correct
4_Conv2d_Mish_Mish.py
started
2026-05-02T20:19:51Z
timingkernelbench.timing
kernel mean / median / std
7.56 / — / 0.19 μs
ref mean / median / std
8.65 / — / 0.40 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1.29x
energyNVML (pynvml)
ratio (ref / kernel)
1.25x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
8481.94 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 convolution, applies Mish, and another Mish.
"""
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):
x = self.conv(x)
x = torch.nn.functional.mish(x)
x = torch.nn.functional.mish(x)
return x
batch_size = 64
in_channels = 64
out_channels = 128
height = width = 256
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 011.1s turn · 11.1s LLM · 0.0s tools
reasoning
**Assessing convolution settings**
I'm thinking about calling `conv2d` to match everything correctly and then applying a custom Mish function. It's important to ensure I pass correctness along the way. I might use the same convolution as a reference, and compute Mish twice to make it accurate. I should also compile the extension and take a look at the GPU specs to make sure everything aligns properly. Let's keep everything consistent!
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) · 13.1s turn · 13.1s LLM · 10,292 chars (click to expand)
turn 113.1s turn · 13.1s LLM · 0.0s tools
reasoning
**Evaluating model instantiation**
I think when evaluating, they might instantiate ModelNew and Model with the same seed, typically making a copy. ModelNew has a conv parameter, so I need to define self.conv the same way. The extension function forward(x,w,b) should return a tensor conv and apply the kernel with torch::conv2d. I need to include torch/extension.h and ensure everything with CUDA is set up correctly. The host function might need declarations in cpp_sources. It’s crucial to test this setup efficiently.
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#include <vector>\n\ntorch::Tensor mish2_cuda(torch::Tensor y);\n\ntorch::Tensor forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::conv2d(x, weight, bias, {1,1}, {0,0}, {1,1}, 1);\n return mish2_cuda(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__device__ __forceinline__ float mish_one(float x) {\n float sp = log1pf(expf(x));\n return x * tanhf(sp);\n}\n\n__global__ void mish2_kernel(float* __restrict__ y, 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 = y[i];\n float m1 = mish_one(v);\n y[i] = mish_one(m1);\n }\n}\n\ntorch::Tensor mish2_cuda(torch::Tensor y) {\n const long long n = y.numel();\n if (n > 0) {\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);\n }\n return y;\n}\n'''\n\n_ext = load_inline(name='mish_conv_ext_v1', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n def forward(self, x):\n return _ext.forward(x, self.conv.weight, self.conv.bias)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'mish_conv_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=mish_conv_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_4_cache/k_87478137d693/mish_conv_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=mish_conv_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_4_cache/k_87478137d693/mish_conv_ext_v1/main.cpp -o main.o
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_4_cache/k_87478137d693/mish_conv_ext_v1/main.cpp: In function 'at::Tensor forward(at::Tensor, at::Tensor, at::Tensor)':
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_4_cache/k_87478137d693/mish_conv_ext_v1/main.cpp:9:27: 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 = torch::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_4_cache/k_87478137d693/mish_conv_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=mish_conv_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/l12_default/original/gpt-5.5-priority/level_2_problem_4_cache/k_87478137d693/mish_conv_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=mish_conv_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/l12_default/original/gpt-5.5-priority/level_2_problem_4_cache/k_87478137d693/mish_conv_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_4_cache/k_87478137d693/mish_conv_ext_v1/cuda.cu(31): error: namespace "at::cuda" has no member "getCurrentCUDAStream"
mish2_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_4_cache/k_87478137d693/mish_conv_ext_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 27.6s turn · 7.6s LLM · 0.0s tools
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 34.9s turn · 4.9s 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 411.9s turn · 11.9s 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=1.42e-05 mean_abs_err=4.87e-07 max_rel_err=1.99e-05
turn 56.6s turn · 6.6s LLM · 0.0s tools
call → static_check
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 610.6s turn · 10.6s LLM · 0.0s tools
reasoning
**Exploring activation approximation**
I'm thinking about experimenting with activation approximation polynomials to speed things up. I notice that convolutions might dominate, but there’s potential for improvement, albeit with risks. For the mish activation, I’m looking at the identity involving tanh and log functions. I need to consider how to manage overflow for large values and implement safeguards. Also, I’d like to ensure that my approach minimizes underflow issues. It seems like I’m on the right track with the formula!
**Refining formula for performance**
I'm considering how to refine my approach for values of x greater than 20, where I can just return x. I also have to think about the PyTorch softplus threshold and its relationship with tanh(x), which is exactly 1 in the float representation. This formula should perform well within tolerance, though it may not be bit-exact. The speed improvements seem substantial, so testing for correctness is definitely necessary to ensure everything is functioning as intended!
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=4.77e-07 mean_abs_err=8.92e-09 max_rel_err=6.71e-07
turn 7 · 1 tool call(s) · 31.6s turn · 31.6s LLM · 6,389 chars (click to expand)
turn 731.6s turn · 31.6s LLM · 0.0s tools
reasoning
**Evaluating thread and block efficiency**
I’m calculating the efficiency of blocks and threads. With 1024 blocks and 256 threads, I’d have about 262k threads, which seems like enough. Each thread is performing 2016*2 operations, leading to around 4k operations serially, though that’s not ideal.
I need a large grid for scheduling since blocks finish at different times. It seems like I want to balance the number of blocks to optimize performance, possibly leaning towards either 1024 or 4096 blocks for efficiency.
**Rethinking block and thread usage**
I’m considering whether to use one element per thread without a loop, but I realize that 65k blocks might not cover all `n` because of the maximum grid size. They seem to have incorporated loops. So, with 65k blocks, I could potentially have many waves, with each block processing 32 elements. It looks like I need to better balance how I'm utilizing threads and blocks to ensure optimal coverage and efficiency.
**Considering polynomial interpolation**
I'm thinking about using polynomial interpolation at runtime, possibly using Horner's method. First, I need the coefficients, and I wonder if I can utilize numpy for analysis. However, I realize that I can't invoke Python directly, except for designated tools. I could create code with a module to calculate the coefficients, but I must remember that the final kernel will need specific constants as well. This sounds like a challenge I'm ready to tackle!
**Creating coefficient generation**
I’m thinking about writing some code to generate coefficients using NumPy. This will help me debug the correctness of my implementation. I'm curious about the specific functions and methods I should use to make it efficient and clear. I’ll consider different approaches and maybe look into ways to visualize the coefficients. It's important to get this right, as debugging will give me insights into any existing issues. Let's see how this unfolds!
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport numpy as np\n\n# Generate Chebyshev approximation coefficients for mish(mish(x)) on [-3, 3].\ndef _mish_np(x):\n e = np.exp(x)\n return x * (e * (e + 2.0) / (e * e + 2.0 * e + 2.0))\ndef _f2_np(x):\n return _mish_np(_mish_np(x))\n_segments = 12\n_degree = 7\n_lo, _hi = -3.0, 3.0\n_h = (_hi - _lo) / _segments\n_coeffs = []\nfor s in range(_segments):\n a = _lo + s * _h\n b = a + _h\n xs = np.linspace(a, b, 400)\n ts = (2*xs - (a+b))/(b-a)\n cs = np.polynomial.chebyshev.chebfit(ts, _f2_np(xs), _degree)\n _coeffs.extend([float(c) for c in cs])\n_coeff_str = ','.join([f'{c:.9e}f' for c in _coeffs])\n\n_cpp_src = r'''\n#include <torch/extension.h>\n#include <ATen/ops/conv2d.h>\n\ntorch::Tensor mish2_cuda(torch::Tensor y);\n\ntorch::Tensor forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = at::conv2d(x, weight, c10::optional<at::Tensor>(bias), c10::IntArrayRef({1,1}), c10::IntArrayRef({0,0}), c10::IntArrayRef({1,1}), 1);\n return mish2_cuda(y);\n}\n'''\n\n_cuda_src = f'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define NSEGS {_segments}\n#define DEG {_degree}\n__device__ __constant__ float coeffs[NSEGS*(DEG+1)] = {{{_coeff_str}}};\n\n__device__ __forceinline__ float mish_one_exact(float x) {{\n if (x > 20.0f) return x;\n float e = expf(x);\n float num = e * (e + 2.0f);\n return x * (num / (num + 2.0f));\n}}\n__device__ __forceinline__ float mish2_exact(float x) {{ return mish_one_exact(mish_one_exact(x)); }}\n\n__device__ __forceinline__ float mish2_approx(float x) {{\n if (x < -3.0f || x > 3.0f) return mish2_exact(x);\n float u = (x + 3.0f) * 2.0f; // h=0.5 => 1/h=2\n int s = (int)u;\n if (s >= NSEGS) s = NSEGS - 1;\n float a = -3.0f + 0.5f * (float)s;\n float t = 4.0f * (x - a) - 1.0f;\n const float* c = coeffs + s*(DEG+1);\n float b1 = 0.0f, b2 = 0.0f;\n #pragma unroll\n for (int j = DEG; j >= 1; --j) {{\n float bj = fmaf(2.0f*t, b1, c[j] - b2);\n b2 = b1; b1 = bj;\n }}\n return fmaf(t, b1, c[0] - b2);\n}}\n\n__global__ void mish2_kernel(float* __restrict__ y, 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 y[i] = mish2_approx(y[i]);\n }}\n}}\n\ntorch::Tensor mish2_cuda(torch::Tensor y) {{\n const long long n = y.numel();\n if (n > 0) {{\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 8192) blocks = 8192;\n mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);\n }}\n return y;\n}}\n'''\n\n_ext = load_inline(name='mish_conv_ext_poly_v1', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n def forward(self, x):\n return _ext.forward(x, self.conv.weight, self.conv.bias)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=3.58e-07 mean_abs_err=8.96e-09 max_rel_err=7.45e+03
turn 813.5s turn · 13.5s LLM · 0.0s tools
reasoning
**Preparing final submission**
I need to finalize our submission, weighing the best options. We haven't benchmarked much beyond the initial submission test. It might be wise to check the static parameters and then submit, considering if the poly function is quicker than the exact activation. Our convolution operations are substantial, so I want to ensure everything is optimized. Also, I should clarify if we need to avoid NumPy to minimize overhead. I need to retrieve and possibly generate the necessary coefficients from the prior code.
**Finalizing code submission**
I think it’s better to stick with the exact formula since it’s simpler, though poly might be faster. I need to decide whether to submit the exact formula or the poly version with Python code, which includes NumPy. I'm concerned about whether using NumPy is okay due to potential dependency risks. I should consider conducting a static check first before the final submission. I’m pondering if I need to submit parallel or just focus on one call for efficiency. Let’s clarify these points before I finalize everything!
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport numpy as np\n\ndef _mish_np(x):\n e = np.exp(x)\n return x * (e * (e + 2.0) / (e * e + 2.0 * e + 2.0))\ndef _f2_np(x):\n return _mish_np(_mish_np(x))\n_segments = 12\n_degree = 7\n_lo, _hi = -3.0, 3.0\n_h = (_hi - _lo) / _segments\n_coeffs = []\nfor s in range(_segments):\n a = _lo + s * _h\n b = a + _h\n xs = np.linspace(a, b, 400)\n ts = (2*xs - (a+b))/(b-a)\n cs = np.polynomial.chebyshev.chebfit(ts, _f2_np(xs), _degree)\n _coeffs.extend([float(c) for c in cs])\n_coeff_str = ','.join([f'{c:.9e}f' for c in _coeffs])\n\n_cpp_src = r'''\n#include <torch/extension.h>\n#include <ATen/ops/conv2d.h>\n\ntorch::Tensor mish2_cuda(torch::Tensor y);\n\ntorch::Tensor forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = at::conv2d(x, weight, c10::optional<at::Tensor>(bias), c10::IntArrayRef({1,1}), c10::IntArrayRef({0,0}), c10::IntArrayRef({1,1}), 1);\n return mish2_cuda(y);\n}\n'''\n\n_cuda_src = f'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define NSEGS {_segments}\n#define DEG {_degree}\n__device__ __constant__ float coeffs[NSEGS*(DEG+1)] = {{{_coeff_str}}};\n\n__device__ __forceinline__ float mish_one_exact(float x) {{\n if (x > 20.0f) return x;\n float e = expf(x);\n float num = e * (e + 2.0f);\n return x * (num / (num + 2.0f));\n}}\n__device__ __forceinline__ float mish2_exact(float x) {{ return mish_one_exact(mish_one_exact(x)); }}\n\n__device__ __forceinline__ float mish2_approx(float x) {{\n if (x < -3.0f || x > 3.0f) return mish2_exact(x);\n float u = (x + 3.0f) * 2.0f;\n int s = (int)u;\n if (s >= NSEGS) s = NSEGS - 1;\n float a = -3.0f + 0.5f * (float)s;\n float t = 4.0f * (x - a) - 1.0f;\n const float* c = coeffs + s*(DEG+1);\n float b1 = 0.0f, b2 = 0.0f;\n #pragma unroll\n for (int j = DEG; j >= 1; --j) {{\n float bj = fmaf(2.0f*t, b1, c[j] - b2);\n b2 = b1; b1 = bj;\n }}\n return fmaf(t, b1, c[0] - b2);\n}}\n\n__global__ void mish2_kernel(float* __restrict__ y, 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 y[i] = mish2_approx(y[i]);\n }}\n}}\n\ntorch::Tensor mish2_cuda(torch::Tensor y) {{\n const long long n = y.numel();\n if (n > 0) {{\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 8192) blocks = 8192;\n mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);\n }}\n return y;\n}}\n'''\n\n_ext = load_inline(name='mish_conv_ext_poly_final', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n def forward(self, x):\n return _ext.forward(x, self.conv.weight, self.conv.bias)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 97.5s turn · 7.5s 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\nimport numpy as np\n\ndef _mish_np(x):\n e = np.exp(x)\n return x * (e * (e + 2.0) / (e * e + 2.0 * e + 2.0))\ndef _f2_np(x):\n return _mish_np(_mish_np(x))\n_segments = 12\n_degree = 7\n_lo, _hi = -3.0, 3.0\n_h = (_hi - _lo) / _segments\n_coeffs = []\nfor s in range(_segments):\n a = _lo + s * _h\n b = a + _h\n xs = np.linspace(a, b, 400)\n ts = (2*xs - (a+b))/(b-a)\n cs = np.polynomial.chebyshev.chebfit(ts, _f2_np(xs), _degree)\n _coeffs.extend([float(c) for c in cs])\n_coeff_str = ','.join([f'{c:.9e}f' for c in _coeffs])\n\n_cpp_src = r'''\n#include <torch/extension.h>\n#include <ATen/ops/conv2d.h>\n\ntorch::Tensor mish2_cuda(torch::Tensor y);\n\ntorch::Tensor forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = at::conv2d(x, weight, c10::optional<at::Tensor>(bias), c10::IntArrayRef({1,1}), c10::IntArrayRef({0,0}), c10::IntArrayRef({1,1}), 1);\n return mish2_cuda(y);\n}\n'''\n\n_cuda_src = f'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define NSEGS {_segments}\n#define DEG {_degree}\n__device__ __constant__ float coeffs[NSEGS*(DEG+1)] = {{{_coeff_str}}};\n\n__device__ __forceinline__ float mish_one_exact(float x) {{\n if (x > 20.0f) return x;\n float e = expf(x);\n float num = e * (e + 2.0f);\n return x * (num / (num + 2.0f));\n}}\n__device__ __forceinline__ float mish2_exact(float x) {{ return mish_one_exact(mish_one_exact(x)); }}\n\n__device__ __forceinline__ float mish2_approx(float x) {{\n if (x < -3.0f || x > 3.0f) return mish2_exact(x);\n float u = (x + 3.0f) * 2.0f;\n int s = (int)u;\n if (s >= NSEGS) s = NSEGS - 1;\n float a = -3.0f + 0.5f * (float)s;\n float t = 4.0f * (x - a) - 1.0f;\n const float* c = coeffs + s*(DEG+1);\n float b1 = 0.0f, b2 = 0.0f;\n #pragma unroll\n for (int j = DEG; j >= 1; --j) {{\n float bj = fmaf(2.0f*t, b1, c[j] - b2);\n b2 = b1; b1 = bj;\n }}\n return fmaf(t, b1, c[0] - b2);\n}}\n\n__global__ void mish2_kernel(float* __restrict__ y, 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 y[i] = mish2_approx(y[i]);\n }}\n}}\n\ntorch::Tensor mish2_cuda(torch::Tensor y) {{\n const long long n = y.numel();\n if (n > 0) {{\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 8192) blocks = 8192;\n mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);\n }}\n return y;\n}}\n'''\n\n_ext = load_inline(name='mish_conv_ext_poly_submit', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n def forward(self, x):\n return _ext.forward(x, self.conv.weight, self.conv.bias)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 7.56 μs
Runtime stats: mean=7.56μs median=?μs std=0.19μs
Numerical precision: max_abs_err=3.58e-07 mean_abs_err=8.96e-09 max_rel_err=7.45e+03
Memory: 4097.97 MB peak (ref: 5089.81 MB, ratio: 0.8051x)
Kernel launches: 7 (ref: 9, fusion ratio: 1.2857)
Energy: 1290.88 mJ/run (ref: 1613.24 mJ, ratio: 1.2497x)
SOL score: 1.0
final submitted kernel (101 lines, 3,243 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
import numpy as np
def _mish_np(x):
e = np.exp(x)
return x * (e * (e + 2.0) / (e * e + 2.0 * e + 2.0))
def _f2_np(x):
return _mish_np(_mish_np(x))
_segments = 12
_degree = 7
_lo, _hi = -3.0, 3.0
_h = (_hi - _lo) / _segments
_coeffs = []
for s in range(_segments):
a = _lo + s * _h
b = a + _h
xs = np.linspace(a, b, 400)
ts = (2*xs - (a+b))/(b-a)
cs = np.polynomial.chebyshev.chebfit(ts, _f2_np(xs), _degree)
_coeffs.extend([float(c) for c in cs])
_coeff_str = ','.join([f'{c:.9e}f' for c in _coeffs])
_cpp_src = r'''
#include <torch/extension.h>
#include <ATen/ops/conv2d.h>
torch::Tensor mish2_cuda(torch::Tensor y);
torch::Tensor forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {
auto y = at::conv2d(x, weight, c10::optional<at::Tensor>(bias), c10::IntArrayRef({1,1}), c10::IntArrayRef({0,0}), c10::IntArrayRef({1,1}), 1);
return mish2_cuda(y);
}
'''
_cuda_src = f'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#define NSEGS {_segments}
#define DEG {_degree}
__device__ __constant__ float coeffs[NSEGS*(DEG+1)] = {{{_coeff_str}}};
__device__ __forceinline__ float mish_one_exact(float x) {{
if (x > 20.0f) return x;
float e = expf(x);
float num = e * (e + 2.0f);
return x * (num / (num + 2.0f));
}}
__device__ __forceinline__ float mish2_exact(float x) {{ return mish_one_exact(mish_one_exact(x)); }}
__device__ __forceinline__ float mish2_approx(float x) {{
if (x < -3.0f || x > 3.0f) return mish2_exact(x);
float u = (x + 3.0f) * 2.0f;
int s = (int)u;
if (s >= NSEGS) s = NSEGS - 1;
float a = -3.0f + 0.5f * (float)s;
float t = 4.0f * (x - a) - 1.0f;
const float* c = coeffs + s*(DEG+1);
float b1 = 0.0f, b2 = 0.0f;
#pragma unroll
for (int j = DEG; j >= 1; --j) {{
float bj = fmaf(2.0f*t, b1, c[j] - b2);
b2 = b1; b1 = bj;
}}
return fmaf(t, b1, c[0] - b2);
}}
__global__ void mish2_kernel(float* __restrict__ y, 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) {{
y[i] = mish2_approx(y[i]);
}}
}}
torch::Tensor mish2_cuda(torch::Tensor y) {{
const long long n = y.numel();
if (n > 0) {{
const int threads = 256;
int blocks = (int)((n + threads - 1) / threads);
if (blocks > 8192) blocks = 8192;
mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);
}}
return y;
}}
'''
_ext = load_inline(name='mish_conv_ext_poly_submit', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)
class ModelNew(nn.Module):
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):
return _ext.forward(x, self.conv.weight, self.conv.bias)