L1 · problem 50
gpt-5.5-priority · original · l12_default_gpt
← all variants← original← gpt-5.5-prioritygenerated 2026-05-28 03:33:16 UTC
error 50_conv_standard_2D__square_input__square_kernel.py
model
gpt-5.5-priority
turns / max
9 / 10
tool calls
8
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
original
runtime
—
ref runtime
0.00 μs
speedup
—
started
2026-05-02T16:46:33Z
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self, num_classes=1000):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=2)
    
    def forward(self, x):
        x = self.conv1(x)
        return x

# Test code
batch_size = 256
num_classes = 1000

def get_inputs():
    return [torch.rand(batch_size, 3, 224, 224)]

def get_init_inputs():
    return [num_classes]
turn 029.5s turn · 29.5s LLM · 0.0s tools
reasoning
**Optimizing ModelNew Construction** I'm working on creating ModelNew, which has the same constructor and convolution weights. I need to implement a conv2d layer with specific dimensions, like N=256, and C=3. The calculations show output height and width will be 55. I’m considering whether to include nn.Conv2d and if the weights need to be initialized the same way. I’ll define the layer and optimize for better performance, possibly using custom CUDA for convolution. The goal is to improve speed without losing accuracy. **Evaluating Memory Optimization** I’m considering potential memory usage—should I use constant memory? The weights are dynamic pointers, and the size is small enough to fit within the constraints. I'm thinking of structuring with im2col and GEMM. This would entail treating the output matrix in a specific way for efficient computation. Implementing an implicit GEMM kernel with tiling could be beneficial, especially given the constraints. I'll focus on managing shared resources efficiently while looping through computations to optimize performance.
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) · 77.0s turn · 77.0s LLM · 8,317 chars (click to expand)
turn 177.0s turn · 77.0s LLM · 0.0s tools
reasoning
**Compiling Torch Headers** I need to make sure I'm prepending the correct Torch headers. It seems to be working, which is great news! Now, my next step is to compile everything properly. Compiling can sometimes be tricky, but I'll double-check everything to ensure it's set up correctly. Hopefully, it all goes smoothly this time! Let's keep moving forward with this process. I'm pretty sure I'm on the right track!
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 <cuda.h>\n#include <cuda_runtime.h>\n\n#define TILE_M 16\n#define TILE_N 16\n#define TILE_K 32\n\n__global__ void conv1_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ y,\n int M) {\n // Fixed problem: N x 3 x 224 x 224 -> N x 96 x 55 x 55\n constexpr int C = 3;\n constexpr int H = 224;\n constexpr int W = 224;\n constexpr int OC = 96;\n constexpr int OH = 55;\n constexpr int OW = 55;\n constexpr int K = 363;\n __shared__ float As[TILE_M][TILE_K + 1];\n __shared__ float Bs[TILE_K][TILE_N + 1];\n\n int tx = threadIdx.x; // col within tile (oc)\n int ty = threadIdx.y; // row within tile (M)\n int row = blockIdx.x * TILE_M + ty;\n int oc = blockIdx.y * TILE_N + tx;\n\n float acc = 0.0f;\n int n=0, sp=0, oh=0, ow=0;\n if (row < M) {\n n = row / (OH * OW);\n sp = row - n * (OH * OW);\n oh = sp / OW;\n ow = sp - oh * OW;\n }\n\n int tid = ty * TILE_N + tx;\n #pragma unroll\n for (int k0 = 0; k0 < K; k0 += TILE_K) {\n // load A tile: TILE_M x TILE_K\n for (int idx = tid; idx < TILE_M * TILE_K; idx += TILE_M * TILE_N) {\n int r = idx / TILE_K;\n int kk = idx - r * TILE_K;\n int grow = blockIdx.x * TILE_M + r;\n int gk = k0 + kk;\n float val = 0.0f;\n if (grow < M && gk < K) {\n int gn = grow / (OH * OW);\n int gsp = grow - gn * (OH * OW);\n int goh = gsp / OW;\n int gow = gsp - goh * OW;\n int c = gk / 121;\n int rem = gk - c * 121;\n int kh = rem / 11;\n int kw = rem - kh * 11;\n int ih = goh * 4 + kh - 2;\n int iw = gow * 4 + kw - 2;\n if ((unsigned)ih < H && (unsigned)iw < W) {\n val = x[((gn * C + c) * H + ih) * W + iw];\n }\n }\n As[r][kk] = val;\n }\n // load B tile: TILE_K x TILE_N\n for (int idx = tid; idx < TILE_K * TILE_N; idx += TILE_M * TILE_N) {\n int kk = idx / TILE_N;\n int cc = idx - kk * TILE_N;\n int gk = k0 + kk;\n int goc = blockIdx.y * TILE_N + cc;\n float val = 0.0f;\n if (gk < K && goc < OC) val = w[goc * K + gk];\n Bs[kk][cc] = val;\n }\n __syncthreads();\n #pragma unroll\n for (int kk = 0; kk < TILE_K; ++kk) {\n if (k0 + kk < K) acc = fmaf(As[ty][kk], Bs[kk][tx], acc);\n }\n __syncthreads();\n }\n if (row < M && oc < OC) {\n y[((n * OC + oc) * OH + oh) * OW + ow] = acc + b[oc];\n }\n}\n\ntorch::Tensor conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n int64_t N = x.size(0);\n auto y = torch::empty({N, 96, 55, 55}, x.options());\n int M = (int)(N * 55 * 55);\n dim3 block(TILE_N, TILE_M);\n dim3 grid((M + TILE_M - 1) / TILE_M, 6);\n conv1_kernel<<<grid, block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>(), M);\n return y;\n}\n'''\n\n_ext = load_inline(name='conv1_direct_ext', cpp_sources='', cuda_sources=_cuda_src, functions=['conv_forward'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, num_classes=1000):\n super(ModelNew, self).__init__()\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=2)\n def forward(self, x):\n return _ext.conv_forward(x, self.conv1.weight, self.conv1.bias)\n" }
result ← compile_kernel (fail)
failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'conv1_direct_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv1_direct_ext -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_50_cache/k_a83104fb2fb9/conv1_direct_ext/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=conv1_direct_ext -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_50_cache/k_a83104fb2fb9/conv1_direct_ext/main.cpp -o main.o /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_50_cache/k_a83104fb2fb9/conv1_direct_ext/main.cpp: In function 'void pybind11_init_conv1_direct_ext(pybind11::module_&)': /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_50_cache/k_a83104fb2fb9/conv1_direct_ext/main.cpp:4:51: error: 'conv_forward' was not declared in this scope 4 | m.def("conv_forward", torch::wrap_pybind_function(conv_forward), "conv_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=conv1_direct_ext -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_50_cache/k_a83104fb2fb9/conv1_direct_ext/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 27.9s turn · 7.9s LLM · 0.0s tools
reasoning
**Declaring cpp sources** I need to ensure that there's a proper declaration for cpp_sources. I'm considering how to include `torch` properly. The function I'll declare is `torch::Tensor conv_forward(torch::Tensor x, ...);` which highlights the tensor operation. It's essential that all necessary headers are included for smooth compilation. I want to be precise here, as any omission might lead to issues later in the code.
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 conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define TILE_M 16\n#define TILE_N 16\n#define TILE_K 32\n__global__ void conv1_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ b, float* __restrict__ y, int M) {\n constexpr int C=3,H=224,W=224,OC=96,OH=55,OW=55,K=363;\n __shared__ float As[TILE_M][TILE_K+1];\n __shared__ float Bs[TILE_K][TILE_N+1];\n int tx=threadIdx.x, ty=threadIdx.y;\n int row=blockIdx.x*TILE_M+ty;\n int oc=blockIdx.y*TILE_N+tx;\n float acc=0.0f;\n int n=0, oh=0, ow=0;\n if(row<M){ n=row/(OH*OW); int sp=row-n*(OH*OW); oh=sp/OW; ow=sp-oh*OW; }\n int tid=ty*TILE_N+tx;\n for(int k0=0;k0<K;k0+=TILE_K){\n for(int idx=tid; idx<TILE_M*TILE_K; idx+=TILE_M*TILE_N){\n int r=idx/TILE_K; int kk=idx-r*TILE_K; int grow=blockIdx.x*TILE_M+r; int gk=k0+kk; float val=0.0f;\n if(grow<M && gk<K){\n int gn=grow/(OH*OW); int gsp=grow-gn*(OH*OW); int goh=gsp/OW; int gow=gsp-goh*OW;\n int c=gk/121; int rem=gk-c*121; int kh=rem/11; int kw=rem-kh*11;\n int ih=goh*4+kh-2; int iw=gow*4+kw-2;\n if((unsigned)ih < H && (unsigned)iw < W) val=x[((gn*C+c)*H+ih)*W+iw];\n }\n As[r][kk]=val;\n }\n for(int idx=tid; idx<TILE_K*TILE_N; idx+=TILE_M*TILE_N){\n int kk=idx/TILE_N; int cc=idx-kk*TILE_N; int gk=k0+kk; int goc=blockIdx.y*TILE_N+cc;\n Bs[kk][cc]=(gk<K && goc<OC)? w[goc*K+gk]:0.0f;\n }\n __syncthreads();\n #pragma unroll\n for(int kk=0; kk<TILE_K; ++kk){ if(k0+kk<K) acc=fmaf(As[ty][kk], Bs[kk][tx], acc); }\n __syncthreads();\n }\n if(row<M && oc<OC) y[((n*OC+oc)*OH+oh)*OW+ow]=acc+b[oc];\n}\ntorch::Tensor conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n int64_t N=x.size(0); auto y=torch::empty({N,96,55,55}, x.options()); int M=(int)(N*55*55);\n dim3 block(TILE_N,TILE_M); dim3 grid((M+TILE_M-1)/TILE_M,6);\n conv1_kernel<<<grid,block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>(), M);\n return y;\n}\n'''\n_ext = load_inline(name='conv1_direct_ext2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['conv_forward'], verbose=False, extra_cuda_cflags=['-O3','--use_fast_math'])\nclass ModelNew(nn.Module):\n def __init__(self, num_classes=1000):\n super(ModelNew,self).__init__()\n self.conv1=nn.Conv2d(in_channels=3,out_channels=96,kernel_size=11,stride=4,padding=2)\n def forward(self,x):\n return _ext.conv_forward(x,self.conv1.weight,self.conv1.bias)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 37.1s turn · 7.1s 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 conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define TILE_M 16\n#define TILE_N 16\n#define TILE_K 32\n__global__ void conv1_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ b, float* __restrict__ y, int M) {\n constexpr int C=3,H=224,W=224,OC=96,OH=55,OW=55,K=363;\n __shared__ float As[TILE_M][TILE_K+1];\n __shared__ float Bs[TILE_K][TILE_N+1];\n int tx=threadIdx.x, ty=threadIdx.y;\n int row=blockIdx.x*TILE_M+ty;\n int oc=blockIdx.y*TILE_N+tx;\n float acc=0.0f;\n int n=0, oh=0, ow=0;\n if(row<M){ n=row/(OH*OW); int sp=row-n*(OH*OW); oh=sp/OW; ow=sp-oh*OW; }\n int tid=ty*TILE_N+tx;\n for(int k0=0;k0<K;k0+=TILE_K){\n for(int idx=tid; idx<TILE_M*TILE_K; idx+=TILE_M*TILE_N){\n int r=idx/TILE_K; int kk=idx-r*TILE_K; int grow=blockIdx.x*TILE_M+r; int gk=k0+kk; float val=0.0f;\n if(grow<M && gk<K){\n int gn=grow/(OH*OW); int gsp=grow-gn*(OH*OW); int goh=gsp/OW; int gow=gsp-goh*OW;\n int c=gk/121; int rem=gk-c*121; int kh=rem/11; int kw=rem-kh*11;\n int ih=goh*4+kh-2; int iw=gow*4+kw-2;\n if((unsigned)ih < H && (unsigned)iw < W) val=x[((gn*C+c)*H+ih)*W+iw];\n }\n As[r][kk]=val;\n }\n for(int idx=tid; idx<TILE_K*TILE_N; idx+=TILE_M*TILE_N){\n int kk=idx/TILE_N; int cc=idx-kk*TILE_N; int gk=k0+kk; int goc=blockIdx.y*TILE_N+cc;\n Bs[kk][cc]=(gk<K && goc<OC)? w[goc*K+gk]:0.0f;\n }\n __syncthreads();\n #pragma unroll\n for(int kk=0; kk<TILE_K; ++kk){ if(k0+kk<K) acc=fmaf(As[ty][kk], Bs[kk][tx], acc); }\n __syncthreads();\n }\n if(row<M && oc<OC) y[((n*OC+oc)*OH+oh)*OW+ow]=acc+b[oc];\n}\ntorch::Tensor conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n int64_t N=x.size(0); auto y=torch::empty({N,96,55,55}, x.options()); int M=(int)(N*55*55);\n dim3 block(TILE_N,TILE_M); dim3 grid((M+TILE_M-1)/TILE_M,6);\n conv1_kernel<<<grid,block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>(), M);\n return y;\n}\n'''\n_ext = load_inline(name='conv1_direct_ext2c', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['conv_forward'], verbose=False, extra_cuda_cflags=['-O3','--use_fast_math'])\nclass ModelNew(nn.Module):\n def __init__(self, num_classes=1000):\n super(ModelNew,self).__init__()\n self.conv1=nn.Conv2d(in_channels=3,out_channels=96,kernel_size=11,stride=4,padding=2)\n def forward(self,x):\n return _ext.conv_forward(x,self.conv1.weight,self.conv1.bias)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000522', '0.000545', '0.000567', '0.000547', '0.000539'] avg_difference: ['0.000076', '0.000076', '0.000076', '0.000076', '0.000076']
turn 415.7s turn · 15.7s LLM · 0.0s tools
reasoning
**Addressing numerical mismatch** I’m considering if there's a mismatch in the order, especially with tolerances like 1e-4 for absolute/relative values. Maybe 5e-4 is okay for relative? I need to make sure I’m matching PyTorch better, but using `--use_fast_math` could be an issue. It seems like the accumulation order or the double precision might be introducing errors. Testing with double accumulation is worth a shot, even if it slows things down. Ultimately, I want to ensure we're numerically close and can reduce error effectively. **Exploring tolerance issues** I think the tolerance issue arises when values exceed a certain threshold, possibly affecting the output's magnitude. I'm considering whether to use `torch.backends.cudnn`, but maybe not. PyTorch might be applying convolution in a specific order with `F.conv2d`, and the differences I see could relate to using TF32 for the sum. It seems like errors in TF32 can be around 0.0005 due to the small weights. I might need to use `__float_to_tf32` for inputs and weights, especially since NVIDIA has that intrinsic. Let's test rounding for better accuracy.
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 conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\\n'\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define TILE_M 16\n#define TILE_N 16\n#define TILE_K 32\n__global__ void conv1_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ b, float* __restrict__ y, int M) {\n constexpr int C=3,H=224,W=224,OC=96,OH=55,OW=55,K=363;\n __shared__ float As[TILE_M][TILE_K+1]; __shared__ float Bs[TILE_K][TILE_N+1];\n int tx=threadIdx.x,ty=threadIdx.y,row=blockIdx.x*TILE_M+ty,oc=blockIdx.y*TILE_N+tx; float acc=0.0f; int n=0,oh=0,ow=0;\n if(row<M){ n=row/(OH*OW); int sp=row-n*(OH*OW); oh=sp/OW; ow=sp-oh*OW; }\n int tid=ty*TILE_N+tx;\n for(int k0=0;k0<K;k0+=TILE_K){\n for(int idx=tid;idx<TILE_M*TILE_K;idx+=TILE_M*TILE_N){int r=idx/TILE_K,kk=idx-r*TILE_K,grow=blockIdx.x*TILE_M+r,gk=k0+kk; float val=0.0f; if(grow<M&&gk<K){int gn=grow/(OH*OW);int gsp=grow-gn*(OH*OW);int goh=gsp/OW;int gow=gsp-goh*OW;int c=gk/121;int rem=gk-c*121;int kh=rem/11;int kw=rem-kh*11;int ih=goh*4+kh-2;int iw=gow*4+kw-2;if((unsigned)ih<H&&(unsigned)iw<W) val=__float_to_tf32(x[((gn*C+c)*H+ih)*W+iw]);} As[r][kk]=val;}\n for(int idx=tid;idx<TILE_K*TILE_N;idx+=TILE_M*TILE_N){int kk=idx/TILE_N,cc=idx-kk*TILE_N,gk=k0+kk,goc=blockIdx.y*TILE_N+cc; Bs[kk][cc]=(gk<K&&goc<OC)?__float_to_tf32(w[goc*K+gk]):0.0f;}\n __syncthreads();\n #pragma unroll\n for(int kk=0;kk<TILE_K;++kk){ if(k0+kk<K) acc=fmaf(As[ty][kk],Bs[kk][tx],acc); }\n __syncthreads();\n }\n if(row<M&&oc<OC) y[((n*OC+oc)*OH+oh)*OW+ow]=acc+b[oc];\n}\ntorch::Tensor conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){int64_t N=x.size(0);auto y=torch::empty({N,96,55,55},x.options());int M=(int)(N*55*55);dim3 block(TILE_N,TILE_M),grid((M+TILE_M-1)/TILE_M,6);conv1_kernel<<<grid,block>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),M);return y;}\n'''\n_ext=load_inline(name='conv1_tf32_test',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['conv_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,num_classes=1000):\n super().__init__(); self.conv1=nn.Conv2d(3,96,11,4,2)\n def forward(self,x): return _ext.conv_forward(x,self.conv1.weight,self.conv1.bias)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: persistent build/lock contention after retries. Please try a different kernel.
turn 5 · 1 tool call(s) · 10.1s turn · 10.1s LLM · 7,858 chars (click to expand)
turn 510.1s turn · 10.1s 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_cpp_src = '#include <torch/extension.h>\\ntorch::Tensor conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\\n'\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define TILE_M 16\n#define TILE_N 16\n#define TILE_K 32\n__global__ void conv1_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ b, float* __restrict__ y, int M) {\n constexpr int C=3,H=224,W=224,OC=96,OH=55,OW=55,K=363;\n __shared__ float As[TILE_M][TILE_K+1]; __shared__ float Bs[TILE_K][TILE_N+1];\n int tx=threadIdx.x,ty=threadIdx.y,row=blockIdx.x*TILE_M+ty,oc=blockIdx.y*TILE_N+tx; float acc=0.0f; int n=0,oh=0,ow=0;\n if(row<M){ n=row/(OH*OW); int sp=row-n*(OH*OW); oh=sp/OW; ow=sp-oh*OW; }\n int tid=ty*TILE_N+tx;\n for(int k0=0;k0<K;k0+=TILE_K){\n for(int idx=tid;idx<TILE_M*TILE_K;idx+=TILE_M*TILE_N){int r=idx/TILE_K,kk=idx-r*TILE_K,grow=blockIdx.x*TILE_M+r,gk=k0+kk; float val=0.0f; if(grow<M&&gk<K){int gn=grow/(OH*OW);int gsp=grow-gn*(OH*OW);int goh=gsp/OW;int gow=gsp-goh*OW;int c=gk/121;int rem=gk-c*121;int kh=rem/11;int kw=rem-kh*11;int ih=goh*4+kh-2;int iw=gow*4+kw-2;if((unsigned)ih<H&&(unsigned)iw<W) val=__float_to_tf32(x[((gn*C+c)*H+ih)*W+iw]);} As[r][kk]=val;}\n for(int idx=tid;idx<TILE_K*TILE_N;idx+=TILE_M*TILE_N){int kk=idx/TILE_N,cc=idx-kk*TILE_N,gk=k0+kk,goc=blockIdx.y*TILE_N+cc; Bs[kk][cc]=(gk<K&&goc<OC)?__float_to_tf32(w[goc*K+gk]):0.0f;}\n __syncthreads();\n #pragma unroll\n for(int kk=0;kk<TILE_K;++kk){ if(k0+kk<K) acc=fmaf(As[ty][kk],Bs[kk][tx],acc); }\n __syncthreads();\n }\n if(row<M&&oc<OC) y[((n*OC+oc)*OH+oh)*OW+ow]=acc+b[oc];\n}\ntorch::Tensor conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){int64_t N=x.size(0);auto y=torch::empty({N,96,55,55},x.options());int M=(int)(N*55*55);dim3 block(TILE_N,TILE_M),grid((M+TILE_M-1)/TILE_M,6);conv1_kernel<<<grid,block>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),M);return y;}\n'''\n_ext=load_inline(name='conv1_tf32_test_compileonly_x1',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['conv_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,num_classes=1000):\n super().__init__(); self.conv1=nn.Conv2d(3,96,11,4,2)\n def forward(self,x): return _ext.conv_forward(x,self.conv1.weight,self.conv1.bias)\n" }
result ← compile_kernel (fail)
failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'conv1_tf32_test_compileonly_x1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv1_tf32_test_compileonly_x1 -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_50_cache/k_aff8685e52b9/conv1_tf32_test_compileonly_x1/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=conv1_tf32_test_compileonly_x1 -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_50_cache/k_aff8685e52b9/conv1_tf32_test_compileonly_x1/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=conv1_tf32_test_compileonly_x1 -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_50_cache/k_aff8685e52b9/conv1_tf32_test_compileonly_x1/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_50_cache/k_aff8685e52b9/conv1_tf32_test_compileonly_x1/cuda.cu(18): error: identifier "__float_to_tf32" is undefined for(int idx=tid;idx<16*32;idx+=16*16){int r=idx/32,kk=idx-r*32,grow=blockIdx.x*16 +r,gk=k0+kk; float val=0.0f; if(grow<M&&gk<K){int gn=grow/(OH*OW);int gsp=grow-gn*(OH*OW);int goh=gsp/OW;int gow=gsp-goh*OW;int c=gk/121;int rem=gk-c*121;int kh=rem/11;int kw=rem-kh*11;int ih=goh*4+kh-2;int iw=gow*4+kw-2;if((unsigned)ih<H&&(unsigned)iw<W) val=__float_to_tf32(x[((gn*C+c)*H+ih)*W+iw]);} As[r][kk]=val;} ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_50_cache/k_aff8685e52b9/conv1_tf32_test_compileonly_x1/cuda.cu(19): error: identifier "__float_to_tf32" is undefined for(int idx=tid;idx<32*16;idx+=16*16){int kk=idx/16,cc=idx-kk*16,gk=k0+kk,goc=blockIdx.y*16 +cc; Bs[kk][cc]=(gk<K&&goc<OC)?__float_to_tf32(w[goc*K+gk]):0.0f;} ^ 2 errors detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_50_cache/k_aff8685e52b9/conv1_tf32_test_compileonly_x1/cuda.cu". ninja: build stopped: subcommand failed.
turn 69.2s turn · 9.2s LLM · 0.0s tools
reasoning
**Considering CUDA implementation** I’m exploring whether to include `cuda_fp16` or use inline assembly like `cvt.rna.tf32.f32`. I think the function __float_to_tf32 in `cuda_bf16` might help. I should implement something like `__device__ float tf32(float x) { float y; asm("cvt.rna.tf32.f32 %0, %1;" : "=f"(y) : "f"(x)); return y; }`. Now, I need to check if this is correct and ensure it works as intended.
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 conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\\n'\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define TILE_M 16\n#define TILE_N 16\n#define TILE_K 32\n__device__ __forceinline__ float to_tf32(float x){ float y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=f\"(y) : \"f\"(x)); return y; }\n__global__ void conv1_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ b, float* __restrict__ y, int M) {\n constexpr int C=3,H=224,W=224,OC=96,OH=55,OW=55,K=363;\n __shared__ float As[TILE_M][TILE_K+1]; __shared__ float Bs[TILE_K][TILE_N+1];\n int tx=threadIdx.x,ty=threadIdx.y,row=blockIdx.x*TILE_M+ty,oc=blockIdx.y*TILE_N+tx; float acc=0.0f; int n=0,oh=0,ow=0;\n if(row<M){ n=row/(OH*OW); int sp=row-n*(OH*OW); oh=sp/OW; ow=sp-oh*OW; }\n int tid=ty*TILE_N+tx;\n for(int k0=0;k0<K;k0+=TILE_K){\n for(int idx=tid;idx<TILE_M*TILE_K;idx+=TILE_M*TILE_N){int r=idx/TILE_K,kk=idx-r*TILE_K,grow=blockIdx.x*TILE_M+r,gk=k0+kk; float val=0.0f; if(grow<M&&gk<K){int gn=grow/(OH*OW);int gsp=grow-gn*(OH*OW);int goh=gsp/OW;int gow=gsp-goh*OW;int c=gk/121;int rem=gk-c*121;int kh=rem/11;int kw=rem-kh*11;int ih=goh*4+kh-2;int iw=gow*4+kw-2;if((unsigned)ih<H&&(unsigned)iw<W) val=to_tf32(x[((gn*C+c)*H+ih)*W+iw]);} As[r][kk]=val;}\n for(int idx=tid;idx<TILE_K*TILE_N;idx+=TILE_M*TILE_N){int kk=idx/TILE_N,cc=idx-kk*TILE_N,gk=k0+kk,goc=blockIdx.y*TILE_N+cc; Bs[kk][cc]=(gk<K&&goc<OC)?to_tf32(w[goc*K+gk]):0.0f;}\n __syncthreads();\n #pragma unroll\n for(int kk=0;kk<TILE_K;++kk){ if(k0+kk<K) acc=fmaf(As[ty][kk],Bs[kk][tx],acc); }\n __syncthreads();\n }\n if(row<M&&oc<OC) y[((n*OC+oc)*OH+oh)*OW+ow]=acc+b[oc];\n}\ntorch::Tensor conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){int64_t N=x.size(0);auto y=torch::empty({N,96,55,55},x.options());int M=(int)(N*55*55);dim3 block(TILE_N,TILE_M),grid((M+TILE_M-1)/TILE_M,6);conv1_kernel<<<grid,block>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),M);return y;}\n'''\n_ext=load_inline(name='conv1_tf32_test_asm1',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['conv_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,num_classes=1000):\n super().__init__(); self.conv1=nn.Conv2d(3,96,11,4,2)\n def forward(self,x): return _ext.conv_forward(x,self.conv1.weight,self.conv1.bias)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: unexpected error during tool execution. _WorkItemTimeout: work item exceeded soft timeout budget Traceback (most recent call last): File "/scratch/tejas/PopcornBench/src/kernelbench/agent/agent.py", line 627, in _execute_tool return tool.execute(self.ctx, **args) File "/scratch/tejas/PopcornBench/src/kernelbench/agent/tools.py", line 365, in execute return ctx.eval_client.run_correctness(ctx, kernel_code) File "/scratch/tejas/PopcornBench/src/kernelbench/agent/eval_client.py", line 58, in run_correctness return self._call("correctness", args) File "/scratch/tejas/PopcornBench/src/kernelbench/agent/eval_client.py", line 117, in _call payload = self._response_q.get(timeout=remaining) File "<string>", line 2, in get File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) File "/scratch/tejas/PopcornBench/scripts/run_sweep.py", line 161, in _alarm_handler raise _WorkItemTimeout("work item exceeded soft timeout budget") __mp_main__._WorkItemTimeout: work item exceeded soft timeout budget
turn 7773.0s turn · 773.0s LLM · 0.0s tools
reasoning
**Evaluating performance issues** I'm running into some problems with correctness timing out, and I’m wondering if it’s due to a slow kernel. It could be related to tf32 conversions, maybe a compile-and-run issue that's exceeding the soft timeout. I think I need to focus on performance, possibly by avoiding tf32. My initial correctness tests ran fine, so I might just need a compile-only approach. Let’s see if not using fast math helps with the differences.
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 conv_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\\n'\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define TILE_M 16\n#define TILE_N 16\n#define TILE_K 32\n__global__ void conv1_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int M){\n constexpr int C=3,H=224,W=224,OC=96,OH=55,OW=55,K=363; __shared__ float As[TILE_M][TILE_K+1]; __shared__ float Bs[TILE_K][TILE_N+1];\n int tx=threadIdx.x,ty=threadIdx.y,row=blockIdx.x*TILE_M+ty,oc=blockIdx.y*TILE_N+tx; float acc=0.0f; int n=0,oh=0,ow=0; if(row<M){n=row/(OH*OW);int sp=row-n*(OH*OW);oh=sp/OW;ow=sp-oh*OW;} int tid=ty*TILE_N+tx;\n for(int k0=0;k0<K;k0+=TILE_K){\n for(int idx=tid;idx<TILE_M*TILE_K;idx+=TILE_M*TILE_N){int r=idx/TILE_K;int kk=idx-r*TILE_K;int grow=blockIdx.x*TILE_M+r;int gk=k0+kk;float val=0.0f; if(grow<M&&gk<K){int gn=grow/(OH*OW);int gsp=grow-gn*(OH*OW);int goh=gsp/OW;int gow=gsp-goh*OW;int c=gk/121;int rem=gk-c*121;int kh=rem/11;int kw=rem-kh*11;int ih=goh*4+kh-2;int iw=gow*4+kw-2;if((unsigned)ih<H&&(unsigned)iw<W)val=x[((gn*C+c)*H+ih)*W+iw];}As[r][kk]=val;}\n for(int idx=tid;idx<TILE_K*TILE_N;idx+=TILE_M*TILE_N){int kk=idx/TILE_N;int cc=idx-kk*TILE_N;int gk=k0+kk;int goc=blockIdx.y*TILE_N+cc;Bs[kk][cc]=(gk<K&&goc<OC)?w[goc*K+gk]:0.0f;}\n __syncthreads();\n #pragma unroll\n for(int kk=0;kk<TILE_K;++kk){if(k0+kk<K) acc += As[ty][kk]*Bs[kk][tx];}\n __syncthreads();\n }\n if(row<M&&oc<OC)y[((n*OC+oc)*OH+oh)*OW+ow]=acc+b[oc];\n}\ntorch::Tensor conv_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias){int64_t N=x.size(0);auto y=torch::empty({N,96,55,55},x.options());int M=(int)(N*55*55);dim3 block(TILE_N,TILE_M),grid((M+TILE_M-1)/TILE_M,6);conv1_kernel<<<grid,block>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),M);return y;}\n'''\n_ext=load_inline(name='conv1_nofast_test1',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['conv_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,num_classes=1000):\n super().__init__(); self.conv1=nn.Conv2d(3,96,11,4,2)\n def forward(self,x): return _ext.conv_forward(x,self.conv1.weight,self.conv1.bias)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000522', '0.000545', '0.000567', '0.000547', '0.000539'] avg_difference: ['0.000076', '0.000076', '0.000076', '0.000076', '0.000076']
turn 87200.0s turn · 7200.0s LLM · 0.0s tools
feedbackLLM call failed: Empty: