L2 P17 · original

correct 17_Conv2d_InstanceNorm_Divide.py

model

gpt-5.5-priority

turns / max

9 / 10

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

4.05 μs

ref runtime

4.95 μs

speedup

1.22x

started

2026-05-02T21:14:54Z

timingkernelbench.timing

kernel runtime

4.05 μs

ref runtime

4.95 μs

speedup

1.22x

kernel mean / median / std

4.05 / — / 0.11 μs

ref mean / median / std

4.95 / — / 0.02 μs

numerical precisioneval_kernel_against_ref

max abs error

7.15e-06

mean abs error

1.13e-07

max rel error

701034.94

mean rel error

0.01

gpu memoryextended_metrics.measure_memory

kernel peak

2050.10 MB

ref peak

2530.19 MB

kernel / ref

0.81x

kernel launches / fusiontorch.profiler

kernel launches

ref launches

fusion ratio (ref / kernel)

3.43x

total cuda time

11668 μs

energyNVML (pynvml)

kernel mJ / run

645.44

ref mJ / run

968.10

ratio (ref / kernel)

1.50x

avg power

159.46 W

ref avg power

195.25 W

SOL (speed-of-light)nsight_hardware_counters

SOL score (0–1)

1.000

dram util %

100.0%

compute util %

64.6%

bottleneck

memory-bound

dominant pipe

tensor-core

arithmetic intensity

—

ridge point

295.22

roofline / occupancynsight_hardware_counters

dram bandwidth

8671.84 GB/s

dram util %

100.0%

fp32 tflops

—

fp32 util %

—

fp16 tflops

—

occupancy %

310.0%

bottleneck

memory-bound

dominant pipe %

64.6%

arithmetic intensity

—

ridge point

295.22

L1 hit %

—

L2 hit %

312.5%

ld sectors / req

40.88

st sectors / req

—

regs / thread

340

smem / block

79360 B

block size

1024

peak bw

3350.00 GB/s

peak fp32 tflops

67.00

peak fp16 tflops

1979.00

warp stalls (top 5)nsight

long_scoreboard

30967.8%

short_scoreboard

400.6%

mio_throttle

80.5%

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 010.8s turn · 10.8s LLM · 0.0s tools

reasoning

**Evaluating model creation** I need to create ModelNew with the same arguments and ensure the convolution weights are properly initialized. It’s likely that I need to instantiate both Model and ModelNew with the same seed to match reference weights, which means the convolution module parameters should also be the same. I’ll define the convolution layer in ModelNew so I can load the weights. The input shape will be 128x64x128x128, and I need to compute the operations efficiently. I should think about using a custom CUDA kernel for optimization while ensuring to fuse instance normalization and division effectively. **Considering optimization techniques** I need to achieve faster performance by replacing instance normalization and division with a fused kernel while keeping the convolution operation in cuDNN. The reference model combines convolution, instance normalization, and division separately. By using torch.conv2d and implementing a custom fused instance norm/div in an extension, I might gain speed. I must also ensure the extension can effectively call at::conv2d or use F.conv2d. Additionally, I’ll try to measure correctness without revealing reference parameters, but I may need to consider fixed shapes and efficiently store convolution outputs to normalize properly. **Planning computational strategy** I need to perform a two-pass approach: first, I'll compute the convolution and store the output y, then accumulate the sum and sum of squares per N and C before normalizing. I realize that a custom convolution might be slower than cuDNN unless I can optimize it effectively, perhaps by using implicit GEMM with tiling. Alternatively, I could opt for cuDNN convolution through torch, while implementing a custom normalization step afterward.

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 114.9s turn · 14.9s LLM · 0.0s tools

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n\ntorch::Tensor inst_norm_div(torch::Tensor x, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n for (int offset = 16; offset > 0; offset >>= 1)\n val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_kernel(const float* __restrict__ x, float* __restrict__ y,\n int rows, int S, float inv_div) {\n int row = blockIdx.x;\n if (row >= rows) return;\n const float* base = x + (long long)row * S;\n float* out = y + (long long)row * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n int S4 = S >> 2;\n const float4* base4 = reinterpret_cast<const float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = sumsq / (float)S - mean * mean;\n if (var < 0.0f) var = 0.0f;\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean;\n float scale = sh_scale;\n float4* out4 = reinterpret_cast<float4*>(out);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale;\n v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale;\n v.w = (v.w - mean) * scale;\n out4[i] = v;\n }\n}\n\ntorch::Tensor inst_norm_div(torch::Tensor x, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n auto y = torch::empty_like(x);\n int64_t N = x.size(0), C = x.size(1), H = x.size(2), W = x.size(3);\n int rows = (int)(N * C);\n int S = (int)(H * W);\n float inv_div = 1.0f / (float)divide_by;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<rows, 256, 0, stream>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='inst_norm_div_ext_v1', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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 y = self.conv(x)\n return _ext.inst_norm_div(y, self.divide_by)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 27.2s turn · 7.2s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\n\ntorch::Tensor inst_norm_div(torch::Tensor x, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n for (int offset = 16; offset > 0; offset >>= 1)\n val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_kernel(const float* __restrict__ x, float* __restrict__ y,\n int rows, int S, float inv_div) {\n int row = blockIdx.x;\n if (row >= rows) return;\n const float* base = x + (long long)row * S;\n float* out = y + (long long)row * S;\n float sum = 0.0f;\n float sumsq = 0.0f;\n int S4 = S >> 2;\n const float4* base4 = reinterpret_cast<const float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = sumsq / (float)S - mean * mean;\n if (var < 0.0f) var = 0.0f;\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean;\n float scale = sh_scale;\n float4* out4 = reinterpret_cast<float4*>(out);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale;\n v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale;\n v.w = (v.w - mean) * scale;\n out4[i] = v;\n }\n}\n\ntorch::Tensor inst_norm_div(torch::Tensor x, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n auto y = torch::empty_like(x);\n int64_t N = x.size(0), C = x.size(1), H = x.size(2), W = x.size(3);\n int rows = (int)(N * C);\n int S = (int)(H * W);\n float inv_div = 1.0f / (float)divide_by;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_kernel<<<rows, 256, 0, stream>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='inst_norm_div_ext_v1', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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 y = self.conv(x)\n return _ext.inst_norm_div(y, 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=7.15e-06 mean_abs_err=1.13e-07 max_rel_err=7.01e+05

turn 3 · 1 tool call(s) · 12.2s turn · 12.2s LLM · 9,417 chars (click to expand)

turn 312.2s turn · 12.2s 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\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n for (int offset = 16; offset > 0; offset >>= 1)\n val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_kernel(const float* __restrict__ x, float* __restrict__ y,\n int rows, int S, float inv_div) {\n int row = blockIdx.x;\n const float* base = x + (long long)row * S;\n float* out = y + (long long)row * S;\n float sum = 0.0f, sumsq = 0.0f;\n int S4 = S >> 2;\n const float4* base4 = reinterpret_cast<const float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = sumsq / (float)S - mean * mean;\n var = fmaxf(var, 0.0f);\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean, scale = sh_scale;\n float4* out4 = reinterpret_cast<float4*>(out);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale;\n v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale;\n v.w = (v.w - mean) * scale;\n out4[i] = v;\n }\n}\n\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n auto yconv = at::conv2d(x, weight, bias, {1,1}, {0,0}, {1,1}, 1);\n auto y = torch::empty_like(yconv);\n int rows = (int)(yconv.size(0) * yconv.size(1));\n int S = (int)(yconv.size(2) * yconv.size(3));\n float inv_div = 1.0f / (float)divide_by;\n inst_norm_div_kernel<<<rows, 256, 0, at::cuda::getCurrentCUDAStream()>>>(yconv.data_ptr<float>(), y.data_ptr<float>(), rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv_inst_norm_div_ext_v2', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['conv_inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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.conv_inst_norm_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 'conv_inst_norm_div_ext_v2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_inst_norm_div_ext_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_17_cache/k_182bed06effb/conv_inst_norm_div_ext_v2/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_inst_norm_div_ext_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/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/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_17_cache/k_182bed06effb/conv_inst_norm_div_ext_v2/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_inst_norm_div_ext_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/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/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_17_cache/k_182bed06effb/conv_inst_norm_div_ext_v2/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_17_cache/k_182bed06effb/conv_inst_norm_div_ext_v2/cuda.cu(69): 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/tejas/PopcornBench/.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/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/conv2d.h) argument types are: (at::Tensor, at::Tensor, at::Tensor, {...}, {...}, {...}, int) auto yconv = at::conv2d(x, weight, bias, {1,1}, {0,0}, {1,1}, 1); ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_17_cache/k_182bed06effb/conv_inst_norm_div_ext_v2/cuda.cu(74): error: type name is not allowed inst_norm_div_kernel<<<rows, 256, 0, at::cuda::getCurrentCUDAStream()>>>(yconv.data_ptr<float>(), y.data_ptr<float>(), rows, S, inv_div); ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_17_cache/k_182bed06effb/conv_inst_norm_div_ext_v2/cuda.cu(74): error: expected an expression inst_norm_div_kernel<<<rows, 256, 0, at::cuda::getCurrentCUDAStream()>>>(yconv.data_ptr<float>(), y.data_ptr<float>(), rows, S, inv_div); ^ 3 errors detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_17_cache/k_182bed06effb/conv_inst_norm_div_ext_v2/cuda.cu". ninja: build stopped: subcommand failed.

turn 413.3s turn · 13.3s 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\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/ops/conv2d.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <optional>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n for (int offset = 16; offset > 0; offset >>= 1) val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31, wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int S, float inv_div) {\n int row = blockIdx.x;\n const float* base = x + (long long)row * S;\n float* out = y + (long long)row * S;\n float sum = 0.0f, sumsq = 0.0f;\n int S4 = S >> 2;\n const float4* base4 = reinterpret_cast<const float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = fmaxf(sumsq / (float)S - mean * mean, 0.0f);\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean, scale = sh_scale;\n float4* out4 = reinterpret_cast<float4*>(out);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale; v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale; v.w = (v.w - mean) * scale;\n out4[i] = v;\n }\n}\n\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n std::optional<at::Tensor> b = bias;\n std::vector<int64_t> stride = {1,1}, padding = {0,0}, dilation = {1,1};\n auto yconv = at::conv2d(x, weight, b, stride, padding, dilation, 1);\n auto y = torch::empty_like(yconv);\n int rows = (int)(yconv.size(0) * yconv.size(1));\n int S = (int)(yconv.size(2) * yconv.size(3));\n float inv_div = 1.0f / (float)divide_by;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n const float* xp = yconv.data_ptr<float>();\n float* yp = y.data_ptr<float>();\n inst_norm_div_kernel<<<rows, 256, 0, stream>>>(xp, yp, rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv_inst_norm_div_ext_v3', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['conv_inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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.conv_inst_norm_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 58.1s turn · 8.1s 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\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/ops/conv2d.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <optional>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n for (int offset = 16; offset > 0; offset >>= 1) val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31, wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int S, float inv_div) {\n int row = blockIdx.x;\n const float* base = x + (long long)row * S;\n float* out = y + (long long)row * S;\n float sum = 0.0f, sumsq = 0.0f;\n int S4 = S >> 2;\n const float4* base4 = reinterpret_cast<const float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = fmaxf(sumsq / (float)S - mean * mean, 0.0f);\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean, scale = sh_scale;\n float4* out4 = reinterpret_cast<float4*>(out);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale; v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale; v.w = (v.w - mean) * scale;\n out4[i] = v;\n }\n}\n\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n std::optional<at::Tensor> b = bias;\n std::vector<int64_t> stride = {1,1}, padding = {0,0}, dilation = {1,1};\n auto yconv = at::conv2d(x, weight, b, stride, padding, dilation, 1);\n auto y = torch::empty_like(yconv);\n int rows = (int)(yconv.size(0) * yconv.size(1));\n int S = (int)(yconv.size(2) * yconv.size(3));\n float inv_div = 1.0f / (float)divide_by;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n const float* xp = yconv.data_ptr<float>();\n float* yp = y.data_ptr<float>();\n inst_norm_div_kernel<<<rows, 256, 0, stream>>>(xp, yp, rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv_inst_norm_div_ext_v3', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['conv_inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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.conv_inst_norm_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=7.15e-06 mean_abs_err=1.13e-07 max_rel_err=7.01e+05

turn 613.2s turn · 13.2s LLM · 0.0s tools

reasoning

**Considering performance optimizations** I'm thinking about the timing and whether I should only submit the final result. Maybe there's a way to compare variants without worrying about correctness related to timing. I wonder if C++'s `at::conv2d` operates the same as `nn.Conv2d`. It might help to avoid separate allocations for `yconv` and the output. If I use the convolution output in-place after computing stats, it could lower memory usage. Okay, I’ll consider using `yconv` as the output to avoid additional memory allocation altogether. **Exploring in-place optimization** I'm examining if I can avoid extra allocations, specifically returning `yconv` without needing additional space. By modifying the convolution output directly, it seems I can effectively maintain performance. This approach keeps input and output separate, but in-place modifications might save some allocation time, which could be beneficial. I’m considering fixing the wrapper call to modify the kernel to work correctly. If I keep everything in a single vector read before the write, I can eliminate race conditions. I think this could speed up the process overall, so implementing it should be worthwhile!

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\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/ops/conv2d.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <optional>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n #pragma unroll\n for (int offset = 16; offset > 0; offset >>= 1) val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31, wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_inplace_kernel(float* __restrict__ x, int rows, int S, float inv_div) {\n int row = blockIdx.x;\n float* base = x + (long long)row * S;\n float sum = 0.0f, sumsq = 0.0f;\n int S4 = S >> 2;\n float4* base4 = reinterpret_cast<float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = fmaxf(sumsq / (float)S - mean * mean, 0.0f);\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean, scale = sh_scale;\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale; v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale; v.w = (v.w - mean) * scale;\n base4[i] = v;\n }\n}\n\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n std::optional<at::Tensor> b = bias;\n std::vector<int64_t> stride = {1,1}, padding = {0,0}, dilation = {1,1};\n auto y = at::conv2d(x, weight, b, stride, padding, dilation, 1);\n int rows = (int)(y.size(0) * y.size(1));\n int S = (int)(y.size(2) * y.size(3));\n float inv_div = 1.0f / (float)divide_by;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_inplace_kernel<<<rows, 256, 0, stream>>>(y.data_ptr<float>(), rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv_inst_norm_div_ext_v4_inplace', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['conv_inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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.conv_inst_norm_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=7.15e-06 mean_abs_err=1.13e-07 max_rel_err=7.01e+05

turn 746.1s turn · 46.1s LLM · 0.0s tools

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\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/ops/conv2d.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <optional>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n #pragma unroll\n for (int offset = 16; offset > 0; offset >>= 1) val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31, wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_inplace_kernel(float* __restrict__ x, int rows, int S, float inv_div) {\n int row = blockIdx.x;\n float* base = x + (long long)row * S;\n float sum = 0.0f, sumsq = 0.0f;\n int S4 = S >> 2;\n float4* base4 = reinterpret_cast<float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = fmaxf(sumsq / (float)S - mean * mean, 0.0f);\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean, scale = sh_scale;\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale; v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale; v.w = (v.w - mean) * scale;\n base4[i] = v;\n }\n}\n\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n std::optional<at::Tensor> b = bias;\n std::vector<int64_t> stride = {1,1}, padding = {0,0}, dilation = {1,1};\n auto y = at::conv2d(x, weight, b, stride, padding, dilation, 1);\n int rows = (int)(y.size(0) * y.size(1));\n int S = (int)(y.size(2) * y.size(3));\n float inv_div = 1.0f / (float)divide_by;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_inplace_kernel<<<rows, 256, 0, stream>>>(y.data_ptr<float>(), rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv_inst_norm_div_ext_v4_inplace', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['conv_inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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.conv_inst_norm_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 824.9s turn · 24.9s LLM · 0.0s tools · FINAL

reasoning

**Optimizing conv2d process** I’m thinking about how the host wrapper for conv2d could help reduce Python overhead and output allocation. That should improve memory usage since in-place operations can be more efficient. That's a good direction to take! I need to submit this soon. I’ll use the same kernel code but should ensure the name is unique, maybe for the final submission. Let’s get everything lined up for that!

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\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by);\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <ATen/ATen.h>\n#include <ATen/ops/conv2d.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <optional>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n #pragma unroll\n for (int offset = 16; offset > 0; offset >>= 1) val += __shfl_down_sync(0xffffffff, val, offset);\n return val;\n}\n__inline__ __device__ float block_reduce_sum(float val) {\n static __shared__ float shared[32];\n int lane = threadIdx.x & 31, wid = threadIdx.x >> 5;\n val = warp_reduce_sum(val);\n if (lane == 0) shared[wid] = val;\n __syncthreads();\n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) val = warp_reduce_sum(val);\n return val;\n}\n__global__ __launch_bounds__(256, 2)\nvoid inst_norm_div_inplace_kernel(float* __restrict__ x, int rows, int S, float inv_div) {\n int row = blockIdx.x;\n float* base = x + (long long)row * S;\n float sum = 0.0f, sumsq = 0.0f;\n int S4 = S >> 2;\n float4* base4 = reinterpret_cast<float4*>(base);\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n sum += v.x + v.y + v.z + v.w;\n sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n __shared__ float sh_mean, sh_scale;\n if (threadIdx.x == 0) {\n float mean = sum / (float)S;\n float var = fmaxf(sumsq / (float)S - mean * mean, 0.0f);\n sh_mean = mean;\n sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;\n }\n __syncthreads();\n float mean = sh_mean, scale = sh_scale;\n for (int i = threadIdx.x; i < S4; i += blockDim.x) {\n float4 v = base4[i];\n v.x = (v.x - mean) * scale; v.y = (v.y - mean) * scale;\n v.z = (v.z - mean) * scale; v.w = (v.w - mean) * scale;\n base4[i] = v;\n }\n}\n\ntorch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by) {\n const c10::cuda::CUDAGuard device_guard(x.device());\n std::optional<at::Tensor> b = bias;\n std::vector<int64_t> stride = {1,1}, padding = {0,0}, dilation = {1,1};\n auto y = at::conv2d(x, weight, b, stride, padding, dilation, 1);\n int rows = (int)(y.size(0) * y.size(1));\n int S = (int)(y.size(2) * y.size(3));\n float inv_div = 1.0f / (float)divide_by;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n inst_norm_div_inplace_kernel<<<rows, 256, 0, stream>>>(y.data_ptr<float>(), rows, S, inv_div);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv_inst_norm_div_ext_final', cpp_sources=cpp_source, cuda_sources=cuda_source,\n functions=['conv_inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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.conv_inst_norm_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: 4.05 μs Runtime stats: mean=4.05μs median=?μs std=0.11μs Numerical precision: max_abs_err=7.15e-06 mean_abs_err=1.13e-07 max_rel_err=7.01e+05 Memory: 2050.1 MB peak (ref: 2530.19 MB, ratio: 0.8103x) Kernel launches: 7 (ref: 24, fusion ratio: 3.4286) Energy: 645.44 mJ/run (ref: 968.1 mJ, ratio: 1.4999x) SOL score: 1.0

final submitted kernel (92 lines, 3,503 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>

torch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by);
'''

cuda_source = r'''
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <ATen/ops/conv2d.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <optional>

__inline__ __device__ float warp_reduce_sum(float val) {
    #pragma unroll
    for (int offset = 16; offset > 0; offset >>= 1) val += __shfl_down_sync(0xffffffff, val, offset);
    return val;
}
__inline__ __device__ float block_reduce_sum(float val) {
    static __shared__ float shared[32];
    int lane = threadIdx.x & 31, wid = threadIdx.x >> 5;
    val = warp_reduce_sum(val);
    if (lane == 0) shared[wid] = val;
    __syncthreads();
    val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;
    if (wid == 0) val = warp_reduce_sum(val);
    return val;
}
__global__ __launch_bounds__(256, 2)
void inst_norm_div_inplace_kernel(float* __restrict__ x, int rows, int S, float inv_div) {
    int row = blockIdx.x;
    float* base = x + (long long)row * S;
    float sum = 0.0f, sumsq = 0.0f;
    int S4 = S >> 2;
    float4* base4 = reinterpret_cast<float4*>(base);
    for (int i = threadIdx.x; i < S4; i += blockDim.x) {
        float4 v = base4[i];
        sum += v.x + v.y + v.z + v.w;
        sumsq += v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w;
    }
    sum = block_reduce_sum(sum);
    sumsq = block_reduce_sum(sumsq);
    __shared__ float sh_mean, sh_scale;
    if (threadIdx.x == 0) {
        float mean = sum / (float)S;
        float var = fmaxf(sumsq / (float)S - mean * mean, 0.0f);
        sh_mean = mean;
        sh_scale = rsqrtf(var + 1.0e-5f) * inv_div;
    }
    __syncthreads();
    float mean = sh_mean, scale = sh_scale;
    for (int i = threadIdx.x; i < S4; i += blockDim.x) {
        float4 v = base4[i];
        v.x = (v.x - mean) * scale; v.y = (v.y - mean) * scale;
        v.z = (v.z - mean) * scale; v.w = (v.w - mean) * scale;
        base4[i] = v;
    }
}

torch::Tensor conv_inst_norm_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divide_by) {
    const c10::cuda::CUDAGuard device_guard(x.device());
    std::optional<at::Tensor> b = bias;
    std::vector<int64_t> stride = {1,1}, padding = {0,0}, dilation = {1,1};
    auto y = at::conv2d(x, weight, b, stride, padding, dilation, 1);
    int rows = (int)(y.size(0) * y.size(1));
    int S = (int)(y.size(2) * y.size(3));
    float inv_div = 1.0f / (float)divide_by;
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    inst_norm_div_inplace_kernel<<<rows, 256, 0, stream>>>(y.data_ptr<float>(), rows, S, inv_div);
    return y;
}
'''

_ext = load_inline(name='conv_inst_norm_div_ext_final', cpp_sources=cpp_source, cuda_sources=cuda_source,
                   functions=['conv_inst_norm_div'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], 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.conv_inst_norm_div(x, self.conv.weight, self.conv.bias, self.divide_by)