compile_fail
54_Conv2d_Multiply_LeakyReLU_GELU.py
started
2026-05-04T07:21:44Z
compile error: Error building extension 'fused_conv_act_ext_final_check': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv_act_ext_final_check -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_
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):
"""
Model that performs a convolution, multiplies by a learnable scalar, applies LeakyReLU, and then GELU.
"""
def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.multiplier = nn.Parameter(torch.randn(multiplier_shape))
self.leaky_relu = nn.LeakyReLU()
def forward(self, x):
x = self.conv(x)
x = x * self.multiplier
x = self.leaky_relu(x)
x = torch.nn.functional.gelu(x)
return x
batch_size = 64
in_channels = 64
out_channels = 64
height, width = 256, 256
kernel_size = 3
multiplier_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, multiplier_shape]turn 010.1s turn · 10.1s LLM · 0.0s tools
call → get_gpu_specs
callget_gpu_specs
{}
result ← get_gpu_specs (ok)
okget_gpu_specs
GPU specs for NVIDIA H100 80GB HBM3:
total memory (runtime): 79.1 GB
spec entry: H100
GPU Architecture: Hopper
GPU Memory: 80GB
Memory Bandwidth: 3.35 TB/s
FP64 TFLOPS: 34
FP64 Tensor Core TFLOPS: 67
FP32 TFLOPS: 67
TF32 Tensor Core TFLOPS: 989 with sparsity
BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity
FP16 Tensor Core TFLOPS: 1979 with sparsity
FP8 Tensor Core TFLOPS: 3958 with sparsity
INT8 Tensor Core TOPS: 3958 with sparsity
Register File Size: 64K 32-bit registers per SM
Maximum number of registers per thread: 255
Maximum number of thread blocks per SM: 32
Shared memory capacity per SM: 228 KB
Maximum shared memory per thread block: 227 KB
turn 1 · 1 tool call(s) · 10.9s turn · 10.9s LLM · 7,821 chars (click to expand)
turn 110.9s turn · 10.9s LLM · 0.0s tools
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <vector>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n\n__global__ void post_kernel(float* __restrict__ y, const float* __restrict__ mult, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n for (long i = idx; i < total; i += stride) {\n long hw = (long)H * W;\n int c = (int)((i / hw) % C);\n float v = y[i] * mult[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n y[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n auto y = at::conv2d(x, weight, bias, {1,1}, {0,0}, {1,1}, 1);\n const int N = (int)y.size(0);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = (long)N*C*H*W;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), multiplier.data_ptr<float>(), total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_v1', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'fused_conv_act_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv_act_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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_01e59c0202ad/fused_conv_act_ext_v1/main.cpp -o main.o
[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=fused_conv_act_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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_01e59c0202ad/fused_conv_act_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=fused_conv_act_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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_01e59c0202ad/fused_conv_act_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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_01e59c0202ad/fused_conv_act_ext_v1/cuda.cu(28): error: more than one instance of overloaded function "at::conv2d" matches the argument list:
function "at::conv2d(const at::Tensor &, const at::Tensor &, const std::optional<at::Tensor> &, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, int64_t)" (declared at line 28 of /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/conv2d.h)
function "at::conv2d(const at::Tensor &, const at::Tensor &, const std::optional<at::Tensor> &, at::IntArrayRef, c10::string_view, at::IntArrayRef, int64_t)" (declared at line 50 of /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/conv2d.h)
argument types are: (at::Tensor, at::Tensor, at::Tensor, {...}, {...}, {...}, int)
auto y = at::conv2d(x, weight, bias, {1,1}, {0,0}, {1,1}, 1);
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_01e59c0202ad/fused_conv_act_ext_v1/cuda.cu(38): error: type name is not allowed
post_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), multiplier.data_ptr<float>(), total, C, H, W);
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_01e59c0202ad/fused_conv_act_ext_v1/cuda.cu(38): error: expected an expression
post_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), multiplier.data_ptr<float>(), total, C, H, W);
^
3 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_01e59c0202ad/fused_conv_act_ext_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 28.2s turn · 8.2s LLM · 0.0s tools
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n long hw = (long)H * W;\n for (long i = idx; i < total; i += stride) {\n int c = (int)((i / hw) % C);\n float v = yp[i] * multp[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n yp[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = y.numel();\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_v2', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 36.1s turn · 6.1s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n long hw = (long)H * W;\n for (long i = idx; i < total; i += stride) {\n int c = (int)((i / hw) % C);\n float v = yp[i] * multp[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n yp[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = y.numel();\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_v2c', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 46.1s turn · 6.1s LLM · 0.0s tools
reasoning
**Assessing performance needs**
I need to focus on performance now. It might be a good idea to profile or submit something to see if it's running faster than before. Profiling could give us useful insights into any improvements. Let’s explore what tools or methods we can use to efficiently assess this. It's all about getting the right feedback on whether there’s any noticeable change in speed!
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n long hw = (long)H * W;\n for (long i = idx; i < total; i += stride) {\n int c = (int)((i / hw) % C);\n float v = yp[i] * multp[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n yp[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = y.numel();\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_v2p', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 139784.0μs (25%) calls=1
#2: aten::convolution 139721.0μs (25%) calls=1
#3: aten::_convolution 139659.0μs (25%) calls=1
#4: aten::cudnn_convolution 127994.0μs (23%) calls=1
#5: aten::add_ 10676.0μs (2%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 4873.89 μs
--- Memory Bandwidth ---
DRAM BW: 11265.5/3350 GB/s (100.0%)
read=5712.7 write=5552.8 GB/s
Hit rates: L2=367.8%
--- Compute Throughput ---
Dominant pipe: tensor-core util=60.0%
Pipe util: FMA=75.8%, Tensor=60.0%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 401.2%
Eligible warps/cycle: 5.07
Launch: regs/thread=312, smem/blk=250240B, block=1536, grid=1618359
--- Memory Access Quality ---
Global ld coalescing: 15.00 sectors/req (15.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 10175.0% ####################
short_scoreboard 669.3% ####################
mio_throttle 144.8% ####################
--- Optimization Hints ---
-> 10175% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=15.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=15.0x (ideal=1.0) -> 15x wasted DRAM BW. Make per-warp accesses contiguous.
turn 519.4s turn · 19.4s LLM · 0.0s tools
reasoning
**Optimizing kernel performance**
I’m noticing that the profile output seems odd, with GPU time at 4.87 ms and conv2d appearing to list CPU. It raises questions about optimization, especially post kernel. The division modulo per element and erff being slow is concerning. I could take a look at PyTorch’s approach using separate multiplier functions. Specializing layouts and avoiding division might yield better performance. Optimizing the blocks in a 2D grid setup could help manage the total number of blocks effectively.
**Examining block configurations**
Currently, I see we have 65,535 blocks with a loop running 16 elements per thread, which might let us reduce grid blocks. The division per element is concerning, and I’m contemplating using 1D blocks instead, maybe computing via bit for channel assignments. With a total of 64,516 for H*W, I could assign blocks to channel-spatial chunks. Kernel launch overhead must be considered, and I wonder if custom kernels with approximated GELU could help optimize further. The memory savings from our current kernel are a strong point!
**Exploring conv2d invocation**
I’m considering whether the at::conv2d invocation from C++ in the extension might introduce extra synchronization steps. That could be important to note! I wonder if utilizing CUDNN autotuning could optimize this further. It seems like the reference might also employ conv2d the same way, so it’s worth investigating what their approach entails. Overall, ensuring these elements are in check could really enhance performance.
**Evaluating conversation improvements**
I think the current conversation setup is good, but I wonder if I could improve it by using a pointwise approach during the conversation. However, I'm not sure about that. Looking at possible technical optimizations, should I explore using PyTorch in-place operations or c10::cuda::CUDACachingAllocator? I’m questioning whether we should measure in the end or if a static check might be sufficient beforehand. It's worth considering both options before finalizing anything.
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n long hw = (long)H * W;\n for (long i = idx; i < total; i += stride) {\n int c = (int)((i / hw) % C);\n float v = yp[i] * multp[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n yp[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = y.numel();\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_final_check', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 67.3s turn · 7.3s LLM · 0.0s tools
reasoning
**Considering optimization options**
I'm thinking that using the --use_fast_math flag could speed up the erff function, which might help with performance. Earlier, the v1 compilation failed when I tried to use fast math, but it seems like I can compile it correctly this time. While it might not achieve bit-matching, I could set a tolerance level of 1e-4. I definitely need to run some tests to see how it behaves!
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n long hw = (long)H * W;\n for (long i = idx; i < total; i += stride) {\n int c = (int)((i / hw) % C);\n float v = yp[i] * multp[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n yp[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = y.numel();\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_fastmath', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 78.5s turn · 8.5s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n long hw = (long)H * W;\n for (long i = idx; i < total; i += stride) {\n int c = (int)((i / hw) % C);\n float v = yp[i] * multp[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n yp[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = y.numel();\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_fastmath_prof', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 124138.0μs (25%) calls=1
#2: aten::convolution 124086.0μs (25%) calls=1
#3: aten::_convolution 124051.0μs (25%) calls=1
#4: aten::cudnn_convolution 112955.0μs (23%) calls=1
#5: aten::add_ 10283.0μs (2%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 4879.10 μs [↑5.2μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 11249.7/3350 GB/s (100.0%)
read=5703.6 write=5546.1 GB/s
Hit rates: L2=367.7%
--- Compute Throughput ---
Dominant pipe: tensor-core util=59.3% [↓0.7% REGRESSED]
Pipe util: FMA=76.8%, Tensor=59.3%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 401.4% [↑0.2% ok]
Eligible warps/cycle: 5.18
Launch: regs/thread=312, smem/blk=250240B, block=1536, grid=1618359
--- Memory Access Quality ---
Global ld coalescing: 15.00 sectors/req (15.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 10151.4% ####################
short_scoreboard 675.2% ####################
mio_throttle 125.1% ####################
--- Optimization Hints ---
-> 10151% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=15.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=15.0x (ideal=1.0) -> 15x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 4873.89→4879.10μs (↑5.22) REGRESSED
Compute%: 59.97→59.32% (↓0.65) REGRESSED
Occupancy: 401.23→401.39% (↑0.16) ok
Eligible warps/cyc: 5.07→5.18 (↑0.11) ok
L2 hit%: 367.82→367.72% (↓0.10) REGRESSED
turn 812.6s turn · 12.6s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel_nc(float* __restrict__ yp, const float* __restrict__ multp, int NC, int C, int HW) {\n int nc = blockIdx.x;\n int c = nc % C;\n float m = multp[c];\n long base = (long)nc * HW;\n const float inv_sqrt2 = 0.70710678118654752440f;\n for (int off = threadIdx.x; off < HW; off += blockDim.x) {\n float v = yp[base + off] * m;\n v = v >= 0.0f ? v : 0.01f * v;\n yp[base + off] = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n int N = (int)y.size(0); int C = (int)y.size(1); int H = (int)y.size(2); int W = (int)y.size(3);\n int HW = H*W; int NC = N*C;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel_nc<<<NC, 256, 0, stream>>>(yp, mp, NC, C, HW);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_nc', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 910.9s turn · 10.9s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel_nc(float* __restrict__ yp, const float* __restrict__ multp, int NC, int C, int HW) {\n int nc = blockIdx.x;\n int c = nc % C;\n float m = multp[c];\n long base = (long)nc * HW;\n const float inv_sqrt2 = 0.70710678118654752440f;\n for (int off = threadIdx.x; off < HW; off += blockDim.x) {\n float v = yp[base + off] * m;\n v = v >= 0.0f ? v : 0.01f * v;\n yp[base + off] = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n int N = (int)y.size(0); int C = (int)y.size(1); int H = (int)y.size(2); int W = (int)y.size(3);\n int HW = H*W; int NC = N*C;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel_nc<<<NC, 256, 0, stream>>>(yp, mp, NC, C, HW);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_nc_prof', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 127424.0μs (25%) calls=1
#2: aten::convolution 127377.0μs (25%) calls=1
#3: aten::_convolution 127344.0μs (25%) calls=1
#4: aten::cudnn_convolution 116100.0μs (23%) calls=1
#5: aten::add_ 10446.0μs (2%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 5016.90 μs [↑137.8μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 11079.3/3350 GB/s (100.0%)
read=5619.6 write=5459.7 GB/s
Hit rates: L2=374.1%
--- Compute Throughput ---
Dominant pipe: tensor-core util=56.0% [↓3.4% REGRESSED]
Pipe util: FMA=57.1%, Tensor=56.0%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 399.3% [↓2.1% REGRESSED]
Eligible warps/cycle: 3.23
Launch: regs/thread=298, smem/blk=250240B, block=1536, grid=1556920
--- Memory Access Quality ---
Global ld coalescing: 16.98 sectors/req (17.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 13558.1% ####################
short_scoreboard 599.2% ####################
mio_throttle 132.0% ####################
--- Optimization Hints ---
-> 13558% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=17.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=17.0x (ideal=1.0) -> 17x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 4879.10→5016.90μs (↑137.79) REGRESSED
Compute%: 59.32→55.97% (↓3.36) REGRESSED
Occupancy: 401.39→399.32% (↓2.07) REGRESSED
Eligible warps/cyc: 5.18→3.23 (↓1.95) REGRESSED
Ld sectors/req: 15.00→16.98 (↑1.98) REGRESSED
L2 hit%: 367.72→374.07% (↑6.36) ok
turn 10 · 1 tool call(s) · 10.2s turn · 10.2s LLM · 6,487 chars (click to expand)
turn 1010.2s turn · 10.2s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__device__ __forceinline__ float act(float v) {\n const float inv_sqrt2 = 0.70710678118654752440f;\n v = v >= 0.0f ? v : 0.01f * v;\n return 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n}\n\n__global__ void post_kernel_vec4(float4* __restrict__ yp4, const float* __restrict__ multp, long total4, int C, int HW4) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n for (long j = idx; j < total4; j += stride) {\n int c = (int)((j / HW4) % C);\n float m = multp[c];\n float4 a = yp4[j];\n a.x = act(a.x * m);\n a.y = act(a.y * m);\n a.z = act(a.z * m);\n a.w = act(a.w * m);\n yp4[j] = a;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n int C = (int)y.size(1); int H = (int)y.size(2); int W = (int)y.size(3);\n int HW4 = (H*W) >> 2;\n long total4 = y.numel() >> 2;\n int threads = 256;\n int blocks = (int)((total4 + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float4* yp = reinterpret_cast<float4*>(y.data_ptr<float>());\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel_vec4<<<blocks, threads, 0, stream>>>(yp, mp, total4, C, HW4);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_vec4', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'fused_conv_act_ext_vec4': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv_act_ext_vec4 -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_222422331616/fused_conv_act_ext_vec4/main.cpp -o main.o
[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=fused_conv_act_ext_vec4 -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_222422331616/fused_conv_act_ext_vec4/cuda.cu -o cuda.cuda.o
FAILED: [code=4] 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=fused_conv_act_ext_vec4 -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_222422331616/fused_conv_act_ext_vec4/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/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/reflection_pad3d_native.h(26): catastrophic error: error while writing generated C++ file: No space left on device
1 catastrophic error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_222422331616/fused_conv_act_ext_vec4/cuda.cu".
Compilation terminated.
ninja: build stopped: subcommand failed.
turn 11 · FINAL · 1 tool call(s) · 25.9s turn · 25.9s LLM · 10,269 chars (click to expand)
turn 1125.9s turn · 25.9s LLM · 0.0s tools · FINAL
reasoning
**Evaluating cache space issues**
I'm thinking about whether there's enough space in the cache. We might need to reduce the number of compilations or opt for unique names since we’ve used many. I wonder if an environment compile would work. However, it seems the tool might be out of space because of too many attempts. I need to ensure any final compile is correct without including unnecessary resources. I should be cautious about submitting anything unproven. I also need to infer correctness while considering kernel sizes.
**Analyzing computation efficiency**
I'm considering whether the code assumes numel is divisible by 4. If the evaluator only checks shapes, it could still pass, but I wonder about operation order. Multiplication, leaky functions, and erf should behave the same. It seems like using vector storage would be beneficial since calculations align to four outputs, enhancing performance with fewer loop iterations. However, I need to keep in mind that the erf calculation may dominate. It's crucial to compile the final version with vector4, but I’m uncertain about testing due to disk space issues.
**Considering code compilation**
I'm thinking that for the final evaluation, I could reduce the compiled code size by only including relevant cpp source declarations and minimizing cuda includes. However, using at::conv2d requires a heavy ATen inclusion, so I should consider that. Maybe I shouldn't use vector4 because it hasn’t been tested yet. While it likely compiles, I suspect issues with loading inline functions that expect definitions in the cuda source. I need to ensure everything is compatible and that "act" does not conflict.
**Evaluating code logic**
I'm working through some code logic for arithmetic with variables. If the height and width aren't divisible by 4, I might be ignoring the remainder, which isn’t ideal. It could be safer to handle this with a scalar, but that adds extra code. I'm also considering how to manage arbitrary kernel sizes while ensuring alignment and memory allocation are correct. Using temporary initializers seems feasible, but I need to check if everything compiles properly and ensure the environment is suitable for the evaluations.
**Optimizing submission logic**
I'm considering how to submit a compiled name in the cache efficiently. If I'm using the extended v2 name and it's already compiled, I might not need to access the disk, which is a plus. But I'll need to ensure that submissions have unique names while leveraging existing cache. If the vector4 submission might fail due to space limitations, I prefer using v2 that has already passed checks. My goal is to maximize speed since the current v2 speed is already decent compared to PyTorch.
**Deciding on submission strategy**
I'm weighing the benefits of using Vector4 because it might be faster, but since the disk is full, I don’t want to take that risk. The final answer should go through a submission tool instead of directly using textual code. I think I should call the submit function using v2 with a possibility of `--use_fast_math`, even though profiling shows no difference. It might be best to use a compiled cache name like `fused_conv_act_ext_v2c` to ensure consistency.
**Considering module compilation**
I'm thinking about the module name, maybe using `fused_conv_act_ext_final_check`. I’m wondering if I need the exact source to compile properly. If I submit without recompilation, what if there's no space in the new directory? This might lead to issues, especially if paths differ. I know PyTorch caches extensions globally, so using the same name may help avoid rebuilding. I also need to consider the risks of loading duplicate modules in the same Python process. Is it safer to execute in a new process to use the cache?
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/ops/conv2d.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <optional>\n\n__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n long stride = (long)blockDim.x * gridDim.x;\n const float inv_sqrt2 = 0.70710678118654752440f;\n long hw = (long)H * W;\n for (long i = idx; i < total; i += stride) {\n int c = (int)((i / hw) % C);\n float v = yp[i] * multp[c];\n v = v >= 0.0f ? v : 0.01f * v;\n float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));\n yp[i] = g;\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {\n std::optional<at::Tensor> b = bias;\n auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n const int C = (int)y.size(1);\n const int H = (int)y.size(2);\n const int W = (int)y.size(3);\n long total = y.numel();\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n float* yp = y.data_ptr<float>();\n const float* mp = multiplier.data_ptr<float>();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_act_ext_final_check', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.multiplier = nn.Parameter(torch.randn(multiplier_shape))\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)\n"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Error building extension 'fused_conv_act_ext_final_check': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv_act_ext_final_check -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_93b315dfee25/fused_conv_act_ext_final_check/main.cpp -o main.o
[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=fused_conv_act_ext_final_check -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_93b315dfee25/fused_conv_act_ext_final_check/cuda.cu -o cuda.cuda.o
FAILED: [code=1] 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=fused_conv_act_ext_final_check -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_93b315dfee25/fused_conv_act_ext_final_check/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
/opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/bits/hashtable.h(396): catastrophic error: error while writing generated C file: No space left on device
1 catastrophic error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_54_cache/k_93b315dfee25/fused_conv_act_ext_final_check/cuda.cu".
Compilation terminated.
ninja: build stopped: subcommand failed.
final submitted kernel (63 lines, 2,417 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cpp_source = r'''
#include <torch/extension.h>
#include <ATen/ATen.h>
torch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier);
'''
cuda_source = r'''
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/ops/conv2d.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>
#include <optional>
__global__ void post_kernel(float* __restrict__ yp, const float* __restrict__ multp, long total, int C, int H, int W) {
long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;
long stride = (long)blockDim.x * gridDim.x;
const float inv_sqrt2 = 0.70710678118654752440f;
long hw = (long)H * W;
for (long i = idx; i < total; i += stride) {
int c = (int)((i / hw) % C);
float v = yp[i] * multp[c];
v = v >= 0.0f ? v : 0.01f * v;
float g = 0.5f * v * (1.0f + erff(v * inv_sqrt2));
yp[i] = g;
}
}
torch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor multiplier) {
std::optional<at::Tensor> b = bias;
auto y = at::conv2d(x, weight, b, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);
const int C = (int)y.size(1);
const int H = (int)y.size(2);
const int W = (int)y.size(3);
long total = y.numel();
int threads = 256;
int blocks = (int)((total + threads - 1) / threads);
if (blocks > 65535) blocks = 65535;
float* yp = y.data_ptr<float>();
const float* mp = multiplier.data_ptr<float>();
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
post_kernel<<<blocks, threads, 0, stream>>>(yp, mp, total, C, H, W);
return y;
}
'''
_ext = load_inline(name='fused_conv_act_ext_final_check', cpp_sources=cpp_source, cuda_sources=cuda_source, functions=['fused_forward'], verbose=False, extra_cflags=['-O3'], extra_cuda_cflags=['-O3'])
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, multiplier_shape):
super(ModelNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.multiplier = nn.Parameter(torch.randn(multiplier_shape))
def forward(self, x):
return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.multiplier)