Kernel Details - 11_vgg16_thread_block_optimization

import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv_weights: nn.ParameterList,
    conv_biases: nn.ParameterList,
    fc_weights: nn.ParameterList,
    fc_biases: nn.ParameterList,
    is_training: bool,
) -> torch.Tensor:
    """
    Implements the VGG16 module.

    Args:
        x (torch.Tensor): Input tensor, shape (batch_size, in_channels, height, width)
        conv_weights (nn.ParameterList): List of convolutional weights
        conv_biases (nn.ParameterList): List of convolutional biases
        fc_weights (nn.ParameterList): List of fully connected weights
        fc_biases (nn.ParameterList): List of fully connected biases
        is_training (bool): Whether in training mode

    Returns:
        torch.Tensor: Output tensor, shape (batch_size, num_classes)
    """
    # Block 1
    x = F.conv2d(x, conv_weights[0], conv_biases[0], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[1], conv_biases[1], padding=1)
    x = F.relu(x)
    x = F.max_pool2d(x, kernel_size=2, stride=2)

    # Block 2
    x = F.conv2d(x, conv_weights[2], conv_biases[2], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[3], conv_biases[3], padding=1)
    x = F.relu(x)
    x = F.max_pool2d(x, kernel_size=2, stride=2)

    # Block 3
    x = F.conv2d(x, conv_weights[4], conv_biases[4], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[5], conv_biases[5], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[6], conv_biases[6], padding=1)
    x = F.relu(x)
    x = F.max_pool2d(x, kernel_size=2, stride=2)

    # Block 4
    x = F.conv2d(x, conv_weights[7], conv_biases[7], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[8], conv_biases[8], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[9], conv_biases[9], padding=1)
    x = F.relu(x)
    x = F.max_pool2d(x, kernel_size=2, stride=2)

    # Block 5
    x = F.conv2d(x, conv_weights[10], conv_biases[10], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[11], conv_biases[11], padding=1)
    x = F.relu(x)
    x = F.conv2d(x, conv_weights[12], conv_biases[12], padding=1)
    x = F.relu(x)
    x = F.max_pool2d(x, kernel_size=2, stride=2)

    # Classifier
    x = torch.flatten(x, 1)
    x = F.linear(x, fc_weights[0], fc_biases[0])
    x = F.relu(x)
    x = F.dropout(x, p=0.0, training=is_training)
    x = F.linear(x, fc_weights[1], fc_biases[1])
    x = F.relu(x)
    x = F.dropout(x, p=0.0, training=is_training)
    x = F.linear(x, fc_weights[2], fc_biases[2])

    return x


class Model(nn.Module):
    def __init__(self, num_classes=1000):
        super(Model, self).__init__()

        # Extract convolutional parameters
        self.conv_weights = nn.ParameterList()
        self.conv_biases = nn.ParameterList()

        # Block 1
        conv = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        # Block 2
        conv = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        # Block 3
        conv = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        # Block 4
        conv = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        # Block 5
        conv = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        conv = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv_weights.append(nn.Parameter(conv.weight.data.clone()))
        self.conv_biases.append(nn.Parameter(conv.bias.data.clone()))

        # Extract fully connected parameters
        self.fc_weights = nn.ParameterList()
        self.fc_biases = nn.ParameterList()

        fc = nn.Linear(512 * 7 * 7, 4096)
        self.fc_weights.append(nn.Parameter(fc.weight.data.clone()))
        self.fc_biases.append(nn.Parameter(fc.bias.data.clone()))

        fc = nn.Linear(4096, 4096)
        self.fc_weights.append(nn.Parameter(fc.weight.data.clone()))
        self.fc_biases.append(nn.Parameter(fc.bias.data.clone()))

        fc = nn.Linear(4096, num_classes)
        self.fc_weights.append(nn.Parameter(fc.weight.data.clone()))
        self.fc_biases.append(nn.Parameter(fc.bias.data.clone()))

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.conv_weights,
            self.conv_biases,
            self.fc_weights,
            self.fc_biases,
            self.training,
        )


# Test code
batch_size = 10
num_classes = 1000


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


def get_init_inputs():
    return [num_classes]

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.randn(batch_size, 3, 224, 224)]

def get_init_inputs():
    return [num_classes]

Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name	11_VGG16
Level ID	3
Task ID	11
Kernel Name	11_vgg16_thread_block_optimization_base
CUDA Speedup (Native)	1.007x
CUDA Speedup (Compile)	0.596x
CUDA Runtime	3.231 ms
PyTorch Runtime (Native)	3.252 ms
PyTorch Runtime (Compile)	1.927 ms
Correct	True
Max Diff (vs. Reference)	0.000000
Model	azure-gpt-4o-2024-08-06
Temperature	0.00

View Experiment Progress Details

Related Kernels (Level 3, Task 11 • 11_VGG16)

Rank	Kernel Name	Runtime (ms)	Speedup Native	Speedup Compile
🥇	optimized_vgg16_with_custom_conv_base	3.19	1.02	0.60
🥈	11_vgg16_atomic_optimization_base	3.20	1.02	0.60
🥉	modular_vgg16_optimized_base_base	3.21	1.01	0.60
4	11_vgg16_shared_memory_reduction_base	3.21	1.01	0.60
5	11_vgg16_unroll_optimization_base_base	3.22	1.01	0.60
6	11_vgg16_warp_aligned_base	3.23	1.01	0.60
7	11_vgg16_thread_block_optimization_base	3.23	1.01	0.60
8	11_VGG16	3.25	1.00	0.59
9	11_vgg16_blocksize_experiment_base	3.31	0.98	0.58
10	11_vgg16_optmaxpool_base	3.33	0.98	0.58
11	11_vgg16_opt_pool_idx_base	3.33	0.98	0.58
12	optimized_vgg16_overlap_base	3.33	0.98	0.58
13	11_vgg16_optwarp_divergence_base	3.33	0.98	0.58
14	11_vgg16_unroll_base	3.35	0.97	0.58
15	vgg16_streams_overlap_base	3.38	0.96	0.57
16	divergence_free_vgg16_base	3.41	0.95	0.56
17	modular_vgg16_edit_1	3.42	0.95	0.56
18	fused_conv2d_relu_base	3.48	0.93	0.55
19	warp_optimized_conv1_edit_1	3.50	0.93	0.55
20	optimized_const_blocksize_base	3.50	0.93	0.55

#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

#define TILE_WIDTH 16

// Optimized kernel for 2D convolution with better thread and block indexing
__global__ void conv2d_optimized_kernel(const float* __restrict__ input, const float* __restrict__ weight, const float* __restrict__ bias,
                                        float* __restrict__ output, int N, int C, int H, int W, int K, int kernel_size, int P) {
    int n = blockIdx.z;
    int k = blockIdx.y;
    int h = blockIdx.x * TILE_WIDTH + threadIdx.y;
    int w = threadIdx.x;

    if (h < H && w < W) {
        float sum = bias[k];
        for (int c = 0; c < C; ++c) {
            for (int kh = 0; kh < kernel_size; ++kh) {
                for (int kw = 0; kw < kernel_size; ++kw) {
                    int ih = h - P + kh;
                    int iw = w - P + kw;
                    if (ih >= 0 && ih < H && iw >= 0 && iw < W) {
                        float in_val = input[n * C * H * W + c * H * W + ih * W + iw];
                        float weight_val = weight[k * C * kernel_size * kernel_size + c * kernel_size * kernel_size + kh * kernel_size + kw];
                        sum += in_val * weight_val;
                    }
                }
            }
        }
        output[n * K * H * W + k * H * W + h * W + w] = sum;
    }
}

// Custom convolution function using the optimized kernel
torch::Tensor custom_conv2d_optimized(torch::Tensor input, torch::Tensor weight, torch::Tensor bias) {
    const int N = input.size(0);
    const int C = input.size(1);
    const int H = input.size(2);
    const int W = input.size(3);
    const int K = weight.size(0);
    const int kernel_size = weight.size(2);
    const int P = kernel_size / 2;

    auto output = torch::empty({N, K, H, W}, input.options());

    dim3 block(TILE_WIDTH, TILE_WIDTH);
    dim3 grid((W + TILE_WIDTH - 1) / TILE_WIDTH, K, N);

    conv2d_optimized_kernel<<<grid, block>>>(
        input.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.data_ptr<float>(),
        output.data_ptr<float>(),
        N, C, H, W, K, kernel_size, P
    );

    return output;
}

// VGG16 forward pass using the optimized convolution kernel
torch::Tensor vgg16_forward_cuda(
    torch::Tensor x,
    std::vector<torch::Tensor> conv_weights,
    std::vector<torch::Tensor> conv_biases,
    std::vector<torch::Tensor> fc_weights,
    std::vector<torch::Tensor> fc_biases,
    bool is_training
) {
    auto current = x;

    // Block 1 - Use optimized conv2d for first layer
    current = custom_conv2d_optimized(current, conv_weights[0], conv_biases[0]);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[1], conv_biases[1], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::max_pool2d(current, /*kernel_size=*/2, /*stride=*/2);

    // Rest of the network using optimized cudnn calls
    // Block 2
    current = torch::conv2d(current, conv_weights[2], conv_biases[2], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[3], conv_biases[3], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::max_pool2d(current, /*kernel_size=*/2, /*stride=*/2);

    // Block 3
    current = torch::conv2d(current, conv_weights[4], conv_biases[4], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[5], conv_biases[5], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[6], conv_biases[6], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::max_pool2d(current, /*kernel_size=*/2, /*stride=*/2);

    // Block 4
    current = torch::conv2d(current, conv_weights[7], conv_biases[7], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[8], conv_biases[8], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[9], conv_biases[9], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::max_pool2d(current, /*kernel_size=*/2, /*stride=*/2);

    // Block 5
    current = torch::conv2d(current, conv_weights[10], conv_biases[10], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[11], conv_biases[11], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::conv2d(current, conv_weights[12], conv_biases[12], /*stride=*/1, /*padding=*/1);
    current = torch::relu(current);
    current = torch::max_pool2d(current, /*kernel_size=*/2, /*stride=*/2);

    // Classifier with aligned memory access
    current = current.flatten(1);
    current = torch::linear(current, fc_weights[0], fc_biases[0]);
    current = torch::relu(current);
    if (is_training) {
        current = torch::dropout(current, /*p=*/0.0, /*train=*/true);
    }
    current = torch::linear(current, fc_weights[1], fc_biases[1]);
    current = torch::relu(current);
    if (is_training) {
        current = torch::dropout(current, /*p=*/0.0, /*train=*/true);
    }
    current = torch::linear(current, fc_weights[2], fc_biases[2]);

    return current;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &vgg16_forward_cuda, "VGG16 forward (CUDA)");
}

Performance Metrics

Metric	Value	Unit	Variance	Samples
Executed Ipc Active	3.250	inst/cycle	0.000	5
Executed Ipc Elapsed	3.056	inst/cycle	0.000	5
Issue Slots Busy	81.298	%	0.006	5
Issued Ipc Active	3.252	inst/cycle	0.000	5
SM Busy	81.298	%	0.006	5
Memory Throughput	32303969295.408	byte/second	249016985067787200.000	5
Mem Busy	47.970	%	0.007	5
Max Bandwidth	32.440	%	0.004	5
L1/TEX Hit Rate	87.178	%	0.002	5
L2 Hit Rate	97.114	%	0.238	5
Mem Pipes Busy	53.300	%	0.009	5
Warp Cycles Per Issued Instruction	16.594	cycle	0.001	5
Warp Cycles Per Executed Instruction	16.610	cycle	0.001	5
Avg. Active Threads Per Warp	31.980		0.000	5
Avg. Not Predicated Off Threads Per Warp	28.930		0.000	5
Max Active Clusters	0.000	cluster	0.000	5
Max Cluster Size	8.000	block	0.000	5
Overall GPU Occupancy	0.000	%	0.000	5
Cluster Occupancy	0.000	%	0.000	5
Block Limit SM	32.000	block	0.000	5
Block Limit Registers	8.000	block	0.000	5
Block Limit Shared Mem	32.000	block	0.000	5
Block Limit Warps	8.000	block	0.000	5
Theoretical Active Warps per SM	64.000	warp	0.000	5
Theoretical Occupancy	100.000	%	0.000	5
Achieved Occupancy	84.438	%	0.004	5
Achieved Active Warps Per SM	54.040	warp	0.001	5

Analysis Rules

Rule	Description
INF HighPipeUtilization	ALU is the highest-utilized pipeline (56.0%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
WRN Occupancy	This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (84.5%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.

Rule

Description

INF HighPipeUtilization

ALU is the highest-utilized pipeline (56.0%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.

WRN Occupancy

This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (84.5%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.

Operation / Metric	Value	Unit
cudaLaunchKernel
CPU Time	3980651.83	μs
Device Time	7765.56	μs
Self CPU Time	3980651.83	μs
Self Device Time	7765.56	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::conv2d
CPU Time	4023994.95	μs
Device Time	3500126.03	μs
Self CPU Time	34588.89	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::convolution
CPU Time	3989406.06	μs
Device Time	3500126.03	μs
Self CPU Time	45478.69	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::_convolution
CPU Time	3943927.36	μs
Device Time	3500126.03	μs
Self CPU Time	91669.44	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::cudnn_convolution
CPU Time	3215908.76	μs
Device Time	2850275.83	μs
Self CPU Time	538897.91	μs
Self Device Time	2850275.83	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::linear
CPU Time	600411.73	μs
Device Time	887851.58	μs
Self CPU Time	10173.67	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B

Status: Completed

45297 warnings generated when compiling for host.
Suppressed 45326 warnings (45279 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.

/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b6_s3_11_vgg16_thread_block_optimization/base/base.cu:8:41 bugprone-easily-swappable-parameters

8 | __global__ void conv2d_optimized_kernel(const float* __restrict__ input, const float* __restrict__ weight, const float* __restrict__ bias,

| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b6_s3_11_vgg16_thread_block_optimization/base/base.cu:8:67: note: the first parameter in the range is 'input'

8 | __global__ void conv2d_optimized_kernel(const float* __restrict__ input, const float* __restrict__ weight, const float* __restrict__ bias,

| ^~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b6_s3_11_vgg16_thread_block_optimization/base/base.cu:8:134: note: the last parameter in the range is 'bias'

8 | __global__ void conv2d_optimized_kernel(const float* __restrict__ input, const float* __restrict__ weight, const float* __restrict__ bias,

| ^~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b6_s3_11_vgg16_thread_block_optimization/base/base.cu:9:69: warning: 2 adjacent parameters of 'conv2d_optimized_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]