L1 P62 · original

error 62_conv_standard_2D__square_input__asymmetric_kernel.py

model

gpt-5.5-priority

turns / max

7 / 10

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

—

ref runtime

0.00 μs

speedup

—

started

2026-05-02T21:55:12Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Performs a standard 2D convolution operation with a square input and an asymmetric kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Size of the convolution kernel (height, width).
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int or tuple, optional): Padding applied to the input. Defaults to 0.
        dilation (int or tuple, optional): Spacing between kernel elements. Defaults to 1.
        groups (int, optional): Number of blocked connections from input channels to output channels. Defaults to 1.
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """
    def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 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_out, width_out).
        """
        return self.conv2d(x)

# Test code
batch_size = 8
in_channels = 32
out_channels = 64
kernel_size = (5, 9)
width = 512
height = 512

def get_inputs():
    x = torch.rand(batch_size, in_channels, height, width)
    return [x]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]  # Provide in_channels, out_channels, kernel_size for initialization

turn 04.5s turn · 4.5s 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) · 34.1s turn · 34.1s LLM · 6,451 chars (click to expand)

turn 134.1s turn · 34.1s 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\ncpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor conv2d_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias,\n int stride_h, int stride_w, int pad_h, int pad_w,\n int dil_h, int dil_w, int groups);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void conv2d_5x9_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 H, int W,\n int Kout, int Hout, int Wout,\n int has_bias) {\n int ox = blockIdx.x * blockDim.x + threadIdx.x;\n int oy = blockIdx.y * blockDim.y + threadIdx.y;\n int z = blockIdx.z;\n int co = z % Kout;\n int n = z / Kout;\n if (ox >= Wout || oy >= Hout || n >= N) return;\n float sum = has_bias ? b[co] : 0.0f;\n const int base_in_n = n * C * H * W;\n const int base_w_co = co * C * 5 * 9;\n #pragma unroll\n for (int ci = 0; ci < 32; ++ci) {\n const float* xp = x + base_in_n + ci * H * W + oy * W + ox;\n const float* wp = w + base_w_co + ci * 45;\n #pragma unroll\n for (int ky = 0; ky < 5; ++ky) {\n const float* xrow = xp + ky * W;\n const float* wrow = wp + ky * 9;\n #pragma unroll\n for (int kx = 0; kx < 9; ++kx) {\n sum = fmaf(xrow[kx], wrow[kx], sum);\n }\n }\n }\n y[((n * Kout + co) * Hout + oy) * Wout + ox] = sum;\n}\n\n__global__ void conv2d_general_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 H, int W,\n int Kout, int KH, int KW, int Hout, int Wout,\n int stride_h, int stride_w, int pad_h, int pad_w,\n int dil_h, int dil_w, int has_bias) {\n int ox = blockIdx.x * blockDim.x + threadIdx.x;\n int oy = blockIdx.y * blockDim.y + threadIdx.y;\n int z = blockIdx.z;\n int co = z % Kout;\n int n = z / Kout;\n if (ox >= Wout || oy >= Hout || n >= N) return;\n float sum = has_bias ? b[co] : 0.0f;\n for (int ci = 0; ci < C; ++ci) {\n for (int ky = 0; ky < KH; ++ky) {\n int iy = oy * stride_h - pad_h + ky * dil_h;\n if ((unsigned)iy >= (unsigned)H) continue;\n for (int kx = 0; kx < KW; ++kx) {\n int ix = ox * stride_w - pad_w + kx * dil_w;\n if ((unsigned)ix >= (unsigned)W) continue;\n sum = fmaf(x[((n*C+ci)*H+iy)*W+ix], w[((co*C+ci)*KH+ky)*KW+kx], sum);\n }\n }\n }\n y[((n * Kout + co) * Hout + oy) * Wout + ox] = sum;\n}\n\ntorch::Tensor conv2d_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias,\n int stride_h, int stride_w, int pad_h, int pad_w,\n int dil_h, int dil_w, int groups) {\n int64_t N = x.size(0), C = x.size(1), H = x.size(2), W = x.size(3);\n int64_t Kout = w.size(0), KH = w.size(2), KW = w.size(3);\n int64_t Hout = (H + 2*pad_h - dil_h*(KH-1) - 1) / stride_h + 1;\n int64_t Wout = (W + 2*pad_w - dil_w*(KW-1) - 1) / stride_w + 1;\n auto y = torch::empty({N, Kout, Hout, Wout}, x.options());\n dim3 block(16,16);\n dim3 grid((Wout+15)/16, (Hout+15)/16, N*Kout);\n int has_bias = bias.numel() > 0;\n if (C==32 && KH==5 && KW==9 && stride_h==1 && stride_w==1 && pad_h==0 && pad_w==0 && dil_h==1 && dil_w==1 && groups==1) {\n conv2d_5x9_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,C,H,W,Kout,Hout,Wout,has_bias);\n } else {\n conv2d_general_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,C,H,W,Kout,KH,KW,Hout,Wout,stride_h,stride_w,pad_h,pad_w,dil_h,dil_w,has_bias);\n }\n return y;\n}\n'''\n\n_ext = load_inline(name='conv2d_5x9_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv2d_forward'], extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n def _pair(v):\n return v if isinstance(v, tuple) else (v, v)\n self.stride = _pair(stride); self.padding = _pair(padding); self.dilation = _pair(dilation); self.groups = groups\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n bias = self.conv2d.bias if self.conv2d.bias is not None else torch.empty(0, device=x.device, dtype=x.dtype)\n return _ext.conv2d_forward(x, self.conv2d.weight, bias, self.stride[0], self.stride[1], self.padding[0], self.padding[1], self.dilation[0], self.dilation[1], self.groups)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 2 · 1 tool call(s) · 41.5s turn · 41.5s LLM · 6,762 chars (click to expand)

turn 241.5s turn · 41.5s 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>\n\ntorch::Tensor conv2d_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias,\n int stride_h, int stride_w, int pad_h, int pad_w,\n int dil_h, int dil_w, int groups);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void conv2d_5x9_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 H, int W,\n int Kout, int Hout, int Wout,\n int has_bias) {\n int ox = blockIdx.x * blockDim.x + threadIdx.x;\n int oy = blockIdx.y * blockDim.y + threadIdx.y;\n int z = blockIdx.z;\n int co = z % Kout;\n int n = z / Kout;\n if (ox >= Wout || oy >= Hout || n >= N) return;\n float sum = has_bias ? b[co] : 0.0f;\n const int base_in_n = n * C * H * W;\n const int base_w_co = co * C * 5 * 9;\n #pragma unroll\n for (int ci = 0; ci < 32; ++ci) {\n const float* xp = x + base_in_n + ci * H * W + oy * W + ox;\n const float* wp = w + base_w_co + ci * 45;\n #pragma unroll\n for (int ky = 0; ky < 5; ++ky) {\n const float* xrow = xp + ky * W;\n const float* wrow = wp + ky * 9;\n #pragma unroll\n for (int kx = 0; kx < 9; ++kx) {\n sum = fmaf(xrow[kx], wrow[kx], sum);\n }\n }\n }\n y[((n * Kout + co) * Hout + oy) * Wout + ox] = sum;\n}\n\n__global__ void conv2d_general_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 H, int W,\n int Kout, int KH, int KW, int Hout, int Wout,\n int stride_h, int stride_w, int pad_h, int pad_w,\n int dil_h, int dil_w, int has_bias) {\n int ox = blockIdx.x * blockDim.x + threadIdx.x;\n int oy = blockIdx.y * blockDim.y + threadIdx.y;\n int z = blockIdx.z;\n int co = z % Kout;\n int n = z / Kout;\n if (ox >= Wout || oy >= Hout || n >= N) return;\n float sum = has_bias ? b[co] : 0.0f;\n for (int ci = 0; ci < C; ++ci) {\n for (int ky = 0; ky < KH; ++ky) {\n int iy = oy * stride_h - pad_h + ky * dil_h;\n if ((unsigned)iy >= (unsigned)H) continue;\n for (int kx = 0; kx < KW; ++kx) {\n int ix = ox * stride_w - pad_w + kx * dil_w;\n if ((unsigned)ix >= (unsigned)W) continue;\n sum = fmaf(x[((n*C+ci)*H+iy)*W+ix], w[((co*C+ci)*KH+ky)*KW+kx], sum);\n }\n }\n }\n y[((n * Kout + co) * Hout + oy) * Wout + ox] = sum;\n}\n\ntorch::Tensor conv2d_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias,\n int stride_h, int stride_w, int pad_h, int pad_w,\n int dil_h, int dil_w, int groups) {\n int64_t N = x.size(0), C = x.size(1), H = x.size(2), W = x.size(3);\n int64_t Kout = w.size(0), KH = w.size(2), KW = w.size(3);\n int64_t Hout = (H + 2*pad_h - dil_h*(KH-1) - 1) / stride_h + 1;\n int64_t Wout = (W + 2*pad_w - dil_w*(KW-1) - 1) / stride_w + 1;\n auto y = torch::empty({N, Kout, Hout, Wout}, x.options());\n dim3 block(16,16);\n dim3 grid((Wout+15)/16, (Hout+15)/16, N*Kout);\n int has_bias = bias.numel() > 0;\n if (C==32 && KH==5 && KW==9 && stride_h==1 && stride_w==1 && pad_h==0 && pad_w==0 && dil_h==1 && dil_w==1 && groups==1) {\n conv2d_5x9_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,C,H,W,Kout,Hout,Wout,has_bias);\n } else {\n conv2d_general_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,C,H,W,Kout,KH,KW,Hout,Wout,stride_h,stride_w,pad_h,pad_w,dil_h,dil_w,has_bias);\n }\n return y;\n}\n'''\n\n_ext = load_inline(name='conv2d_5x9_ext_v1c', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv2d_forward'], extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n def _pair(v):\n return v if isinstance(v, tuple) else (v, v)\n self.stride = _pair(stride); self.padding = _pair(padding); self.dilation = _pair(dilation); self.groups = groups\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n bias = self.conv2d.bias if self.conv2d.bias is not None else torch.empty(0, device=x.device, dtype=x.dtype)\n return _ext.conv2d_forward(x, self.conv2d.weight, bias, self.stride[0], self.stride[1], self.padding[0], self.padding[1], self.dilation[0], self.dilation[1], self.groups)\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000514', '0.000503', '0.000524', '0.000509', '0.000498'] avg_difference: ['0.000073', '0.000073', '0.000073', '0.000073', '0.000073']

turn 324.6s turn · 24.6s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor conv2d_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias,int stride_h,int stride_w,int pad_h,int pad_w,int dil_h,int dil_w,int groups);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__global__ void conv2d_5x9_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int H,int W,int Kout,int Hout,int Wout,int has_bias){\n int ox=blockIdx.x*blockDim.x+threadIdx.x; int oy=blockIdx.y*blockDim.y+threadIdx.y; int z=blockIdx.z; int co=z%Kout; int n=z/Kout; if(ox>=Wout||oy>=Hout) return; double sum=has_bias?(double)b[co]:0.0; int base=n*C*H*W; int bw=co*C*45;\n #pragma unroll\n for(int ci=0;ci<32;++ci){ const float* xp=x+base+ci*H*W+oy*W+ox; const float* wp=w+bw+ci*45;\n #pragma unroll\n for(int ky=0;ky<5;++ky){ const float* xr=xp+ky*W; const float* wr=wp+ky*9;\n #pragma unroll\n for(int kx=0;kx<9;++kx) sum += (double)xr[kx]*(double)wr[kx]; }}\n y[((n*Kout+co)*Hout+oy)*Wout+ox]=(float)sum; }\n__global__ void conv2d_general_kernel(const float*x,const float*w,const float*b,float*y,int N,int C,int H,int W,int Kout,int KH,int KW,int Hout,int Wout,int sh,int sw,int ph,int pw,int dh,int dw,int has_bias){int ox=blockIdx.x*blockDim.x+threadIdx.x;int oy=blockIdx.y*blockDim.y+threadIdx.y;int z=blockIdx.z;int co=z%Kout;int n=z/Kout;if(ox>=Wout||oy>=Hout)return;double sum=has_bias?b[co]:0.0;for(int ci=0;ci<C;++ci)for(int ky=0;ky<KH;++ky){int iy=oy*sh-ph+ky*dh;if((unsigned)iy>=(unsigned)H)continue;for(int kx=0;kx<KW;++kx){int ix=ox*sw-pw+kx*dw;if((unsigned)ix>=(unsigned)W)continue;sum+=(double)x[((n*C+ci)*H+iy)*W+ix]*(double)w[((co*C+ci)*KH+ky)*KW+kx];}}y[((n*Kout+co)*Hout+oy)*Wout+ox]=(float)sum;}\ntorch::Tensor conv2d_forward(torch::Tensor x,torch::Tensor w,torch::Tensor bias,int sh,int sw,int ph,int pw,int dh,int dw,int groups){int64_t N=x.size(0),C=x.size(1),H=x.size(2),W=x.size(3),Kout=w.size(0),KH=w.size(2),KW=w.size(3);int64_t Hout=(H+2*ph-dh*(KH-1)-1)/sh+1,Wout=(W+2*pw-dw*(KW-1)-1)/sw+1;auto y=torch::empty({N,Kout,Hout,Wout},x.options());dim3 block(16,16),grid((Wout+15)/16,(Hout+15)/16,N*Kout);int hb=bias.numel()>0;if(C==32&&KH==5&&KW==9&&sh==1&&sw==1&&ph==0&&pw==0&&dh==1&&dw==1&&groups==1)conv2d_5x9_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,H,W,Kout,Hout,Wout,hb);else conv2d_general_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,H,W,Kout,KH,KW,Hout,Wout,sh,sw,ph,pw,dh,dw,hb);return y;}\n'''\n_ext=load_inline(name='conv2d_5x9_ext_double',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:int=1,padding:int=0,dilation:int=1,groups:int=1,bias:bool=False):\n super().__init__(); self.conv2d=nn.Conv2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,dilation=dilation,groups=groups,bias=bias)\n p=lambda v:v if isinstance(v,tuple) else (v,v); self.stride=p(stride); self.padding=p(padding); self.dilation=p(dilation); self.groups=groups\n def forward(self,x):\n bias=self.conv2d.bias if self.conv2d.bias is not None else torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv2d_forward(x,self.conv2d.weight,bias,self.stride[0],self.stride[1],self.padding[0],self.padding[1],self.dilation[0],self.dilation[1],self.groups)\n" }

result ← run_correctness (fail)

turn 416.0s turn · 16.0s 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\ncpp_src='''#include <torch/extension.h>\\ntorch::Tensor conv2d_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias,int stride_h,int stride_w,int pad_h,int pad_w,int dil_h,int dil_w,int groups);'''\ncuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__global__ void k(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int H,int W,int Kout,int Hout,int Wout,int hb){int ox=blockIdx.x*blockDim.x+threadIdx.x, oy=blockIdx.y*blockDim.y+threadIdx.y,z=blockIdx.z,co=z%Kout,n=z/Kout;if(ox>=Wout||oy>=Hout)return;float sum=hb?b[co]:0.f;int base=n*C*H*W,bw=co*C*45;#pragma unroll\nfor(int ci=0;ci<32;++ci){const float*xp=x+base+ci*H*W+oy*W+ox;const float*wp=w+bw+ci*45;#pragma unroll\nfor(int ky=0;ky<5;++ky){const float*xr=xp+ky*W;const float*wr=wp+ky*9;#pragma unroll\nfor(int kx=0;kx<9;++kx){sum=fmaf(__float_to_tf32(xr[kx]),__float_to_tf32(wr[kx]),sum);}}}y[((n*Kout+co)*Hout+oy)*Wout+ox]=sum;}\n__global__ void kg(const float*x,const float*w,const float*b,float*y,int N,int C,int H,int W,int Kout,int KH,int KW,int Hout,int Wout,int sh,int sw,int ph,int pw,int dh,int dw,int hb){int ox=blockIdx.x*blockDim.x+threadIdx.x,oy=blockIdx.y*blockDim.y+threadIdx.y,z=blockIdx.z,co=z%Kout,n=z/Kout;if(ox>=Wout||oy>=Hout)return;float sum=hb?b[co]:0.f;for(int ci=0;ci<C;++ci)for(int ky=0;ky<KH;++ky){int iy=oy*sh-ph+ky*dh;if((unsigned)iy>=(unsigned)H)continue;for(int kx=0;kx<KW;++kx){int ix=ox*sw-pw+kx*dw;if((unsigned)ix>=(unsigned)W)continue;sum=fmaf(__float_to_tf32(x[((n*C+ci)*H+iy)*W+ix]),__float_to_tf32(w[((co*C+ci)*KH+ky)*KW+kx]),sum);}}y[((n*Kout+co)*Hout+oy)*Wout+ox]=sum;}\ntorch::Tensor conv2d_forward(torch::Tensor x,torch::Tensor w,torch::Tensor bias,int sh,int sw,int ph,int pw,int dh,int dw,int groups){int64_t N=x.size(0),C=x.size(1),H=x.size(2),W=x.size(3),Kout=w.size(0),KH=w.size(2),KW=w.size(3);int64_t Hout=(H+2*ph-dh*(KH-1)-1)/sh+1,Wout=(W+2*pw-dw*(KW-1)-1)/sw+1;auto y=torch::empty({N,Kout,Hout,Wout},x.options());dim3 block(16,16),grid((Wout+15)/16,(Hout+15)/16,N*Kout);int hb=bias.numel()>0;if(C==32&&KH==5&&KW==9&&sh==1&&sw==1&&ph==0&&pw==0&&dh==1&&dw==1&&groups==1)k<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,H,W,Kout,Hout,Wout,hb);else kg<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,H,W,Kout,KH,KW,Hout,Wout,sh,sw,ph,pw,dh,dw,hb);return y;}\n'''\n_ext=load_inline(name='conv2d_tf32_test',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:int=1,padding:int=0,dilation:int=1,groups:int=1,bias:bool=False):\n super().__init__();self.conv2d=nn.Conv2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,dilation=dilation,groups=groups,bias=bias);p=lambda v:v if isinstance(v,tuple) else (v,v);self.stride=p(stride);self.padding=p(padding);self.dilation=p(dilation);self.groups=groups\n def forward(self,x):\n bias=self.conv2d.bias if self.conv2d.bias is not None else torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv2d_forward(x,self.conv2d.weight,bias,self.stride[0],self.stride[1],self.padding[0],self.padding[1],self.dilation[0],self.dilation[1],self.groups)\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) · 17.6s turn · 17.6s LLM · 9,714 chars (click to expand)

turn 517.6s turn · 17.6s LLM · 0.0s tools

reasoning

**Troubleshooting compilation issues** I’m facing potential issues with multiple modules compiling. It might be worth trying to compile them separately. Plus, the code formatting with #pragma mid-line might not work correctly, as it mentions lock contention. Maybe I should consider using a different name for something to avoid conflicts? It does seem different this time. If it keeps failing, I'll just retry the compilation and see what happens next!

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src='''#include <torch/extension.h>\\ntorch::Tensor conv2d_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias,int stride_h,int stride_w,int pad_h,int pad_w,int dil_h,int dil_w,int groups);'''\ncuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__global__ void k(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int N,int C,int H,int W,int Kout,int Hout,int Wout,int hb){\n int ox=blockIdx.x*blockDim.x+threadIdx.x, oy=blockIdx.y*blockDim.y+threadIdx.y,z=blockIdx.z,co=z%Kout,n=z/Kout;\n if(ox>=Wout||oy>=Hout)return;\n float sum=hb?b[co]:0.f;int base=n*C*H*W,bw=co*C*45;\n #pragma unroll\n for(int ci=0;ci<32;++ci){const float*xp=x+base+ci*H*W+oy*W+ox;const float*wp=w+bw+ci*45;\n #pragma unroll\n for(int ky=0;ky<5;++ky){const float*xr=xp+ky*W;const float*wr=wp+ky*9;\n #pragma unroll\n for(int kx=0;kx<9;++kx){sum=fmaf(__float_to_tf32(xr[kx]),__float_to_tf32(wr[kx]),sum);}}}\n y[((n*Kout+co)*Hout+oy)*Wout+ox]=sum;}\n__global__ void kg(const float*x,const float*w,const float*b,float*y,int N,int C,int H,int W,int Kout,int KH,int KW,int Hout,int Wout,int sh,int sw,int ph,int pw,int dh,int dw,int hb){int ox=blockIdx.x*blockDim.x+threadIdx.x,oy=blockIdx.y*blockDim.y+threadIdx.y,z=blockIdx.z,co=z%Kout,n=z/Kout;if(ox>=Wout||oy>=Hout)return;float sum=hb?b[co]:0.f;for(int ci=0;ci<C;++ci)for(int ky=0;ky<KH;++ky){int iy=oy*sh-ph+ky*dh;if((unsigned)iy>=(unsigned)H)continue;for(int kx=0;kx<KW;++kx){int ix=ox*sw-pw+kx*dw;if((unsigned)ix>=(unsigned)W)continue;sum=fmaf(__float_to_tf32(x[((n*C+ci)*H+iy)*W+ix]),__float_to_tf32(w[((co*C+ci)*KH+ky)*KW+kx]),sum);}}y[((n*Kout+co)*Hout+oy)*Wout+ox]=sum;}\ntorch::Tensor conv2d_forward(torch::Tensor x,torch::Tensor w,torch::Tensor bias,int sh,int sw,int ph,int pw,int dh,int dw,int groups){int64_t N=x.size(0),C=x.size(1),H=x.size(2),W=x.size(3),Kout=w.size(0),KH=w.size(2),KW=w.size(3);int64_t Hout=(H+2*ph-dh*(KH-1)-1)/sh+1,Wout=(W+2*pw-dw*(KW-1)-1)/sw+1;auto y=torch::empty({N,Kout,Hout,Wout},x.options());dim3 block(16,16),grid((Wout+15)/16,(Hout+15)/16,N*Kout);int hb=bias.numel()>0;if(C==32&&KH==5&&KW==9&&sh==1&&sw==1&&ph==0&&pw==0&&dh==1&&dw==1&&groups==1)k<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,H,W,Kout,Hout,Wout,hb);else kg<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),hb?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,C,H,W,Kout,KH,KW,Hout,Wout,sh,sw,ph,pw,dh,dw,hb);return y;}\n'''\n_ext=load_inline(name='conv2d_tf32_test2',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:int=1,padding:int=0,dilation:int=1,groups:int=1,bias:bool=False):\n super().__init__();self.conv2d=nn.Conv2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,dilation=dilation,groups=groups,bias=bias);p=lambda v:v if isinstance(v,tuple) else (v,v);self.stride=p(stride);self.padding=p(padding);self.dilation=p(dilation);self.groups=groups\n def forward(self,x):\n bias=self.conv2d.bias if self.conv2d.bias is not None else torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv2d_forward(x,self.conv2d.weight,bias,self.stride[0],self.stride[1],self.padding[0],self.padding[1],self.dilation[0],self.dilation[1],self.groups)\n" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'conv2d_tf32_test2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv2d_tf32_test2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_62_cache/k_0c22b7c4b180/conv2d_tf32_test2/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=conv2d_tf32_test2 -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_62_cache/k_0c22b7c4b180/conv2d_tf32_test2/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=conv2d_tf32_test2 -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_62_cache/k_0c22b7c4b180/conv2d_tf32_test2/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_62_cache/k_0c22b7c4b180/conv2d_tf32_test2/cuda.cu(17): error: identifier "__float_to_tf32" is undefined for(int kx=0;kx<9;++kx){sum=fmaf(__float_to_tf32(xr[kx]),__float_to_tf32(wr[kx]),sum);}}} ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_62_cache/k_0c22b7c4b180/conv2d_tf32_test2/cuda.cu(19): error: identifier "__float_to_tf32" is undefined void kg(const float*x,const float*w,const float*b,float*y,int N,int C,int H,int W,int Kout,int KH,int KW,int Hout,int Wout,int sh,int sw,int ph,int pw,int dh,int dw,int hb){int ox=blockIdx.x*blockDim.x+threadIdx.x,oy=blockIdx.y*blockDim.y+threadIdx.y,z=blockIdx.z,co=z%Kout,n=z/Kout;if(ox>=Wout||oy>=Hout)return;float sum=hb?b[co]:0.f;for(int ci=0;ci<C;++ci)for(int ky=0;ky<KH;++ky){int iy=oy*sh-ph+ky*dh;if((unsigned)iy>=(unsigned)H)continue;for(int kx=0;kx<KW;++kx){int ix=ox*sw-pw+kx*dw;if((unsigned)ix>=(unsigned)W)continue;sum=fmaf(__float_to_tf32(x[((n*C+ci)*H+iy)*W+ix]),__float_to_tf32(w[((co*C+ci)*KH+ky)*KW+kx]),sum);}}y[((n*Kout+co)*Hout+oy)*Wout+ox]=sum;} ^ 2 errors detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_1_problem_62_cache/k_0c22b7c4b180/conv2d_tf32_test2/cuda.cu". ninja: build stopped: subcommand failed.

turn 65874.1s turn · 5874.1s LLM · 0.0s tools

feedbackLLM call failed: _WorkItemTimeout: work item exceeded soft timeout budget