L3 P11 · original

correct 11_VGG16.py

model

gpt-5.5-priority

turns / max

1 / 1

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

5.62 μs

ref runtime

3.41 μs

speedup

0.61x

started

2026-05-05T00:32:51Z

timingkernelbench.timing

kernel runtime

5.62 μs

ref runtime

3.41 μs

speedup

0.61x

kernel mean / median / std

5.62 / — / 32.00 μs

ref mean / median / std

3.41 / — / 0.02 μs

numerical precisioneval_kernel_against_ref

max abs error

2.19e-07

mean abs error

3.76e-08

max rel error

0.02

mean rel error

2.12e-05

gpu memoryextended_metrics.measure_memory

kernel peak

1370.45 MB

ref peak

1585.83 MB

kernel / ref

0.86x

kernel launches / fusiontorch.profiler

kernel launches

122

ref launches

150

fusion ratio (ref / kernel)

1.23x

total cuda time

9403 μs

energyNVML (pynvml)

kernel mJ / run

486.54

ref mJ / run

486.32

ratio (ref / kernel)

1.00x

avg power

202.81 W

ref avg power

142.30 W

SOL (speed-of-light)heuristic_from_speedup

SOL score (0–1)

0.061

dram util %

—

compute util %

—

bottleneck

unknown

dominant pipe

—

arithmetic intensity

—

ridge point

—

roofline / occupancyheuristic

dram bandwidth

—

dram util %

—

fp32 tflops

—

fp32 util %

—

fp16 tflops

—

occupancy %

—

bottleneck

—

dominant pipe %

—

arithmetic intensity

—

ridge point

—

L1 hit %

—

L2 hit %

—

ld sectors / req

—

st sectors / req

—

regs / thread

—

smem / block

—

block size

—

peak bw

—

peak fp32 tflops

—

peak fp16 tflops

—

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):
        """
        Initialize the VGG16 model.
        
        :param num_classes: The number of output classes (default is 1000 for ImageNet)
        """
        super(Model, self).__init__()
        
        # VGG16 architecture: 5 blocks of convolutional layers followed by max pooling
        self.features = nn.Sequential(
            # Block 1
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Block 2
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Block 3
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Block 4
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Block 5
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        
        # Fully connected layers
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.0),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.0),
            nn.Linear(4096, num_classes)
        )
    
    def forward(self, x):
        """
        Forward pass of the VGG16 model.
        
        :param x: The input tensor, shape (batch_size, 3, 224, 224)
        :return: The output tensor, shape (batch_size, num_classes)
        """
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

# Test code
batch_size = 10
num_classes = 1000

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

def get_init_inputs():
    return [num_classes]

turn 0 · FINAL · 1 tool call(s) · 60.4s turn · 60.4s LLM · 8,938 chars (click to expand)

turn 060.4s turn · 60.4s LLM · 0.0s tools · FINAL

reasoning

**Evaluating model implementation** I’m thinking about implementing a classifier as a single custom module, but convolution dominates the operation. I’m considering random input and fixed weights for the model, but I have to maintain an exact tolerance of 1e-4 in fp32 and possibly factor in TF32 differences with PyTorch. It seems that my benchmark may need to include only the forward pass, and I'm weighing the idea of using custom kernels to speed up the process. I want to make sure I preserve the state_dict effectively while exploring the possibility of dropout. **Exploring layer combinations** I'm considering combining three linear layers into one custom module. Using `torch.addmm` for linear operations seems like a potential approach since `nn.Linear` utilizes it too. I'm thinking about using ReLU and noting that a dropout with p=0 would essentially perform no operation, which is a minor point. It might be useful to define features the same way while separating the classifier parameters. I also need to ensure that the output meets the necessary tolerance levels, particularly for convolutional output shapes. **Considering max pooling and ReLU** I'm thinking about optimizing kernel usage in pooling layers. It seems that using the maximum of ReLU values can save resources, as I could compute the maximum by fusing the ReLU and max pooling operations into one custom operation. This means I can apply the ReLU function to the maximum values right after the last convolution in each block instead of running separate kernels for the ReLU and max pooling. It could streamline processing!

call → submit_kernel

callsubmit_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>\n\ntorch::Tensor relu_inplace(torch::Tensor x);\ntorch::Tensor relu_maxpool2d(torch::Tensor x);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void relu_inplace_kernel(float* __restrict__ x, long long n) {\n long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n if (i < n) {\n float v = x[i];\n x[i] = v > 0.0f ? v : 0.0f;\n }\n}\n\n// Input and output are logical NCHW tensors stored in channels-last physical order.\n// Computes MaxPool2d(k=2,s=2)(ReLU(x)) in one pass. Since max(ReLU(a_i)) == ReLU(max(a_i)).\n__global__ void relu_maxpool2d_cl_kernel(const float* __restrict__ x,\n float* __restrict__ y,\n int N, int C, int H, int W) {\n int H2 = H >> 1;\n int W2 = W >> 1;\n long long total = (long long)N * H2 * W2 * C;\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n if (idx >= total) return;\n\n int c = (int)(idx % C);\n long long t = idx / C;\n int ow = (int)(t % W2);\n t /= W2;\n int oh = (int)(t % H2);\n int n = (int)(t / H2);\n\n int ih = oh << 1;\n int iw = ow << 1;\n long long base = (((long long)n * H + ih) * W + iw) * C + c;\n float a = x[base];\n float b = x[base + C];\n float d = x[base + (long long)W * C];\n float e = x[base + ((long long)W + 1) * C];\n float m = a > b ? a : b;\n m = m > d ? m : d;\n m = m > e ? m : e;\n y[idx] = m > 0.0f ? m : 0.0f;\n}\n\ntorch::Tensor relu_inplace(torch::Tensor x) {\n const long long n = x.numel();\n if (n == 0) return x;\n const int threads = 256;\n const int blocks = (int)((n + threads - 1) / threads);\n relu_inplace_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);\n return x;\n}\n\ntorch::Tensor relu_maxpool2d(torch::Tensor x) {\n const int N = (int)x.size(0);\n const int C = (int)x.size(1);\n const int H = (int)x.size(2);\n const int W = (int)x.size(3);\n auto y = torch::empty({N, C, H/2, W/2}, x.options().memory_format(torch::MemoryFormat::ChannelsLast));\n long long total = (long long)N * (H/2) * (W/2) * C;\n if (total == 0) return y;\n const int threads = 256;\n const int blocks = (int)((total + threads - 1) / threads);\n relu_maxpool2d_cl_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), y.data_ptr<float>(), N, C, H, W);\n return y;\n}\n'''\n\n_vgg_ext = load_inline(\n name='vgg16_fused_relu_pool_ext',\n cpp_sources=[_cpp_src],\n cuda_sources=[_cuda_src],\n functions=['relu_inplace', 'relu_maxpool2d'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n verbose=False,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self, num_classes=1000):\n super(ModelNew, self).__init__()\n self.features = nn.Sequential(\n nn.Conv2d(3, 64, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(64, 128, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(128, 128, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(128, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(256, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n )\n self.classifier = nn.Sequential(\n nn.Linear(512 * 7 * 7, 4096),\n nn.ReLU(inplace=True),\n nn.Dropout(p=0.0),\n nn.Linear(4096, 4096),\n nn.ReLU(inplace=True),\n nn.Dropout(p=0.0),\n nn.Linear(4096, num_classes),\n )\n # Keep convolution activations/weights in NHWC layout for faster cuDNN kernels.\n self.features.to(memory_format=torch.channels_last)\n\n def forward(self, x):\n # Convert the input once; subsequent convolutions preserve channels-last layout.\n x = x.contiguous(memory_format=torch.channels_last)\n f = self.features\n ext = _vgg_ext\n\n x = ext.relu_inplace(f[0](x))\n x = ext.relu_maxpool2d(f[2](x))\n\n x = ext.relu_inplace(f[5](x))\n x = ext.relu_maxpool2d(f[7](x))\n\n x = ext.relu_inplace(f[10](x))\n x = ext.relu_inplace(f[12](x))\n x = ext.relu_maxpool2d(f[14](x))\n\n x = ext.relu_inplace(f[17](x))\n x = ext.relu_inplace(f[19](x))\n x = ext.relu_maxpool2d(f[21](x))\n\n x = ext.relu_inplace(f[24](x))\n x = ext.relu_inplace(f[26](x))\n x = ext.relu_maxpool2d(f[28](x))\n\n x = torch.flatten(x, 1)\n c = self.classifier\n x = ext.relu_inplace(c[0](x))\n x = ext.relu_inplace(c[3](x))\n x = c[6](x)\n return x\n" }

result ← submit_kernel (ok)

oksubmit_kernel submit_kernel PASSED: (5 / 5) correctness trials all passed. Kernel runtime: 5.62 μs Runtime stats: mean=5.62μs median=?μs std=32.00μs Numerical precision: max_abs_err=2.19e-07 mean_abs_err=3.76e-08 max_rel_err=1.65e-02 Memory: 1370.45 MB peak (ref: 1585.83 MB, ratio: 0.8642x) Kernel launches: 122 (ref: 150, fusion ratio: 1.2295) Energy: 486.54 mJ/run (ref: 486.32 mJ, ratio: 0.9995x) SOL score: 0.0607

final submitted kernel (171 lines, 5,901 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

_cpp_src = r'''
#include <torch/extension.h>

torch::Tensor relu_inplace(torch::Tensor x);
torch::Tensor relu_maxpool2d(torch::Tensor x);
'''

_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>

__global__ void relu_inplace_kernel(float* __restrict__ x, long long n) {
    long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        float v = x[i];
        x[i] = v > 0.0f ? v : 0.0f;
    }
}

// Input and output are logical NCHW tensors stored in channels-last physical order.
// Computes MaxPool2d(k=2,s=2)(ReLU(x)) in one pass.  Since max(ReLU(a_i)) == ReLU(max(a_i)).
__global__ void relu_maxpool2d_cl_kernel(const float* __restrict__ x,
                                          float* __restrict__ y,
                                          int N, int C, int H, int W) {
    int H2 = H >> 1;
    int W2 = W >> 1;
    long long total = (long long)N * H2 * W2 * C;
    long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= total) return;

    int c = (int)(idx % C);
    long long t = idx / C;
    int ow = (int)(t % W2);
    t /= W2;
    int oh = (int)(t % H2);
    int n = (int)(t / H2);

    int ih = oh << 1;
    int iw = ow << 1;
    long long base = (((long long)n * H + ih) * W + iw) * C + c;
    float a = x[base];
    float b = x[base + C];
    float d = x[base + (long long)W * C];
    float e = x[base + ((long long)W + 1) * C];
    float m = a > b ? a : b;
    m = m > d ? m : d;
    m = m > e ? m : e;
    y[idx] = m > 0.0f ? m : 0.0f;
}

torch::Tensor relu_inplace(torch::Tensor x) {
    const long long n = x.numel();
    if (n == 0) return x;
    const int threads = 256;
    const int blocks = (int)((n + threads - 1) / threads);
    relu_inplace_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), n);
    return x;
}

torch::Tensor relu_maxpool2d(torch::Tensor x) {
    const int N = (int)x.size(0);
    const int C = (int)x.size(1);
    const int H = (int)x.size(2);
    const int W = (int)x.size(3);
    auto y = torch::empty({N, C, H/2, W/2}, x.options().memory_format(torch::MemoryFormat::ChannelsLast));
    long long total = (long long)N * (H/2) * (W/2) * C;
    if (total == 0) return y;
    const int threads = 256;
    const int blocks = (int)((total + threads - 1) / threads);
    relu_maxpool2d_cl_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
        x.data_ptr<float>(), y.data_ptr<float>(), N, C, H, W);
    return y;
}
'''

_vgg_ext = load_inline(
    name='vgg16_fused_relu_pool_ext',
    cpp_sources=[_cpp_src],
    cuda_sources=[_cuda_src],
    functions=['relu_inplace', 'relu_maxpool2d'],
    extra_cflags=['-O3'],
    extra_cuda_cflags=['-O3', '--use_fast_math'],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self, num_classes=1000):
        super(ModelNew, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.0),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.0),
            nn.Linear(4096, num_classes),
        )
        # Keep convolution activations/weights in NHWC layout for faster cuDNN kernels.
        self.features.to(memory_format=torch.channels_last)

    def forward(self, x):
        # Convert the input once; subsequent convolutions preserve channels-last layout.
        x = x.contiguous(memory_format=torch.channels_last)
        f = self.features
        ext = _vgg_ext

        x = ext.relu_inplace(f[0](x))
        x = ext.relu_maxpool2d(f[2](x))

        x = ext.relu_inplace(f[5](x))
        x = ext.relu_maxpool2d(f[7](x))

        x = ext.relu_inplace(f[10](x))
        x = ext.relu_inplace(f[12](x))
        x = ext.relu_maxpool2d(f[14](x))

        x = ext.relu_inplace(f[17](x))
        x = ext.relu_inplace(f[19](x))
        x = ext.relu_maxpool2d(f[21](x))

        x = ext.relu_inplace(f[24](x))
        x = ext.relu_inplace(f[26](x))
        x = ext.relu_maxpool2d(f[28](x))

        x = torch.flatten(x, 1)
        c = self.classifier
        x = ext.relu_inplace(c[0](x))
        x = ext.relu_inplace(c[3](x))
        x = c[6](x)
        return x