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11_VGG16 • modular_vgg16_optimized_base_base

Level 3 • Task 11
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 modular_vgg16_optimized_base_base
CUDA Speedup (Native) 1.013x
CUDA Speedup (Compile) 0.600x
CUDA Runtime 3.210 ms
PyTorch Runtime (Native) 3.252 ms
PyTorch Runtime (Compile) 1.927 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 1.00
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 WARP_SIZE 32
#define BLOCK_SIZE 256
#define TILE_SIZE 16

// Device function for computing convolution at a single point
__device__ __forceinline__ float compute_conv_point(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    int ih, int iw, int H, int W,
    int C, int kh, int kw) {
    if (ih >= 0 && ih < H && iw >= 0 && iw < W) {
        return input[ih * W + iw] * weight[kh * 3 + kw];
    }
    return 0.0f;
}

// Device function for computing bias and ReLU
__device__ __forceinline__ float bias_relu(float x, float bias_val) {
    return max(x + bias_val, 0.0f);
}

// Device function for computing convolution sum
__device__ __forceinline__ float compute_conv_sum(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    int h, int w, int H, int W,
    int C, int P) {
    float sum = 0.0f;
    
    #pragma unroll
    for (int c = 0; c < C; ++c) {
        const float* input_c = input + c * H * W;
        const float* weight_c = weight + c * 9;  // 3x3 kernel
        
        #pragma unroll
        for (int kh = 0; kh < 3; ++kh) {
            #pragma unroll
            for (int kw = 0; kw < 3; ++kw) {
                int ih = h - P + kh;
                int iw = w - P + kw;
                sum += compute_conv_point(input_c, weight_c, ih, iw, H, W, C, kh, kw);
            }
        }
    }
    return sum;
}

// Main convolution kernel
template<int CHANNELS>
__global__ void conv2d_modular_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int N, int H, int W, int K, int P) {
    
    extern __shared__ float shared_mem[];
    
    const int tid = threadIdx.y * blockDim.x + threadIdx.x;
    const int n = blockIdx.z;
    const int k = blockIdx.y;
    const int h = blockIdx.x * blockDim.y + threadIdx.y;
    const int w = threadIdx.x;
    
    if (k >= K || h >= H || w >= W) return;
    
    // Compute convolution for this position
    const float* input_n = input + n * CHANNELS * H * W;
    const float* weight_k = weight + k * CHANNELS * 9;
    float sum = compute_conv_sum(input_n, weight_k, h, w, H, W, CHANNELS, P);
    
    // Apply bias and ReLU
    sum = bias_relu(sum, bias[k]);
    
    // Write result
    output[n * K * H * W + k * H * W + h * W + w] = sum;
}

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
) {
    const int N = x.size(0);
    const int C = x.size(1);
    const int H = x.size(2);
    const int W = x.size(3);
    const int K = conv_weights[0].size(0);
    const int P = 1;  // padding

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

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

    // Launch specialized kernel for first conv layer
    conv2d_modular_kernel<3><<<grid, block, TILE_SIZE * TILE_SIZE * sizeof(float)>>>(
        x.data_ptr<float>(),
        conv_weights[0].data_ptr<float>(),
        conv_biases[0].data_ptr<float>(),
        current.data_ptr<float>(),
        N, H, W, K, P
    );

    current = torch::relu(current);
    
    // Rest of the network using optimized cudnn calls
    for (int i = 1; i < 13; ++i) {
        current = torch::conv2d(current, conv_weights[i], conv_biases[i], 
                              /*stride=*/1, /*padding=*/1);
        current = torch::relu(current);
        if (i == 1 || i == 3 || i == 6 || i == 9 || i == 12) {
            current = torch::max_pool2d(current, /*kernel_size=*/2, /*stride=*/2);
        }
    }

    // Fully connected layers
    current = current.flatten(1);
    for (int i = 0; i < 3; ++i) {
        current = torch::linear(current, fc_weights[i], fc_biases[i]);
        if (i < 2) {
            current = torch::relu(current);
            if (is_training) {
                current = torch::dropout(current, /*p=*/0.0, /*train=*/true);
            }
        }
    }

    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 1.550 inst/cycle 0.000 5
Executed Ipc Elapsed 0.936 inst/cycle 0.000 5
Issue Slots Busy 40.270 % 0.022 5
Issued Ipc Active 1.610 inst/cycle 0.000 5
SM Busy 40.270 % 0.022 5
Memory Throughput 138660993171.938 byte/second 3765903752125453824.000 5
Mem Busy 31.070 % 0.186 5
Max Bandwidth 21.494 % 0.084 5
L1/TEX Hit Rate 84.488 % 0.000 5
L2 Hit Rate 77.784 % 0.247 5
Mem Pipes Busy 20.378 % 0.075 5
Warp Cycles Per Issued Instruction 18.234 cycle 0.006 5
Warp Cycles Per Executed Instruction 18.934 cycle 0.006 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.650 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 6.000 block 0.000 5
Block Limit Shared Mem 16.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 48.000 warp 0.000 5
Theoretical Occupancy 75.000 % 0.000 5
Achieved Occupancy 45.976 % 0.027 5
Achieved Active Warps Per SM 29.422 warp 0.011 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (27.9%) 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.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy (75.0%) is limited by the number of required registers. The difference between calculated theoretical (75.0%) and measured achieved occupancy (46.1%) 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 3223084.90 μs
Device Time 6085.28 μs
Self CPU Time 3223084.90 μs
Self Device Time 6085.28 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 3371216.67 μs
Device Time 2831326.17 μs
Self CPU Time 27918.04 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 3343298.63 μs
Device Time 2831326.17 μs
Self CPU Time 35232.32 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 3308066.31 μs
Device Time 2831326.17 μs
Self CPU Time 72403.08 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 2686588.61 μs
Device Time 2306073.68 μs
Self CPU Time 489543.48 μs
Self Device Time 2306073.68 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 568280.91 μs
Device Time 717212.14 μs
Self CPU Time 8115.70 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 
45304 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/b10_s1_modular_vgg16_optimized_base/base/base.cu:13:28 bugprone-easily-swappable-parameters
13 | int ih, int iw, int H, int W,
| ^~~~~~
14 | int C, int kh, int kw) {
| ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:13:32: note: the first parameter in the range is 'W'
13 | int ih, int iw, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:14:9: note: the last parameter in the range is 'C'
14 | int C, int kh, int kw) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:28:5: warning: 2 adjacent parameters of 'compute_conv_sum' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
28 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
29 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:28:31: note: the first parameter in the range is 'input'
28 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:29:31: note: the last parameter in the range is 'weight'
29 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:30:5: warning: 3 adjacent parameters of 'compute_conv_sum' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
30 | int h, int w, int H, int W,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:30:9: note: the first parameter in the range is 'h'
30 | int h, int w, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:30:23: note: the last parameter in the range is 'H'
30 | int h, int w, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:31:5: warning: 2 adjacent parameters of 'compute_conv_sum' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
31 | int C, int P) {
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:31:9: note: the first parameter in the range is 'C'
31 | int C, int P) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:31:16: note: the last parameter in the range is 'P'
31 | int C, int P) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:36:32: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
36 | const float* input_c = input + c * H * W;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:36:40: note: make conversion explicit to silence this warning
4 | const float* input_c = input + c * H * W;
| ^~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:36:40: note: perform multiplication in a wider type
36 | const float* input_c = input + c * H * W;
| ^~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:37:33: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
37 | const float* weight_c = weight + c * 9; // 3x3 kernel
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:37:42: note: make conversion explicit to silence this warning
37 | const float* weight_c = weight + c * 9; // 3x3 kernel
| ^~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:37:42: note: perform multiplication in a wider type
37 | const float* weight_c = weight + c * 9; // 3x3 kernel
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:55:5: warning: 3 adjacent parameters of 'conv2d_modular_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
55 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
56 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
57 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:55:31: note: the first parameter in the range is 'input'
55 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:57:31: note: the last parameter in the range is 'bias'
57 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:59:5: warning: 2 adjacent parameters of 'conv2d_modular_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
59 | int N, int H, int W, int K, int P) {
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:59:9: note: the first parameter in the range is 'N'
59 | int N, int H, int W, int K, int P) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:59:16: note: the last parameter in the range is 'H'
59 | int N, int H, int W, int K, int P) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:59:26: warning: 2 adjacent parameters of 'conv2d_modular_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
59 | int N, int H, int W, int K, int P) {
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:59:30: note: the first parameter in the range is 'K'
59 | int N, int H, int W, int K, int P) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:59:37: note: the last parameter in the range is 'P'
59 | int N, int H, int W, int K, int P) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:63:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | const int tid = threadIdx.y * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:64:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
64 | const int n = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:65:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
65 | const int k = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:66:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
66 | const int h = blockIdx.x * blockDim.y + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:67:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
67 | const int w = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:72:28: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
72 | const float* input_n = input + n * CHANNELS * H * W;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:72:36: note: make conversion explicit to silence this warning
72 | const float* input_n = input + n * CHANNELS * H * W;
| ^~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:72:36: note: perform multiplication in a wider type
72 | const float* input_n = input + n * CHANNELS * H * W;
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:73:29: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
73 | const float* weight_k = weight + k * CHANNELS * 9;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:73:38: note: make conversion explicit to silence this warning
73 | const float* weight_k = weight + k * CHANNELS * 9;
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:73:38: note: perform multiplication in a wider type
73 | const float* weight_k = weight + k * CHANNELS * 9;
| ^~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:84:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
84 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:86:5: warning: 2 adjacent parameters of 'vgg16_forward_cuda' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
86 | std::vector<torch::Tensor> conv_biases,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
87 | std::vector<torch::Tensor> fc_weights,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:86:32: note: the first parameter in the range is 'conv_biases'
86 | std::vector<torch::Tensor> conv_biases,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:87:32: note: the last parameter in the range is 'fc_weights'
87 | std::vector<torch::Tensor> fc_weights,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:91:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
91 | const int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:92:15: warning: Value stored to 'C' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
92 | const int C = x.size(1);
| ^ ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:92:15: note: Value stored to 'C' during its initialization is never read
92 | const int C = x.size(1);
| ^ ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:92:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
92 | const int C = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:93:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
93 | const int H = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:94:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | const int W = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:95:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | const int K = conv_weights[0].size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:106:45: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
106 | conv2d_modular_kernel<3><<<grid, block, TILE_SIZE * TILE_SIZE * sizeof(float)>>>(
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:7:19: note: expanded from macro 'TILE_SIZE'
7 | #define TILE_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:106:45: note: make conversion explicit to silence this warning
106 | conv2d_modular_kernel<3><<<grid, block, TILE_SIZE * TILE_SIZE * sizeof(float)>>>(
| ^
| static_cast<unsigned long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:7:19: note: expanded from macro 'TILE_SIZE'
7 | #define TILE_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:106:45: note: perform multiplication in a wider type
106 | conv2d_modular_kernel<3><<<grid, block, TILE_SIZE * TILE_SIZE * sizeof(float)>>>(
| ^
| static_cast<long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b10_s1_modular_vgg16_optimized_base/base/base.cu:7:19: note: expanded from macro 'TILE_SIZE'
7 | #define TILE_SIZE 16
| ^~