correct
81_Gemm_Swish_Divide_Clamp_Tanh_Clamp.py
started
2026-05-02T09:18:50Z
timingkernelbench.timing
kernel mean / median / std
2.72 / — / 0.01 μs
ref mean / median / std
2.85 / — / 0.09 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
2.60x
energyNVML (pynvml)
ratio (ref / kernel)
0.50x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
1454.83 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 gemm, swish, divide, clamp, tanh, and clamp operations.
"""
def __init__(self, in_features, out_features, bias=True):
super(Model, self).__init__()
self.gemm = nn.Linear(in_features, out_features, bias=bias)
def forward(self, x):
"""
Args:
x (torch.Tensor): Input tensor of shape (batch_size, in_features).
Returns:
torch.Tensor: Output tensor of shape (batch_size, out_features).
"""
x = self.gemm(x)
x = x * torch.sigmoid(x) # Swish activation
x = x / 2.0
x = torch.clamp(x, min=-1.0, max=1.0) # Clamp between -1 and 1
x = torch.tanh(x) # Tanh activation
x = torch.clamp(x, min=-1.0, max=1.0) # Clamp between -1 and 1
return x
batch_size = 1024
in_features = 8192
out_features = 8192
def get_inputs():
return [torch.rand(batch_size, in_features)]
def get_init_inputs():
return [in_features, out_features]turn 04.4s turn · 4.4s LLM · 0.0s tools
reasoning
**Optimizing CUDA in Python**
I need to craft a complete Python implementation with a CUDA extension to optimize a Linear operation involving large matrices, around 68 billion ops. I'm considering using PyTorch with cuBLAS and elementwise functions, and I'm wondering if torch.matmul would provide a speed boost. It seems like using a custom CUDA kernel would be necessary to replace the forward operation. I might explore cuBLAS wrapped in C++ for efficiency and possibly fuse my operations. I need to verify the GPU environment for this!
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) · 28.8s turn · 28.8s LLM · 6,280 chars (click to expand)
turn 128.8s turn · 28.8s 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\n_cpp_src = r'''\n#include <torch/extension.h>\n\nvoid activation_launcher(torch::Tensor y);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, bool has_bias) {\n torch::Tensor y;\n if (has_bias) {\n y = at::addmm(bias, x, weight.t());\n } else {\n y = at::matmul(x, weight.t());\n }\n activation_launcher(y);\n return y;\n}\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void activation_kernel(float* __restrict__ data, long long n) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (long long i = idx; i < n; i += stride) {\n float v = data[i];\n float sig = 1.0f / (1.0f + expf(-v));\n float u = 0.5f * v * sig;\n u = fminf(1.0f, fmaxf(-1.0f, u));\n data[i] = tanhf(u);\n }\n}\n\nvoid activation_launcher(torch::Tensor y) {\n const long long n = y.numel();\n if (n == 0) return;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n activation_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);\n}\n'''\n\n_ext = load_inline(name='fused_linear_swish_tanh_ext_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['fused_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, bias=True):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features, bias=bias)\n\n def forward(self, x):\n b = self.gemm.bias\n if b is None:\n b = torch.empty(0, device=x.device, dtype=x.dtype)\n has_b = False\n else:\n has_b = True\n return _ext.fused_forward(x, self.gemm.weight, b, has_b)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'fused_linear_swish_tanh_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_linear_swish_tanh_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_81_cache/k_883de76d6fa4/fused_linear_swish_tanh_ext_v1/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=fused_linear_swish_tanh_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_81_cache/k_883de76d6fa4/fused_linear_swish_tanh_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=fused_linear_swish_tanh_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_81_cache/k_883de76d6fa4/fused_linear_swish_tanh_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/l12_default/original/gpt-5.5-priority/level_2_problem_81_cache/k_883de76d6fa4/fused_linear_swish_tanh_ext_v1/cuda.cu(28): error: namespace "at::cuda" has no member "getCurrentCUDAStream"
activation_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);
^
1 error detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_81_cache/k_883de76d6fa4/fused_linear_swish_tanh_ext_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 26.4s turn · 6.4s LLM · 0.0s tools
reasoning
**Considering CUDA includes**
I'm thinking about what needs to be included for proper functionality. I definitely need to include `ATen/cuda/CUDAContext.h`. Oh, and maybe I should also consider adding a `c10` cuda stream include? That could be important for managing CUDA streams effectively. This should help ensure everything is set up correctly for working with CUDA in my code. It's crucial to get all the necessary components in place!
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 37.8s turn · 7.8s 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=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 48.3s turn · 8.3s 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=5.54e-06 mean_abs_err=1.48e-07 max_rel_err=1.05e-05
turn 513.9s turn · 13.9s LLM · 0.0s tools
reasoning
**Optimizing mathematical approximation**
I'm looking to improve the efficiency of my computations by using a fast approximated method like sigmoid or tanh polynomial. I'm exploring whether I can reduce the error to below 0.023 over certain ranges. My current tests only involve five random samples for validation, which only gives limited insights. I’m considering higher-order polynomial approximations for improved accuracy but need to ensure the overall maximum error is acceptable for larger datasets. Testing will be crucial!
**Exploring performance optimizations**
I'm considering using an approximate method for exponentiation to speed up calculations. Implementing the tanh function with fast math might also be viable. I'm thinking about fusing bias and activation after matrix multiplication, instead of using the addmm function, since internal copies could impact performance. For specific matrix sizes, bias may need to be expanded. I should explore using PyTorch's optimized F.linear directly for better efficiency and test for accuracy. I’m curious if C++’s `at::linear` could provide a performance boost.
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 66.1s turn · 6.1s 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=5.54e-06 mean_abs_err=1.48e-07 max_rel_err=1.05e-05
turn 734.1s turn · 34.1s LLM · 0.0s tools
reasoning
**Considering matrix representation**
I’m thinking about how row-major C (M, N) relates to column-major C^T(N, M). So, if I'm computing C_col (N x M) as W_col, I need to figure out how W in row-major format (N, K) transforms into column-major (K, N) when the pointer is interpreted differently. There’s a bit of complexity in deriving this, and I want to ensure I’m getting the dimensions right! Let's break this down further to simplify.
call → static_check
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 816.3s turn · 16.3s LLM · 0.0s tools · FINAL
reasoning
**Submitting final code**
I need to submit the final code now. I’m considering whether to use torch operations in C++ for addmm instead of creating a custom GEMM. This might be faster due to activation fusions. However, the reference implementation combines GEMM with various torch kernels, so the speedup might only be modest. I’ll also make sure the extension name is unique before finalizing everything. Let’s get this done!
call → submit_kernel
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 2.72 μs
Runtime stats: mean=2.72μs median=?μs std=0.01μs
Numerical precision: max_abs_err=5.54e-06 mean_abs_err=1.48e-07 max_rel_err=1.05e-05
Memory: 609.06 MB peak (ref: 673.06 MB, ratio: 0.9049x)
Kernel launches: 5 (ref: 13, fusion ratio: 2.6)
Energy: 483.08 mJ/run (ref: 241.94 mJ, ratio: 0.5008x)
SOL score: 1.0
final submitted kernel (74 lines, 2,122 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>
void activation_launcher(torch::Tensor y);
torch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, bool has_bias) {
torch::Tensor y;
if (has_bias) {
y = at::linear(x, weight, bias);
} else {
y = at::linear(x, weight, c10::nullopt);
}
activation_launcher(y);
return y;
}
'''
_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
__global__ void activation_kernel(float* __restrict__ data, long long n) {
long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;
long long stride = (long long)blockDim.x * gridDim.x;
for (long long i = idx; i < n; i += stride) {
float v = data[i];
float sig = 1.0f / (1.0f + __expf(-v));
float u = 0.5f * v * sig;
u = fminf(1.0f, fmaxf(-1.0f, u));
data[i] = tanhf(u);
}
}
void activation_launcher(torch::Tensor y) {
long long n = y.numel();
if (n == 0) return;
const int threads = 256;
int blocks = (int)((n + threads - 1) / threads);
if (blocks > 65535) blocks = 65535;
activation_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);
}
'''
_ext = load_inline(
name='fused_linear_swish_tanh_ext_final',
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=['fused_forward'],
extra_cflags=['-O3'],
extra_cuda_cflags=['-O3', '--use_fast_math'],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(ModelNew, self).__init__()
self.gemm = nn.Linear(in_features, out_features, bias=bias)
def forward(self, x):
b = self.gemm.bias
if b is None:
b = torch.empty(0, device=x.device, dtype=x.dtype)
has_bias = False
else:
has_bias = True
return _ext.fused_forward(x, self.gemm.weight, b, has_bias)