correct
17_Conv2d_InstanceNorm_Divide.py
started
2026-05-04T07:01:38Z
timingkernelbench.timing
kernel mean / median / std
9.67 / — / 0.14 μs
ref mean / median / std
10.10 / — / 0.02 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
3.43x
energyNVML (pynvml)
ratio (ref / kernel)
1.01x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
7975.10 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 Instance Normalization, and divides by a constant.
"""
def __init__(self, in_channels, out_channels, kernel_size, divide_by):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.instance_norm = nn.InstanceNorm2d(out_channels)
self.divide_by = divide_by
def forward(self, x):
x = self.conv(x)
x = self.instance_norm(x)
x = x / self.divide_by
return x
batch_size = 128
in_channels = 64
out_channels = 128
height = width = 128
kernel_size = 3
divide_by = 2.0
def get_inputs():
return [torch.rand(batch_size, in_channels, height, width)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size, divide_by]turn 010.1s turn · 10.1s LLM · 0.0s tools
reasoning
**Evaluating CUDA implementation**
I need to craft a Python model using the CUDA extension and determine how to initialize weights properly. For ModelNew, I should have convolution weights randomized like the reference model. I'll probably need to copy the state_dict between models and define matching parameters. The forward method needs to support inputs and implement convolution with instance normalization. Also, I must ensure it's optimized and that the calculations remain efficient, given the high compute demands of convolution operations in PyTorch.
**Optimizing convolution operations**
I need to calculate y = (conv - mean) / sqrt(var + eps) and then divide. The challenge is that I can't store convolution outputs directly, so I might reference them during normalization. Should I use cuDNN for faster performance or rely on a custom kernel? Calling at::conv2d might be an option, but I want to ensure I'm not falling back on slower methods. Custom normalization could speed things up, especially since instance normalization involves multiple passes. I'll explore the best way to integrate these efficiently.
**Ensuring correct instance normalization**
I need to confirm that instance normalization computes the mean and variance of the convolution output correctly. I’m considering whether to use Torch operations directly in C++ for efficiency. Maybe implementing a custom kernel could help reduce processing time as well. I really want to make sure everything works smoothly with the calculations while optimizing for performance, so keeping track of these details is crucial to ensure the correctness of the results.
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) · 21.6s turn · 21.6s LLM · 9,041 chars (click to expand)
turn 121.6s turn · 21.6s 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_src = r'''\n#include <torch/extension.h>\n#include <ATen/ops/conv2d.h>\n\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide);\n\ntorch::Tensor fused_conv_instnorm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide) {\n auto y = at::conv2d(x, weight, bias, {1,1}, {0,0}, {1,1}, 1);\n return inst_norm_div_cuda(y, divide);\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__global__ void inst_norm_div_kernel(float* __restrict__ y, int total_nc, int S, float inv_div) {\n int nc = blockIdx.x;\n if (nc >= total_nc) return;\n float* base = y + (long long)nc * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n float v = base[i];\n sum += v;\n sumsq = fmaf(v, v, sumsq);\n }\n __shared__ float sh_sum[256];\n __shared__ float sh_sumsq[256];\n sh_sum[threadIdx.x] = sum;\n sh_sumsq[threadIdx.x] = sumsq;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (threadIdx.x < stride) {\n sh_sum[threadIdx.x] += sh_sum[threadIdx.x + stride];\n sh_sumsq[threadIdx.x] += sh_sumsq[threadIdx.x + stride];\n }\n __syncthreads();\n }\n float mean = sh_sum[0] / (float)S;\n float var = sh_sumsq[0] / (float)S - mean * mean;\n var = var < 0.0f ? 0.0f : var;\n float scale = rsqrtf(var + 1.0e-5f) * inv_div;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n base[i] = (base[i] - mean) * scale;\n }\n}\n\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide) {\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 const int S = H * W;\n const int total_nc = N * C;\n const int threads = 256;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<total_nc, threads, 0, stream>>>(y.data_ptr<float>(), total_nc, S, (float)(1.0 / divide));\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_in_v1', cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=['fused_conv_instnorm_div'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, divide_by):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.instance_norm = nn.InstanceNorm2d(out_channels)\n self.divide_by = float(divide_by)\n def forward(self, x):\n return _ext.fused_conv_instnorm_div(x, self.conv.weight, self.conv.bias, self.divide_by)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'fused_conv_in_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv_in_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_17_cache/k_1fba6772d6f0/fused_conv_in_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=fused_conv_in_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_17_cache/k_1fba6772d6f0/fused_conv_in_v1/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_17_cache/k_1fba6772d6f0/fused_conv_in_v1/main.cpp: In function 'at::Tensor fused_conv_instnorm_div(at::Tensor, at::Tensor, at::Tensor, double)':
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_17_cache/k_1fba6772d6f0/fused_conv_in_v1/main.cpp:9:24: 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
9 | auto y = at::conv2d(x, weight, 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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_17_cache/k_1fba6772d6f0/fused_conv_in_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=fused_conv_in_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_17_cache/k_1fba6772d6f0/fused_conv_in_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.
turn 26.5s turn · 6.5s LLM · 0.0s tools
reasoning
**Considering code improvements**
I need to fix the `std::optional` implementation. It seems there's a potential issue or improvement there. I'm also thinking about making the cache name more unique. That could help avoid conflicts down the line. It’s fascinating how small changes can significantly impact the clarity and efficiency of the code. Maybe I should brainstorm some naming conventions that keep it clear yet distinctive. Those adjustments could really enhance the overall structure!
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\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide);\n\ntorch::Tensor fused_conv_instnorm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide) {\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 return inst_norm_div_cuda(y, divide);\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__global__ void inst_norm_div_kernel(float* __restrict__ y, int total_nc, int S, float inv_div) {\n int nc = blockIdx.x;\n float* base = y + (long long)nc * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n float v = base[i];\n sum += v;\n sumsq = fmaf(v, v, sumsq);\n }\n __shared__ float sh_sum[256];\n __shared__ float sh_sumsq[256];\n sh_sum[threadIdx.x] = sum;\n sh_sumsq[threadIdx.x] = sumsq;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (threadIdx.x < stride) {\n sh_sum[threadIdx.x] += sh_sum[threadIdx.x + stride];\n sh_sumsq[threadIdx.x] += sh_sumsq[threadIdx.x + stride];\n }\n __syncthreads();\n }\n float mean = sh_sum[0] / (float)S;\n float var = sh_sumsq[0] / (float)S - mean * mean;\n var = var < 0.0f ? 0.0f : var;\n float scale = rsqrtf(var + 1.0e-5f) * inv_div;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n base[i] = (base[i] - mean) * scale;\n }\n}\n\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide) {\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 const int S = H * W;\n const int total_nc = N * C;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<total_nc, 256, 0, stream>>>(y.data_ptr<float>(), total_nc, S, (float)(1.0 / divide));\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_in_v2', cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=['fused_conv_instnorm_div'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, divide_by):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.instance_norm = nn.InstanceNorm2d(out_channels)\n self.divide_by = float(divide_by)\n def forward(self, x):\n return _ext.fused_conv_instnorm_div(x, self.conv.weight, self.conv.bias, self.divide_by)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 35.3s turn · 5.3s 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\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide);\n\ntorch::Tensor fused_conv_instnorm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide) {\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 return inst_norm_div_cuda(y, divide);\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__global__ void inst_norm_div_kernel(float* __restrict__ y, int total_nc, int S, float inv_div) {\n int nc = blockIdx.x;\n float* base = y + (long long)nc * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n float v = base[i];\n sum += v;\n sumsq = fmaf(v, v, sumsq);\n }\n __shared__ float sh_sum[256];\n __shared__ float sh_sumsq[256];\n sh_sum[threadIdx.x] = sum;\n sh_sumsq[threadIdx.x] = sumsq;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (threadIdx.x < stride) {\n sh_sum[threadIdx.x] += sh_sum[threadIdx.x + stride];\n sh_sumsq[threadIdx.x] += sh_sumsq[threadIdx.x + stride];\n }\n __syncthreads();\n }\n float mean = sh_sum[0] / (float)S;\n float var = sh_sumsq[0] / (float)S - mean * mean;\n var = var < 0.0f ? 0.0f : var;\n float scale = rsqrtf(var + 1.0e-5f) * inv_div;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n base[i] = (base[i] - mean) * scale;\n }\n}\n\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide) {\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 const int S = H * W;\n const int total_nc = N * C;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<total_nc, 256, 0, stream>>>(y.data_ptr<float>(), total_nc, S, (float)(1.0 / divide));\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_in_v2', cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=['fused_conv_instnorm_div'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, divide_by):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.instance_norm = nn.InstanceNorm2d(out_channels)\n self.divide_by = float(divide_by)\n def forward(self, x):\n return _ext.fused_conv_instnorm_div(x, self.conv.weight, self.conv.bias, self.divide_by)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=8.11e-06 mean_abs_err=1.35e-07 max_rel_err=5.38e+05
turn 46.4s turn · 6.4s 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\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide);\n\ntorch::Tensor fused_conv_instnorm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide) {\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 return inst_norm_div_cuda(y, divide);\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__global__ void inst_norm_div_kernel(float* __restrict__ y, int total_nc, int S, float inv_div) {\n int nc = blockIdx.x;\n float* base = y + (long long)nc * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n float v = base[i];\n sum += v;\n sumsq = fmaf(v, v, sumsq);\n }\n __shared__ float sh_sum[256];\n __shared__ float sh_sumsq[256];\n sh_sum[threadIdx.x] = sum;\n sh_sumsq[threadIdx.x] = sumsq;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (threadIdx.x < stride) {\n sh_sum[threadIdx.x] += sh_sum[threadIdx.x + stride];\n sh_sumsq[threadIdx.x] += sh_sumsq[threadIdx.x + stride];\n }\n __syncthreads();\n }\n float mean = sh_sum[0] / (float)S;\n float var = sh_sumsq[0] / (float)S - mean * mean;\n var = var < 0.0f ? 0.0f : var;\n float scale = rsqrtf(var + 1.0e-5f) * inv_div;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n base[i] = (base[i] - mean) * scale;\n }\n}\n\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide) {\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 const int S = H * W;\n const int total_nc = N * C;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<total_nc, 256, 0, stream>>>(y.data_ptr<float>(), total_nc, S, (float)(1.0 / divide));\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_in_v2', cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=['fused_conv_instnorm_div'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, divide_by):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.instance_norm = nn.InstanceNorm2d(out_channels)\n self.divide_by = float(divide_by)\n def forward(self, x):\n return _ext.fused_conv_instnorm_div(x, self.conv.weight, self.conv.bias, self.divide_by)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 110622.0μs (25%) calls=1
#2: aten::convolution 110574.0μs (25%) calls=1
#3: aten::_convolution 110540.0μs (25%) calls=1
#4: aten::cudnn_convolution 99443.0μs (23%) calls=1
#5: aten::add_ 10152.0μs (2%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 4508.67 μs
--- Memory Bandwidth ---
DRAM BW: 7991.3/3350 GB/s (100.0%)
read=4222.5 write=3768.8 GB/s
Hit rates: L2=318.2%
--- Compute Throughput ---
Dominant pipe: tensor-core util=64.5%
Pipe util: FMA=41.7%, Tensor=64.5%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 310.5%
Eligible warps/cycle: 2.93
Launch: regs/thread=332, smem/blk=81152B, block=1024, grid=1179652
--- Memory Access Quality ---
Global ld coalescing: 28.99 sectors/req (29.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 17726.8% ####################
short_scoreboard 360.4% ####################
mio_throttle 78.9% ###############
--- Optimization Hints ---
-> 17727% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=29.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=29.0x (ideal=1.0) -> 29x wasted DRAM BW. Make per-warp accesses contiguous.
turn 517.1s turn · 17.1s LLM · 0.0s tools
reasoning
**Evaluating custom implementation**
It sounds like I'm considering using a C++ wrapper for a batch normalization function. However, I wonder if a custom implementation might be faster. I need to check if using at::conv2d is permitted by static analysis, and I'm questioning whether that's considered reward hacking. Since my goal is to replace only the normalization with a custom kernel, that should be fine. I also have to consider if the static check will flag calls to Torch functions. Let's proceed with this plan!
call → static_check
callstatic_check
{
"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\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide);\n\ntorch::Tensor fused_conv_instnorm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide) {\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 return inst_norm_div_cuda(y, divide);\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__global__ void inst_norm_div_kernel(float* __restrict__ y, int total_nc, int S, float inv_div) {\n int nc = blockIdx.x;\n float* base = y + (long long)nc * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n float v = base[i];\n sum += v;\n sumsq = fmaf(v, v, sumsq);\n }\n __shared__ float sh_sum[256];\n __shared__ float sh_sumsq[256];\n sh_sum[threadIdx.x] = sum;\n sh_sumsq[threadIdx.x] = sumsq;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (threadIdx.x < stride) {\n sh_sum[threadIdx.x] += sh_sum[threadIdx.x + stride];\n sh_sumsq[threadIdx.x] += sh_sumsq[threadIdx.x + stride];\n }\n __syncthreads();\n }\n float mean = sh_sum[0] / (float)S;\n float var = sh_sumsq[0] / (float)S - mean * mean;\n var = var < 0.0f ? 0.0f : var;\n float scale = rsqrtf(var + 1.0e-5f) * inv_div;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n base[i] = (base[i] - mean) * scale;\n }\n}\n\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide) {\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 const int S = H * W;\n const int total_nc = N * C;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<total_nc, 256, 0, stream>>>(y.data_ptr<float>(), total_nc, S, (float)(1.0 / divide));\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_in_v2', cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=['fused_conv_instnorm_div'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, divide_by):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.instance_norm = nn.InstanceNorm2d(out_channels)\n self.divide_by = float(divide_by)\n def forward(self, x):\n return _ext.fused_conv_instnorm_div(x, self.conv.weight, self.conv.bias, self.divide_by)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 622.2s turn · 22.2s LLM · 0.0s tools · FINAL
reasoning
**Evaluating output execution**
I'm checking the final output to see if it has a unique extension name. It seems like the evaluation might only execute once. I think caching is okay, but I wonder what happens with multiple submissions — I guess a stable name would be necessary. Also, if I need to use `std::optional`, do I need to include the optional library? I noticed something odd with profile occupancy regulations, but it shouldn't be an issue with cuDNN.
call → submit_kernel
callsubmit_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\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide);\n\ntorch::Tensor fused_conv_instnorm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide) {\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 return inst_norm_div_cuda(y, divide);\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__global__ void inst_norm_div_kernel(float* __restrict__ y, int total_nc, int S, float inv_div) {\n int nc = blockIdx.x;\n float* base = y + (long long)nc * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n float v = base[i];\n sum += v;\n sumsq = fmaf(v, v, sumsq);\n }\n __shared__ float sh_sum[256];\n __shared__ float sh_sumsq[256];\n sh_sum[threadIdx.x] = sum;\n sh_sumsq[threadIdx.x] = sumsq;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (threadIdx.x < stride) {\n sh_sum[threadIdx.x] += sh_sum[threadIdx.x + stride];\n sh_sumsq[threadIdx.x] += sh_sumsq[threadIdx.x + stride];\n }\n __syncthreads();\n }\n float mean = sh_sum[0] / (float)S;\n float var = sh_sumsq[0] / (float)S - mean * mean;\n var = var < 0.0f ? 0.0f : var;\n float scale = rsqrtf(var + 1.0e-5f) * inv_div;\n for (int i = threadIdx.x; i < S; i += blockDim.x) {\n base[i] = (base[i] - mean) * scale;\n }\n}\n\ntorch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide) {\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 const int S = H * W;\n const int total_nc = N * C;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<total_nc, 256, 0, stream>>>(y.data_ptr<float>(), total_nc, S, (float)(1.0 / divide));\n return y;\n}\n'''\n\n_ext = load_inline(name='fused_conv_in_final', cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=['fused_conv_instnorm_div'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, divide_by):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.instance_norm = nn.InstanceNorm2d(out_channels)\n self.divide_by = float(divide_by)\n def forward(self, x):\n return _ext.fused_conv_instnorm_div(x, self.conv.weight, self.conv.bias, self.divide_by)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 9.67 μs
Runtime stats: mean=9.67μs median=?μs std=0.14μs
Numerical precision: max_abs_err=8.11e-06 mean_abs_err=1.35e-07 max_rel_err=5.38e+05
Memory: 1537.81 MB peak (ref: 2530.19 MB, ratio: 0.6078x)
Kernel launches: 7 (ref: 24, fusion ratio: 3.4286)
Energy: 5897.76 mJ/run (ref: 5980.24 mJ, ratio: 1.014x)
SOL score: 1.0
final submitted kernel (78 lines, 2,855 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>
torch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide);
torch::Tensor fused_conv_instnorm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide) {
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);
return inst_norm_div_cuda(y, divide);
}
'''
cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void inst_norm_div_kernel(float* __restrict__ y, int total_nc, int S, float inv_div) {
int nc = blockIdx.x;
float* base = y + (long long)nc * S;
float sum = 0.0f;
float sumsq = 0.0f;
for (int i = threadIdx.x; i < S; i += blockDim.x) {
float v = base[i];
sum += v;
sumsq = fmaf(v, v, sumsq);
}
__shared__ float sh_sum[256];
__shared__ float sh_sumsq[256];
sh_sum[threadIdx.x] = sum;
sh_sumsq[threadIdx.x] = sumsq;
__syncthreads();
for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {
if (threadIdx.x < stride) {
sh_sum[threadIdx.x] += sh_sum[threadIdx.x + stride];
sh_sumsq[threadIdx.x] += sh_sumsq[threadIdx.x + stride];
}
__syncthreads();
}
float mean = sh_sum[0] / (float)S;
float var = sh_sumsq[0] / (float)S - mean * mean;
var = var < 0.0f ? 0.0f : var;
float scale = rsqrtf(var + 1.0e-5f) * inv_div;
for (int i = threadIdx.x; i < S; i += blockDim.x) {
base[i] = (base[i] - mean) * scale;
}
}
torch::Tensor inst_norm_div_cuda(torch::Tensor y, double divide) {
const int N = (int)y.size(0);
const int C = (int)y.size(1);
const int H = (int)y.size(2);
const int W = (int)y.size(3);
const int S = H * W;
const int total_nc = N * C;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
inst_norm_div_kernel<<<total_nc, 256, 0, stream>>>(y.data_ptr<float>(), total_nc, S, (float)(1.0 / divide));
return y;
}
'''
_ext = load_inline(name='fused_conv_in_final', cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=['fused_conv_instnorm_div'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, divide_by):
super(ModelNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.instance_norm = nn.InstanceNorm2d(out_channels)
self.divide_by = float(divide_by)
def forward(self, x):
return _ext.fused_conv_instnorm_div(x, self.conv.weight, self.conv.bias, self.divide_by)