compile_fail
1_Conv2D_ReLU_BiasAdd.py
started
2026-05-05T00:38:16Z
compile error: Error building extension 'conv_relu_bias_fused_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_relu_bias_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 -O3 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_1_cache/k_222
timingkernelbench.timing
kernel mean / median / std
—
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a convolution, applies ReLU, and adds a bias term.
"""
def __init__(self, in_channels, out_channels, kernel_size, bias_shape):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.bias = nn.Parameter(torch.randn(bias_shape))
def forward(self, x):
x = self.conv(x)
x = torch.relu(x)
x = x + self.bias
return x
batch_size = 128
in_channels = 64
out_channels = 128
height = width = 128
kernel_size = 3
bias_shape = (out_channels, 1, 1)
def get_inputs():
return [torch.rand(batch_size, in_channels, height, width)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size, bias_shape]turn 0 · FINAL · 1 tool call(s) · 28.8s turn · 28.8s LLM · 11,266 chars (click to expand)
turn 028.8s turn · 28.8s LLM · 0.0s tools · FINAL
reasoning
**Developing a custom PyTorch kernel**
I need to create a Python function for a custom 3x3 convolution with specific parameters: a stride of 1, no padding, an output size of 126x126, with an input batch size of 128, 64 input channels, and 128 output channels. I want it to match certain precision requirements and perform optimally. To achieve this, I'm considering leveraging PyTorch's cuDNN optimizations and possibly using a custom CUDA kernel. I need to ensure that my approach is faster than the reference implementation while incorporating specific operations efficiently.
**Optimizing convolution operations**
I'm analyzing ways to speed up a custom convolution by potentially reducing PyTorch overhead, perhaps by using an extension that fuses ReLU and bias operations. I wonder if there's a risk of falling back to the reference implementation; however, invoking at::conv2d might still be acceptable. By finalizing my code to call torch.ops with a custom CUDA kernel, I could eliminate some Python operations. I also need to consider handling weights from the reference model and ensure I compare properly after copying the state_dict.
**Defining model parameters**
I need to ensure that my class has parameters for self.conv and bias. It's essential to define parameters with the same names. In the ModelNew constructor, I should utilize nn.Conv2d for the convolutional layer and nn.Parameter for any other parameters. This way, I can maintain consistency in how parameters are defined and used throughout my model, which is crucial for ensuring it functions properly. I've got to keep track of this as I build the model!
**Evaluating function generation**
I'm working through an issue where the load_inline functions automatically generate PYBIND for functions in cpp_sources. But what happens when a function in cuda_sources isn't included in cpp? I realize that we have a wrapper in cpp, which might help address this concern. It's an interesting puzzle, as I'm curious about how to ensure everything integrates smoothly. I need to keep this in mind for our next steps in refining the process!
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_cpp_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <vector>\n\nvoid relu_bias_inplace_cuda(torch::Tensor y, torch::Tensor bias);\n\ntorch::Tensor conv_relu_bias_forward(torch::Tensor x,\n torch::Tensor weight,\n torch::Tensor conv_bias,\n torch::Tensor add_bias) {\n // Same convolution parameters as nn.Conv2d(..., kernel_size=3) in the task:\n // stride=1, padding=0, dilation=1, groups=1. Use ATen/cuDNN for the heavy\n // convolution, then fuse ReLU and the channel bias add in one CUDA pass.\n auto y = at::conv2d(x, weight, conv_bias, {1, 1}, {0, 0}, {1, 1}, 1);\n relu_bias_inplace_cuda(y, add_bias);\n return y;\n}\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void relu_bias_kernel(float* __restrict__ y,\n const float* __restrict__ bias,\n long total,\n int C,\n int HW) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n for (; idx < total; idx += stride) {\n int c = (int)((idx / HW) % C);\n float v = y[idx];\n v = v > 0.0f ? v : 0.0f;\n y[idx] = v + bias[c];\n }\n}\n\nvoid relu_bias_inplace_cuda(torch::Tensor y, torch::Tensor bias) {\n const long total = y.numel();\n if (total == 0) return;\n const int C = (int)y.size(1);\n const int HW = (int)(y.size(2) * y.size(3));\n const int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n relu_bias_kernel<<<blocks, threads, 0, stream>>>(\n y.data_ptr<float>(), bias.data_ptr<float>(), total, C, HW);\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"conv_relu_bias_fused_ext_v1\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"conv_relu_bias_forward\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, bias_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.bias = nn.Parameter(torch.randn(bias_shape))\n\n def forward(self, x):\n return _ext.conv_relu_bias_forward(x, self.conv.weight, self.conv.bias, self.bias)\n"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Error building extension 'conv_relu_bias_fused_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_relu_bias_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 -O3 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_1_cache/k_2227f0602f3d/conv_relu_bias_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=conv_relu_bias_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 -O3 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_1_cache/k_2227f0602f3d/conv_relu_bias_fused_ext_v1/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_1_cache/k_2227f0602f3d/conv_relu_bias_fused_ext_v1/main.cpp: In function 'at::Tensor conv_relu_bias_forward(at::Tensor, at::Tensor, at::Tensor, at::Tensor)':
/scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_1_cache/k_2227f0602f3d/conv_relu_bias_fused_ext_v1/main.cpp:16:24: error: call of overloaded 'conv2d(at::Tensor&, at::Tensor&, at::Tensor&, <brace-enclosed initializer list>, <brace-enclosed initializer list>, <brace-enclosed initializer list>, int)' is ambiguous
16 | auto y = at::conv2d(x, weight, conv_bias, {1, 1}, {0, 0}, {1, 1}, 1);
| ~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/Functions.h:607,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ExpandUtils.h:5,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/input_metadata.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/function.h:8,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/custom_function.h:8,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:6,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/extension.h:6,
from /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_1_cache/k_2227f0602f3d/conv_relu_bias_fused_ext_v1/main.cpp:1:
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/conv2d.h:28:19: note: candidate: 'at::Tensor at::conv2d(const Tensor&, const Tensor&, const std::optional<Tensor>&, IntArrayRef, IntArrayRef, IntArrayRef, int64_t)'
28 | inline at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) {
| ^~~~~~
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/conv2d.h:50:19: note: candidate: 'at::Tensor at::conv2d(const Tensor&, const Tensor&, const std::optional<Tensor>&, IntArrayRef, c10::string_view, IntArrayRef, int64_t)'
50 | inline at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) {
| ^~~~~~
[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=conv_relu_bias_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 -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_1_cache/k_2227f0602f3d/conv_relu_bias_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
ninja: build stopped: subcommand failed.
final submitted kernel (80 lines, 2,666 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
_cpp_src = r"""
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <vector>
void relu_bias_inplace_cuda(torch::Tensor y, torch::Tensor bias);
torch::Tensor conv_relu_bias_forward(torch::Tensor x,
torch::Tensor weight,
torch::Tensor conv_bias,
torch::Tensor add_bias) {
// Same convolution parameters as nn.Conv2d(..., kernel_size=3) in the task:
// stride=1, padding=0, dilation=1, groups=1. Use ATen/cuDNN for the heavy
// convolution, then fuse ReLU and the channel bias add in one CUDA pass.
auto y = at::conv2d(x, weight, conv_bias, {1, 1}, {0, 0}, {1, 1}, 1);
relu_bias_inplace_cuda(y, add_bias);
return y;
}
"""
_cuda_src = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void relu_bias_kernel(float* __restrict__ y,
const float* __restrict__ bias,
long total,
int C,
int HW) {
long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;
long stride = (long)blockDim.x * gridDim.x;
for (; idx < total; idx += stride) {
int c = (int)((idx / HW) % C);
float v = y[idx];
v = v > 0.0f ? v : 0.0f;
y[idx] = v + bias[c];
}
}
void relu_bias_inplace_cuda(torch::Tensor y, torch::Tensor bias) {
const long total = y.numel();
if (total == 0) return;
const int C = (int)y.size(1);
const int HW = (int)(y.size(2) * y.size(3));
const int threads = 256;
int blocks = (int)((total + threads - 1) / threads);
if (blocks > 4096) blocks = 4096;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
relu_bias_kernel<<<blocks, threads, 0, stream>>>(
y.data_ptr<float>(), bias.data_ptr<float>(), total, C, HW);
}
"""
_ext = load_inline(
name="conv_relu_bias_fused_ext_v1",
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=["conv_relu_bias_forward"],
extra_cflags=["-O3"],
extra_cuda_cflags=["-O3"],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias_shape):
super(ModelNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.bias = nn.Parameter(torch.randn(bias_shape))
def forward(self, x):
return _ext.conv_relu_bias_forward(x, self.conv.weight, self.conv.bias, self.bias)