correct
87_conv_pointwise_2D.py
started
2026-05-02T19:37:41Z
timingkernelbench.timing
kernel mean / median / std
8.05 / — / 33.00 μs
ref mean / median / std
4.65 / — / 0.16 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
4.00x
energyNVML (pynvml)
ratio (ref / kernel)
1.50x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
2857.36 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):
"""
Performs a pointwise 2D convolution operation.
Args:
in_channels (int): Number of channels in the input tensor.
out_channels (int): Number of channels produced by the convolution.
bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
"""
def __init__(self, in_channels: int, out_channels: int, bias: bool = False):
super(Model, self).__init__()
self.conv1d = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs the pointwise 2D convolution.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).
Returns:
torch.Tensor: Output tensor of shape (batch_size, out_channels, height, width).
"""
return self.conv1d(x)
# Test code
batch_size = 16
in_channels = 64
out_channels = 128
width = 1024
height = 1024
def get_inputs():
x = torch.rand(batch_size, in_channels, height, width)
return [x]
def get_init_inputs():
return [in_channels, out_channels]turn 05.4s turn · 5.4s 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) · 32.6s turn · 32.6s LLM · 8,446 chars (click to expand)
turn 132.6s turn · 32.6s LLM · 0.0s tools
reasoning
**Evaluating TF32 settings**
I’m wondering about the default settings for TF32 in different libraries. It seems like `torch.backends.cuda.matmul.allow_tf32` is set to false, while `cudnn.allow_tf32` is true, which may impact convolution operations. I need to consider whether the correctness of output is affected when comparing values using TF32. The tolerance could lead to discrepancies, especially if full FP32 comparisons show differences greater than >1e-4. The evaluator might disable TF32, but I’ll confirm that with the tools for correctness. Let's compile this information.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int TILE_S = 128;\nconstexpr int TILE_O = 16;\nconstexpr int THREADS = 256;\n\n__global__ void conv1x1_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ y,\n int N, int C, int O, int HW, bool has_bias) {\n extern __shared__ float sh[];\n float* xs = sh; // C x TILE_S\n float* ws = xs + C * TILE_S; // TILE_O x C\n int tile_s = blockIdx.x;\n int tile_o = blockIdx.y;\n int n = blockIdx.z;\n int tid = threadIdx.x;\n int s0 = tile_s * TILE_S;\n int o0 = tile_o * TILE_O;\n\n int x_elems = C * TILE_S;\n for (int idx = tid; idx < x_elems; idx += THREADS) {\n int c = idx / TILE_S;\n int ss = idx - c * TILE_S;\n int s = s0 + ss;\n xs[idx] = (s < HW) ? x[((n * C + c) * HW + s)] : 0.0f;\n }\n int w_elems = TILE_O * C;\n for (int idx = tid; idx < w_elems; idx += THREADS) {\n int oo = idx / C;\n int c = idx - oo * C;\n int o = o0 + oo;\n ws[idx] = (o < O) ? w[o * C + c] : 0.0f;\n }\n __syncthreads();\n\n int out_elems = TILE_O * TILE_S;\n for (int idx = tid; idx < out_elems; idx += THREADS) {\n int oo = idx / TILE_S;\n int ss = idx - oo * TILE_S;\n int s = s0 + ss;\n int o = o0 + oo;\n if (s < HW && o < O) {\n float sum = has_bias ? b[o] : 0.0f;\n #pragma unroll 64\n for (int c = 0; c < C; ++c) {\n sum = fmaf(ws[oo * C + c], xs[c * TILE_S + ss], sum);\n }\n y[((n * O + o) * HW + s)] = sum;\n }\n }\n}\n\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias) {\n const int N = (int)x.size(0);\n const int C = (int)x.size(1);\n const int H = (int)x.size(2);\n const int Wd = (int)x.size(3);\n const int O = (int)w.size(0);\n const int HW = H * Wd;\n auto y = torch::empty({N, O, H, Wd}, x.options());\n dim3 block(THREADS);\n dim3 grid((HW + TILE_S - 1) / TILE_S, (O + TILE_O - 1) / TILE_O, N);\n size_t shmem = (size_t)(C * TILE_S + TILE_O * C) * sizeof(float);\n conv1x1_kernel<<<grid, block, shmem, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), w.data_ptr<float>(), has_bias ? bias.data_ptr<float>() : nullptr,\n y.data_ptr<float>(), N, C, O, HW, has_bias);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv1x1_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['conv1x1_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv1d = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias)\n self._empty_bias = torch.empty(0, device='cuda')\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b = self.conv1d.bias\n if b is None:\n b = self._empty_bias\n has = False\n else:\n has = True\n return _ext.conv1x1_forward(x, self.conv1d.weight, b, has)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'conv1x1_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv1x1_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_1_problem_87_cache/k_485c26919d72/conv1x1_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=conv1x1_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_1_problem_87_cache/k_485c26919d72/conv1x1_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=conv1x1_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_1_problem_87_cache/k_485c26919d72/conv1x1_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_1_problem_87_cache/k_485c26919d72/conv1x1_ext_v1/cuda.cu(72): error: namespace "at::cuda" has no member "getCurrentCUDAStream"
conv1x1_kernel<<<grid, block, shmem, at::cuda::getCurrentCUDAStream()>>>(
^
1 error detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_87_cache/k_485c26919d72/conv1x1_ext_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 28.9s turn · 8.9s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int TILE_S = 128;\nconstexpr int TILE_O = 16;\nconstexpr int THREADS = 256;\n\n__global__ void conv1x1_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ y,\n int N, int C, int O, int HW, bool has_bias) {\n extern __shared__ float sh[];\n float* xs = sh;\n float* ws = xs + C * TILE_S;\n int tile_s = blockIdx.x;\n int tile_o = blockIdx.y;\n int n = blockIdx.z;\n int tid = threadIdx.x;\n int s0 = tile_s * TILE_S;\n int o0 = tile_o * TILE_O;\n for (int idx = tid; idx < C * TILE_S; idx += THREADS) {\n int c = idx / TILE_S;\n int ss = idx - c * TILE_S;\n int s = s0 + ss;\n xs[idx] = (s < HW) ? x[((n * C + c) * HW + s)] : 0.0f;\n }\n for (int idx = tid; idx < TILE_O * C; idx += THREADS) {\n int oo = idx / C;\n int c = idx - oo * C;\n int o = o0 + oo;\n ws[idx] = (o < O) ? w[o * C + c] : 0.0f;\n }\n __syncthreads();\n for (int idx = tid; idx < TILE_O * TILE_S; idx += THREADS) {\n int oo = idx / TILE_S;\n int ss = idx - oo * TILE_S;\n int s = s0 + ss;\n int o = o0 + oo;\n if (s < HW && o < O) {\n float sum = has_bias ? b[o] : 0.0f;\n #pragma unroll 64\n for (int c = 0; c < C; ++c) {\n sum = fmaf(ws[oo * C + c], xs[c * TILE_S + ss], sum);\n }\n y[((n * O + o) * HW + s)] = sum;\n }\n }\n}\n\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias) {\n const int N = (int)x.size(0);\n const int C = (int)x.size(1);\n const int H = (int)x.size(2);\n const int Wd = (int)x.size(3);\n const int O = (int)w.size(0);\n const int HW = H * Wd;\n auto y = torch::empty({N, O, H, Wd}, x.options());\n dim3 grid((HW + TILE_S - 1) / TILE_S, (O + TILE_O - 1) / TILE_O, N);\n size_t shmem = (size_t)(C * TILE_S + TILE_O * C) * sizeof(float);\n conv1x1_kernel<<<grid, THREADS, shmem, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), w.data_ptr<float>(), has_bias ? bias.data_ptr<float>() : nullptr,\n y.data_ptr<float>(), N, C, O, HW, has_bias);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv1x1_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['conv1x1_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv1d = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias)\n self.register_buffer('_empty_bias', torch.empty(0), persistent=False)\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b = self.conv1d.bias\n if b is None:\n b = self._empty_bias\n has = False\n else:\n has = True\n return _ext.conv1x1_forward(x, self.conv1d.weight, b, has)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 37.0s turn · 7.0s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int TILE_S = 128;\nconstexpr int TILE_O = 16;\nconstexpr int THREADS = 256;\n\n__global__ void conv1x1_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ y,\n int N, int C, int O, int HW, bool has_bias) {\n extern __shared__ float sh[];\n float* xs = sh;\n float* ws = xs + C * TILE_S;\n int tile_s = blockIdx.x;\n int tile_o = blockIdx.y;\n int n = blockIdx.z;\n int tid = threadIdx.x;\n int s0 = tile_s * TILE_S;\n int o0 = tile_o * TILE_O;\n for (int idx = tid; idx < C * TILE_S; idx += THREADS) {\n int c = idx / TILE_S;\n int ss = idx - c * TILE_S;\n int s = s0 + ss;\n xs[idx] = (s < HW) ? x[((n * C + c) * HW + s)] : 0.0f;\n }\n for (int idx = tid; idx < TILE_O * C; idx += THREADS) {\n int oo = idx / C;\n int c = idx - oo * C;\n int o = o0 + oo;\n ws[idx] = (o < O) ? w[o * C + c] : 0.0f;\n }\n __syncthreads();\n for (int idx = tid; idx < TILE_O * TILE_S; idx += THREADS) {\n int oo = idx / TILE_S;\n int ss = idx - oo * TILE_S;\n int s = s0 + ss;\n int o = o0 + oo;\n if (s < HW && o < O) {\n float sum = has_bias ? b[o] : 0.0f;\n #pragma unroll 64\n for (int c = 0; c < C; ++c) {\n sum = fmaf(ws[oo * C + c], xs[c * TILE_S + ss], sum);\n }\n y[((n * O + o) * HW + s)] = sum;\n }\n }\n}\n\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias) {\n const int N = (int)x.size(0);\n const int C = (int)x.size(1);\n const int H = (int)x.size(2);\n const int Wd = (int)x.size(3);\n const int O = (int)w.size(0);\n const int HW = H * Wd;\n auto y = torch::empty({N, O, H, Wd}, x.options());\n dim3 grid((HW + TILE_S - 1) / TILE_S, (O + TILE_O - 1) / TILE_O, N);\n size_t shmem = (size_t)(C * TILE_S + TILE_O * C) * sizeof(float);\n conv1x1_kernel<<<grid, THREADS, shmem, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), w.data_ptr<float>(), has_bias ? bias.data_ptr<float>() : nullptr,\n y.data_ptr<float>(), N, C, O, HW, has_bias);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv1x1_ext_v2c', cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=['conv1x1_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv1d = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias)\n self.register_buffer('_empty_bias', torch.empty(0), persistent=False)\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b = self.conv1d.bias\n if b is None:\n b = self._empty_bias\n has = False\n else:\n has = True\n return _ext.conv1x1_forward(x, self.conv1d.weight, b, has)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000540', '0.000527', '0.000539', '0.000522', '0.000538']
avg_difference: ['0.000068', '0.000068', '0.000068', '0.000068', '0.000068']
turn 4 · 1 tool call(s) · 7.7s turn · 7.7s LLM · 8,076 chars (click to expand)
turn 47.7s turn · 7.7s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\nconstexpr int TILE_S=128; constexpr int TILE_O=16; constexpr int THREADS=256;\n__global__ void conv1x1_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int O,int HW,bool has_bias){\n extern __shared__ float sh[]; float* xs=sh; float* ws=xs+C*TILE_S; int ts=blockIdx.x,to=blockIdx.y,n=blockIdx.z,tid=threadIdx.x; int s0=ts*TILE_S,o0=to*TILE_O;\n for(int idx=tid; idx<C*TILE_S; idx+=THREADS){int c=idx/TILE_S, ss=idx-c*TILE_S, s=s0+ss; xs[idx]=(s<HW)?__float_to_tf32(x[((n*C+c)*HW+s)]):0.f;}\n for(int idx=tid; idx<TILE_O*C; idx+=THREADS){int oo=idx/C,c=idx-oo*C,o=o0+oo; ws[idx]=(o<O)?__float_to_tf32(w[o*C+c]):0.f;}\n __syncthreads();\n for(int idx=tid; idx<TILE_O*TILE_S; idx+=THREADS){int oo=idx/TILE_S,ss=idx-oo*TILE_S,s=s0+ss,o=o0+oo; if(s<HW && o<O){float sum=has_bias?b[o]:0.f; #pragma unroll 64\n for(int c=0;c<C;++c){sum=fmaf(ws[oo*C+c],xs[c*TILE_S+ss],sum);} y[((n*O+o)*HW+s)]=sum;}}\n}\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias){int N=x.size(0),C=x.size(1),H=x.size(2),Wd=x.size(3),O=w.size(0),HW=H*Wd; auto y=torch::empty({N,O,H,Wd},x.options()); dim3 grid((HW+TILE_S-1)/TILE_S,(O+TILE_O-1)/TILE_O,N); size_t shmem=(size_t)(C*TILE_S+TILE_O*C)*sizeof(float); conv1x1_kernel<<<grid,THREADS,shmem,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,O,HW,has_bias); return y;}\n'''\n_ext=load_inline(name='conv1x1_ext_tf32_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv1x1_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,bias:bool=False):\n super().__init__(); self.conv1d=nn.Conv2d(in_channels,out_channels,1,1,0,bias=bias); self.register_buffer('_empty_bias',torch.empty(0),persistent=False)\n def forward(self,x):\n b=self.conv1d.bias; return _ext.conv1x1_forward(x,self.conv1d.weight,self._empty_bias if b is None else b,b is not None)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'conv1x1_ext_tf32_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv1x1_ext_tf32_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_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_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=conv1x1_ext_tf32_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_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=conv1x1_ext_tf32_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_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_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_v1/cuda.cu(12): error: identifier "__float_to_tf32" is undefined
for(int idx=tid; idx<C*TILE_S; idx+=THREADS){int c=idx/TILE_S, ss=idx-c*TILE_S, s=s0+ss; xs[idx]=(s<HW)?__float_to_tf32(x[((n*C+c)*HW+s)]):0.f;}
^
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_v1/cuda.cu(13): error: identifier "__float_to_tf32" is undefined
for(int idx=tid; idx<TILE_O*C; idx+=THREADS){int oo=idx/C,c=idx-oo*C,o=o0+oo; ws[idx]=(o<O)?__float_to_tf32(w[o*C+c]):0.f;}
^
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_v1/cuda.cu(15): error: "#" not expected here
for(int idx=tid; idx<TILE_O*TILE_S; idx+=THREADS){int oo=idx/TILE_S,ss=idx-oo*TILE_S,s=s0+ss,o=o0+oo; if(s<HW && o<O){float sum=has_bias?b[o]:0.f; #pragma unroll 64
^
/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_v1/cuda.cu(15): error: expected an expression
for(int idx=tid; idx<TILE_O*TILE_S; idx+=THREADS){int oo=idx/TILE_S,ss=idx-oo*TILE_S,s=s0+ss,o=o0+oo; if(s<HW && o<O){float sum=has_bias?b[o]:0.f; #pragma unroll 64
^
4 errors detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_87_cache/k_0a44c55a7178/conv1x1_ext_tf32_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 5 · 1 tool call(s) · 7.9s turn · 7.9s LLM · 7,423 chars (click to expand)
turn 57.9s turn · 7.9s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\nconstexpr int TILE_S=128; constexpr int TILE_O=16; constexpr int THREADS=256;\n__device__ __forceinline__ float tf32_round(float a){ float r; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=f\"(r) : \"f\"(a)); return r; }\n__global__ void conv1x1_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int O,int HW,bool has_bias){\n extern __shared__ float sh[]; float* xs=sh; float* ws=xs+C*TILE_S; int ts=blockIdx.x,to=blockIdx.y,n=blockIdx.z,tid=threadIdx.x; int s0=ts*TILE_S,o0=to*TILE_O;\n for(int idx=tid; idx<C*TILE_S; idx+=THREADS){int c=idx/TILE_S; int ss=idx-c*TILE_S; int s=s0+ss; xs[idx]=(s<HW)?tf32_round(x[((n*C+c)*HW+s)]):0.f;}\n for(int idx=tid; idx<TILE_O*C; idx+=THREADS){int oo=idx/C; int c=idx-oo*C; int o=o0+oo; ws[idx]=(o<O)?tf32_round(w[o*C+c]):0.f;}\n __syncthreads();\n for(int idx=tid; idx<TILE_O*TILE_S; idx+=THREADS){int oo=idx/TILE_S; int ss=idx-oo*TILE_S; int s=s0+ss; int o=o0+oo; if(s<HW && o<O){float sum=has_bias?b[o]:0.f;\n #pragma unroll 64\n for(int c=0;c<C;++c){sum=fmaf(ws[oo*C+c],xs[c*TILE_S+ss],sum);} y[((n*O+o)*HW+s)]=sum;}}\n}\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias){int N=x.size(0),C=x.size(1),H=x.size(2),Wd=x.size(3),O=w.size(0),HW=H*Wd; auto y=torch::empty({N,O,H,Wd},x.options()); dim3 grid((HW+TILE_S-1)/TILE_S,(O+TILE_O-1)/TILE_O,N); size_t shmem=(size_t)(C*TILE_S+TILE_O*C)*sizeof(float); conv1x1_kernel<<<grid,THREADS,shmem,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,O,HW,has_bias); return y;}\n'''\n_ext=load_inline(name='conv1x1_ext_tf32_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv1x1_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,bias:bool=False):\n super().__init__(); self.conv1d=nn.Conv2d(in_channels,out_channels,1,1,0,bias=bias); self.register_buffer('_empty_bias',torch.empty(0),persistent=False)\n def forward(self,x):\n b=self.conv1d.bias; return _ext.conv1x1_forward(x,self.conv1d.weight,self._empty_bias if b is None else b,b is not None)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'conv1x1_ext_tf32_v2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv1x1_ext_tf32_v2 -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_1_problem_87_cache/k_79b0d7c27dbf/conv1x1_ext_tf32_v2/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=conv1x1_ext_tf32_v2 -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_1_problem_87_cache/k_79b0d7c27dbf/conv1x1_ext_tf32_v2/cuda.cu -o cuda.cuda.o
FAILED: [code=255] 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=conv1x1_ext_tf32_v2 -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_1_problem_87_cache/k_79b0d7c27dbf/conv1x1_ext_tf32_v2/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 120; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 164; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 188; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 214; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 238; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 283; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 317; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 330; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 345; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_001d58de_00000000-6_cuda.ptx, line 358; error : Arguments mismatch for instruction 'cvt'
ptxas fatal : Ptx assembly aborted due to errors
ninja: build stopped: subcommand failed.
turn 68.3s turn · 8.3s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\nconstexpr int TILE_S=128; constexpr int TILE_O=16; constexpr int THREADS=256;\n__device__ __forceinline__ float tf32_round(float a){ unsigned r; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=r\"(r) : \"f\"(a)); return __uint_as_float(r); }\n__global__ void conv1x1_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int O,int HW,bool has_bias){\n extern __shared__ float sh[]; float* xs=sh; float* ws=xs+C*TILE_S; int ts=blockIdx.x,to=blockIdx.y,n=blockIdx.z,tid=threadIdx.x; int s0=ts*TILE_S,o0=to*TILE_O;\n for(int idx=tid; idx<C*TILE_S; idx+=THREADS){int c=idx/TILE_S; int ss=idx-c*TILE_S; int s=s0+ss; xs[idx]=(s<HW)?tf32_round(x[((n*C+c)*HW+s)]):0.f;}\n for(int idx=tid; idx<TILE_O*C; idx+=THREADS){int oo=idx/C; int c=idx-oo*C; int o=o0+oo; ws[idx]=(o<O)?tf32_round(w[o*C+c]):0.f;}\n __syncthreads();\n for(int idx=tid; idx<TILE_O*TILE_S; idx+=THREADS){int oo=idx/TILE_S; int ss=idx-oo*TILE_S; int s=s0+ss; int o=o0+oo; if(s<HW && o<O){float sum=has_bias?b[o]:0.f;\n #pragma unroll 64\n for(int c=0;c<C;++c){sum=fmaf(ws[oo*C+c],xs[c*TILE_S+ss],sum);} y[((n*O+o)*HW+s)]=sum;}}\n}\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias){int N=x.size(0),C=x.size(1),H=x.size(2),Wd=x.size(3),O=w.size(0),HW=H*Wd; auto y=torch::empty({N,O,H,Wd},x.options()); dim3 grid((HW+TILE_S-1)/TILE_S,(O+TILE_O-1)/TILE_O,N); size_t shmem=(size_t)(C*TILE_S+TILE_O*C)*sizeof(float); conv1x1_kernel<<<grid,THREADS,shmem,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,O,HW,has_bias); return y;}\n'''\n_ext=load_inline(name='conv1x1_ext_tf32_v3', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv1x1_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,bias:bool=False):\n super().__init__(); self.conv1d=nn.Conv2d(in_channels,out_channels,1,1,0,bias=bias); self.register_buffer('_empty_bias',torch.empty(0),persistent=False)\n def forward(self,x):\n b=self.conv1d.bias; return _ext.conv1x1_forward(x,self.conv1d.weight,self._empty_bias if b is None else b,b is not None)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000122', '0.000120', '0.000119', '0.000130', '0.000121']
avg_difference: ['0.000000', '0.000000', '0.000000', '0.000000', '0.000000']
turn 729.3s turn · 29.3s LLM · 0.0s tools
reasoning
**Evaluating tensor core calculations**
I'm trying to refine my calculations: I'm thinking about using a maximum difference of 1e-4 and possibly avoiding fmaf to see how it works out. Maybe I'll explore using __fmul_rn and __fadd_rn instead. I'm curious if Tensor Core uses fma or not since it seems to handle accumulation differently. I should test different variants despite having limited calls. My initial full FP32 test didn’t meet the criteria. TF32 looks promising, so I'll consider calculating references to keep it within limits. Let's check if adjusting accumulators makes a difference.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);'''\ncuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\nconstexpr int TILE_S=128,TILE_O=16,THREADS=256;\n__device__ __forceinline__ float tf32_round(float a){unsigned r; asm(\"cvt.rna.tf32.f32 %0, %1;\":\"=r\"(r):\"f\"(a)); return __uint_as_float(r);} \n__global__ void k(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int O,int HW,bool hb){extern __shared__ float sh[]; float* xs=sh; float* ws=xs+C*TILE_S; int s0=blockIdx.x*TILE_S,o0=blockIdx.y*TILE_O,n=blockIdx.z,tid=threadIdx.x; for(int idx=tid;idx<C*TILE_S;idx+=THREADS){int c=idx/TILE_S,ss=idx-c*TILE_S,s=s0+ss; xs[idx]=(s<HW)?tf32_round(x[(n*C+c)*HW+s]):0.f;} for(int idx=tid;idx<TILE_O*C;idx+=THREADS){int oo=idx/C,c=idx-oo*C,o=o0+oo; ws[idx]=(o<O)?tf32_round(w[o*C+c]):0.f;} __syncthreads(); for(int idx=tid;idx<TILE_O*TILE_S;idx+=THREADS){int oo=idx/TILE_S,ss=idx-oo*TILE_S,s=s0+ss,o=o0+oo; if(s<HW&&o<O){float s0a=0.f,s1=0.f,s2=0.f,s3=0.f; for(int c=0;c<C;c+=4){s0a=fmaf(ws[oo*C+c],xs[c*TILE_S+ss],s0a); s1=fmaf(ws[oo*C+c+1],xs[(c+1)*TILE_S+ss],s1); s2=fmaf(ws[oo*C+c+2],xs[(c+2)*TILE_S+ss],s2); s3=fmaf(ws[oo*C+c+3],xs[(c+3)*TILE_S+ss],s3);} float sum=((s0a+s1)+(s2+s3))+(hb?b[o]:0.f); y[(n*O+o)*HW+s]=sum;}}}\ntorch::Tensor conv1x1_forward(torch::Tensor x,torch::Tensor w,torch::Tensor bias,bool hb){int N=x.size(0),C=x.size(1),H=x.size(2),Wd=x.size(3),O=w.size(0),HW=H*Wd; auto y=torch::empty({N,O,H,Wd},x.options()); dim3 grid((HW+TILE_S-1)/TILE_S,(O+TILE_O-1)/TILE_O,N); k<<<grid,THREADS,(size_t)(C*TILE_S+TILE_O*C)*4,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,O,HW,hb); return y;}'''\n_ext=load_inline(name='conv1x1_ext_tf32_4acc',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv1x1_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,bias:bool=False): super().__init__(); self.conv1d=nn.Conv2d(in_channels,out_channels,1,1,0,bias=bias); self.register_buffer('_empty_bias',torch.empty(0),persistent=False)\n def forward(self,x):\n b=self.conv1d.bias; return _ext.conv1x1_forward(x,self.conv1d.weight,self._empty_bias if b is None else b,b is not None)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000122', '0.000120', '0.000119', '0.000130', '0.000121']
avg_difference: ['0.000000', '0.000000', '0.000000', '0.000000', '0.000000']
turn 814.2s turn · 14.2s LLM · 0.0s tools
reasoning
**Considering computational methods**
I’m thinking about trying something without fmaf, maybe by using PyTorch. It seems like the use of fast math could be causing issues with fmaf. I might compile without fast math, but I wonder if the expression would still use fma anyway. It’s a bit tricky trying to figure out the best approach, and I want to make sure I’m doing this in the most efficient way possible!
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);'''\ncuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\nconstexpr int TILE_S=128,TILE_O=16,THREADS=256;\n__global__ void k(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int O,int HW,bool hb){extern __shared__ float sh[]; float* xs=sh; float* ws=xs+C*TILE_S; int s0=blockIdx.x*TILE_S,o0=blockIdx.y*TILE_O,n=blockIdx.z,tid=threadIdx.x; for(int idx=tid;idx<C*TILE_S;idx+=THREADS){int c=idx/TILE_S,ss=idx-c*TILE_S,s=s0+ss; xs[idx]=(s<HW)?x[(n*C+c)*HW+s]:0.f;} for(int idx=tid;idx<TILE_O*C;idx+=THREADS){int oo=idx/C,c=idx-oo*C,o=o0+oo; ws[idx]=(o<O)?w[o*C+c]:0.f;} __syncthreads(); for(int idx=tid;idx<TILE_O*TILE_S;idx+=THREADS){int oo=idx/TILE_S,ss=idx-oo*TILE_S,s=s0+ss,o=o0+oo; if(s<HW&&o<O){float sum=hb?b[o]:0.f; for(int c=0;c<C;++c){sum += ws[oo*C+c]*xs[c*TILE_S+ss];} y[(n*O+o)*HW+s]=sum;}}}\ntorch::Tensor conv1x1_forward(torch::Tensor x,torch::Tensor w,torch::Tensor bias,bool hb){int N=x.size(0),C=x.size(1),H=x.size(2),Wd=x.size(3),O=w.size(0),HW=H*Wd; auto y=torch::empty({N,O,H,Wd},x.options()); dim3 grid((HW+TILE_S-1)/TILE_S,(O+TILE_O-1)/TILE_O,N); k<<<grid,THREADS,(size_t)(C*TILE_S+TILE_O*C)*4,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,O,HW,hb); return y;}'''\n_ext=load_inline(name='conv1x1_ext_fp32_nofma',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv1x1_forward'],extra_cuda_cflags=['-O3','--fmad=false'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,bias:bool=False): super().__init__(); self.conv1d=nn.Conv2d(in_channels,out_channels,1,1,0,bias=bias); self.register_buffer('_empty_bias',torch.empty(0),persistent=False)\n def forward(self,x):\n b=self.conv1d.bias; return _ext.conv1x1_forward(x,self.conv1d.weight,self._empty_bias if b is None else b,b is not None)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000540', '0.000527', '0.000538', '0.000522', '0.000538']
avg_difference: ['0.000068', '0.000068', '0.000068', '0.000068', '0.000068']
turn 925.8s turn · 25.8s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/cuda/CUDABlas.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n\n__global__ void add_bias_kernel(float* __restrict__ y, const float* __restrict__ b, int N, int O, int HW) {\n long long total = (long long)N * O * HW;\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (; idx < total; idx += stride) {\n int o = (idx / HW) % O;\n y[idx] += b[o];\n }\n}\n\ntorch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias) {\n const int N = (int)x.size(0);\n const int C = (int)x.size(1);\n const int H = (int)x.size(2);\n const int Wd = (int)x.size(3);\n const int O = (int)w.size(0);\n const int HW = H * Wd;\n\n auto y = torch::empty({N, O, H, Wd}, x.options());\n\n // Each batch is a row-major GEMM: Y[O,HW] = W[O,C] * X[C,HW].\n // cuBLAS is column-major, so compute Y^T[HW,O] = X^T[HW,C] * W^T[C,O]\n // without any data movement. This gives the desired contiguous NCHW output.\n cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();\n cublasSetStream(handle, at::cuda::getCurrentCUDAStream());\n cublasSetMathMode(handle, CUBLAS_TF32_TENSOR_OP_MATH);\n\n const float alpha = 1.0f;\n const float beta = 0.0f;\n cublasSgemmStridedBatched(\n handle,\n CUBLAS_OP_N, CUBLAS_OP_N,\n HW, O, C,\n &alpha,\n x.data_ptr<float>(), HW, (long long)C * HW,\n w.data_ptr<float>(), C, 0LL,\n &beta,\n y.data_ptr<float>(), HW, (long long)O * HW,\n N);\n\n if (has_bias) {\n long long total = (long long)N * O * HW;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n add_bias_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n y.data_ptr<float>(), bias.data_ptr<float>(), N, O, HW);\n }\n return y;\n}\n'''\n\n_ext = load_inline(\n name='conv1x1_cublas_ext_final',\n cpp_sources=cpp_src,\n cuda_sources=cuda_src,\n functions=['conv1x1_forward'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcublas'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv1d = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias)\n self.register_buffer('_empty_bias', torch.empty(0), persistent=False)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b = self.conv1d.bias\n if b is None:\n return _ext.conv1x1_forward(x, self.conv1d.weight, self._empty_bias, False)\n return _ext.conv1x1_forward(x, self.conv1d.weight, b, True)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 8.05 μs
Runtime stats: mean=8.05μs median=?μs std=33.00μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 12321.06 MB peak (ref: 12321.06 MB, ratio: 1.0x)
Kernel launches: 1 (ref: 4, fusion ratio: 4.0)
Energy: 644.96 mJ/run (ref: 968.3 mJ, ratio: 1.5013x)
SOL score: 0.8529
final submitted kernel (91 lines, 3,120 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 conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias);
'''
cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDABlas.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
__global__ void add_bias_kernel(float* __restrict__ y, const float* __restrict__ b, int N, int O, int HW) {
long long total = (long long)N * O * HW;
long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;
long long stride = (long long)blockDim.x * gridDim.x;
for (; idx < total; idx += stride) {
int o = (idx / HW) % O;
y[idx] += b[o];
}
}
torch::Tensor conv1x1_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias, bool has_bias) {
const int N = (int)x.size(0);
const int C = (int)x.size(1);
const int H = (int)x.size(2);
const int Wd = (int)x.size(3);
const int O = (int)w.size(0);
const int HW = H * Wd;
auto y = torch::empty({N, O, H, Wd}, x.options());
// Each batch is a row-major GEMM: Y[O,HW] = W[O,C] * X[C,HW].
// cuBLAS is column-major, so compute Y^T[HW,O] = X^T[HW,C] * W^T[C,O]
// without any data movement. This gives the desired contiguous NCHW output.
cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
cublasSetStream(handle, at::cuda::getCurrentCUDAStream());
cublasSetMathMode(handle, CUBLAS_TF32_TENSOR_OP_MATH);
const float alpha = 1.0f;
const float beta = 0.0f;
cublasSgemmStridedBatched(
handle,
CUBLAS_OP_N, CUBLAS_OP_N,
HW, O, C,
&alpha,
x.data_ptr<float>(), HW, (long long)C * HW,
w.data_ptr<float>(), C, 0LL,
&beta,
y.data_ptr<float>(), HW, (long long)O * HW,
N);
if (has_bias) {
long long total = (long long)N * O * HW;
int threads = 256;
int blocks = (int)((total + threads - 1) / threads);
if (blocks > 65535) blocks = 65535;
add_bias_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
y.data_ptr<float>(), bias.data_ptr<float>(), N, O, HW);
}
return y;
}
'''
_ext = load_inline(
name='conv1x1_cublas_ext_final',
cpp_sources=cpp_src,
cuda_sources=cuda_src,
functions=['conv1x1_forward'],
extra_cuda_cflags=['-O3'],
extra_ldflags=['-lcublas'],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self, in_channels: int, out_channels: int, bias: bool = False):
super(ModelNew, self).__init__()
self.conv1d = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias)
self.register_buffer('_empty_bias', torch.empty(0), persistent=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
b = self.conv1d.bias
if b is None:
return _ext.conv1x1_forward(x, self.conv1d.weight, self._empty_bias, False)
return _ext.conv1x1_forward(x, self.conv1d.weight, b, True)