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11_VGG16 • vgg16_streams_overlap_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 vgg16_streams_overlap_base
CUDA Speedup (Native) 0.962x
CUDA Speedup (Compile) 0.570x
CUDA Runtime 3.382 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 WARP_SIZE 32
#define BLOCK_SIZE 256
#define TILE_SIZE 16

// Kernel for 2D convolution with atomic operations optimization
__global__ void conv2d_atomic_kernel(const float* input, const float* weight, const float* bias,
                                      float* output, int N, int C, int H, int W,
                                      int K, int P, int stride) {
    extern __shared__ float shared_mem[];
    float* tile = shared_mem;

    int n = blockIdx.z;
    int k = blockIdx.y;
    int h = blockIdx.x * TILE_SIZE + threadIdx.y;
    int w = threadIdx.x;

    float sum = 0.0f;
    if (k < K && h < H && w < W) {
        sum = bias[k];

        #pragma unroll
        for (int c = 0; c < C; ++c) {
            #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;
                    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 * 3 * 3 + c * 3 * 3 + kh * 3 + kw];
                        sum += in_val * weight_val;
                    }
                }
            }
        }
    }
    __syncthreads();

    if (k < K && h < H && w < W) {
        atomicAdd(&output[n * K * H * W + k * H * W + h * W + w], sum);
    }
}

// Optimized VGG16 forward pass using the custom optimized convolution
torch::Tensor optimized_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 = conv_weights[0].size(2) / 2;

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

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

    size_t shared_mem_size = TILE_SIZE * TILE_SIZE * sizeof(float);

    // Create CUDA streams
    cudaStream_t stream1, stream2;
    cudaStreamCreate(&stream1);
    cudaStreamCreate(&stream2);

    // Launch the first layer convolution in stream1
    conv2d_atomic_kernel<<<grid, block, shared_mem_size, stream1>>>(
        x.data_ptr<float>(),
        conv_weights[0].data_ptr<float>(),
        conv_biases[0].data_ptr<float>(),
        output.data_ptr<float>(),
        N, C, H, W, K, P, 1
    );

    auto current = torch::relu(output);

    // Use stream2 for the rest of the network
    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);
        // Apply max pooling after every block except the first layer of block 1
        if (i == 1 || i == 3 || i == 6 || i == 9 || i == 12) {
            current = torch::max_pool2d(current, /*kernel_size=*/2, /*stride=*/2);
        }
    }

    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]);

    // Destroy CUDA streams
    cudaStreamDestroy(stream1);
    cudaStreamDestroy(stream2);

    return current;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &optimized_vgg16_forward_cuda, "Optimized VGG16 forward (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.456 inst/cycle 0.000 5
Executed Ipc Elapsed 0.858 inst/cycle 0.000 5
Issue Slots Busy 37.600 % 0.073 5
Issued Ipc Active 1.504 inst/cycle 0.000 5
SM Busy 37.600 % 0.073 5
Memory Throughput 233254296363.148 byte/second 6079548632647208960.000 5
Mem Busy 30.590 % 0.114 5
Max Bandwidth 22.268 % 0.059 5
L1/TEX Hit Rate 84.490 % 0.000 5
L2 Hit Rate 68.486 % 0.198 5
Mem Pipes Busy 20.340 % 0.049 5
Warp Cycles Per Issued Instruction 20.512 cycle 0.420 5
Warp Cycles Per Executed Instruction 21.176 cycle 0.444 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.230 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 47.306 % 0.067 5
Achieved Active Warps Per SM 30.276 warp 0.028 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (26.2%) 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 (47.6%) 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 4415232.55 μs
Device Time 11897.44 μs
Self CPU Time 4415232.55 μs
Self Device Time 11897.44 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 5704621.72 μs
Device Time 5072894.38 μs
Self CPU Time 49528.86 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 5655092.86 μs
Device Time 5072894.38 μs
Self CPU Time 64258.61 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 5590834.25 μs
Device Time 5072894.38 μs
Self CPU Time 128354.46 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 4433255.23 μs
Device Time 4132312.33 μs
Self CPU Time 592323.65 μs
Self Device Time 4132312.33 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 930343.67 μs
Device Time 1282300.03 μs
Self CPU Time 15324.29 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 
45296 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/b9_s2_vgg16_streams_overlap/base/base.cu:10:38 bugprone-easily-swappable-parameters
10 | __global__ void conv2d_atomic_kernel(const float* input, const float* weight, const float* bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:10:51: note: the first parameter in the range is 'input'
10 | __global__ void conv2d_atomic_kernel(const float* input, const float* weight, const float* bias,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:10:92: note: the last parameter in the range is 'bias'
10 | __global__ void conv2d_atomic_kernel(const float* input, const float* weight, const float* bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:11:54: warning: 2 adjacent parameters of 'conv2d_atomic_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
11 | float* output, int N, int C, int H, int W,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:11:58: note: the first parameter in the range is 'N'
11 | float* output, int N, int C, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:11:65: note: the last parameter in the range is 'C'
11 | float* output, int N, int C, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:12:39: warning: 3 adjacent parameters of 'conv2d_atomic_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | int K, int P, int stride) {
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:12:43: note: the first parameter in the range is 'K'
12 | int K, int P, int stride) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:12:57: note: the last parameter in the range is 'stride'
12 | int K, int P, int stride) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:14:12: warning: Value stored to 'tile' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
14 | float* tile = shared_mem;
| ^~~~ ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:14:12: note: Value stored to 'tile' during its initialization is never read
14 | float* tile = shared_mem;
| ^~~~ ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:16:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | int n = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:17:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | int k = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:18:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | int h = blockIdx.x * TILE_SIZE + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:19:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | int w = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:51:19: 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]
51 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:53:5: warning: 2 adjacent parameters of 'optimized_vgg16_forward_cuda' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
53 | std::vector<torch::Tensor> conv_biases,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
54 | std::vector<torch::Tensor> fc_weights,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:53:32: note: the first parameter in the range is 'conv_biases'
53 | std::vector<torch::Tensor> conv_biases,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:54:32: note: the last parameter in the range is 'fc_weights'
54 | std::vector<torch::Tensor> fc_weights,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:58:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
58 | const int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:59:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | const int C = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:60:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
60 | const int H = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:61:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
61 | const int W = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:62:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
62 | 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/b9_s2_vgg16_streams_overlap/base/base.cu:63:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | const int P = conv_weights[0].size(2) / 2;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/base/base.cu:70:30: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
70 | size_t shared_mem_size = TILE_SIZE * TILE_SIZE * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_11/b9_s2_vgg16_streams_overlap/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/b9_s2_vgg16_streams_overlap/base/base.cu:70:30: note: make conversion explicit to silence this warning
70 | size_t shared_mem_size = 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/b9_s2_vgg16_streams_overlap/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/b9_s2_vgg16_streams_overlap/base/base.cu:70:30: note: perform multiplication in a wider type
70 | size_t shared_mem_size = 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/b9_s2_vgg16_streams_overlap/base/base.cu:7:19: note: expanded from macro 'TILE_SIZE'
7 | #define TILE_SIZE 16
| ^~