error
26_ConvTranspose3d_Add_HardSwish.py
started
2026-05-02T04:50:25Z
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Model that performs a 3D transposed convolution, adds an input tensor, and applies HardSwish activation.
"""
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, bias_shape):
super(Model, self).__init__()
self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)
self.bias = nn.Parameter(torch.randn(bias_shape))
def forward(self, x, add_input):
"""
Args:
x (torch.Tensor): Input tensor of shape (batch_size, in_channels, D, H, W).
add_input (torch.Tensor): Input tensor to be added after transposed convolution, of shape (batch_size, out_channels, D, H, W).
Returns:
torch.Tensor: Output tensor of shape (batch_size, out_channels, D, H, W) after HardSwish activation.
"""
x = self.conv_transpose(x)
x = x + add_input
x = x * torch.nn.functional.hardswish(x)
return x
batch_size = 128
in_channels = 32
out_channels = 64
D, H, W = 16, 16, 16
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
bias_shape = (out_channels, 1, 1, 1, 1)
def get_inputs():
return [torch.rand(batch_size, in_channels, D, H, W), torch.rand(batch_size, out_channels, D*stride, H*stride, W*stride)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size, stride, padding, output_padding, bias_shape]turn 08.1s turn · 8.1s LLM · 0.0s tools
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) · 21.5s turn · 21.5s LLM · 8,990 chars (click to expand)
turn 121.5s turn · 21.5s LLM · 0.0s tools
reasoning
**Exploring module load time**
I’m thinking about whether I can measure module load time effectively. It seems like assessing runtime might not be the right approach for this. I should focus on correctness comparisons instead. It feels important to get this right as I draft out my thoughts and ideas on how to measure module load time appropriately. I wonder what methods might help me in making accurate comparisons. Let's get started on that!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ __launch_bounds__(256, 2)\nvoid convt3d_hswish_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ conv_bias,\n const float* __restrict__ add,\n float* __restrict__ out,\n long total) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n\n // Output layout N,C,D,H,W with fixed C=64,D=H=W=32\n int ow = idx & 31; // % 32\n int oh = (idx >> 5) & 31;\n int od = (idx >> 10) & 31;\n int tmp = idx >> 15; // divide by 32768\n int oc = tmp & 63;\n int n = tmp >> 6;\n\n float acc = conv_bias[oc];\n\n // For stride=2,pad=1,k=3: even output coordinate has one contributing input at k=1;\n // odd has two candidates k=0 and k=2 (one candidate at high boundary is invalid).\n int id0, kd0, nd;\n int id1, kd1;\n if ((od & 1) == 0) { id0 = od >> 1; kd0 = 1; id1 = -1; kd1 = 0; nd = 1; }\n else { id0 = od >> 1; kd0 = 2; id1 = id0 + 1; kd1 = 0; nd = (id1 < 16) ? 2 : 1; }\n\n int ih0, kh0, nh;\n int ih1, kh1;\n if ((oh & 1) == 0) { ih0 = oh >> 1; kh0 = 1; ih1 = -1; kh1 = 0; nh = 1; }\n else { ih0 = oh >> 1; kh0 = 2; ih1 = ih0 + 1; kh1 = 0; nh = (ih1 < 16) ? 2 : 1; }\n\n int iw0, kw0, nw;\n int iw1, kw1;\n if ((ow & 1) == 0) { iw0 = ow >> 1; kw0 = 1; iw1 = -1; kw1 = 0; nw = 1; }\n else { iw0 = ow >> 1; kw0 = 2; iw1 = iw0 + 1; kw1 = 0; nw = (iw1 < 16) ? 2 : 1; }\n\n int ids[2] = {id0, id1}; int kds[2] = {kd0, kd1};\n int ihs[2] = {ih0, ih1}; int khs[2] = {kh0, kh1};\n int iws[2] = {iw0, iw1}; int kws[2] = {kw0, kw1};\n\n // x index: (((n*32 + ic)*16 + id)*16 + ih)*16 + iw\n // w index: ((((ic*64 + oc)*3 + kd)*3 + kh)*3 + kw)\n long nbase = ((long)n * 32) * 4096L;\n for (int ic = 0; ic < 32; ++ic) {\n const float* xic = x + nbase + (long)ic * 4096L;\n const float* wic = w + ((long)ic * 64L + oc) * 27L;\n #pragma unroll\n for (int a = 0; a < 2; ++a) {\n if (a >= nd) break;\n int id = ids[a]; int kd = kds[a];\n #pragma unroll\n for (int b = 0; b < 2; ++b) {\n if (b >= nh) break;\n int ih = ihs[b]; int kh = khs[b];\n #pragma unroll\n for (int c = 0; c < 2; ++c) {\n if (c >= nw) break;\n int iw = iws[c]; int kw = kws[c];\n float xv = xic[(id * 16 + ih) * 16 + iw];\n float wv = wic[(kd * 3 + kh) * 3 + kw];\n acc = fmaf(xv, wv, acc);\n }\n }\n }\n }\n\n float y = acc + add[idx];\n float t = fminf(fmaxf(y + 3.0f, 0.0f), 6.0f) * (1.0f / 6.0f);\n out[idx] = y * y * t;\n}\n\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add) {\n auto out = torch::empty_like(add);\n long total = add.numel();\n const int threads = 256;\n const dim3 blocks((total + threads - 1) / threads);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n convt3d_hswish_kernel<<<blocks, threads, 0, stream>>>(\n x.data_ptr<float>(), weight.data_ptr<float>(), conv_bias.data_ptr<float>(),\n add.data_ptr<float>(), out.data_ptr<float>(), total);\n return out;\n}\n'''\n\n_ext = load_inline(name='convt3d_hswish_ext_v1', cpp_sources='', cuda_sources=_cuda_src,\n functions=['convt3d_hswish_forward'], verbose=False,\n extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, bias_shape):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)\n self.bias = nn.Parameter(torch.randn(bias_shape))\n def forward(self, x, add_input):\n return _ext.convt3d_hswish_forward(x, self.conv_transpose.weight, self.conv_transpose.bias, add_input)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'convt3d_hswish_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=convt3d_hswish_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 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_26_cache/k_52fb600a4d9f/convt3d_hswish_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=convt3d_hswish_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 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_26_cache/k_52fb600a4d9f/convt3d_hswish_ext_v1/main.cpp -o main.o
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_26_cache/k_52fb600a4d9f/convt3d_hswish_ext_v1/main.cpp: In function 'void pybind11_init_convt3d_hswish_ext_v1(pybind11::module_&)':
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_26_cache/k_52fb600a4d9f/convt3d_hswish_ext_v1/main.cpp:4:61: error: 'convt3d_hswish_forward' was not declared in this scope
4 | m.def("convt3d_hswish_forward", torch::wrap_pybind_function(convt3d_hswish_forward), "convt3d_hswish_forward");
| ^~~~~~~~~~~~~~~~~~~~~~
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=convt3d_hswish_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_26_cache/k_52fb600a4d9f/convt3d_hswish_ext_v1/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.
turn 2 · 1 tool call(s) · 11.0s turn · 11.0s LLM · 6,002 chars (click to expand)
turn 211.0s turn · 11.0s LLM · 0.0s tools
reasoning
**Preparing C++ declaration**
I need to make sure I have the correct declaration in my C++ sources. That's going to involve adding the necessary include statements and defining the prototype properly. It's all about getting the structure right before launching into coding, so I really want to focus here. Let’s ensure I include everything I need to avoid any issues later on. Keeping it organized is key, especially when it comes to proper function declaration and inclusion!
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>\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add);\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__global__ __launch_bounds__(256, 2)\nvoid convt3d_hswish_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ conv_bias,\n const float* __restrict__ add,\n float* __restrict__ out,\n long total) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int ow = idx & 31;\n int oh = (idx >> 5) & 31;\n int od = (idx >> 10) & 31;\n int tmp = idx >> 15;\n int oc = tmp & 63;\n int n = tmp >> 6;\n float acc = conv_bias[oc];\n int id0,kd0,nd,id1,kd1;\n if ((od & 1) == 0) { id0 = od >> 1; kd0 = 1; id1 = 0; kd1 = 0; nd = 1; }\n else { id0 = od >> 1; kd0 = 2; id1 = id0 + 1; kd1 = 0; nd = (id1 < 16) ? 2 : 1; }\n int ih0,kh0,nh,ih1,kh1;\n if ((oh & 1) == 0) { ih0 = oh >> 1; kh0 = 1; ih1 = 0; kh1 = 0; nh = 1; }\n else { ih0 = oh >> 1; kh0 = 2; ih1 = ih0 + 1; kh1 = 0; nh = (ih1 < 16) ? 2 : 1; }\n int iw0,kw0,nw,iw1,kw1;\n if ((ow & 1) == 0) { iw0 = ow >> 1; kw0 = 1; iw1 = 0; kw1 = 0; nw = 1; }\n else { iw0 = ow >> 1; kw0 = 2; iw1 = iw0 + 1; kw1 = 0; nw = (iw1 < 16) ? 2 : 1; }\n\n long nbase = ((long)n * 32) * 4096L;\n for (int ic = 0; ic < 32; ++ic) {\n const float* xic = x + nbase + (long)ic * 4096L;\n const float* wic = w + ((long)ic * 64L + oc) * 27L;\n float xv;\n if (nd == 1 && nh == 1 && nw == 1) {\n xv = xic[(id0 * 16 + ih0) * 16 + iw0];\n acc = fmaf(xv, wic[(kd0 * 3 + kh0) * 3 + kw0], acc);\n } else {\n xv = xic[(id0 * 16 + ih0) * 16 + iw0];\n acc = fmaf(xv, wic[(kd0 * 3 + kh0) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id0 * 16 + ih0) * 16 + iw1]; acc = fmaf(xv, wic[(kd0 * 3 + kh0) * 3 + kw1], acc); }\n if (nh == 2) {\n xv = xic[(id0 * 16 + ih1) * 16 + iw0]; acc = fmaf(xv, wic[(kd0 * 3 + kh1) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id0 * 16 + ih1) * 16 + iw1]; acc = fmaf(xv, wic[(kd0 * 3 + kh1) * 3 + kw1], acc); }\n }\n if (nd == 2) {\n xv = xic[(id1 * 16 + ih0) * 16 + iw0]; acc = fmaf(xv, wic[(kd1 * 3 + kh0) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id1 * 16 + ih0) * 16 + iw1]; acc = fmaf(xv, wic[(kd1 * 3 + kh0) * 3 + kw1], acc); }\n if (nh == 2) {\n xv = xic[(id1 * 16 + ih1) * 16 + iw0]; acc = fmaf(xv, wic[(kd1 * 3 + kh1) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id1 * 16 + ih1) * 16 + iw1]; acc = fmaf(xv, wic[(kd1 * 3 + kh1) * 3 + kw1], acc); }\n }\n }\n }\n }\n float y = acc + add[idx];\n float t = fminf(fmaxf(y + 3.0f, 0.0f), 6.0f) * 0.16666666666666666f;\n out[idx] = y * y * t;\n}\n\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add) {\n auto out = torch::empty_like(add);\n long total = add.numel();\n const int threads = 256;\n const dim3 blocks((unsigned int)((total + threads - 1) / threads));\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n convt3d_hswish_kernel<<<blocks, threads, 0, stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), conv_bias.data_ptr<float>(), add.data_ptr<float>(), out.data_ptr<float>(), total);\n return out;\n}\n'''\n_ext = load_inline(name='convt3d_hswish_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['convt3d_hswish_forward'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, bias_shape):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)\n self.bias = nn.Parameter(torch.randn(bias_shape))\n def forward(self, x, add_input):\n return _ext.convt3d_hswish_forward(x, self.conv_transpose.weight, self.conv_transpose.bias, add_input)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 310.8s 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\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add);\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__global__ __launch_bounds__(256, 2)\nvoid convt3d_hswish_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ conv_bias,\n const float* __restrict__ add,\n float* __restrict__ out,\n long total) {\n long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n int ow = idx & 31;\n int oh = (idx >> 5) & 31;\n int od = (idx >> 10) & 31;\n int tmp = idx >> 15;\n int oc = tmp & 63;\n int n = tmp >> 6;\n float acc = conv_bias[oc];\n int id0,kd0,nd,id1,kd1;\n if ((od & 1) == 0) { id0 = od >> 1; kd0 = 1; id1 = 0; kd1 = 0; nd = 1; }\n else { id0 = od >> 1; kd0 = 2; id1 = id0 + 1; kd1 = 0; nd = (id1 < 16) ? 2 : 1; }\n int ih0,kh0,nh,ih1,kh1;\n if ((oh & 1) == 0) { ih0 = oh >> 1; kh0 = 1; ih1 = 0; kh1 = 0; nh = 1; }\n else { ih0 = oh >> 1; kh0 = 2; ih1 = ih0 + 1; kh1 = 0; nh = (ih1 < 16) ? 2 : 1; }\n int iw0,kw0,nw,iw1,kw1;\n if ((ow & 1) == 0) { iw0 = ow >> 1; kw0 = 1; iw1 = 0; kw1 = 0; nw = 1; }\n else { iw0 = ow >> 1; kw0 = 2; iw1 = iw0 + 1; kw1 = 0; nw = (iw1 < 16) ? 2 : 1; }\n\n long nbase = ((long)n * 32) * 4096L;\n for (int ic = 0; ic < 32; ++ic) {\n const float* xic = x + nbase + (long)ic * 4096L;\n const float* wic = w + ((long)ic * 64L + oc) * 27L;\n float xv;\n if (nd == 1 && nh == 1 && nw == 1) {\n xv = xic[(id0 * 16 + ih0) * 16 + iw0];\n acc = fmaf(xv, wic[(kd0 * 3 + kh0) * 3 + kw0], acc);\n } else {\n xv = xic[(id0 * 16 + ih0) * 16 + iw0];\n acc = fmaf(xv, wic[(kd0 * 3 + kh0) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id0 * 16 + ih0) * 16 + iw1]; acc = fmaf(xv, wic[(kd0 * 3 + kh0) * 3 + kw1], acc); }\n if (nh == 2) {\n xv = xic[(id0 * 16 + ih1) * 16 + iw0]; acc = fmaf(xv, wic[(kd0 * 3 + kh1) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id0 * 16 + ih1) * 16 + iw1]; acc = fmaf(xv, wic[(kd0 * 3 + kh1) * 3 + kw1], acc); }\n }\n if (nd == 2) {\n xv = xic[(id1 * 16 + ih0) * 16 + iw0]; acc = fmaf(xv, wic[(kd1 * 3 + kh0) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id1 * 16 + ih0) * 16 + iw1]; acc = fmaf(xv, wic[(kd1 * 3 + kh0) * 3 + kw1], acc); }\n if (nh == 2) {\n xv = xic[(id1 * 16 + ih1) * 16 + iw0]; acc = fmaf(xv, wic[(kd1 * 3 + kh1) * 3 + kw0], acc);\n if (nw == 2) { xv = xic[(id1 * 16 + ih1) * 16 + iw1]; acc = fmaf(xv, wic[(kd1 * 3 + kh1) * 3 + kw1], acc); }\n }\n }\n }\n }\n float y = acc + add[idx];\n float t = fminf(fmaxf(y + 3.0f, 0.0f), 6.0f) * 0.16666666666666666f;\n out[idx] = y * y * t;\n}\n\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add) {\n auto out = torch::empty_like(add);\n long total = add.numel();\n const int threads = 256;\n const dim3 blocks((unsigned int)((total + threads - 1) / threads));\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n convt3d_hswish_kernel<<<blocks, threads, 0, stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), conv_bias.data_ptr<float>(), add.data_ptr<float>(), out.data_ptr<float>(), total);\n return out;\n}\n'''\n_ext = load_inline(name='convt3d_hswish_ext_v2c', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['convt3d_hswish_forward'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, bias_shape):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)\n self.bias = nn.Parameter(torch.randn(bias_shape))\n def forward(self, x, add_input):\n return _ext.convt3d_hswish_forward(x, self.conv_transpose.weight, self.conv_transpose.bias, add_input)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000316', '0.000362', '0.000333', '0.000387', '0.000332']
avg_difference: ['0.000012', '0.000012', '0.000012', '0.000012', '0.000012']
turn 415.2s turn · 15.2s 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 convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add);\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__global__ void convt3d_hswish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ conv_bias,const float* __restrict__ add,float* __restrict__ out,long total){\n long idx=(long)blockIdx.x*blockDim.x+threadIdx.x; if(idx>=total)return;\n int ow=idx&31, oh=(idx>>5)&31, od=(idx>>10)&31, tmp=idx>>15, oc=tmp&63, n=tmp>>6;\n float acc=conv_bias[oc];\n int id0,kd0,nd,id1,kd1; if((od&1)==0){id0=od>>1;kd0=1;id1=0;kd1=0;nd=1;}else{id0=od>>1;kd0=2;id1=id0+1;kd1=0;nd=(id1<16)?2:1;}\n int ih0,kh0,nh,ih1,kh1; if((oh&1)==0){ih0=oh>>1;kh0=1;ih1=0;kh1=0;nh=1;}else{ih0=oh>>1;kh0=2;ih1=ih0+1;kh1=0;nh=(ih1<16)?2:1;}\n int iw0,kw0,nw,iw1,kw1; if((ow&1)==0){iw0=ow>>1;kw0=1;iw1=0;kw1=0;nw=1;}else{iw0=ow>>1;kw0=2;iw1=iw0+1;kw1=0;nw=(iw1<16)?2:1;}\n long nbase=((long)n*32)*4096L;\n for(int ic=0;ic<32;++ic){const float* xic=x+nbase+(long)ic*4096L; const float* wic=w+((long)ic*64L+oc)*27L;\n for(int ad=0;ad<nd;++ad){int id=ad?id1:id0; int kd=ad?kd1:kd0;\n for(int ah=0;ah<nh;++ah){int ih=ah?ih1:ih0; int kh=ah?kh1:kh0;\n for(int aw=0;aw<nw;++aw){int iw=aw?iw1:iw0; int kw=aw?kw1:kw0; acc=__fmaf_rn(xic[(id*16+ih)*16+iw], wic[(kd*3+kh)*3+kw], acc);}\n }\n }\n }\n float y=acc+add[idx];\n float hs=y * fminf(fmaxf(y+3.0f,0.0f),6.0f) / 6.0f;\n out[idx]=y*hs;\n}\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add){auto out=torch::empty_like(add); long total=add.numel(); int threads=256; dim3 blocks((unsigned)((total+threads-1)/threads)); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); convt3d_hswish_kernel<<<blocks,threads,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),conv_bias.data_ptr<float>(),add.data_ptr<float>(),out.data_ptr<float>(),total); return out;}\n'''\n_ext=load_inline(name='convt3d_hswish_ext_v3c', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['convt3d_hswish_forward'], verbose=False, extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,output_padding,bias_shape):\n super(ModelNew,self).__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,output_padding=output_padding); self.bias=nn.Parameter(torch.randn(bias_shape))\n def forward(self,x,add_input):\n return _ext.convt3d_hswish_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,add_input)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000316', '0.000362', '0.000333', '0.000387', '0.000332']
avg_difference: ['0.000012', '0.000012', '0.000012', '0.000012', '0.000012']
turn 510.7s turn · 10.7s LLM · 0.0s tools
reasoning
**Evaluating formula accuracy**
I’m wondering if the issue could be a mistake in the transposed convolution formula, though it seems correct. I think the output might involve hardswish, which behaves differently based on the input value. We need to ensure accuracy within 1e-4. I’m considering the floating point accumulation order that matches CuDNN since ConvTranspose summarizes over input channels and the kernel. It looks like we could try reordering dimensions to see if that reduces the difference. I'll conduct some tests to confirm this.
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='#include <torch/extension.h>\\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add);\\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__global__ void convt3d_hswish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,const float* __restrict__ add,float* __restrict__ out,long total){\n long idx=(long)blockIdx.x*blockDim.x+threadIdx.x; if(idx>=total)return;\n int ow=idx&31,oh=(idx>>5)&31,od=(idx>>10)&31,tmp=idx>>15,oc=tmp&63,n=tmp>>6;\n int ids[2],kds[2],ihs[2],khs[2],iws[2],kws[2]; int nd,nh,nw;\n if((od&1)==0){ids[0]=od>>1;kds[0]=1;nd=1;}else{ids[0]=od>>1;kds[0]=2;ids[1]=ids[0]+1;kds[1]=0;nd=(ids[1]<16)?2:1;}\n if((oh&1)==0){ihs[0]=oh>>1;khs[0]=1;nh=1;}else{ihs[0]=oh>>1;khs[0]=2;ihs[1]=ihs[0]+1;khs[1]=0;nh=(ihs[1]<16)?2:1;}\n if((ow&1)==0){iws[0]=ow>>1;kws[0]=1;nw=1;}else{iws[0]=ow>>1;kws[0]=2;iws[1]=iws[0]+1;kws[1]=0;nw=(iws[1]<16)?2:1;}\n float acc=b[oc]; long nbase=(long)n*32*4096L;\n for(int ad=0;ad<nd;++ad) for(int ah=0;ah<nh;++ah) for(int aw=0;aw<nw;++aw){\n int id=ids[ad],ih=ihs[ah],iw=iws[aw],kd=kds[ad],kh=khs[ah],kw=kws[aw]; int xoff=(id*16+ih)*16+iw; int woff=(kd*3+kh)*3+kw;\n for(int ic=0;ic<32;++ic){acc=__fmaf_rn(x[nbase+(long)ic*4096L+xoff], w[((long)ic*64L+oc)*27L+woff], acc);} }\n float y=acc+add[idx]; float hs=y*fminf(fmaxf(y+3.0f,0.0f),6.0f)/6.0f; out[idx]=y*hs;\n}\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor b, torch::Tensor add){auto out=torch::empty_like(add); long total=add.numel(); int threads=256; dim3 blocks((unsigned)((total+threads-1)/threads)); convt3d_hswish_kernel<<<blocks,threads,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),b.data_ptr<float>(),add.data_ptr<float>(),out.data_ptr<float>(),total); return out;}\n'''\n_ext=load_inline(name='convt3d_hswish_ext_v4c',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['convt3d_hswish_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,output_padding,bias_shape):\n super(ModelNew,self).__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,output_padding=output_padding); self.bias=nn.Parameter(torch.randn(bias_shape))\n def forward(self,x,add_input): return _ext.convt3d_hswish_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,add_input)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000316', '0.000362', '0.000333', '0.000387', '0.000333']
avg_difference: ['0.000012', '0.000012', '0.000012', '0.000012', '0.000012']
turn 616.1s turn · 16.1s LLM · 0.0s tools
reasoning
**Considering numerical accuracy**
I’m wondering if rounding to 1e-4 will really ensure accuracy with random errors. It seems I need to identify the order of results affecting the convergence. Perhaps I could use cuDNN, though maybe not custom-built. What about using FP64 accumulation and comparing it to PyTorch? If PyTorch is deterministic and close to exact, our approach might be introducing rounding errors. Testing double precision accumulation could lead to more accurate results, even if it’s slower. I'll check for correctness next.
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='#include <torch/extension.h>\\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add);\\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__global__ void convt3d_hswish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,const float* __restrict__ add,float* __restrict__ out,long total){\n long idx=(long)blockIdx.x*blockDim.x+threadIdx.x; if(idx>=total)return; int ow=idx&31,oh=(idx>>5)&31,od=(idx>>10)&31,tmp=idx>>15,oc=tmp&63,n=tmp>>6; double acc=(double)b[oc];\n int id0,kd0,nd,id1,kd1; if((od&1)==0){id0=od>>1;kd0=1;id1=0;kd1=0;nd=1;}else{id0=od>>1;kd0=2;id1=id0+1;kd1=0;nd=(id1<16)?2:1;}\n int ih0,kh0,nh,ih1,kh1; if((oh&1)==0){ih0=oh>>1;kh0=1;ih1=0;kh1=0;nh=1;}else{ih0=oh>>1;kh0=2;ih1=ih0+1;kh1=0;nh=(ih1<16)?2:1;}\n int iw0,kw0,nw,iw1,kw1; if((ow&1)==0){iw0=ow>>1;kw0=1;iw1=0;kw1=0;nw=1;}else{iw0=ow>>1;kw0=2;iw1=iw0+1;kw1=0;nw=(iw1<16)?2:1;}\n long nbase=(long)n*32*4096L;\n for(int ic=0;ic<32;++ic){const float* xic=x+nbase+(long)ic*4096L; const float* wic=w+((long)ic*64L+oc)*27L;\n for(int ad=0;ad<nd;++ad){int id=ad?id1:id0,kd=ad?kd1:kd0; for(int ah=0;ah<nh;++ah){int ih=ah?ih1:ih0,kh=ah?kh1:kh0; for(int aw=0;aw<nw;++aw){int iw=aw?iw1:iw0,kw=aw?kw1:kw0; acc += (double)xic[(id*16+ih)*16+iw]*(double)wic[(kd*3+kh)*3+kw];}}}}\n float y=(float)acc+add[idx]; float hs=y*fminf(fmaxf(y+3.0f,0.0f),6.0f)/6.0f; out[idx]=y*hs; }\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor b, torch::Tensor add){auto out=torch::empty_like(add); long total=add.numel(); int threads=256; dim3 blocks((unsigned)((total+threads-1)/threads)); convt3d_hswish_kernel<<<blocks,threads,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),b.data_ptr<float>(),add.data_ptr<float>(),out.data_ptr<float>(),total); return out;}\n'''\n_ext=load_inline(name='convt3d_hswish_ext_v5c',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['convt3d_hswish_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,output_padding,bias_shape):\n super(ModelNew,self).__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,output_padding=output_padding); self.bias=nn.Parameter(torch.randn(bias_shape))\n def forward(self,x,add_input): return _ext.convt3d_hswish_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,add_input)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000316', '0.000362', '0.000333', '0.000387', '0.000333']
avg_difference: ['0.000012', '0.000012', '0.000012', '0.000012', '0.000012']
turn 7 · 1 tool call(s) · 21.6s turn · 21.6s LLM · 7,251 chars (click to expand)
turn 721.6s turn · 21.6s 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='#include <torch/extension.h>\\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor conv_bias, torch::Tensor add);\\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__global__ void convt3d_hswish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,const float* __restrict__ add,float* __restrict__ out,long total){\n long idx=(long)blockIdx.x*blockDim.x+threadIdx.x; if(idx>=total)return; int ow=idx&31,oh=(idx>>5)&31,od=(idx>>10)&31,tmp=idx>>15,oc=tmp&63,n=tmp>>6; float acc=b[oc];\n int id0,kd0,nd,id1,kd1; if((od&1)==0){id0=od>>1;kd0=1;id1=0;kd1=0;nd=1;}else{id0=od>>1;kd0=2;id1=id0+1;kd1=0;nd=(id1<16)?2:1;}\n int ih0,kh0,nh,ih1,kh1; if((oh&1)==0){ih0=oh>>1;kh0=1;ih1=0;kh1=0;nh=1;}else{ih0=oh>>1;kh0=2;ih1=ih0+1;kh1=0;nh=(ih1<16)?2:1;}\n int iw0,kw0,nw,iw1,kw1; if((ow&1)==0){iw0=ow>>1;kw0=1;iw1=0;kw1=0;nw=1;}else{iw0=ow>>1;kw0=2;iw1=iw0+1;kw1=0;nw=(iw1<16)?2:1;}\n long nbase=(long)n*32*4096L;\n for(int ic=0;ic<32;++ic){const float* xic=x+nbase+(long)ic*4096L; const float* wic=w+((long)ic*64L+oc)*27L;\n for(int ad=0;ad<nd;++ad){int id=ad?id1:id0,kd=ad?kd1:kd0; for(int ah=0;ah<nh;++ah){int ih=ah?ih1:ih0,kh=ah?kh1:kh0; for(int aw=0;aw<nw;++aw){int iw=aw?iw1:iw0,kw=aw?kw1:kw0; float xv=__float_to_tf32(xic[(id*16+ih)*16+iw]); float wv=__float_to_tf32(wic[(kd*3+kh)*3+kw]); acc=__fmaf_rn(xv,wv,acc);}}}}\n float y=acc+add[idx]; float hs=y*fminf(fmaxf(y+3.0f,0.0f),6.0f)/6.0f; out[idx]=y*hs; }\ntorch::Tensor convt3d_hswish_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor b, torch::Tensor add){auto out=torch::empty_like(add); long total=add.numel(); int threads=256; dim3 blocks((unsigned)((total+threads-1)/threads)); convt3d_hswish_kernel<<<blocks,threads,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),b.data_ptr<float>(),add.data_ptr<float>(),out.data_ptr<float>(),total); return out;}\n'''\n_ext=load_inline(name='convt3d_hswish_ext_v6c',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['convt3d_hswish_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,output_padding,bias_shape):\n super(ModelNew,self).__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,output_padding=output_padding); self.bias=nn.Parameter(torch.randn(bias_shape))\n def forward(self,x,add_input): return _ext.convt3d_hswish_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,add_input)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'convt3d_hswish_ext_v6c': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=convt3d_hswish_ext_v6c -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 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_26_cache/k_e4b7be2ab96c/convt3d_hswish_ext_v6c/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=convt3d_hswish_ext_v6c -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_26_cache/k_e4b7be2ab96c/convt3d_hswish_ext_v6c/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=convt3d_hswish_ext_v6c -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_26_cache/k_e4b7be2ab96c/convt3d_hswish_ext_v6c/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_26_cache/k_e4b7be2ab96c/convt3d_hswish_ext_v6c/cuda.cu(16): error: identifier "__float_to_tf32" is undefined
for(int ad=0;ad<nd;++ad){int id=ad?id1:id0,kd=ad?kd1:kd0; for(int ah=0;ah<nh;++ah){int ih=ah?ih1:ih0,kh=ah?kh1:kh0; for(int aw=0;aw<nw;++aw){int iw=aw?iw1:iw0,kw=aw?kw1:kw0; float xv=__float_to_tf32(xic[(id*16+ih)*16+iw]); float wv=__float_to_tf32(wic[(kd*3+kh)*3+kw]); acc=__fmaf_rn(xv,wv,acc);}}}}
^
1 error detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_26_cache/k_e4b7be2ab96c/convt3d_hswish_ext_v6c/cuda.cu".
ninja: build stopped: subcommand failed.
turn 822.7s turn · 22.7s LLM · 0.0s tools
reasoning
**Evaluating CUDA kernel usage**
I need to replace the default forward function with a custom CUDA kernel for better performance. However, I’m not sure if routing a fallback option is necessary. It’s a bit tricky since it could complicate things. On one hand, not routing the fallback might streamline the process, but on the other hand, it could lead to issues if the custom kernel doesn't work as expected. I'll have to think carefully about this implementation choice.
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 93.6s turn · 3.6s LLM · 0.0s tools
call → static_check
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.