correct
69_Conv2d_HardSwish_ReLU.py
started
2026-05-05T01:18:59Z
timingkernelbench.timing
kernel mean / median / std
1.55 / — / 0.13 μs
ref mean / median / std
1.88 / — / 0.78 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1.57x
energyNVML (pynvml)
ratio (ref / kernel)
0.20x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
5576.76 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, applies HardSwish, and then ReLU.
"""
def __init__(self, in_channels, out_channels, kernel_size):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
def forward(self, x):
"""
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 = torch.nn.functional.hardswish(x)
x = torch.relu(x)
return x
batch_size = 128
in_channels = 8
out_channels = 64
height, width = 128, 128
kernel_size = 3
def get_inputs():
return [torch.rand(batch_size, in_channels, height, width)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size]turn 054.0s turn · 54.0s LLM · 0.0s tools · FINAL
reasoning
**Optimizing convolution with PyTorch**
I’m considering optimizing convolution operations with PyTorch, specifically focusing on separable activation functions. It might be necessary to implement a custom convolution for specific configurations — like using implicit GEMM tiling. My aim is to efficiently handle the weights and reduce reads by tiling spatial and output channels. This could lead to better performance, but I need to be mindful of potential thread count issues while maximizing output computations. So, careful balancing is key here!
**Evaluating CUDA and activation fusion**
I'm considering using torch.ops for potential speed gains with custom CUDA. Maybe I could implement torch.nn.functional.conv2d followed by a custom activation to see if it’s faster. I think fusing the activation function with the convolution might yield benefits, especially since the output convolution doesn't need a custom kernel. However, I need to ensure correctness and efficiency, given that I'm dealing with large activation outputs. Saving memory by fusing operations could really help optimize performance!
**Optimizing convolution and activation**
I’m considering how the convolution returns a tensor, and then a kernel computes the final output. The reference uses convolution with hardswish and ReLU, while I could use a fused activation for faster performance with one kernel instead of two. Additionally, I can modify the convolution output in place to avoid memory allocation, which might enhance efficiency. Since the model specifies using a custom CUDA kernel to replace the forward pass, I need to ensure everything ties in well with nn.Conv2d and F.conv2d seamlessly.
**Writing the host wrapper**
I need to create a host wrapper that returns a Tensor and launches the necessary functionality. I could use torch.utils.cpp_extension.load_inline with both cuda_sources and cpp_sources. It's interesting that load_inline automatically binds functions found in cpp_sources, and those must be declared there. If a function is defined in cuda_sources, it may still be visible. Also, it seems load_inline concatenates cpp_sources into one main .cpp file and keeps cuda separate, which seems efficient for binding expectations.
call → submit_kernel
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 1.55 μs
Runtime stats: mean=1.55μs median=?μs std=0.13μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 593.16 MB peak (ref: 1089.29 MB, ratio: 0.5445x)
Kernel launches: 7 (ref: 11, fusion ratio: 1.5714)
Energy: 1209.94 mJ/run (ref: 242.28 mJ, ratio: 0.2002x)
SOL score: 1.0
final submitted kernel (76 lines, 2,157 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/ATen.h>
at::Tensor hardswish_relu_inplace(at::Tensor x);
"""
_cuda_src = r"""
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <algorithm>
#include <cstdint>
__global__ void hardswish_relu_inplace_kernel(float* __restrict__ x, int64_t n) {
int64_t tid = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;
int64_t stride = (int64_t)blockDim.x * gridDim.x;
const float one_sixth = 0.16666666666666666f;
for (int64_t i = tid; i < n; i += stride) {
float v = x[i];
float y;
// relu(hardswish(v)) simplifies to this piecewise form.
if (v <= 0.0f) {
y = 0.0f;
} else if (v < 3.0f) {
y = v * (v + 3.0f) * one_sixth;
} else {
y = v;
}
x[i] = y;
}
}
at::Tensor hardswish_relu_inplace(at::Tensor x) {
const int64_t n = x.numel();
if (n == 0) return x;
constexpr int threads = 256;
int64_t needed = (n + threads - 1) / threads;
int blocks = (int)std::min<int64_t>(needed, 4096);
hardswish_relu_inplace_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
x.data_ptr<float>(), n);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return x;
}
"""
_ext = load_inline(
name="fused_hardswish_relu_inplace_ext_v1",
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=["hardswish_relu_inplace"],
extra_cflags=["-O3"],
extra_cuda_cflags=["-O3", "--use_fast_math"],
verbose=False,
)
class ModelNew(nn.Module):
"""
Conv2d followed by fused HardSwish+ReLU. The convolution module is kept so
the parameter names/state_dict match the reference implementation exactly.
"""
def __init__(self, in_channels, out_channels, kernel_size):
super(ModelNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
def forward(self, x):
y = self.conv(x)
return _ext.hardswish_relu_inplace(y)