L3 P11 · popcorn

compile_fail 11_pipeline_three_stage_mlp.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

2026-05-05T00:17:32Z

compile error: Error building extension 'packed_mlp_fused_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=packed_mlp_fused_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_3_problem_11_cache/k_587dd209733c/pa

timingkernelbench.timing

kernel runtime

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ref runtime

—

speedup

—

kernel mean / median / std

—

ref mean / median / std

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reference PyTorch source (the prompt the agent sees)

"""
Three-stage MLP on packed activations: h1 = relu(h0 @ W1), h2 = relu(h1 @ W2), out = h2 @ W3.
Dimensions are fixed; weights are trainable parameters with default init.
"""

import torch
import torch.nn as nn

from kernelbench.distributed_collectives import default_device

B = 16
H0 = 48
H1 = 64
H2 = 40
H3 = 24


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.W1 = nn.Linear(H0, H1, bias=False)
        self.W2 = nn.Linear(H1, H2, bias=False)
        self.W3 = nn.Linear(H2, H3, bias=False)

    def forward(self, h0: torch.Tensor) -> torch.Tensor:
        h1 = torch.relu(self.W1(h0))
        h2 = torch.relu(self.W2(h1))
        return self.W3(h2)


def get_inputs():
    dev = default_device()
    p = popcorn_pri
    return [torch.randn(p.jitter_int(B), H0, device=dev)]


def get_init_inputs():
    return []

turn 0 · FINAL · 1 tool call(s) · 16.5s turn · 16.5s LLM · 7,747 chars (click to expand)

turn 016.5s turn · 16.5s 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\nB = 16\nH0 = 48\nH1 = 64\nH2 = 40\nH3 = 24\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B_ 16\n#define H0_ 48\n#define H1_ 64\n#define H2_ 40\n#define H3_ 24\n\n__global__ __launch_bounds__(128, 4)\nvoid mlp_fused_kernel(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n int b = blockIdx.x;\n int tid = threadIdx.x;\n __shared__ float h1[H1_];\n __shared__ float h2[H2_];\n\n const float* x = h0 + b * H0_;\n\n if (tid < H1_) {\n const float* w = W1 + tid * H0_;\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H0_; ++k) {\n s = fmaf(x[k], w[k], s);\n }\n h1[tid] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n\n if (tid < H2_) {\n const float* w = W2 + tid * H1_;\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H1_; ++k) {\n s = fmaf(h1[k], w[k], s);\n }\n h2[tid] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n\n if (tid < H3_) {\n const float* w = W3 + tid * H2_;\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H2_; ++k) {\n s = fmaf(h2[k], w[k], s);\n }\n out[b * H3_ + tid] = s;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B_, H3_}, h0.options());\n mlp_fused_kernel<<<B_, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n'''\n\n_ext = load_inline(\n name='packed_mlp_fused_ext_v1',\n cpp_sources='',\n cuda_sources=_cuda_src,\n functions=['mlp_forward'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1 = nn.Linear(H0, H1, bias=False)\n self.W2 = nn.Linear(H1, H2, bias=False)\n self.W3 = nn.Linear(H2, H3, bias=False)\n\n def forward(self, h0: torch.Tensor) -> torch.Tensor:\n return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)\n" }

result ← submit_kernel (fail)

failsubmit_kernel submit_kernel FAILED: kernel did not compile. Error building extension 'packed_mlp_fused_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=packed_mlp_fused_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_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_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=packed_mlp_fused_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_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_ext_v1/main.cpp -o main.o /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_ext_v1/main.cpp: In function 'void pybind11_init_packed_mlp_fused_ext_v1(pybind11::module_&)': /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_ext_v1/main.cpp:4:50: error: 'mlp_forward' was not declared in this scope 4 | m.def("mlp_forward", torch::wrap_pybind_function(mlp_forward), "mlp_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=packed_mlp_fused_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_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=packed_mlp_fused_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_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_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_ext_v1/cuda.cu(63): error: namespace "at::cuda" has no member "getCurrentCUDAStream" mlp_fused_kernel<<<16, 128, 0, at::cuda::getCurrentCUDAStream()>>>( ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l123_single_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_587dd209733c/packed_mlp_fused_ext_v1/cuda.cu". ninja: build stopped: subcommand failed.

final submitted kernel (95 lines, 2,459 chars) — full source, untruncated

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

B = 16
H0 = 48
H1 = 64
H2 = 40
H3 = 24

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

#define B_ 16
#define H0_ 48
#define H1_ 64
#define H2_ 40
#define H3_ 24

__global__ __launch_bounds__(128, 4)
void mlp_fused_kernel(const float* __restrict__ h0,
                      const float* __restrict__ W1,
                      const float* __restrict__ W2,
                      const float* __restrict__ W3,
                      float* __restrict__ out) {
    int b = blockIdx.x;
    int tid = threadIdx.x;
    __shared__ float h1[H1_];
    __shared__ float h2[H2_];

    const float* x = h0 + b * H0_;

    if (tid < H1_) {
        const float* w = W1 + tid * H0_;
        float s = 0.0f;
        #pragma unroll
        for (int k = 0; k < H0_; ++k) {
            s = fmaf(x[k], w[k], s);
        }
        h1[tid] = s > 0.0f ? s : 0.0f;
    }
    __syncthreads();

    if (tid < H2_) {
        const float* w = W2 + tid * H1_;
        float s = 0.0f;
        #pragma unroll
        for (int k = 0; k < H1_; ++k) {
            s = fmaf(h1[k], w[k], s);
        }
        h2[tid] = s > 0.0f ? s : 0.0f;
    }
    __syncthreads();

    if (tid < H3_) {
        const float* w = W3 + tid * H2_;
        float s = 0.0f;
        #pragma unroll
        for (int k = 0; k < H2_; ++k) {
            s = fmaf(h2[k], w[k], s);
        }
        out[b * H3_ + tid] = s;
    }
}

torch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {
    auto out = torch::empty({B_, H3_}, h0.options());
    mlp_fused_kernel<<<B_, 128, 0, at::cuda::getCurrentCUDAStream()>>>(
        h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());
    return out;
}
'''

_ext = load_inline(
    name='packed_mlp_fused_ext_v1',
    cpp_sources='',
    cuda_sources=_cuda_src,
    functions=['mlp_forward'],
    extra_cuda_cflags=['-O3', '--use_fast_math'],
    with_cuda=True,
    verbose=False,
)

class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()
        self.W1 = nn.Linear(H0, H1, bias=False)
        self.W2 = nn.Linear(H1, H2, bias=False)
        self.W3 = nn.Linear(H2, H3, bias=False)

    def forward(self, h0: torch.Tensor) -> torch.Tensor:
        return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)