAILED: [code=2] cuda.cuda.o /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=gcn_norm_agg_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -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_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/cuda.cu(48): error: namespace "at::cuda" has no member "getCurrentCUDAStream" cudaStream_t stream = at::cuda::getCurrentCUDAStream(); ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_1_problem_11_cache/k_072edf36ddf1/gcn_norm_agg_ext_v1/cuda.cu". ninja: build stopped: subcommand failed.

final submitted kernel (80 lines, 2,248 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline


_cuda_src = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__global__ void gcn_norm_agg_kernel(
    const int* __restrict__ row_ptr,
    const int* __restrict__ col_idx,
    const float* __restrict__ node_feat,
    const float* __restrict__ degrees,
    float* __restrict__ out,
    int num_nodes,
    int feat_dim)
{
    int dst = blockIdx.x;
    int tid = threadIdx.x;
    if (dst >= num_nodes) return;

    int start = row_ptr[dst];
    int end   = row_ptr[dst + 1];
    float deg_dst = degrees[dst];

    for (int f = tid; f < feat_dim; f += blockDim.x) {
        float acc = 0.0f;
        #pragma unroll 1
        for (int e = start; e < end; ++e) {
            int src = col_idx[e];
            float norm = 1.0f / sqrtf(deg_dst * degrees[src]);
            acc += node_feat[src * feat_dim + f] * norm;
        }
        out[dst * feat_dim + f] = acc;
    }
}

torch::Tensor gcn_forward(torch::Tensor row_ptr,
                          torch::Tensor col_idx,
                          torch::Tensor node_feat,
                          torch::Tensor degrees) {
    const int num_nodes = (int)row_ptr.numel() - 1;
    const int feat_dim = (int)node_feat.size(1);
    auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());

    const int threads = 128;
    dim3 grid(num_nodes);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    gcn_norm_agg_kernel<<<grid, threads, 0, stream>>>(
        row_ptr.data_ptr<int>(),
        col_idx.data_ptr<int>(),
        node_feat.data_ptr<float>(),
        degrees.data_ptr<float>(),
        out.data_ptr<float>(),
        num_nodes,
        feat_dim);
    return out;
}
'''

_ext = load_inline(
    name='gcn_norm_agg_ext_v1',
    cpp_sources='',
    cuda_sources=_cuda_src,
    functions=['gcn_forward'],
    extra_cuda_cflags=['-O3'],
    with_cuda=True,
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:
        return _ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)