L1 P73 · original

error 73_conv_transposed_3D_asymmetric_input_square_kernel__strided_padded__grouped.py

model

gpt-5.5-priority

turns / max

9 / 10

tool calls

tokens (total)

wall clock

—

truncated

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

class Model(nn.Module):
    """
    Performs a 3D transposed convolution operation with asymmetric input and square kernel.
    The input is padded before the convolution.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        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: int, stride: int = 1, padding: int = 0, output_padding: int = 0, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.conv_transpose3d = nn.ConvTranspose3d(in_channels, out_channels, kernel_size=(kernel_size, kernel_size, kernel_size), stride=stride, padding=padding, groups=groups, bias=bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 3D transposed convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, depth_out, height_out, width_out).
        """
        return self.conv_transpose3d(x)

# Test code
batch_size = 4
in_channels = 32
out_channels = 32
kernel_size = 3
depth = 32
height = 64
width = 128
stride = 2
padding = 1
groups = 4

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, groups]

turn 022.8s turn · 22.8s 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) · 79.8s turn · 79.8s LLM · 6,810 chars (click to expand)

turn 179.8s turn · 79.8s 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 convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void convt3d_s2k3_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n float* __restrict__ y,\n long N, long Cin, long D, long H, long W,\n long Cout, long G,\n long Do, long Ho, long Wo) {\n long idx = blockIdx.x * blockDim.x + threadIdx.x;\n long total = N * Cout * Do * Ho * Wo;\n if (idx >= total) return;\n\n long t = idx;\n long ow = t % Wo; t /= Wo;\n long oh = t % Ho; t /= Ho;\n long od = t % Do; t /= Do;\n long oc = t % Cout; t /= Cout;\n long n = t;\n\n const long out_per_g = Cout / G;\n const long in_per_g = Cin / G;\n const long g = oc / out_per_g;\n const long ocg = oc - g * out_per_g;\n const long ic_start = g * in_per_g;\n\n float acc = 0.0f;\n\n // stride=2, padding=1, kernel=3 specialized. For each dim, only k with (o+1-k) even.\n int kd0 = ((od + 1) & 1) ? 0 : 1; // parity matching first kd\n #pragma unroll\n for (int kd = kd0; kd < 3; kd += 2) {\n long id = (od + 1 - kd) >> 1;\n if ((unsigned long)id >= (unsigned long)D) continue;\n int kh0 = ((oh + 1) & 1) ? 0 : 1;\n #pragma unroll\n for (int kh = kh0; kh < 3; kh += 2) {\n long ih = (oh + 1 - kh) >> 1;\n if ((unsigned long)ih >= (unsigned long)H) continue;\n int kw0 = ((ow + 1) & 1) ? 0 : 1;\n #pragma unroll\n for (int kw = kw0; kw < 3; kw += 2) {\n long iw = (ow + 1 - kw) >> 1;\n if ((unsigned long)iw >= (unsigned long)W) continue;\n long x_base = ((n * Cin + ic_start) * D + id) * H * W + ih * W + iw;\n long w_base = (((ic_start) * out_per_g + ocg) * 3 + kd) * 3 * 3 + kh * 3 + kw;\n #pragma unroll 1\n for (long ii = 0; ii < in_per_g; ++ii) {\n acc += x[x_base + ii * D * H * W] * w[w_base + ii * out_per_g * 27];\n }\n }\n }\n }\n y[idx] = acc;\n}\n\n__global__ void convt3d_generic_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n float* __restrict__ y,\n long N, long Cin, long D, long H, long W,\n long Cout, long K, long stride, long padding, long G,\n long Do, long Ho, long Wo) {\n long idx = blockIdx.x * blockDim.x + threadIdx.x;\n long total = N * Cout * Do * Ho * Wo;\n if (idx >= total) return;\n long t=idx; long ow=t%Wo; t/=Wo; long oh=t%Ho; t/=Ho; long od=t%Do; t/=Do; long oc=t%Cout; t/=Cout; long n=t;\n long out_per_g=Cout/G, in_per_g=Cin/G, g=oc/out_per_g, ocg=oc-g*out_per_g, ic_start=g*in_per_g;\n float acc=0.0f;\n for(long kd=0; kd<K; ++kd){ long zd=od+padding-kd; if(zd%stride) continue; long id=zd/stride; if((unsigned long)id>=(unsigned long)D) continue;\n for(long kh=0; kh<K; ++kh){ long zh=oh+padding-kh; if(zh%stride) continue; long ih=zh/stride; if((unsigned long)ih>=(unsigned long)H) continue;\n for(long kw=0; kw<K; ++kw){ long zw=ow+padding-kw; if(zw%stride) continue; long iw=zw/stride; if((unsigned long)iw>=(unsigned long)W) continue;\n long xb=((n*Cin+ic_start)*D+id)*H*W+ih*W+iw;\n long wb=(((ic_start)*out_per_g+ocg)*K+kd)*K*K+kh*K+kw;\n for(long ii=0; ii<in_per_g; ++ii) acc += x[xb+ii*D*H*W]*w[wb+ii*out_per_g*K*K*K];\n }\n }\n }\n y[idx]=acc;\n}\n\ntorch::Tensor convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups) {\n auto N = x.size(0), Cin = x.size(1), D = x.size(2), H = x.size(3), W = x.size(4);\n auto K = w.size(2);\n auto Cout = w.size(1) * groups;\n auto Do = (D - 1) * stride - 2 * padding + K;\n auto Ho = (H - 1) * stride - 2 * padding + K;\n auto Wo = (W - 1) * stride - 2 * padding + K;\n auto y = torch::empty({N, Cout, Do, Ho, Wo}, x.options());\n long total = N*Cout*Do*Ho*Wo;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n if (x.scalar_type() == torch::kFloat32 && K == 3 && stride == 2 && padding == 1) {\n convt3d_s2k3_kernel<<<blocks, threads>>>(x.data_ptr<float>(), w.data_ptr<float>(), y.data_ptr<float>(), N,Cin,D,H,W,Cout,groups,Do,Ho,Wo);\n } else {\n convt3d_generic_kernel<<<blocks, threads>>>(x.data_ptr<float>(), w.data_ptr<float>(), y.data_ptr<float>(), N,Cin,D,H,W,Cout,K,stride,padding,groups,Do,Ho,Wo);\n }\n return y;\n}\n'''\n\n_ext = load_inline(name='convt3d_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['convt3d_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, kernel_size: int, stride: int = 1, padding: int = 0, output_padding: int = 0, groups: int = 1, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv_transpose3d = nn.ConvTranspose3d(in_channels, out_channels, kernel_size=(kernel_size, kernel_size, kernel_size), stride=stride, padding=padding, groups=groups, bias=bias)\n self.stride = stride\n self.padding = padding\n self.groups = groups\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.convt3d_forward(x.contiguous(), self.conv_transpose3d.weight.contiguous(), int(self.stride), int(self.padding), int(self.groups))\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 2 · 1 tool call(s) · 11.3s turn · 11.3s LLM · 7,120 chars (click to expand)

turn 211.3s turn · 11.3s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void convt3d_s2k3_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n float* __restrict__ y,\n long N, long Cin, long D, long H, long W,\n long Cout, long G,\n long Do, long Ho, long Wo) {\n long idx = blockIdx.x * blockDim.x + threadIdx.x;\n long total = N * Cout * Do * Ho * Wo;\n if (idx >= total) return;\n\n long t = idx;\n long ow = t % Wo; t /= Wo;\n long oh = t % Ho; t /= Ho;\n long od = t % Do; t /= Do;\n long oc = t % Cout; t /= Cout;\n long n = t;\n\n const long out_per_g = Cout / G;\n const long in_per_g = Cin / G;\n const long g = oc / out_per_g;\n const long ocg = oc - g * out_per_g;\n const long ic_start = g * in_per_g;\n\n float acc = 0.0f;\n\n // stride=2, padding=1, kernel=3 specialized. For each dim, only k with (o+1-k) even.\n int kd0 = ((od + 1) & 1) ? 0 : 1; // parity matching first kd\n #pragma unroll\n for (int kd = kd0; kd < 3; kd += 2) {\n long id = (od + 1 - kd) >> 1;\n if ((unsigned long)id >= (unsigned long)D) continue;\n int kh0 = ((oh + 1) & 1) ? 0 : 1;\n #pragma unroll\n for (int kh = kh0; kh < 3; kh += 2) {\n long ih = (oh + 1 - kh) >> 1;\n if ((unsigned long)ih >= (unsigned long)H) continue;\n int kw0 = ((ow + 1) & 1) ? 0 : 1;\n #pragma unroll\n for (int kw = kw0; kw < 3; kw += 2) {\n long iw = (ow + 1 - kw) >> 1;\n if ((unsigned long)iw >= (unsigned long)W) continue;\n long x_base = ((n * Cin + ic_start) * D + id) * H * W + ih * W + iw;\n long w_base = (((ic_start) * out_per_g + ocg) * 3 + kd) * 3 * 3 + kh * 3 + kw;\n #pragma unroll 1\n for (long ii = 0; ii < in_per_g; ++ii) {\n acc += x[x_base + ii * D * H * W] * w[w_base + ii * out_per_g * 27];\n }\n }\n }\n }\n y[idx] = acc;\n}\n\n__global__ void convt3d_generic_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n float* __restrict__ y,\n long N, long Cin, long D, long H, long W,\n long Cout, long K, long stride, long padding, long G,\n long Do, long Ho, long Wo) {\n long idx = blockIdx.x * blockDim.x + threadIdx.x;\n long total = N * Cout * Do * Ho * Wo;\n if (idx >= total) return;\n long t=idx; long ow=t%Wo; t/=Wo; long oh=t%Ho; t/=Ho; long od=t%Do; t/=Do; long oc=t%Cout; t/=Cout; long n=t;\n long out_per_g=Cout/G, in_per_g=Cin/G, g=oc/out_per_g, ocg=oc-g*out_per_g, ic_start=g*in_per_g;\n float acc=0.0f;\n for(long kd=0; kd<K; ++kd){ long zd=od+padding-kd; if(zd%stride) continue; long id=zd/stride; if((unsigned long)id>=(unsigned long)D) continue;\n for(long kh=0; kh<K; ++kh){ long zh=oh+padding-kh; if(zh%stride) continue; long ih=zh/stride; if((unsigned long)ih>=(unsigned long)H) continue;\n for(long kw=0; kw<K; ++kw){ long zw=ow+padding-kw; if(zw%stride) continue; long iw=zw/stride; if((unsigned long)iw>=(unsigned long)W) continue;\n long xb=((n*Cin+ic_start)*D+id)*H*W+ih*W+iw;\n long wb=(((ic_start)*out_per_g+ocg)*K+kd)*K*K+kh*K+kw;\n for(long ii=0; ii<in_per_g; ++ii) acc += x[xb+ii*D*H*W]*w[wb+ii*out_per_g*K*K*K];\n }\n }\n }\n y[idx]=acc;\n}\n\ntorch::Tensor convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups) {\n auto N = x.size(0), Cin = x.size(1), D = x.size(2), H = x.size(3), W = x.size(4);\n auto K = w.size(2);\n auto Cout = w.size(1) * groups;\n auto Do = (D - 1) * stride - 2 * padding + K;\n auto Ho = (H - 1) * stride - 2 * padding + K;\n auto Wo = (W - 1) * stride - 2 * padding + K;\n auto y = torch::empty({N, Cout, Do, Ho, Wo}, x.options());\n long total = N*Cout*Do*Ho*Wo;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n if (x.scalar_type() == torch::kFloat32 && K == 3 && stride == 2 && padding == 1) {\n convt3d_s2k3_kernel<<<blocks, threads>>>(x.data_ptr<float>(), w.data_ptr<float>(), y.data_ptr<float>(), N,Cin,D,H,W,Cout,groups,Do,Ho,Wo);\n } else {\n convt3d_generic_kernel<<<blocks, threads>>>(x.data_ptr<float>(), w.data_ptr<float>(), y.data_ptr<float>(), N,Cin,D,H,W,Cout,K,stride,padding,groups,Do,Ho,Wo);\n }\n return y;\n}\n'''\n\n_ext = load_inline(name='convt3d_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['convt3d_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, kernel_size: int, stride: int = 1, padding: int = 0, output_padding: int = 0, groups: int = 1, bias: bool = False):\n super(ModelNew, self).__init__()\n self.conv_transpose3d = nn.ConvTranspose3d(in_channels, out_channels, kernel_size=(kernel_size, kernel_size, kernel_size), stride=stride, padding=padding, groups=groups, bias=bias)\n self.stride = stride\n self.padding = padding\n self.groups = groups\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.convt3d_forward(x.contiguous(), self.conv_transpose3d.weight.contiguous(), int(self.stride), int(self.padding), int(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: ['0.837179', '0.837973', '0.819179', '0.818838', '0.825152'] avg_difference: ['0.134337', '0.134347', '0.134317', '0.134325', '0.134329']

turn 329.7s turn · 29.7s LLM · 0.0s tools

reasoning

**Diagnosing with tools** I need to diagnose something using a small manual. The tools available don’t have custom scripts, so I'm focusing on their correctness. One idea I have is to create a kernel variant by flipping the k to check if that affects correctness. It’s a simple approach, but I wonder if it’ll give me the clarity I’m looking for. Let’s see how this goes!

call → run_correctness

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.696079', '0.697628', '0.738580', '0.711483', '0.735803'] avg_difference: ['0.058495', '0.058484', '0.058503', '0.058502', '0.058495']

turn 411.8s turn · 11.8s LLM · 0.0s tools

reasoning

**Analyzing model parameters** I’m considering how the function `run_correctness` initializes `ModelNew` and its relationship with `output_padding`. It seems like there’s a possibility of mismatches with the group settings because of how the inputs are being passed. If the evaluator uses keyword arguments for groups, we might need to clarify that. In some cases, the tool looks for a prompt discrepancy, which might affect the `state_dict` copy. So, if `output_padding` isn't zero and `groups` equals one, I could adjust `actual_groups` accordingly. Let's test this out!

call → run_correctness

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.834982', '0.860861', '0.885382', '0.838154', '0.838166'] avg_difference: ['0.140116', '0.140124', '0.140089', '0.140107', '0.140096']

turn 520.2s turn · 20.2s LLM · 0.0s tools

reasoning

**Testing model correctness** I’m wondering if `load_state_dict` might fail silently if the `ModelNew` state has an extension. It seems like it could still work if there's no copy, possibly resulting in a static state. I want to test this by using the forward call `self.conv_transpose3d(x)` in `ModelNew` to check for correctness. If there’s a mismatch, it suggests that the weights might not have been copied. I’ll reference `run_correctness` with `ModelNew` for this test.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nclass ModelNew(nn.Module):\n def __init__(self, in_channels:int, out_channels:int, kernel_size:int, stride:int=1, padding:int=0, output_padding:int=0, groups:int=1, bias:bool=False):\n super().__init__()\n self.conv_transpose3d=nn.ConvTranspose3d(in_channels,out_channels,(kernel_size,kernel_size,kernel_size),stride=stride,padding=padding,groups=groups,bias=bias)\n def forward(self,x):\n return self.conv_transpose3d(x)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00

turn 68.6s turn · 8.6s LLM · 0.0s tools

reasoning

**Diagnosing weight issues** I need to address some math errors after copying weights. It seems like comparing with a reference and using groups might help. The pure module passed with default groups, so groups equal one. I think our math issues may come from indexing. Maybe I'll write a simpler, more generic formula using PyTorch, testing different variants. I could utilize a scatter kernel with atomics in the formula to confirm correctness. Implementing zero for each input channel might take time but will help verify the formula.

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 convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups);\\n'\ncuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__global__ void scatter(const float*x,const float*w,float*y,long N,long Cin,long D,long H,long W,long Cout,long G,long Do,long Ho,long Wo){\n long idx=blockIdx.x*blockDim.x+threadIdx.x; long total=N*Cin*D*H*W; if(idx>=total)return; long t=idx; long iw=t%W;t/=W; long ih=t%H;t/=H; long id=t%D;t/=D; long ic=t%Cin;t/=Cin; long n=t;\n long ipg=Cin/G, opg=Cout/G, g=ic/ipg; long oc_start=g*opg; float xv=x[idx];\n for(long ocg=0; ocg<opg; ++ocg) for(int kd=0;kd<3;kd++){ long od=id*2-1+kd; if((unsigned long)od>=(unsigned long)Do) continue; for(int kh=0;kh<3;kh++){ long oh=ih*2-1+kh; if((unsigned long)oh>=(unsigned long)Ho) continue; for(int kw=0;kw<3;kw++){ long ow=iw*2-1+kw; if((unsigned long)ow>=(unsigned long)Wo) continue; long oc=oc_start+ocg; long yi=((((n*Cout+oc)*Do+od)*Ho+oh)*Wo+ow); long wi=((((ic*opg+ocg)*3+kd)*3+kh)*3+kw); atomicAdd(y+yi,xv*w[wi]); }}}\n}\ntorch::Tensor convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups){auto N=x.size(0),Cin=x.size(1),D=x.size(2),H=x.size(3),W=x.size(4);auto K=w.size(2);auto Cout=w.size(1)*groups;auto Do=(D-1)*stride-2*padding+K;auto Ho=(H-1)*stride-2*padding+K;auto Wo=(W-1)*stride-2*padding+K;auto y=torch::zeros({N,Cout,Do,Ho,Wo},x.options());long total=N*Cin*D*H*W; scatter<<<(total+255)/256,256>>>(x.data_ptr<float>(),w.data_ptr<float>(),y.data_ptr<float>(),N,Cin,D,H,W,Cout,groups,Do,Ho,Wo); return y;}\n'''\n_ext=load_inline(name='convt3d_scatter_test',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['convt3d_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,stride:int=1,padding:int=0,output_padding:int=0,groups:int=1,bias:bool=False):\n super().__init__(); self.conv_transpose3d=nn.ConvTranspose3d(in_channels,out_channels,(kernel_size,kernel_size,kernel_size),stride=stride,padding=padding,groups=groups,bias=bias); self.stride=stride; self.padding=padding; self.groups=groups\n def forward(self,x): return _ext.convt3d_forward(x.contiguous(),self.conv_transpose3d.weight.contiguous(),int(self.stride),int(self.padding),int(self.groups))\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 7495.6s turn · 495.6s 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\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups);\n'''\ncuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__global__ void k(const float*x,const float*w,float*y,long N,long Cin,long D,long H,long W,long Cout,long G,long Do,long Ho,long Wo){long idx=blockIdx.x*blockDim.x+threadIdx.x,total=N*Cout*Do*Ho*Wo;if(idx>=total)return;long t=idx;long ow=t%Wo;t/=Wo;long oh=t%Ho;t/=Ho;long od=t%Do;t/=Do;long oc=t%Cout;t/=Cout;long n=t;long opg=Cout/G,ipg=Cin/G,g=oc/opg,ocg=oc-g*opg,ics=g*ipg;float acc=0;int kd0=(int)((od+1)&1);for(int kd=kd0;kd<3;kd+=2){long id=(od+1-kd)>>1;if((unsigned long)id>=(unsigned long)D)continue;int kh0=(int)((oh+1)&1);for(int kh=kh0;kh<3;kh+=2){long ih=(oh+1-kh)>>1;if((unsigned long)ih>=(unsigned long)H)continue;int kw0=(int)((ow+1)&1);for(int kw=kw0;kw<3;kw+=2){long iw=(ow+1-kw)>>1;if((unsigned long)iw>=(unsigned long)W)continue;long xb=((n*Cin+ics)*D+id)*H*W+ih*W+iw;long wb=(((ics)*opg+ocg)*3+kd)*9+kh*3+kw;for(long ii=0;ii<ipg;++ii)acc+=x[xb+ii*D*H*W]*w[wb+ii*opg*27];}}}y[idx]=acc;}\ntorch::Tensor convt3d_forward(torch::Tensor x, torch::Tensor w, int64_t stride, int64_t padding, int64_t groups){auto N=x.size(0),Cin=x.size(1),D=x.size(2),H=x.size(3),W=x.size(4);auto K=w.size(2);auto Cout=w.size(1)*groups;auto Do=(D-1)*stride-2*padding+K;auto Ho=(H-1)*stride-2*padding+K;auto Wo=(W-1)*stride-2*padding+K;auto y=torch::empty({N,Cout,Do,Ho,Wo},x.options());long total=N*Cout*Do*Ho*Wo;k<<<(total+255)/256,256>>>(x.data_ptr<float>(),w.data_ptr<float>(),y.data_ptr<float>(),N,Cin,D,H,W,Cout,groups,Do,Ho,Wo);return y;}\n'''\n_ext=load_inline(name='convt3d_corrected',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['convt3d_forward'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,stride:int=1,padding:int=0,output_padding:int=0,groups:int=1,bias:bool=False):\n super().__init__(); self.conv_transpose3d=nn.ConvTranspose3d(in_channels,out_channels,(kernel_size,kernel_size,kernel_size),stride=stride,padding=padding,groups=groups,bias=bias); self.stride=stride; self.padding=padding; self.groups=groups\n def forward(self,x): return _ext.convt3d_forward(x.contiguous(),self.conv_transpose3d.weight.contiguous(),int(self.stride),int(self.padding),int(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: ['0.000260', '0.000249', '0.000258', '0.000267', '0.000251'] avg_difference: ['0.000025', '0.000025', '0.000025', '0.000025', '0.000025']

turn 87200.0s turn · 7200.0s LLM · 0.0s tools

feedbackLLM call failed: Empty: