compile_fail
9_ResNet18.py
started
2026-05-05T02:53:43Z
compile error: /lib/x86_64-linux-gnu/libstdc++.so.6: version `CXXABI_1.3.15' not found (required by /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_3_problem_9_cache/k_b6f870dd0803/resnet18_fused_add_relu_ext/resnet18_fused_add_relu_ext.so)
timingkernelbench.timing
kernel mean / median / std
—
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
"""
:param in_channels: Number of input channels
:param out_channels: Number of output channels
:param stride: Stride for the first convolutional layer
:param downsample: Downsample layer for the shortcut connection
"""
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
self.stride = stride
def forward(self, x):
"""
:param x: Input tensor, shape (batch_size, in_channels, height, width)
:return: Output tensor, shape (batch_size, out_channels, height, width)
"""
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Model(nn.Module):
def __init__(self, num_classes=1000):
"""
:param num_classes: Number of output classes
"""
super(Model, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(BasicBlock, 64, 2, stride=1)
self.layer2 = self._make_layer(BasicBlock, 128, 2, stride=2)
self.layer3 = self._make_layer(BasicBlock, 256, 2, stride=2)
self.layer4 = self._make_layer(BasicBlock, 512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * BasicBlock.expansion, num_classes)
def _make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if stride != 1 or self.in_channels != out_channels * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels * block.expansion),
)
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
"""
:param x: Input tensor, shape (batch_size, 3, height, width)
:return: Output tensor, shape (batch_size, num_classes)
"""
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
# Test code
batch_size = 2
num_classes = 1000
input_shape = (batch_size, 3, 224, 224)
def get_inputs():
return [torch.rand(input_shape)]
def get_init_inputs():
return [num_classes]turn 0 · FINAL · 1 tool call(s) · 81.4s turn · 81.4s LLM · 14,257 chars (click to expand)
turn 081.4s turn · 81.4s LLM · 0.0s tools · FINAL
reasoning
**Evaluating ResNet18 implementation**
I need to submit code for the full ResNet18 forward implementation. It's important to consider using Torch operations, aiming for faster performance than the reference. The task requires a custom CUDA kernel, possibly using load_inline for some fused operations. Writing all convolution kernels isn't feasible, so I might use torch.nn modules and call Torch ops instead, though it could jeopardize speed. There's also the need to match parameter names if I define the same architecture.
**Folding operations in ResNet implementation**
I’m considering the cuDNN sequence for conv+BN+ReLU, which has many operations. Folding the batch normalization (BN) into the convolution layer reduces these operations. I want to use Torch operations instead of a custom kernel, except maybe for average pooling and fully connected layers. In the forward pass, I can calculate effective weights and biases from the BN parameters. However, computing folded weights each time costs only a tiny amount. For residual connections, I might need a custom CUDA kernel for addition and ReLU operations.
**Handling BatchNorm in training mode**
The user wants to use fp32, and if the model isn't training, BatchNorm defaults to training mode being true. The reference initializes randomly, and since BatchNorm uses batch statistics, outputs may differ when running evaluations. I need to account for whether ModelNew is in training mode. Could I use the original module's forward method if self.training is involved? But I might be missing fallbacks or detection that make it work like the PyTorch reference. Many benchmarks seem to use model.eval() post-instantiation, so timing is critical for correctness.
**Evaluating CUDA extension**
I’m analyzing the custom CUDA extension named `add_relu_cuda`. It seems like it adds the identity value to the output while also applying clamping, and it’s designed for contiguous tensors, which is interesting. It looks like the convolution outputs are contiguous, but I'm not completely sure if the downsample or identity tensors maintain that contiguity based on the previous output. This will require a bit more checking to ensure everything aligns correctly.
**Considering tensor calculations**
I’m thinking about how tensors interact with state_dict in terms of caching. If I reference copies and state_dict is loaded after cache, that might not work well. I wonder if overriding load_state_dict to clear the cache could help. When evaluating models, I’m not so sure if the cache is necessary. I need to be careful about grad requirements during benchmarking as they tie to numerical stability. It seems like I can tolerate small differences in deep networks, maybe within 1e-4.
**Evaluating folding changes**
I’m thinking about how folding changes can be minimal, but I can't apply a convolution post-scaling. The reference for the batch normalization (BN) seems to apply a factor of 0.999995, and I need to consider how this interacts with rounding errors, especially for larger activations. It looks like untrained activations won’t cause huge issues. Still, if BN running stats change, it could lead to random scale values. I wonder if we should use torch.allclose to check correctness. While we can't test everything, maybe an optional exact mode would be helpful for evaluations.
**Considering timing and kernel usage**
I’m pondering the timing of the object if there's no prior correctness; maybe I need to implement some initial warm-ups. I might require a custom kernel that utilizes both out and identity. It seems compiling an extension at module load time could be beneficial, but I should be mindful of potential issues when using load_inline functions with void returns. I wonder if the input being non-CUDA affects anything. If tests use torch.rand without specifying a device, and tasks shift to CUDA, that might cause complications as get_inputs typically returns CPU but the harness likely shifts it.
**Considering CUDA and channels_last**
I'm reflecting on CPU correctness and CUDA expectations, wondering if I should use PyTorch when `x.is_cuda` is false. I need to include imports and could possibly optimize using `channels_last`. This raises questions about converting modules and inputs. Additionally, I think about cuDNN being faster with convolutions, and whether channel formats like `NHWC` could be beneficial. If I convert in `__init__`, it may impact how I load `state_dict`. Still, it seems like working with `channels_last` formatting could speed things up!
**Evaluating tensor layouts**
I'm considering whether our custom `add_relu` function handles contiguous layouts correctly, especially since `channels_last` defaults aren't contiguous, but can be made so in a specific memory format. It seems that tensor indexing in CUDA wouldn't encounter issues with this format, provided both tensors share the same layout.
I'll need to ensure that the shapes for operations like `add` align properly, particularly for the first residual layer and when dealing with downsample and identity connections. I’m left pondering if different strides between identity and output tensors could pose any trouble, but they’re likely consistent.
**Analyzing tensor operations**
I'm thinking about the end stages where I have `avg_pool` and `flatten` operations. The `channels_last` tensor from `flatten(x, 1)` might copy data, and the output from `avg_pool` has a shape of `(B, C, 1, 1)`, which makes me wonder about the strides. The flattened shape probably needs to be contiguous since `F.linear` expects that. If it's not, a copy might occur, which isn't ideal. I could ensure the tensor is contiguous from the start with `torch.channels_last`. Also, will weight folding return to default contiguous `NCHW` format?
**Checking CUDA compatibility**
I need to check CUDA compatibility, though I wonder if I could omit some parts. Specifically, I’m considering templates with scalar types—only using float, right? I might also need to use AT_DISPATCH_FLOATING_TYPES_AND_HALF, but that seems limited to fp32. Since the task is for fp32, I should also include cuda_runtime. These considerations are crucial for ensuring everything functions properly together, so I'll stay focused on the necessary details.
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n\n\n_cpp_src = r'''\n#include <torch/extension.h>\n\nvoid add_relu_cuda(torch::Tensor a, torch::Tensor b);\n\nvoid add_relu_(torch::Tensor a, torch::Tensor b) {\n add_relu_cuda(a, b);\n}\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cuda_runtime.h>\n\n__global__ void add_relu_kernel(float* __restrict__ a, const float* __restrict__ b, long long n) {\n long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n if (i < n) {\n float v = a[i] + b[i];\n a[i] = v > 0.0f ? v : 0.0f;\n }\n}\n\nvoid add_relu_cuda(torch::Tensor a, torch::Tensor b) {\n const long long n = a.numel();\n if (n == 0) return;\n const at::cuda::CUDAGuard device_guard(a.device());\n const int threads = 256;\n const int blocks = (int)((n + threads - 1) / threads);\n add_relu_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n (float*)a.data_ptr<float>(), (const float*)b.data_ptr<float>(), n);\n}\n'''\n\n_ext = load_inline(\n name='resnet18_fused_add_relu_ext',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['add_relu_'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\n\nclass BasicBlock(nn.Module):\n expansion = 1\n\n def __init__(self, in_channels, out_channels, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(out_channels)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(out_channels)\n self.downsample = downsample\n self.stride = stride\n\n\nclass ModelNew(nn.Module):\n def __init__(self, num_classes=1000):\n super(ModelNew, self).__init__()\n self.in_channels = 64\n\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n self.bn1 = nn.BatchNorm2d(64)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n self.layer1 = self._make_layer(BasicBlock, 64, 2, stride=1)\n self.layer2 = self._make_layer(BasicBlock, 128, 2, stride=2)\n self.layer3 = self._make_layer(BasicBlock, 256, 2, stride=2)\n self.layer4 = self._make_layer(BasicBlock, 512, 2, stride=2)\n\n self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n self.fc = nn.Linear(512 * BasicBlock.expansion, num_classes)\n self._fuse_cache = {}\n # Keep convolution operands in NHWC-friendly layout when the module is moved to CUDA.\n self.to(memory_format=torch.channels_last)\n\n def _make_layer(self, block, out_channels, blocks, stride=1):\n downsample = None\n if stride != 1 or self.in_channels != out_channels * block.expansion:\n downsample = nn.Sequential(\n nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),\n nn.BatchNorm2d(out_channels * block.expansion),\n )\n\n layers = []\n layers.append(block(self.in_channels, out_channels, stride, downsample))\n self.in_channels = out_channels * block.expansion\n for _ in range(1, blocks):\n layers.append(block(self.in_channels, out_channels))\n return nn.Sequential(*layers)\n\n def _apply(self, fn):\n r = super()._apply(fn)\n self._fuse_cache = {}\n return r\n\n def load_state_dict(self, state_dict, strict=True):\n r = super().load_state_dict(state_dict, strict)\n self._fuse_cache = {}\n return r\n\n def _conv_bn(self, x, conv, bn, key):\n # Training mode needs batch statistics and running-stat updates, so use exact modules.\n if self.training:\n return bn(conv(x))\n\n # Eval mode: fold BatchNorm into the convolution. This removes every BN kernel and\n # its memory traffic while preserving the inference computation.\n ver = (conv.weight._version, bn.weight._version, bn.bias._version,\n bn.running_mean._version, bn.running_var._version,\n conv.weight.device, conv.weight.dtype)\n item = self._fuse_cache.get(key)\n if item is None or item[0] != ver:\n with torch.no_grad():\n scale = bn.weight * torch.rsqrt(bn.running_var + bn.eps)\n bias = bn.bias - bn.running_mean * scale\n w = conv.weight * scale.reshape(-1, 1, 1, 1)\n if w.is_cuda:\n w = w.contiguous(memory_format=torch.channels_last)\n else:\n w = w.contiguous()\n bias = bias.contiguous()\n self._fuse_cache[key] = (ver, w, bias)\n else:\n w, bias = item[1], item[2]\n\n return F.conv2d(x, w, bias, conv.stride, conv.padding, conv.dilation, conv.groups)\n\n def _block_forward(self, block, x, prefix):\n identity = x\n\n out = self._conv_bn(x, block.conv1, block.bn1, prefix + '.conv1')\n torch.relu_(out)\n out = self._conv_bn(out, block.conv2, block.bn2, prefix + '.conv2')\n\n if block.downsample is not None:\n identity = self._conv_bn(x, block.downsample[0], block.downsample[1], prefix + '.downsample')\n\n if out.is_cuda:\n _ext.add_relu_(out, identity)\n else:\n out.add_(identity)\n torch.relu_(out)\n return out\n\n def forward(self, x):\n if x.is_cuda:\n x = x.contiguous(memory_format=torch.channels_last)\n\n x = self._conv_bn(x, self.conv1, self.bn1, 'conv1')\n torch.relu_(x)\n x = self.maxpool(x)\n\n x = self._block_forward(self.layer1[0], x, 'layer1.0')\n x = self._block_forward(self.layer1[1], x, 'layer1.1')\n x = self._block_forward(self.layer2[0], x, 'layer2.0')\n x = self._block_forward(self.layer2[1], x, 'layer2.1')\n x = self._block_forward(self.layer3[0], x, 'layer3.0')\n x = self._block_forward(self.layer3[1], x, 'layer3.1')\n x = self._block_forward(self.layer4[0], x, 'layer4.0')\n x = self._block_forward(self.layer4[1], x, 'layer4.1')\n\n x = self.avgpool(x)\n x = torch.flatten(x, 1)\n x = self.fc(x)\n return x\n"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
/lib/x86_64-linux-gnu/libstdc++.so.6: version `CXXABI_1.3.15' not found (required by /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_3_problem_9_cache/k_b6f870dd0803/resnet18_fused_add_relu_ext/resnet18_fused_add_relu_ext.so)
final submitted kernel (178 lines, 6,512 chars) — full source, untruncated
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
_cpp_src = r'''
#include <torch/extension.h>
void add_relu_cuda(torch::Tensor a, torch::Tensor b);
void add_relu_(torch::Tensor a, torch::Tensor b) {
add_relu_cuda(a, b);
}
'''
_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <cuda_runtime.h>
__global__ void add_relu_kernel(float* __restrict__ a, const float* __restrict__ b, long long n) {
long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
float v = a[i] + b[i];
a[i] = v > 0.0f ? v : 0.0f;
}
}
void add_relu_cuda(torch::Tensor a, torch::Tensor b) {
const long long n = a.numel();
if (n == 0) return;
const at::cuda::CUDAGuard device_guard(a.device());
const int threads = 256;
const int blocks = (int)((n + threads - 1) / threads);
add_relu_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
(float*)a.data_ptr<float>(), (const float*)b.data_ptr<float>(), n);
}
'''
_ext = load_inline(
name='resnet18_fused_add_relu_ext',
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=['add_relu_'],
extra_cflags=['-O3'],
extra_cuda_cflags=['-O3'],
verbose=False,
)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
self.stride = stride
class ModelNew(nn.Module):
def __init__(self, num_classes=1000):
super(ModelNew, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(BasicBlock, 64, 2, stride=1)
self.layer2 = self._make_layer(BasicBlock, 128, 2, stride=2)
self.layer3 = self._make_layer(BasicBlock, 256, 2, stride=2)
self.layer4 = self._make_layer(BasicBlock, 512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * BasicBlock.expansion, num_classes)
self._fuse_cache = {}
# Keep convolution operands in NHWC-friendly layout when the module is moved to CUDA.
self.to(memory_format=torch.channels_last)
def _make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if stride != 1 or self.in_channels != out_channels * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels * block.expansion),
)
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, out_channels))
return nn.Sequential(*layers)
def _apply(self, fn):
r = super()._apply(fn)
self._fuse_cache = {}
return r
def load_state_dict(self, state_dict, strict=True):
r = super().load_state_dict(state_dict, strict)
self._fuse_cache = {}
return r
def _conv_bn(self, x, conv, bn, key):
# Training mode needs batch statistics and running-stat updates, so use exact modules.
if self.training:
return bn(conv(x))
# Eval mode: fold BatchNorm into the convolution. This removes every BN kernel and
# its memory traffic while preserving the inference computation.
ver = (conv.weight._version, bn.weight._version, bn.bias._version,
bn.running_mean._version, bn.running_var._version,
conv.weight.device, conv.weight.dtype)
item = self._fuse_cache.get(key)
if item is None or item[0] != ver:
with torch.no_grad():
scale = bn.weight * torch.rsqrt(bn.running_var + bn.eps)
bias = bn.bias - bn.running_mean * scale
w = conv.weight * scale.reshape(-1, 1, 1, 1)
if w.is_cuda:
w = w.contiguous(memory_format=torch.channels_last)
else:
w = w.contiguous()
bias = bias.contiguous()
self._fuse_cache[key] = (ver, w, bias)
else:
w, bias = item[1], item[2]
return F.conv2d(x, w, bias, conv.stride, conv.padding, conv.dilation, conv.groups)
def _block_forward(self, block, x, prefix):
identity = x
out = self._conv_bn(x, block.conv1, block.bn1, prefix + '.conv1')
torch.relu_(out)
out = self._conv_bn(out, block.conv2, block.bn2, prefix + '.conv2')
if block.downsample is not None:
identity = self._conv_bn(x, block.downsample[0], block.downsample[1], prefix + '.downsample')
if out.is_cuda:
_ext.add_relu_(out, identity)
else:
out.add_(identity)
torch.relu_(out)
return out
def forward(self, x):
if x.is_cuda:
x = x.contiguous(memory_format=torch.channels_last)
x = self._conv_bn(x, self.conv1, self.bn1, 'conv1')
torch.relu_(x)
x = self.maxpool(x)
x = self._block_forward(self.layer1[0], x, 'layer1.0')
x = self._block_forward(self.layer1[1], x, 'layer1.1')
x = self._block_forward(self.layer2[0], x, 'layer2.0')
x = self._block_forward(self.layer2[1], x, 'layer2.1')
x = self._block_forward(self.layer3[0], x, 'layer3.0')
x = self._block_forward(self.layer3[1], x, 'layer3.1')
x = self._block_forward(self.layer4[0], x, 'layer4.0')
x = self._block_forward(self.layer4[1], x, 'layer4.1')
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x