correct
32_Conv2d_Scaling_Min.py
started
2026-05-02T16:46:32Z
timingkernelbench.timing
kernel mean / median / std
6.47 / — / 0.04 μs
ref mean / median / std
7.94 / — / 0.02 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1.12x
energyNVML (pynvml)
ratio (ref / kernel)
1.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
9063.20 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):
"""
Model that performs a convolution, scales the output, and then applies a minimum operation.
"""
def __init__(self, in_channels, out_channels, kernel_size, scale_factor):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.scale_factor = scale_factor
def forward(self, x):
"""
Args:
x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).
Returns:
torch.Tensor: Output tensor of shape (batch_size, out_channels, height, width).
"""
x = self.conv(x)
x = x * self.scale_factor
x = torch.min(x, dim=1, keepdim=True)[0] # Minimum along channel dimension
return x
batch_size = 64
in_channels = 64
out_channels = 128
height = width = 256
kernel_size = 3
scale_factor = 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, scale_factor]turn 012.0s turn · 12.0s LLM · 0.0s tools
reasoning
**Creating ModelNew module**
I'm focused on creating a Python module called ModelNew. First, I need to determine if it should evaluate copies of weights and whether it uses the same constructor as the Model, likely employing state_dict.
I'll instantiate both Model and ModelNew with the same arguments and copy the weights. For the convolution operation, I need to define parameters like kernel size and strides to match. I’m aiming for a custom forward method that processes data efficiently without taking too much computational power, which could get complicated.
**Exploring convolution options**
I’m considering using PyTorch's Conv2d, which likely runs really fast with cuDNN. However, a custom direct convolution might be slower. The reference suggests that a direct fused implementation could lower memory usage, yet it might be more compute-intensive with around 300G MACs, potentially taking about 3ms at 100 TFLOPS. The memory for weights is relatively small, but the input is quite large at 1GB. I’m wondering if using torch conv is a better option, considering the need for custom CUDA implementations.
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) · 67.3s turn · 67.3s LLM · 8,846 chars (click to expand)
turn 167.3s turn · 67.3s 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_src = r'''\n#include <torch/extension.h>\n#include <vector>\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {\n auto y = at::conv2d(x, weight, bias, {1,1}, {0,0}, {1,1}, 1);\n return reduce_min_scale_cuda(y, scale);\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#include <float.h>\n\n__global__ void min_scale_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int C, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * C * HW + hw);\n float mn = p[0] * scale;\n #pragma unroll 8\n for (int c = 1; c < 128; ++c) {\n float v = p[(long long)c * HW] * scale;\n mn = fminf(mn, v);\n }\n out[idx] = mn;\n}\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {\n TORCH_CHECK(y.is_cuda(), \"y must be CUDA\");\n TORCH_CHECK(y.scalar_type() == at::kFloat, \"only float32 supported\");\n int64_t N = y.size(0);\n int64_t C = y.size(1);\n int64_t H = y.size(2);\n int64_t W = y.size(3);\n auto out = torch::empty({N, 1, H, W}, y.options());\n int total = (int)(N * H * W);\n int HW = (int)(H * W);\n const int threads = 256;\n const int blocks = (total + threads - 1) / threads;\n min_scale_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, (float)scale);\n C10_CUDA_KERNEL_LAUNCH_CHECK();\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_conv_min_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, scale_factor):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.scale_factor = float(scale_factor)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'fused_conv_min_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv_min_ext_v1 -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_32_cache/k_f0896d9e0dbf/fused_conv_min_ext_v1/main.cpp -o main.o
FAILED: [code=1] main.o
/opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv_min_ext_v1 -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_32_cache/k_f0896d9e0dbf/fused_conv_min_ext_v1/main.cpp -o main.o
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_32_cache/k_f0896d9e0dbf/fused_conv_min_ext_v1/main.cpp: In function 'at::Tensor fused_forward(at::Tensor, at::Tensor, at::Tensor, double)':
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_32_cache/k_f0896d9e0dbf/fused_conv_min_ext_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/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/ATen/Functions.h:607,
from /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ExpandUtils.h:5,
from /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/input_metadata.h:4,
from /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/function.h:8,
from /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/custom_function.h:8,
from /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:6,
from /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/extension.h:6,
from /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_32_cache/k_f0896d9e0dbf/fused_conv_min_ext_v1/main.cpp:1:
/scratch/tejas/PopcornBench/.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/tejas/PopcornBench/.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_min_ext_v1 -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_32_cache/k_f0896d9e0dbf/fused_conv_min_ext_v1/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ninja: build stopped: subcommand failed.
turn 27.8s turn · 7.8s 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 <vector>\n#include <optional>\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {\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 reduce_min_scale_cuda(y, scale);\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#include <float.h>\n\n__global__ void min_scale_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int C, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * C * HW + hw);\n float mn = p[0] * scale;\n #pragma unroll\n for (int c = 1; c < 128; ++c) {\n float v = p[(long long)c * HW] * scale;\n mn = fminf(mn, v);\n }\n out[idx] = mn;\n}\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {\n int64_t N = y.size(0);\n int64_t C = y.size(1);\n int64_t H = y.size(2);\n int64_t W = y.size(3);\n auto out = torch::empty({N, 1, H, W}, y.options());\n int total = (int)(N * H * W);\n int HW = (int)(H * W);\n const int threads = 256;\n const int blocks = (total + threads - 1) / threads;\n min_scale_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, (float)scale);\n C10_CUDA_KERNEL_LAUNCH_CHECK();\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_conv_min_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, scale_factor):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.scale_factor = float(scale_factor)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 35.2s 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 <vector>\n#include <optional>\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {\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 reduce_min_scale_cuda(y, scale);\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#include <float.h>\n\n__global__ void min_scale_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int C, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * C * HW + hw);\n float mn = p[0] * scale;\n #pragma unroll\n for (int c = 1; c < 128; ++c) {\n float v = p[(long long)c * HW] * scale;\n mn = fminf(mn, v);\n }\n out[idx] = mn;\n}\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {\n int64_t N = y.size(0);\n int64_t C = y.size(1);\n int64_t H = y.size(2);\n int64_t W = y.size(3);\n auto out = torch::empty({N, 1, H, W}, y.options());\n int total = (int)(N * H * W);\n int HW = (int)(H * W);\n const int threads = 256;\n const int blocks = (total + threads - 1) / threads;\n min_scale_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, (float)scale);\n C10_CUDA_KERNEL_LAUNCH_CHECK();\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_conv_min_ext_v2c', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, scale_factor):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.scale_factor = float(scale_factor)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 410.8s turn · 10.8s LLM · 0.0s tools
reasoning
**Considering performance optimization**
I need to focus on performance and ensure that I'm submitting only the final returns. I see that there might be opportunities to optimize and reduce, but I wonder if what I have is already sufficient. So, I think we should submit, but first, a static check is necessary just to be sure everything's in order. It's essential to make sure that everything runs smoothly before moving forward!
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 <vector>\n#include <optional>\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {\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 reduce_min_scale_cuda(y, scale);\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#include <float.h>\n\n__global__ void min_scale_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int C, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * C * HW + hw);\n float mn = p[0] * scale;\n #pragma unroll\n for (int c = 1; c < 128; ++c) {\n float v = p[(long long)c * HW] * scale;\n mn = fminf(mn, v);\n }\n out[idx] = mn;\n}\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {\n int64_t N = y.size(0);\n int64_t C = y.size(1);\n int64_t H = y.size(2);\n int64_t W = y.size(3);\n auto out = torch::empty({N, 1, H, W}, y.options());\n int total = (int)(N * H * W);\n int HW = (int)(H * W);\n const int threads = 256;\n const int blocks = (total + threads - 1) / threads;\n min_scale_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, (float)scale);\n C10_CUDA_KERNEL_LAUNCH_CHECK();\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_conv_min_ext_v2s', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, scale_factor):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.scale_factor = float(scale_factor)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 5 · 1 tool call(s) · 14.8s turn · 14.8s LLM · 6,368 chars (click to expand)
turn 514.8s turn · 14.8s LLM · 0.0s tools
reasoning
**Considering performance optimizations**
I need to think about whether to measure the submission only. Before finalizing, I might want to consider a direct fused method for speed, but there's a high risk involved. The submit call returns no reference, so we may need to run it once at the end. However, if the overhead is the same, then reducing the kernel might not yield better performance compared to the optimized Torch approach. There are also calculations around multiplying and scaling that could improve efficiency.
**Exploring kernel optimizations**
I'm considering that multiplication after the min function is fine for selected values, like with +2. However, for negatives, using max(x)*scale seems necessary. There’s the question of handling NaNs because convolution shouldn’t produce any. I wonder if fminf's behavior differs from torch.min in terms of NaN. I'm also pondering making our construction more flexible with out_channels while evaluating the best way to optimize for memory and threads. Maybe I won't fully unroll loops if it complicates the logic.
**Evaluating convolution strategies**
I’m leaning towards using C for safety in the loop since unrolling it with the C variable might not be reliable. Specializing if C equals 128 could be a good idea, and I might want to check that condition. The output of at::conv2d might be non-contiguous, while NCHW is likely contiguous. If we’re using channels_last with cudnn, should I call at::cudnn_convolution? Hmm, maybe I should just go with at::conv2d and then reduce, but the output tensor allocation seems quite large. Reusing it isn’t feasible, though.
**Understanding compilation options**
I’m considering whether to use torch.compile but quickly deciding against it. Instead, I think I should focus on improving positive scaling and ensuring correctness during the compilation process. It's important for me to think through how these elements interact with each other, making sure everything is functioning as intended. There’s a lot to think about, but I feel confident that this approach will lead to better outcomes.
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 <optional>\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {\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 reduce_min_scale_cuda(y, scale);\n}\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n#include <float.h>\n\n__global__ void min_scale_pos_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * 128 * HW + hw);\n float mn = p[0];\n #pragma unroll\n for (int c = 1; c < 128; ++c) {\n float v = p[(long long)c * HW];\n mn = fminf(mn, v);\n }\n out[idx] = mn * scale;\n}\n\n__global__ void min_scale_generic_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int C, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * C * HW + hw);\n float best = p[0];\n if (scale >= 0.0f) {\n for (int c = 1; c < C; ++c) best = fminf(best, p[(long long)c * HW]);\n } else {\n for (int c = 1; c < C; ++c) best = fmaxf(best, p[(long long)c * HW]);\n }\n out[idx] = best * scale;\n}\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {\n int64_t N = y.size(0), C = y.size(1), H = y.size(2), W = y.size(3);\n auto out = torch::empty({N, 1, H, W}, y.options());\n int total = (int)(N * H * W);\n int HW = (int)(H * W);\n const int threads = 256;\n const int blocks = (total + threads - 1) / threads;\n float sc = (float)scale;\n if (C == 128 && sc >= 0.0f) {\n min_scale_pos_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, HW, sc);\n } else {\n min_scale_generic_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, sc);\n }\n C10_CUDA_KERNEL_LAUNCH_CHECK();\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_conv_min_ext_v3c', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, scale_factor):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.scale_factor = float(scale_factor)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 68.7s turn · 8.7s LLM · 0.0s tools
reasoning
**Considering function customizability**
I'm thinking about whether I should submit a runtime or if using at::conv2d is too standard, which might not fit the custom requirement. It seems like there’s a distinction between static and dynamic functions, and I should check which one is more appropriate. Maybe I should finalize the current approach first before submitting. I'll aim for clarity and ensure everything aligns with the requirements.
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 <optional>\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {\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 reduce_min_scale_cuda(y, scale);\n}\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n#include <float.h>\n\n__global__ void min_scale_pos_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * 128 * HW + hw);\n float mn = p[0];\n #pragma unroll\n for (int c = 1; c < 128; ++c) {\n float v = p[(long long)c * HW];\n mn = fminf(mn, v);\n }\n out[idx] = mn * scale;\n}\n\n__global__ void min_scale_generic_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int C, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * C * HW + hw);\n float best = p[0];\n if (scale >= 0.0f) {\n for (int c = 1; c < C; ++c) best = fminf(best, p[(long long)c * HW]);\n } else {\n for (int c = 1; c < C; ++c) best = fmaxf(best, p[(long long)c * HW]);\n }\n out[idx] = best * scale;\n}\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {\n int64_t N = y.size(0), C = y.size(1), H = y.size(2), W = y.size(3);\n auto out = torch::empty({N, 1, H, W}, y.options());\n int total = (int)(N * H * W);\n int HW = (int)(H * W);\n const int threads = 256;\n const int blocks = (total + threads - 1) / threads;\n float sc = (float)scale;\n if (C == 128 && sc >= 0.0f) {\n min_scale_pos_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, HW, sc);\n } else {\n min_scale_generic_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, sc);\n }\n C10_CUDA_KERNEL_LAUNCH_CHECK();\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_conv_min_ext_v3s', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, scale_factor):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.scale_factor = float(scale_factor)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 710.6s turn · 10.6s LLM · 0.0s tools · FINAL
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 <optional>\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {\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 reduce_min_scale_cuda(y, scale);\n}\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n#include <float.h>\n\n__global__ void min_scale_pos_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * 128 * HW + hw);\n float mn = p[0];\n #pragma unroll\n for (int c = 1; c < 128; ++c) {\n float v = p[(long long)c * HW];\n mn = fminf(mn, v);\n }\n out[idx] = mn * scale;\n}\n\n__global__ void min_scale_generic_kernel(const float* __restrict__ y, float* __restrict__ out,\n int total, int C, int HW, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int n = idx / HW;\n int hw = idx - n * HW;\n const float* p = y + ((long long)n * C * HW + hw);\n float best = p[0];\n if (scale >= 0.0f) {\n for (int c = 1; c < C; ++c) best = fminf(best, p[(long long)c * HW]);\n } else {\n for (int c = 1; c < C; ++c) best = fmaxf(best, p[(long long)c * HW]);\n }\n out[idx] = best * scale;\n}\n\ntorch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {\n int64_t N = y.size(0), C = y.size(1), H = y.size(2), W = y.size(3);\n auto out = torch::empty({N, 1, H, W}, y.options());\n int total = (int)(N * H * W);\n int HW = (int)(H * W);\n const int threads = 256;\n const int blocks = (total + threads - 1) / threads;\n float sc = (float)scale;\n if (C == 128 && sc >= 0.0f) {\n min_scale_pos_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, HW, sc);\n } else {\n min_scale_generic_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, sc);\n }\n C10_CUDA_KERNEL_LAUNCH_CHECK();\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_conv_min_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, scale_factor):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.scale_factor = float(scale_factor)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 6.47 μs
Runtime stats: mean=6.47μs median=?μs std=0.04μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 4097.97 MB peak (ref: 5089.81 MB, ratio: 0.8051x)
Kernel launches: 8 (ref: 9, fusion ratio: 1.125)
Energy: 929.36 mJ/run (ref: 929.1 mJ, ratio: 0.9997x)
SOL score: 1.0
final submitted kernel (84 lines, 3,198 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 <optional>
torch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale);
torch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double scale) {
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 reduce_min_scale_cuda(y, scale);
}
'''
cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <float.h>
__global__ void min_scale_pos_kernel(const float* __restrict__ y, float* __restrict__ out,
int total, int HW, float scale) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= total) return;
int n = idx / HW;
int hw = idx - n * HW;
const float* p = y + ((long long)n * 128 * HW + hw);
float mn = p[0];
#pragma unroll
for (int c = 1; c < 128; ++c) {
float v = p[(long long)c * HW];
mn = fminf(mn, v);
}
out[idx] = mn * scale;
}
__global__ void min_scale_generic_kernel(const float* __restrict__ y, float* __restrict__ out,
int total, int C, int HW, float scale) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= total) return;
int n = idx / HW;
int hw = idx - n * HW;
const float* p = y + ((long long)n * C * HW + hw);
float best = p[0];
if (scale >= 0.0f) {
for (int c = 1; c < C; ++c) best = fminf(best, p[(long long)c * HW]);
} else {
for (int c = 1; c < C; ++c) best = fmaxf(best, p[(long long)c * HW]);
}
out[idx] = best * scale;
}
torch::Tensor reduce_min_scale_cuda(torch::Tensor y, double scale) {
int64_t N = y.size(0), C = y.size(1), H = y.size(2), W = y.size(3);
auto out = torch::empty({N, 1, H, W}, y.options());
int total = (int)(N * H * W);
int HW = (int)(H * W);
const int threads = 256;
const int blocks = (total + threads - 1) / threads;
float sc = (float)scale;
if (C == 128 && sc >= 0.0f) {
min_scale_pos_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, HW, sc);
} else {
min_scale_generic_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), out.data_ptr<float>(), total, (int)C, HW, sc);
}
C10_CUDA_KERNEL_LAUNCH_CHECK();
return out;
}
'''
_ext = load_inline(name='fused_conv_min_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src,
functions=['fused_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, scale_factor):
super(ModelNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.scale_factor = float(scale_factor)
def forward(self, x):
return _ext.fused_forward(x, self.conv.weight, self.conv.bias, self.scale_factor)