correct
4_Conv2d_Mish_Mish.py
started
2026-05-04T03:13:02Z
timingkernelbench.timing
kernel mean / median / std
6.38 / — / 0.20 μs
ref mean / median / std
8.64 / — / 0.38 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
2.25x
energyNVML (pynvml)
ratio (ref / kernel)
1.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
5632.19 GB/s
warp stalls (top 5)nsight
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 Mish, and another Mish.
"""
def __init__(self, in_channels, out_channels, kernel_size):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
def forward(self, x):
x = self.conv(x)
x = torch.nn.functional.mish(x)
x = torch.nn.functional.mish(x)
return x
batch_size = 64
in_channels = 64
out_channels = 128
height = width = 256
kernel_size = 3
def get_inputs():
return [torch.rand(batch_size, in_channels, height, width)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size]turn 011.9s turn · 11.9s LLM · 0.0s tools
reasoning
**Optimizing convolution implementation**
I need to produce code for a conv2d operation with a 3x3 kernel, no padding, and bias included. The input and output dimensions are 64x64x256x256 and 64x128x254x254 respectively. The operations will be substantial, potentially around 30 billion. I might consider using a custom extension in PyTorch or leveraging torch.nn.Conv2d for efficiency. There’s a possibility of fusing activation functions for speed and effectiveness, and I'll explore how to use custom CUDA to optimize performance while maintaining simplicity.
**Exploring convolution efficiency**
I'm looking at a convolution operation that combines conv2d with two in-place mish activations using PyTorch. While torch.nn.functional.conv2d can handle this, creating a custom CUDA kernel might be necessary for performance. It could be faster to use a single activation kernel instead of two since that eliminates Python overhead. The convolution operation is demanding, potentially consuming considerable memory and time, especially due to the size of the output. Fusing the double mish with the convolution could optimize the process, but achieving efficiency with fixed dimensions is challenging.
**Considering PyTorch and TF32**
I’m thinking about PyTorch and whether the conv2d operation uses TF32, which is likely the case. When using torch.rand, it defaults to fp32. I need to ensure that the tolerances match at around 1e-4. It seems crucial to keep this in mind while implementing the convolution, as the performance and accuracy could be impacted by the differences in precision. Making sure these elements align is essential for achieving the desired results in the model.
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) · 43.6s turn · 43.6s LLM · 7,255 chars (click to expand)
turn 143.6s turn · 43.6s LLM · 0.0s tools
call → compile_kernel
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'conv_mish2_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_mish2_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_4_cache/k_15c1c402fe36/conv_mish2_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=conv_mish2_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_4_cache/k_15c1c402fe36/conv_mish2_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=conv_mish2_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_4_cache/k_15c1c402fe36/conv_mish2_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_4_cache/k_15c1c402fe36/conv_mish2_ext_v1/cuda.cu(30): 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(input, weight, bias, {1,1}, {0,0}, {1,1}, 1);
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_15c1c402fe36/conv_mish2_ext_v1/cuda.cu(35): error: type name is not allowed
mish2_inplace_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_15c1c402fe36/conv_mish2_ext_v1/cuda.cu(35): error: expected an expression
mish2_inplace_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);
^
3 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_15c1c402fe36/conv_mish2_ext_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 212.8s turn · 12.8s LLM · 0.0s tools
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 35.4s turn · 5.4s LLM · 0.0s tools
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.42e-05 mean_abs_err=4.87e-07 max_rel_err=1.99e-05
turn 45.3s turn · 5.3s LLM · 0.0s tools
call → profile_kernel
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 129714.0μs (25%) calls=1
#2: aten::convolution 129637.0μs (25%) calls=1
#3: aten::_convolution 129592.0μs (25%) calls=1
#4: aten::cudnn_convolution 120112.0μs (23%) calls=1
#5: aten::add_ 8743.0μs (2%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 7523.10 μs
--- Memory Bandwidth ---
DRAM BW: 8499.5/3350 GB/s (100.0%)
read=4147.1 write=4352.3 GB/s
Hit rates: L2=326.8%
--- Compute Throughput ---
Dominant pipe: tensor-core util=68.2%
Pipe util: FMA=68.1%, Tensor=68.2%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 310.0%
Eligible warps/cycle: 3.92
Launch: regs/thread=340, smem/blk=79104B, block=1024, grid=2424705
--- Memory Access Quality ---
Global ld coalescing: 28.50 sectors/req (28.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 7289.2% ####################
short_scoreboard 417.0% ####################
mio_throttle 73.7% ##############
--- Optimization Hints ---
-> 7289% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=28.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=28.5x (ideal=1.0) -> 28x wasted DRAM BW. Make per-warp accesses contiguous.
turn 512.1s turn · 12.1s LLM · 0.0s tools
reasoning
**Evaluating GPU Performance**
I'm analyzing the GPU time, which is around 7523 microseconds, but I’m unsure if that's good or if I need a reference to compare it against. Should I consider optimizing by using torch.compile, particularly for custom convolution? It seems the runtime might be inconsistent. The profiling shows a convolution taking 120112 microseconds, which raises questions about activation kernels. I could also use an approximate formula for the Mish function to improve efficiency.
**Exploring Kernel Optimization**
I'm looking into using the Mish function twice for better performance when the input is above a certain threshold. There might be something to gain by implementing a fully fused convolution and activation using cuDNN, but I'm not sure how to go about it. Maybe I could use at::cudnn_convolution. Fusing the bias into the activation kernel could also help eliminate unnecessary operations. I want to make sure I'm correctly mapping the output while ensuring optimal performance.
**Optimizing Memory Usage**
I’m thinking about how to save memory in PyTorch by modifying the wrapper to eliminate the bias from the convolution operation. Instead, I can add the bias directly in the activation kernel, which might enhance performance. I need to ensure that the correctness of this approach is maintained. Passing the channel and height-width should make the process faster. I really want to get this optimization right for better efficiency!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\n#include <ATen/ops/conv2d.h>\n\nvoid bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias);\n\ntorch::Tensor conv_mish2(torch::Tensor input, torch::Tensor weight, torch::Tensor bias) {\n c10::optional<at::Tensor> no_bias = c10::nullopt;\n auto y = at::conv2d(input, weight, no_bias, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n bias_mish2_inplace_cuda(y, bias);\n return y;\n}\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float mish_once(float x) {\n float sp = (x > 20.0f) ? x : log1pf(expf(x));\n return x * tanhf(sp);\n}\n\n__global__ void bias_mish2_kernel(float* __restrict__ y, const float* __restrict__ b, int64_t n, int C, int HW) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n for (int64_t i = idx; i < n; i += stride) {\n int c = (int)((i / HW) % C);\n float v = y[i] + b[c];\n v = mish_once(v);\n v = mish_once(v);\n y[i] = v;\n }\n}\n\nvoid bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias) {\n int64_t n = y.numel();\n int C = (int)y.size(1);\n int HW = (int)(y.size(2) * y.size(3));\n int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n bias_mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), bias.data_ptr<float>(), n, C, HW);\n}\n'''\n\n_ext = load_inline(name='conv_mish2_ext_v3', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv_mish2'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n def forward(self, x):\n return _ext.conv_mish2(x, self.conv.weight, self.conv.bias)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 65.2s turn · 5.2s 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_src = r'''\n#include <torch/extension.h>\n#include <ATen/ops/conv2d.h>\n\nvoid bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias);\n\ntorch::Tensor conv_mish2(torch::Tensor input, torch::Tensor weight, torch::Tensor bias) {\n c10::optional<at::Tensor> no_bias = c10::nullopt;\n auto y = at::conv2d(input, weight, no_bias, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n bias_mish2_inplace_cuda(y, bias);\n return y;\n}\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float mish_once(float x) {\n float sp = (x > 20.0f) ? x : log1pf(expf(x));\n return x * tanhf(sp);\n}\n\n__global__ void bias_mish2_kernel(float* __restrict__ y, const float* __restrict__ b, int64_t n, int C, int HW) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n for (int64_t i = idx; i < n; i += stride) {\n int c = (int)((i / HW) % C);\n float v = y[i] + b[c];\n v = mish_once(v);\n v = mish_once(v);\n y[i] = v;\n }\n}\n\nvoid bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias) {\n int64_t n = y.numel();\n int C = (int)y.size(1);\n int HW = (int)(y.size(2) * y.size(3));\n int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n bias_mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), bias.data_ptr<float>(), n, C, HW);\n}\n'''\n\n_ext = load_inline(name='conv_mish2_ext_v3', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv_mish2'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n def forward(self, x):\n return _ext.conv_mish2(x, self.conv.weight, self.conv.bias)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.42e-05 mean_abs_err=4.87e-07 max_rel_err=1.99e-05
turn 75.4s turn · 5.4s 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_src = r'''\n#include <torch/extension.h>\n#include <ATen/ops/conv2d.h>\n\nvoid bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias);\n\ntorch::Tensor conv_mish2(torch::Tensor input, torch::Tensor weight, torch::Tensor bias) {\n c10::optional<at::Tensor> no_bias = c10::nullopt;\n auto y = at::conv2d(input, weight, no_bias, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);\n bias_mish2_inplace_cuda(y, bias);\n return y;\n}\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float mish_once(float x) {\n float sp = (x > 20.0f) ? x : log1pf(expf(x));\n return x * tanhf(sp);\n}\n\n__global__ void bias_mish2_kernel(float* __restrict__ y, const float* __restrict__ b, int64_t n, int C, int HW) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n for (int64_t i = idx; i < n; i += stride) {\n int c = (int)((i / HW) % C);\n float v = y[i] + b[c];\n v = mish_once(v);\n v = mish_once(v);\n y[i] = v;\n }\n}\n\nvoid bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias) {\n int64_t n = y.numel();\n int C = (int)y.size(1);\n int HW = (int)(y.size(2) * y.size(3));\n int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n bias_mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), bias.data_ptr<float>(), n, C, HW);\n}\n'''\n\n_ext = load_inline(name='conv_mish2_ext_v3', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv_mish2'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n def forward(self, x):\n return _ext.conv_mish2(x, self.conv.weight, self.conv.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 150440.0μs (25%) calls=1
#2: aten::convolution 150391.0μs (25%) calls=1
#3: aten::_convolution 150361.0μs (25%) calls=1
#4: aten::cudnn_convolution 149505.0μs (25%) calls=1
--- Timing ---
GPU time: 6315.07 μs [↓1208.0μs ok]
--- Memory Bandwidth ---
DRAM BW: 5595.1/3350 GB/s (100.0%)
read=2688.1 write=2907.0 GB/s
Hit rates: L2=276.4%
--- Compute Throughput ---
Dominant pipe: tensor-core util=66.4% [↓1.8% REGRESSED]
Pipe util: FMA=60.9%, Tensor=66.4%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 226.4% [↓83.6% REGRESSED]
Eligible warps/cycle: 4.96
Launch: regs/thread=322, smem/blk=78080B, block=896, grid=360193
--- Memory Access Quality ---
Global ld coalescing: 24.50 sectors/req (24.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 4626.8% ####################
short_scoreboard 399.5% ####################
mio_throttle 97.4% ###################
--- Optimization Hints ---
-> 4627% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=24.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=24.5x (ideal=1.0) -> 24x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 7523.10→6315.07μs (↓1208.03) ok
Compute%: 68.22→66.38% (↓1.84) REGRESSED
Occupancy: 309.98→226.36% (↓83.63) REGRESSED
Eligible warps/cyc: 3.92→4.96 (↑1.03) ok
Ld sectors/req: 28.50→24.50 (↓4.00) ok
L2 hit%: 326.83→276.38% (↓50.46) REGRESSED
turn 812.3s turn · 12.3s LLM · 0.0s tools
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 95.3s turn · 5.3s LLM · 0.0s tools
call → run_correctness
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'conv_mish2_ext_v4_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_mish2_ext_v4_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_4_cache/k_9553053f58ed/conv_mish2_ext_v4/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_mish2_ext_v4_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_4_cache/k_9553053f58ed/conv_mish2_ext_v4/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_9553053f58ed/conv_mish2_ext_v4/main.cpp: In function 'at::Tensor conv_mish2(at::Tensor, at::Tensor, at::Tensor)':
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_9553053f58ed/conv_mish2_ext_v4/main.cpp:11:28: error: too few arguments to function 'void bias_mish2_inplace_cuda(at::Tensor, at::Tensor)'
11 | bias_mish2_inplace_cuda(y);
| ~~~~~~~~~~~~~~~~~~~~~~~^~~
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_9553053f58ed/conv_mish2_ext_v4/main.cpp:6:6: note: declared here
6 | void bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias);
| ^~~~~~~~~~~~~~~~~~~~~~~
[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_mish2_ext_v4_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_4_cache/k_9553053f58ed/conv_mish2_ext_v4/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.
turn 105.7s turn · 5.7s LLM · 0.0s tools
reasoning
**Addressing code issues**
Oh no, I realized I left out bias in the correctness code! The compiled version had it right, so I need to rerun it to fix that. It's important that I get this right so that the outputs are as accurate as possible. I'll make sure to double-check everything this time to avoid missing anything else. Let's tackle this and make it better!
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.42e-05 mean_abs_err=4.87e-07 max_rel_err=1.99e-05
turn 114.8s turn · 4.8s LLM · 0.0s tools
call → profile_kernel
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 119250.0μs (25%) calls=1
#2: aten::convolution 119203.0μs (25%) calls=1
#3: aten::_convolution 119166.0μs (25%) calls=1
#4: aten::cudnn_convolution 118442.0μs (25%) calls=1
--- Timing ---
GPU time: 6284.96 μs [↓30.1μs ok]
--- Memory Bandwidth ---
DRAM BW: 5644.7/3350 GB/s (100.0%)
read=2710.4 write=2934.2 GB/s
Hit rates: L2=283.4%
--- Compute Throughput ---
Dominant pipe: tensor-core util=63.8% [↓2.5% REGRESSED]
Pipe util: FMA=46.1%, Tensor=63.8%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 223.2% [↓3.1% REGRESSED]
Eligible warps/cycle: 2.22
Launch: regs/thread=314, smem/blk=78080B, block=896, grid=302850
--- Memory Access Quality ---
Global ld coalescing: 26.48 sectors/req (26.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 6256.0% ####################
short_scoreboard 372.2% ####################
mio_throttle 71.2% ##############
--- Optimization Hints ---
-> 6256% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=26.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=26.5x (ideal=1.0) -> 26x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 6315.07→6284.96μs (↓30.11) ok
Compute%: 66.38→63.83% (↓2.55) REGRESSED
Occupancy: 226.36→223.23% (↓3.12) REGRESSED
Eligible warps/cyc: 4.96→2.22 (↓2.74) REGRESSED
Ld sectors/req: 24.50→26.48 (↑1.98) REGRESSED
L2 hit%: 276.38→283.42% (↑7.04) ok
turn 127.0s turn · 7.0s LLM · 0.0s tools
reasoning
**Optimizing CUDA kernel**
I'm confirming that v4b is correct, with a runtime around 6285. Now, I need to decide if further optimization is possible. The task states that a custom CUDA kernel can replace PyTorch, which currently uses at::conv2d and a custom mish function. Usually, it's acceptable for custom kernels to only involve activations, but could we use torch.compile? It might not work since using at::conv2d in C++ could get tricky. Still, the potential performance boost seems significant, so I should submit v4b after doing a static check.
call → static_check
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 134.9s turn · 4.9s LLM · 0.0s tools · FINAL
call → submit_kernel
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 6.38 μs
Runtime stats: mean=6.38μs median=?μs std=0.20μs
Numerical precision: max_abs_err=1.42e-05 mean_abs_err=4.87e-07 max_rel_err=1.99e-05
Memory: 4096.97 MB peak (ref: 5088.81 MB, ratio: 0.8051x)
Kernel launches: 4 (ref: 9, fusion ratio: 2.25)
Energy: 965.46 mJ/run (ref: 964.98 mJ, ratio: 0.9995x)
SOL score: 1.0
final submitted kernel (60 lines, 2,052 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/ops/conv2d.h>
void bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias);
torch::Tensor conv_mish2(torch::Tensor input, torch::Tensor weight, torch::Tensor bias) {
c10::optional<at::Tensor> no_bias = c10::nullopt;
auto y = at::conv2d(input, weight, no_bias, at::IntArrayRef({1,1}), at::IntArrayRef({0,0}), at::IntArrayRef({1,1}), 1);
bias_mish2_inplace_cuda(y, bias);
return y;
}
'''
cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
__device__ __forceinline__ float mish_once(float x) {
float sp = (x > 20.0f) ? x : log1pf(expf(x));
return x * tanhf(sp);
}
__global__ void bias_mish2_rows_kernel(float* __restrict__ y, const float* __restrict__ b, int HW, int C) {
int nc = blockIdx.x;
int c = nc % C;
float bv = b[c];
int64_t base = (int64_t)nc * HW;
for (int hw = threadIdx.x; hw < HW; hw += blockDim.x) {
float v = y[base + hw] + bv;
v = mish_once(v);
v = mish_once(v);
y[base + hw] = v;
}
}
void bias_mish2_inplace_cuda(torch::Tensor y, torch::Tensor bias) {
int C = (int)y.size(1);
int HW = (int)(y.size(2) * y.size(3));
int NC = (int)(y.size(0) * y.size(1));
int threads = 256;
bias_mish2_rows_kernel<<<NC, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), bias.data_ptr<float>(), HW, C);
}
'''
_ext = load_inline(name='conv_mish2_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv_mish2'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size):
super(ModelNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
def forward(self, x):
return _ext.conv_mish2(x, self.conv.weight, self.conv.bias)