correct
30_Softsign.py
started
2026-05-02T03:44:38Z
timingkernelbench.timing
kernel mean / median / std
4.54 / — / 0.16 μs
ref mean / median / std
14.90 / — / 0.09 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
3.00x
energyNVML (pynvml)
ratio (ref / kernel)
4.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
2895.67 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 Softsign activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softsign activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with Softsign applied, same shape as input.
"""
return x / (1 + torch.abs(x))
batch_size = 4096
dim = 393216
def get_inputs():
x = torch.rand(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs neededturn 06.5s turn · 6.5s LLM · 0.0s tools
reasoning
**Evaluating model creation**
I’m considering creating a ModelNew for softsign with a huge tensor size, approximately 1.61 billion elements. This seems quite challenging! I’ll need to implement an elementwise operation using x/(1+abs(x)). I’m wondering if I can use PyTorch memory bandwidth and optimize by potentially fusing kernels. Also, I should implement a CUDA extension and ensure that I handle any input shapes correctly. I'll gather the required specs and see how I can manage allocating memory efficiently.
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) · 26.6s turn · 26.6s LLM · 8,374 chars (click to expand)
turn 126.6s turn · 26.6s LLM · 0.0s tools
reasoning
**Clarifying output accuracy**
I’m thinking about the accuracy tolerance, which is 1e-4 for output values between 0 and 1. I wonder what the approximate reciprocal would be — maybe around 2 units in the last place? I'm curious if I need to focus on using something like `1.0f / denom` for division to ensure precision. It’s a bit technical, but I want to make sure I'm getting this right!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <stdint.h>\n\n__device__ __forceinline__ float softsign_f(float x) {\n return x / (1.0f + fabsf(x));\n}\n\n__global__ void softsign_kernel_scalar(const float* __restrict__ x, float* __restrict__ y, int64_t n) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n for (int64_t i = idx; i < n; i += stride) {\n float v = x[i];\n y[i] = softsign_f(v);\n }\n}\n\n__global__ void softsign_kernel_vec4(const float4* __restrict__ x4, float4* __restrict__ y4, int64_t n4) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 v = x4[i];\n float4 o;\n o.x = softsign_f(v.x);\n o.y = softsign_f(v.y);\n o.z = softsign_f(v.z);\n o.w = softsign_f(v.w);\n y4[i] = o;\n }\n}\n\ntorch::Tensor softsign_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int64_t n = x.numel();\n if (n == 0) return y;\n const int threads = 256;\n const int max_blocks = 65535;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n const float* xp = x.data_ptr<float>();\n float* yp = y.data_ptr<float>();\n uintptr_t xa = reinterpret_cast<uintptr_t>(xp);\n uintptr_t ya = reinterpret_cast<uintptr_t>(yp);\n if (((xa | ya) & 15) == 0 && (n % 4 == 0)) {\n int64_t n4 = n >> 2;\n int blocks = (int)((n4 + threads - 1) / threads);\n if (blocks > max_blocks) blocks = max_blocks;\n softsign_kernel_vec4<<<blocks, threads, 0, stream>>>(reinterpret_cast<const float4*>(xp), reinterpret_cast<float4*>(yp), n4);\n } else {\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > max_blocks) blocks = max_blocks;\n softsign_kernel_scalar<<<blocks, threads, 0, stream>>>(xp, yp, n);\n }\n return y;\n}\n'''\n\n_ext = load_inline(\n name='softsign_ext_v1',\n cpp_sources='',\n cuda_sources=_cuda_src,\n functions=['softsign_forward'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.softsign_forward(x)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'softsign_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=softsign_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/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_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=softsign_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/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_ext_v1/main.cpp -o main.o
/scratch/tejas/PopcornBench/runs/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_ext_v1/main.cpp: In function 'void pybind11_init_softsign_ext_v1(pybind11::module_&)':
/scratch/tejas/PopcornBench/runs/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_ext_v1/main.cpp:4:55: error: 'softsign_forward' was not declared in this scope
4 | m.def("softsign_forward", torch::wrap_pybind_function(softsign_forward), "softsign_forward");
| ^~~~~~~~~~~~~~~~
[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=softsign_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 --use_fast_math -std=c++17 -c /scratch/tejas/PopcornBench/runs/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_ext_v1/cuda.cu -o cuda.cuda.o
FAILED: [code=2] cuda.cuda.o
/usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=softsign_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 --use_fast_math -std=c++17 -c /scratch/tejas/PopcornBench/runs/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_ext_v1/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
/scratch/tejas/PopcornBench/runs/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_ext_v1/cuda.cu(43): error: namespace "at::cuda" has no member "getCurrentCUDAStream"
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
^
1 error detected in the compilation of "/scratch/tejas/PopcornBench/runs/full_l12/original/gpt-5.5-priority/level_1_problem_30_cache/k_f87ce7b5d5b9/softsign_ext_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 27.2s turn · 7.2s LLM · 0.0s tools
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 36.8s turn · 6.8s LLM · 0.0s tools
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=5.96e-08 mean_abs_err=8.32e-09 max_rel_err=1.60e-07
turn 413.8s turn · 13.8s LLM · 0.0s tools
call → static_check
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 521.3s turn · 21.3s LLM · 0.0s tools · FINAL
call → submit_kernel
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 4.54 μs
Runtime stats: mean=4.54μs median=?μs std=0.16μs
Numerical precision: max_abs_err=5.96e-08 mean_abs_err=8.32e-09 max_rel_err=1.60e-07
Memory: 12288.0 MB peak (ref: 18432.0 MB, ratio: 0.6667x)
Kernel launches: 2 (ref: 6, fusion ratio: 3.0)
Energy: 478.28 mJ/run (ref: 1913.34 mJ, ratio: 4.0005x)
SOL score: 0.8644
final submitted kernel (84 lines, 2,652 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>
torch::Tensor softsign_forward(torch::Tensor x);
'''
_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdint.h>
__device__ __forceinline__ float softsign_f(float x) {
return x / (1.0f + fabsf(x));
}
__global__ void softsign_kernel_scalar(const float* __restrict__ x, float* __restrict__ y, int64_t n) {
int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;
int64_t stride = (int64_t)blockDim.x * gridDim.x;
for (int64_t i = idx; i < n; i += stride) {
float v = x[i];
y[i] = softsign_f(v);
}
}
__global__ void softsign_kernel_vec4(const float4* __restrict__ x4, float4* __restrict__ y4, int64_t n4) {
int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;
int64_t stride = (int64_t)blockDim.x * gridDim.x;
for (int64_t i = idx; i < n4; i += stride) {
float4 v = x4[i];
float4 o;
o.x = softsign_f(v.x);
o.y = softsign_f(v.y);
o.z = softsign_f(v.z);
o.w = softsign_f(v.w);
y4[i] = o;
}
}
torch::Tensor softsign_forward(torch::Tensor x) {
auto y = torch::empty_like(x);
int64_t n = x.numel();
if (n == 0) return y;
const int threads = 256;
const int max_blocks = 65535;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
const float* xp = x.data_ptr<float>();
float* yp = y.data_ptr<float>();
uintptr_t xa = reinterpret_cast<uintptr_t>(xp);
uintptr_t ya = reinterpret_cast<uintptr_t>(yp);
if (((xa | ya) & 15) == 0 && (n % 4 == 0)) {
int64_t n4 = n >> 2;
int blocks = (int)((n4 + threads - 1) / threads);
if (blocks > max_blocks) blocks = max_blocks;
softsign_kernel_vec4<<<blocks, threads, 0, stream>>>(reinterpret_cast<const float4*>(xp), reinterpret_cast<float4*>(yp), n4);
} else {
int blocks = (int)((n + threads - 1) / threads);
if (blocks > max_blocks) blocks = max_blocks;
softsign_kernel_scalar<<<blocks, threads, 0, stream>>>(xp, yp, n);
}
return y;
}
'''
_ext = load_inline(
name='softsign_ext_final',
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=['softsign_forward'],
extra_cuda_cflags=['-O3', '--use_fast_math'],
extra_cflags=['-O3'],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return _ext.softsign_forward(x)