L2 · problem 29
gpt-5.5-priority · original · l12_all_gpt
← all variants← original← gpt-5.5-prioritygenerated 2026-05-28 03:33:15 UTC
compile_fail 29_Matmul_Mish_Mish.py
model
gpt-5.5-priority
turns / max
15 / 15
tool calls
15
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
original
runtime
—
ref runtime
-1.00 μs
speedup
—
started
2026-05-04T07:53:58Z
compile error: Error building extension 'double_mish_ext_final': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=double_mish_ext_final -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_b5ac6cecb9ce/doubl
timingkernelbench.timing
kernel runtime
—
ref runtime
—
speedup
—
kernel mean / median / std
—
ref mean / median / std
—
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a matrix multiplication, applies Mish, and applies Mish again.
    """
    def __init__(self, in_features, out_features):
        super(Model, self).__init__()
        self.linear = nn.Linear(in_features, out_features)

    def forward(self, x):
        x = self.linear(x)
        x = torch.nn.functional.mish(x)
        x = torch.nn.functional.mish(x)
        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 010.6s turn · 10.6s LLM · 0.0s tools
reasoning
**Optimizing a Python model** I need to craft a Python file, and I’m considering how to evaluate transfers from weights. If ModelNew has the same constructor, I’ll likely copy the state dict. I wonder if I need to define a linear attribute since parameters should exist with nn.Linear. For the forward operation, the shape is fixed at 1024x8192 in fp32. I'm concerned about optimizing the massive GEMM operation and whether to use cuBLAS with fused activation. Maybe torch ops could work, but I need to ensure it’s faster! **Implementing custom extension** I think that a custom extension with just the activation function might be sufficient if the overhead on processing 67 million elements with Mish twice is too high. I wonder if I can write an extension function using torch::addmm, although that might not allow for a custom CUDA kernel for GEMM. The goal is to produce something faster by reducing the PyTorch Mish calls to a single fused kernel. I plan to implement double_mish_cuda with in-place mutations after the linear output to save on memory allocation. **Considering PyTorch Mish implementation** I think the PyTorch Mish function for fp32 probably uses softplus with a threshold of 20. I need to ensure that the numeric values stay within 1e-4 accuracy. These calculations will be based on a linear transformation of the random weights. It’s important to double-check that I’m handling the values correctly to achieve the expected results. I wonder if there are any special considerations I should keep in mind for this implementation!
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 114.3s turn · 14.3s 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\n_cpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor double_mish_cuda(torch::Tensor x);\n\ntorch::Tensor double_mish_forward(torch::Tensor x) {\n return double_mish_cuda(x);\n}\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.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 double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one(v);\n v = mish_one(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n const int64_t n = x.numel();\n if (n == 0) return x;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(\n name='double_mish_ext_v1',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['double_mish_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← compile_kernel (fail)
failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'double_mish_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=double_mish_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_2d43fdf59704/double_mish_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=double_mish_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/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/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_2d43fdf59704/double_mish_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=double_mish_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/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/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_2d43fdf59704/double_mish_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/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_2d43fdf59704/double_mish_ext_v1/cuda.cu(31): error: namespace "at::cuda" has no member "getCurrentCUDAStream" double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n); ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_2d43fdf59704/double_mish_ext_v1/cuda.cu". ninja: build stopped: subcommand failed.
turn 24.2s turn · 4.2s 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\ntorch::Tensor double_mish_cuda(torch::Tensor x);\n\ntorch::Tensor double_mish_forward(torch::Tensor x) {\n return double_mish_cuda(x);\n}\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.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 double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one(v);\n v = mish_one(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n const int64_t n = x.numel();\n if (n == 0) return x;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(\n name='double_mish_ext_v2',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['double_mish_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 34.3s turn · 4.3s 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\n_cpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor double_mish_cuda(torch::Tensor x);\n\ntorch::Tensor double_mish_forward(torch::Tensor x) {\n return double_mish_cuda(x);\n}\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.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 double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one(v);\n v = mish_one(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n const int64_t n = x.numel();\n if (n == 0) return x;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(\n name='double_mish_ext_v2c',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['double_mish_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\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 47.3s turn · 7.3s LLM · 0.0s tools
call → profile_kernel
callprofile_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\ntorch::Tensor double_mish_cuda(torch::Tensor x);\n\ntorch::Tensor double_mish_forward(torch::Tensor x) {\n return double_mish_cuda(x);\n}\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.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 double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one(v);\n v = mish_one(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n const int64_t n = x.numel();\n if (n == 0) return x;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(\n name='double_mish_ext_v2p',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['double_mish_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← profile_kernel (ok)
okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::linear 77378.0μs (50%) calls=1 #2: aten::addmm 77194.0μs (50%) calls=1 #3: aten::t 110.0μs (0%) calls=1 #4: aten::transpose 59.0μs (0%) calls=1 #5: aten::as_strided 19.0μs (0%) calls=1 --- Timing --- GPU time: 2705.28 μs --- Memory Bandwidth --- DRAM BW: 1290.3/3350 GB/s (38.5%) read=999.9 write=290.4 GB/s Hit rates: L2=141.5% --- Compute Throughput --- Dominant pipe: FP32 util=124.0% Pipe util: FMA=124.0%, Tensor=4.5% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 98.9% Eligible warps/cycle: 6.24 Launch: regs/thread=277, smem/blk=35840B, block=384, grid=4608 --- Memory Access Quality --- Global ld coalescing: 8.00 sectors/req (8.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 674.6% #################### short_scoreboard 95.8% ################### mio_throttle 14.0% ## --- Optimization Hints --- -> 675% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=8.0x (ideal=1.0) -> 8x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
turn 517.3s turn · 17.3s 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\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x) { return double_mish_cuda(x); }\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float mish_one_fast(float x) {\n if (x > 20.0f) return x;\n float e = expf(x);\n return x * (e * (e + 2.0f)) / (e * e + 2.0f * e + 2.0f);\n}\n\n__global__ void double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one_fast(v);\n v = mish_one_fast(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n int64_t n = x.numel();\n if (n == 0) return x;\n int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(name='double_mish_ext_v3c', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'],\n with_cuda=True, verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=4.17e-07 mean_abs_err=9.35e-09 max_rel_err=6.62e-07
turn 65.0s turn · 5.0s LLM · 0.0s tools
call → profile_kernel
callprofile_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>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x) { return double_mish_cuda(x); }\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float mish_one_fast(float x) {\n if (x > 20.0f) return x;\n float e = expf(x);\n return x * (e * (e + 2.0f)) / (e * e + 2.0f * e + 2.0f);\n}\n\n__global__ void double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one_fast(v);\n v = mish_one_fast(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n int64_t n = x.numel();\n if (n == 0) return x;\n int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(name='double_mish_ext_v3p', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'],\n with_cuda=True, verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← profile_kernel (ok)
okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::linear 72516.0μs (50%) calls=1 #2: aten::addmm 72369.0μs (50%) calls=1 #3: aten::t 84.0μs (0%) calls=1 #4: aten::transpose 41.0μs (0%) calls=1 #5: aten::as_strided 11.0μs (0%) calls=1 --- Timing --- GPU time: 2708.13 μs [↑2.8μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 1619.5/3350 GB/s (48.3%) [↑9.8% ok] read=1246.7 write=372.9 GB/s Hit rates: L2=140.7% --- Compute Throughput --- Dominant pipe: FP32 util=114.3% [↓9.7% REGRESSED] Pipe util: FMA=114.3%, Tensor=3.9% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 98.6% [↓0.3% REGRESSED] Eligible warps/cycle: 3.57 Launch: regs/thread=278, smem/blk=35840B, block=384, grid=4608 --- Memory Access Quality --- Global ld coalescing: 8.00 sectors/req (8.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 1103.8% #################### short_scoreboard 101.5% #################### mio_throttle 42.0% ######## --- Optimization Hints --- -> 1104% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=8.0x (ideal=1.0) -> 8x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 2705.28→2708.13μs (↑2.85) REGRESSED DRAM BW%: 38.52→48.34% (↑9.83) ok Compute%: 124.00→114.31% (↓9.69) REGRESSED Occupancy: 98.88→98.63% (↓0.25) REGRESSED Eligible warps/cyc: 6.24→3.57 (↓2.67) REGRESSED L2 hit%: 141.51→140.69% (↓0.82) REGRESSED
turn 711.1s turn · 11.1s 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\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x) { return double_mish_cuda(x); }\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n__device__ __forceinline__ float mish_one_fast(float x) { if (x > 20.0f) return x; float e=expf(x); return x*(e*(e+2.0f))/(e*e+2.0f*e+2.0f); }\n__global__ void double_mish_kernel(float* p, long n){ long idx=(long)blockIdx.x*blockDim.x+threadIdx.x; long stride=(long)blockDim.x*gridDim.x; for(long i=idx;i<n;i+=stride){float v=p[i]; v=mish_one_fast(v); v=mish_one_fast(v); p[i]=v;}}\ntorch::Tensor double_mish_cuda(torch::Tensor x){ long n=x.numel(); if(!n)return x; int th=256; int bl=(n+th-1)/th; if(bl>4096)bl=4096; double_mish_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),n); return x;}\n'''\n_ext=load_inline(name='double_mish_ext_tf32test', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'], with_cuda=True, verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super().__init__(); self.linear=nn.Linear(in_features,out_features)\n def forward(self,x):\n torch.backends.cuda.matmul.allow_tf32 = True\n y=self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (4 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000443'] avg_difference: ['0.000030']
turn 88.1s turn · 8.1s 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_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x){return double_mish_cuda(x);}\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n__device__ __forceinline__ float mish_one_fast(float x){ if(x>20.0f) return x; float e=__expf(x); return x*(e*(e+2.0f))/(e*e+2.0f*e+2.0f); }\n__global__ void double_mish_kernel(float* __restrict__ p, 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=p[i]; v=mish_one_fast(v); v=mish_one_fast(v); p[i]=v; }}\ntorch::Tensor double_mish_cuda(torch::Tensor x){ int64_t n=x.numel(); if(n==0)return x; int th=256; int bl=(int)((n+th-1)/th); if(bl>4096)bl=4096; double_mish_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),n); return x;}\n'''\n_ext=load_inline(name='double_mish_ext_v4fastmath', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], with_cuda=True, verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features): super().__init__(); self.linear=nn.Linear(in_features,out_features)\n def forward(self,x):\n y=self.linear(x)\n return _ext.double_mish_forward(y)\n" }
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=1.04e-08 max_rel_err=7.43e-07
turn 97.1s turn · 7.1s LLM · 0.0s tools
reasoning
**Considering performance optimizations** I'm thinking about how to optimize the profile, possibly by using static later. I could use a vectorized float4 to reduce overhead and improve memory coalescing. Each thread can handle float4 since it's contiguous. While activation is compute-heavy, vectorization might not have a massive impact. Still, maintaining the same number of threads per element is essential. If n is a multiple of 4, I can use float4 pointers, vectorized loads, and stores for better efficiency, but I'll need to test for correctness.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x){return double_mish_cuda(x);}\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n__device__ __forceinline__ float mish_one_fast(float x){ if(x>20.0f) return x; float e=__expf(x); return x*(e*(e+2.0f))/(e*e+2.0f*e+2.0f); }\n__device__ __forceinline__ float dm(float v){ v=mish_one_fast(v); return mish_one_fast(v); }\n__global__ void double_mish4_kernel(float4* __restrict__ p4, 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 a=p4[i]; a.x=dm(a.x); a.y=dm(a.y); a.z=dm(a.z); a.w=dm(a.w); p4[i]=a; }}\n__global__ void double_mish_kernel(float* __restrict__ p, 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){ p[i]=dm(p[i]); }}\ntorch::Tensor double_mish_cuda(torch::Tensor x){ int64_t n=x.numel(); if(n==0)return x; int th=256; if(((uintptr_t)x.data_ptr<float>() & 15)==0 && (n%4)==0){ int64_t n4=n/4; int bl=(int)((n4+th-1)/th); if(bl>4096)bl=4096; double_mish4_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>((float4*)x.data_ptr<float>(),n4); } else { int bl=(int)((n+th-1)/th); if(bl>4096)bl=4096; double_mish_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),n); } return x;}\n'''\n_ext=load_inline(name='double_mish_ext_v5vec', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], with_cuda=True, verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features): super().__init__(); self.linear=nn.Linear(in_features,out_features)\n def forward(self,x):\n y=self.linear(x)\n return _ext.double_mish_forward(y)\n" }
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=1.04e-08 max_rel_err=7.43e-07
turn 105.5s turn · 5.5s LLM · 0.0s tools
reasoning
**Considering runtimes** I need to compare the runtime, but I should only focus on submitting the final results. It might be worth profiling to gather some data and insights on how long things are taking. This way, I can notice any differences and understand what's working best. I wonder what the final data will look like once I analyze everything thoroughly! I'm curious to see if I can make any improvements based on the findings.
call → profile_kernel
callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x){return double_mish_cuda(x);}\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n__device__ __forceinline__ float mish_one_fast(float x){ if(x>20.0f) return x; float e=__expf(x); return x*(e*(e+2.0f))/(e*e+2.0f*e+2.0f); }\n__device__ __forceinline__ float dm(float v){ v=mish_one_fast(v); return mish_one_fast(v); }\n__global__ void double_mish4_kernel(float4* __restrict__ p4, 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 a=p4[i]; a.x=dm(a.x); a.y=dm(a.y); a.z=dm(a.z); a.w=dm(a.w); p4[i]=a; }}\n__global__ void double_mish_kernel(float* __restrict__ p, 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){ p[i]=dm(p[i]); }}\ntorch::Tensor double_mish_cuda(torch::Tensor x){ int64_t n=x.numel(); if(n==0)return x; int th=256; if(((uintptr_t)x.data_ptr<float>() & 15)==0 && (n%4)==0){ int64_t n4=n/4; int bl=(int)((n4+th-1)/th); if(bl>4096)bl=4096; double_mish4_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>((float4*)x.data_ptr<float>(),n4); } else { int bl=(int)((n+th-1)/th); if(bl>4096)bl=4096; double_mish_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),n); } return x;}\n'''\n_ext=load_inline(name='double_mish_ext_v5vecp', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], with_cuda=True, verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features): super().__init__(); self.linear=nn.Linear(in_features,out_features)\n def forward(self,x):\n y=self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← profile_kernel (ok)
okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::linear 75292.0μs (50%) calls=1 #2: aten::addmm 75137.0μs (50%) calls=1 #3: aten::t 98.0μs (0%) calls=1 #4: aten::transpose 52.0μs (0%) calls=1 #5: aten::as_strided 17.0μs (0%) calls=1 --- Timing --- GPU time: 2706.50 μs [↓1.6μs ok] --- Memory Bandwidth --- DRAM BW: 2221.2/3350 GB/s (66.3%) [↑18.0% ok] read=1690.4 write=530.9 GB/s Hit rates: L2=140.7% --- Compute Throughput --- Dominant pipe: FP32 util=101.9% [↓12.4% REGRESSED] Pipe util: FMA=101.9%, Tensor=0.3% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 94.2% [↓4.5% REGRESSED] Eligible warps/cycle: 2.40 Launch: regs/thread=274, smem/blk=35840B, block=384, grid=4608 --- Memory Access Quality --- Global ld coalescing: 20.00 sectors/req (20.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 1939.1% #################### short_scoreboard 216.1% #################### mio_throttle 73.4% ############## --- Optimization Hints --- -> 1939% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=20.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=20.0x (ideal=1.0) -> 20x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 2708.13→2706.50μs (↓1.63) ok DRAM BW%: 48.34→66.31% (↑17.96) ok Compute%: 114.31→101.94% (↓12.37) REGRESSED Occupancy: 98.63→94.15% (↓4.47) REGRESSED Eligible warps/cyc: 3.57→2.40 (↓1.17) REGRESSED Ld sectors/req: 8.00→20.00 (↑12.00) REGRESSED L2 hit%: 140.69→140.67% (↓0.01) REGRESSED
turn 1121.3s turn · 21.3s LLM · 0.0s tools
reasoning
**Testing polynomial approximation** I'm considering the need for a polynomial approximation within a defined range of [-4,4] and ensuring it meets specific tolerance levels, especially for PyTorch functions. I will evaluate whether Chebyshev degree 16 can be effective with computations done using numpy. Then, I should think about defining coefficients in CUDA using Horner's method. For smooth functions like softplus, I want to see if I can keep the error under 1e-4. Let’s get this all tested!
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\ndef _make_coeffs(deg=16, rng=4.0):\n xs = _np.linspace(-rng, rng, 20001, dtype=_np.float64)\n def mish(z):\n return z * _np.tanh(_np.log1p(_np.exp(z)))\n ys = mish(mish(xs))\n ch = _np.polynomial.chebyshev.Chebyshev.fit(xs, ys, deg, domain=[-rng, rng])\n poly = ch.convert(kind=_np.polynomial.Polynomial)\n return poly.coef.astype(_np.float32)\n_coeff = _make_coeffs(16, 4.0)\n_coeff_str = ','.join([f'{c:.9g}f' for c in _coeff])\n_deg = len(_coeff)-1\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x){return double_mish_cuda(x);}\n'''\n_cuda_src=f'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n__constant__ float coeff[{len(_coeff)}] = {{{_coeff_str}}};\n__device__ __forceinline__ float mish_one_fast(float x){{ if(x>20.0f) return x; float e=__expf(x); return x*(e*(e+2.0f))/(e*e+2.0f*e+2.0f); }}\n__device__ __forceinline__ float exact_dm(float v){{ v=mish_one_fast(v); return mish_one_fast(v); }}\n__device__ __forceinline__ float poly_dm(float x){{\n if (x < -4.0f || x > 4.0f) return exact_dm(x);\n float y = coeff[{_deg}];\n #pragma unroll\n for(int j={_deg-1}; j>=0; --j) y = fmaf(y, x, coeff[j]);\n return y;\n}}\n__global__ void double_mish_kernel(float* __restrict__ p, 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){{ p[i]=poly_dm(p[i]); }} }}\ntorch::Tensor double_mish_cuda(torch::Tensor x){{ int64_t n=x.numel(); if(n==0)return x; int th=256; int bl=(int)((n+th-1)/th); if(bl>4096)bl=4096; double_mish_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),n); return x;}}\n'''\n_ext=load_inline(name='double_mish_ext_poly16', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], with_cuda=True, verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features): super().__init__(); self.linear=nn.Linear(in_features,out_features)\n def forward(self,x):\n y=self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000799', '0.000799', '0.000799', '0.000799', '0.000799'] avg_difference: ['0.000421', '0.000421', '0.000421', '0.000421', '0.000421']
turn 12 · 1 tool call(s) · 10.8s turn · 10.8s LLM · 6,891 chars (click to expand)
turn 1210.8s turn · 10.8s 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\nimport numpy as _np\n\ndef _make_coeffs(deg=32, rng=4.0):\n xs = _np.linspace(-rng, rng, 100001, dtype=_np.float64)\n def mish(z): return z * _np.tanh(_np.log1p(_np.exp(z)))\n ys = mish(mish(xs))\n ch = _np.polynomial.chebyshev.Chebyshev.fit(xs, ys, deg, domain=[-rng, rng])\n return ch.convert(kind=_np.polynomial.Polynomial).coef.astype(_np.float32)\n_coeff=_make_coeffs(32,4.0); _coeff_str=','.join([f'{c:.9g}f' for c in _coeff]); _deg=len(_coeff)-1\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x){return double_mish_cuda(x);}\n'''\n_cuda_src=f'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n__constant__ float coeff[{len(_coeff)}] = {{{_coeff_str}}};\n__device__ __forceinline__ float mish_one_fast(float x){{ if(x>20.0f) return x; float e=__expf(x); return x*(e*(e+2.0f))/(e*e+2.0f*e+2.0f); }}\n__device__ __forceinline__ float exact_dm(float v){{ v=mish_one_fast(v); return mish_one_fast(v); }}\n__device__ __forceinline__ float poly_dm(float x){{ if (x < -4.0f || x > 4.0f) return exact_dm(x); float y=coeff[{_deg}]; #pragma unroll for(int j={_deg-1}; j>=0; --j) y=fmaf(y,x,coeff[j]); return y; }}\n__global__ void double_mish_kernel(float* __restrict__ p, 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) p[i]=poly_dm(p[i]); }}\ntorch::Tensor double_mish_cuda(torch::Tensor x){{ int64_t n=x.numel(); if(n==0)return x; int th=256; int bl=(int)((n+th-1)/th); if(bl>4096)bl=4096; double_mish_kernel<<<bl,th,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),n); return x;}}\n'''\n_ext=load_inline(name='double_mish_ext_poly32b', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['double_mish_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], with_cuda=True, verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features): super().__init__(); self.linear=nn.Linear(in_features,out_features)\n def forward(self,x):\n y=self.linear(x); return _ext.double_mish_forward(y)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: kernel did not compile. Error building extension 'double_mish_ext_poly32b': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=double_mish_ext_poly32b -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_3746ab37b6bf/double_mish_ext_poly32b/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=double_mish_ext_poly32b -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/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/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_3746ab37b6bf/double_mish_ext_poly32b/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=double_mish_ext_poly32b -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/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/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_3746ab37b6bf/double_mish_ext_poly32b/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_3746ab37b6bf/double_mish_ext_poly32b/cuda.cu(11): error: "#" not expected here float poly_dm(float x){ if (x < -4.0f || x > 4.0f) return exact_dm(x); float y=coeff[32]; #pragma unroll for(int j=31; j>=0; --j) y=fmaf(y,x,coeff[j]); return y; } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_3746ab37b6bf/double_mish_ext_poly32b/cuda.cu(11): error: expected an expression float poly_dm(float x){ if (x < -4.0f || x > 4.0f) return exact_dm(x); float y=coeff[32]; #pragma unroll for(int j=31; j>=0; --j) y=fmaf(y,x,coeff[j]); return y; } ^ 2 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_3746ab37b6bf/double_mish_ext_poly32b/cuda.cu". ninja: build stopped: subcommand failed.
turn 1310.5s turn · 10.5s LLM · 0.0s tools
call → static_check
callstatic_check { "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>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x) { return double_mish_cuda(x); }\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float mish_one_fast(float x) {\n if (x > 20.0f) return x;\n float e = __expf(x);\n return x * (e * (e + 2.0f)) / (e * e + 2.0f * e + 2.0f);\n}\n\n__global__ void double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one_fast(v);\n v = mish_one_fast(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n int64_t n = x.numel();\n if (n == 0) return x;\n int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(\n name='double_mish_ext_final',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['double_mish_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← static_check (ok)
okstatic_check static_check PASSED: no violations or warnings detected.
turn 144.5s turn · 4.5s 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\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor double_mish_cuda(torch::Tensor x);\ntorch::Tensor double_mish_forward(torch::Tensor x) { return double_mish_cuda(x); }\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float mish_one_fast(float x) {\n if (x > 20.0f) return x;\n float e = __expf(x);\n return x * (e * (e + 2.0f)) / (e * e + 2.0f * e + 2.0f);\n}\n\n__global__ void double_mish_kernel(float* __restrict__ p, 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 = p[i];\n v = mish_one_fast(v);\n v = mish_one_fast(v);\n p[i] = v;\n }\n}\n\ntorch::Tensor double_mish_cuda(torch::Tensor x) {\n int64_t n = x.numel();\n if (n == 0) return x;\n int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 4096) blocks = 4096;\n double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n'''\n\n_ext = load_inline(\n name='double_mish_ext_final',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['double_mish_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features):\n super(ModelNew, self).__init__()\n self.linear = nn.Linear(in_features, out_features)\n\n def forward(self, x):\n y = self.linear(x)\n return _ext.double_mish_forward(y)\n" }
result ← submit_kernel (fail)
failsubmit_kernel submit_kernel FAILED: kernel did not compile. Error building extension 'double_mish_ext_final': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=double_mish_ext_final -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_b5ac6cecb9ce/double_mish_ext_final/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=double_mish_ext_final -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_b5ac6cecb9ce/double_mish_ext_final/main.cpp -o main.o /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_b5ac6cecb9ce/double_mish_ext_final/main.cpp:9:1: fatal error: error writing to /var/tmp/ccTJInbH.s: No space left on device 9 | } | ^ compilation terminated. [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=double_mish_ext_final -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/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/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_29_cache/k_b5ac6cecb9ce/double_mish_ext_final/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.
final submitted kernel (63 lines, 1,786 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 double_mish_cuda(torch::Tensor x);
torch::Tensor double_mish_forward(torch::Tensor x) { return double_mish_cuda(x); }
'''

_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>

__device__ __forceinline__ float mish_one_fast(float x) {
    if (x > 20.0f) return x;
    float e = __expf(x);
    return x * (e * (e + 2.0f)) / (e * e + 2.0f * e + 2.0f);
}

__global__ void double_mish_kernel(float* __restrict__ p, 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 = p[i];
        v = mish_one_fast(v);
        v = mish_one_fast(v);
        p[i] = v;
    }
}

torch::Tensor double_mish_cuda(torch::Tensor x) {
    int64_t n = x.numel();
    if (n == 0) return x;
    int threads = 256;
    int blocks = (int)((n + threads - 1) / threads);
    if (blocks > 4096) blocks = 4096;
    double_mish_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);
    return x;
}
'''

_ext = load_inline(
    name='double_mish_ext_final',
    cpp_sources=_cpp_src,
    cuda_sources=_cuda_src,
    functions=['double_mish_forward'],
    extra_cflags=['-O3'],
    extra_cuda_cflags=['-O3', '--use_fast_math'],
    with_cuda=True,
    verbose=False,
)

class ModelNew(nn.Module):
    def __init__(self, in_features, out_features):
        super(ModelNew, self).__init__()
        self.linear = nn.Linear(in_features, out_features)

    def forward(self, x):
        y = self.linear(x)
        return _ext.double_mish_forward(y)