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4_LeNet5 • modular_device_functions_base_base

Level 3 • Task 4
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv1_weight: nn.Parameter,
    conv1_bias: nn.Parameter,
    conv2_weight: nn.Parameter,
    conv2_bias: nn.Parameter,
    fc1_weight: nn.Parameter,
    fc1_bias: nn.Parameter,
    fc2_weight: nn.Parameter,
    fc2_bias: nn.Parameter,
    fc3_weight: nn.Parameter,
    fc3_bias: nn.Parameter,
) -> torch.Tensor:
    """
    Implements a LeNet-5 architecture with ReLU activation.

    Args:
        x (torch.Tensor): The input tensor, shape (batch_size, 1, 32, 32)
        conv1_weight (nn.Parameter): Parameters for first conv layer
        conv1_bias (nn.Parameter): Parameters for first conv layer
        conv2_weight (nn.Parameter): Parameters for second conv layer
        conv2_bias (nn.Parameter): Parameters for second conv layer
        fc1_weight (nn.Parameter): Parameters for first FC layer
        fc1_bias (nn.Parameter): Parameters for first FC layer
        fc2_weight (nn.Parameter): Parameters for second FC layer
        fc3_weight (nn.Parameter): Parameters for third FC layer
        fc3_bias (nn.Parameter): Parameters for third FC layer

    Returns:
        torch.Tensor: The output tensor, shape (batch_size, num_classes)
    """
    # First convolutional layer with ReLU activation and max pooling
    x = F.conv2d(x, conv1_weight, conv1_bias, stride=1)
    x = F.relu(x)
    x = F.max_pool2d(x, kernel_size=2, stride=2)

    # Second convolutional layer with ReLU activation and max pooling
    x = F.conv2d(x, conv2_weight, conv2_bias, stride=1)
    x = F.relu(x)
    x = F.max_pool2d(x, kernel_size=2, stride=2)

    # Flatten the output for the fully connected layers
    x = x.view(-1, 16 * 5 * 5)

    # First fully connected layer with ReLU activation
    x = F.linear(x, fc1_weight, fc1_bias)
    x = F.relu(x)

    # Second fully connected layer with ReLU activation
    x = F.linear(x, fc2_weight, fc2_bias)
    x = F.relu(x)

    # Final fully connected layer
    x = F.linear(x, fc3_weight, fc3_bias)

    return x


class Model(nn.Module):
    def __init__(self, num_classes):
        """
        LeNet-5 architecture implementation in PyTorch.

        :param num_classes: The number of output classes.
        """
        super(Model, self).__init__()

        # Extract parameters from convolutional layers
        conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1)
        self.conv1_weight = nn.Parameter(conv1.weight.data.clone())
        self.conv1_bias = nn.Parameter(conv1.bias.data.clone())

        conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)
        self.conv2_weight = nn.Parameter(conv2.weight.data.clone())
        self.conv2_bias = nn.Parameter(conv2.bias.data.clone())

        # Extract parameters from fully connected layers
        fc1 = nn.Linear(in_features=16 * 5 * 5, out_features=120)
        self.fc1_weight = nn.Parameter(fc1.weight.data.clone())
        self.fc1_bias = nn.Parameter(fc1.bias.data.clone())

        fc2 = nn.Linear(in_features=120, out_features=84)
        self.fc2_weight = nn.Parameter(fc2.weight.data.clone())
        self.fc2_bias = nn.Parameter(fc2.bias.data.clone())

        fc3 = nn.Linear(in_features=84, out_features=num_classes)
        self.fc3_weight = nn.Parameter(fc3.weight.data.clone())
        self.fc3_bias = nn.Parameter(fc3.bias.data.clone())

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.conv1_weight,
            self.conv1_bias,
            self.conv2_weight,
            self.conv2_bias,
            self.fc1_weight,
            self.fc1_bias,
            self.fc2_weight,
            self.fc2_bias,
            self.fc3_weight,
            self.fc3_bias,
        )


# Test code for the LeNet-5 model
batch_size = 1
num_classes = 10


def get_inputs():
    return [torch.randn(batch_size, 1, 32, 32)]


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):
        """
        LeNet-5 architecture implementation in PyTorch.

        :param num_classes: The number of output classes.
        """
        super(Model, self).__init__()
        
        # Convolutional layers
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)
        
        # Fully connected layers
        self.fc1 = nn.Linear(in_features=16*5*5, out_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=84)
        self.fc3 = nn.Linear(in_features=84, out_features=num_classes)
    
    def forward(self, x):
        """
        Forward pass of the LeNet-5 model.

        :param x: The input tensor, shape (batch_size, 1, 32, 32)
        :return: The output tensor, shape (batch_size, num_classes)
        """
        # First convolutional layer with ReLU activation and max pooling
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        
        # Second convolutional layer with ReLU activation and max pooling
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        
        # Flatten the output for the fully connected layers
        x = x.view(-1, 16*5*5)
        
        # First fully connected layer with ReLU activation
        x = F.relu(self.fc1(x))
        
        # Second fully connected layer with ReLU activation
        x = F.relu(self.fc2(x))
        
        # Final fully connected layer
        x = self.fc3(x)
        
        return x

# Test code for the LeNet-5 model
batch_size = 1
num_classes = 10

def get_inputs():
    return [torch.randn(batch_size, 1, 32, 32)]

def get_init_inputs():
    return [num_classes]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 4_LeNet5
Level ID 3
Task ID 4
Kernel Name modular_device_functions_base_base
CUDA Speedup (Native) 2.247x
CUDA Speedup (Compile) 1.383x
CUDA Runtime 0.052 ms
PyTorch Runtime (Native) 0.117 ms
PyTorch Runtime (Compile) 0.072 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 0.50
View Experiment Progress Details

Related Kernels (Level 3, Task 4 • 4_LeNet5)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 4_LeNet5_fused_even_edit_1 0.05 2.38 1.47
🥇 4_LeNet5_fused_even_base 0.05 2.38 1.47
🥉 warp_uniform_optimized_base 0.05 2.29 1.41
4 modular_device_functions_base_base 0.05 2.25 1.38
4 4_LeNet5 0.05 2.25 1.38
4 optimized_sync_4_lenet5_base 0.05 2.25 1.38
4 4_LeNet5_strided_loops_edit_1 0.05 2.25 1.38
4 4_LeNet5_warp_divergence_edit_1 0.05 2.25 1.38
4 4_LeNet5_atomic_optimization_base 0.05 2.25 1.38
4 4_LeNet5_unroll_loops_base 0.05 2.25 1.38
4 4_lenet5_shared_mem_optimization_base 0.05 2.25 1.38
4 4_LeNet5_strided_loops_base 0.05 2.25 1.38
4 4_LeNet5_shared_memory_reduction_base 0.05 2.25 1.38
14 4_lenet5_workload_balancing_base 0.05 2.20 1.36
14 4_LeNet5_shared_memory_atomic_base 0.05 2.20 1.36
14 4_LeNet5_shared_memory_base 0.05 2.20 1.36
14 4_lenet5_memory_coalescing_edit_1 0.05 2.20 1.36
14 balanced_workload_distribution_base 0.05 2.20 1.36
14 no_divergence_fast_base 0.05 2.20 1.36
14 4_LeNet5_unroll_loops_edit_1 0.05 2.20 1.36 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cublas_v2.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDAUtils.h>

// Device function for ReLU activation
__device__ float relu(float x) {
    return fmaxf(0.0f, x);
}

// Optimized ReLU kernel using device function
__global__ void relu_kernel(float* input, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int stride = blockDim.x * gridDim.x;

    for (int i = idx; i < size; i += stride) {
        input[i] = relu(input[i]);
    }
}

// Device function for max pooling
__device__ float max_pool(const float* input, int height, int width, int in_h_start, int in_w_start, int pool_height, int pool_width) {
    float max_val = -FLT_MAX;
    for (int i = in_h_start; i < in_h_start + pool_height; ++i) {
        for (int j = in_w_start; j < in_w_start + pool_width; ++j) {
            float val = input[i * width + j];
            max_val = fmaxf(max_val, val);
        }
    }
    return max_val;
}

// Optimized max pooling kernel using device function
__global__ void max_pool2d_kernel(
    const float* __restrict__ input, float* __restrict__ output,
    int batch_size, int channels, int height, int width,
    int pool_height, int pool_width, int stride
) {
    int out_h = (height - pool_height) / stride + 1;
    int out_w = (width - pool_width) / stride + 1;
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int stride_threads = blockDim.x * gridDim.x;

    for (; idx < batch_size * channels * out_h * out_w; idx += stride_threads) {
        int b = idx / (channels * out_h * out_w);
        int c = (idx / (out_h * out_w)) % channels;
        int h = (idx / out_w) % out_h;
        int w = idx % out_w;

        int in_h_start = h * stride;
        int in_w_start = w * stride;

        output[idx] = max_pool(&input[((b * channels + c) * height) * width], height, width, in_h_start, in_w_start, pool_height, pool_width);
    }
}

// Device function for linear transformation
__device__ float linear_transform(const float* input, const float* weight, float bias, int in_features) {
    float val = bias;
    for (int i = 0; i < in_features; ++i) {
        val += input[i] * weight[i];
    }
    return val;
}

// Optimized linear layer kernel using device function
__global__ void linear_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int in_features, int out_features
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int stride = blockDim.x * gridDim.x;

    for (; idx < out_features; idx += stride) {
        output[idx] = linear_transform(input, &weight[idx * in_features], bias[idx], in_features);
    }
}

// Forward function for the LeNet-5 architecture
torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor conv1_weight, torch::Tensor conv1_bias,
    torch::Tensor conv2_weight, torch::Tensor conv2_bias,
    torch::Tensor fc1_weight, torch::Tensor fc1_bias,
    torch::Tensor fc2_weight, torch::Tensor fc2_bias,
    torch::Tensor fc3_weight, torch::Tensor fc3_bias
) {
    // Ensure inputs are on CUDA
    x = x.to(torch::kCUDA);
    conv1_weight = conv1_weight.to(torch::kCUDA);
    conv1_bias = conv1_bias.to(torch::kCUDA);
    conv2_weight = conv2_weight.to(torch::kCUDA);
    conv2_bias = conv2_bias.to(torch::kCUDA);
    fc1_weight = fc1_weight.to(torch::kCUDA);
    fc1_bias = fc1_bias.to(torch::kCUDA);
    fc2_weight = fc2_weight.to(torch::kCUDA);
    fc2_bias = fc2_bias.to(torch::kCUDA);
    fc3_weight = fc3_weight.to(torch::kCUDA);
    fc3_bias = fc3_bias.to(torch::kCUDA);

    const int block_size = 256;
    const int max_blocks = 32;

    // First convolutional layer
    auto conv1 = torch::conv2d(x, conv1_weight, conv1_bias, {1, 1});
    relu_kernel<<<max_blocks, block_size>>>(conv1.data_ptr<float>(), conv1.numel());
    auto pool1 = torch::max_pool2d(conv1, {2, 2}, {2, 2});

    // Second convolutional layer
    auto conv2 = torch::conv2d(pool1, conv2_weight, conv2_bias, {1, 1});
    relu_kernel<<<max_blocks, block_size>>>(conv2.data_ptr<float>(), conv2.numel());
    auto pool2 = torch::max_pool2d(conv2, {2, 2}, {2, 2});

    // Flatten the output
    auto flat = pool2.view({pool2.size(0), -1});

    // First fully connected layer
    auto fc1 = torch::linear(flat, fc1_weight, fc1_bias);
    relu_kernel<<<max_blocks, block_size>>>(fc1.data_ptr<float>(), fc1.numel());

    // Second fully connected layer
    auto fc2 = torch::linear(fc1, fc2_weight, fc2_bias);
    relu_kernel<<<max_blocks, block_size>>>(fc2.data_ptr<float>(), fc2.numel());

    // Final fully connected layer
    auto fc3 = torch::linear(fc2, fc3_weight, fc3_bias);

    return fc3;
}

// PyBind11 module definition
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "LeNet-5 forward pass");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv2d
CPU Time 1015717.38 μs
Device Time 278716.12 μs
Self CPU Time 40902.06 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 974815.32 μs
Device Time 278716.12 μs
Self CPU Time 52949.64 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 921865.68 μs
Device Time 278716.12 μs
Self CPU Time 108705.34 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
cudaLaunchKernel
CPU Time 680875.27 μs
Device Time 40022.29 μs
Self CPU Time 680875.27 μs
Self Device Time 40022.29 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 679635.60 μs
Device Time 149615.70 μs
Self CPU Time 59322.96 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::zero_
CPU Time 111523.75 μs
Device Time 980954.69 μs
Self CPU Time 24333.13 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::fill_
CPU Time 87192.19 μs
Device Time 980954.69 μs
Self CPU Time 32098.95 μs
Self Device Time 980954.69 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 980954.69 μs
Self CPU Time 0.00 μs
Self Device Time 980954.69 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 
45315 warnings generated when compiling for host.
Suppressed 45346 warnings (45299 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_4/b6_s1_modular_device_functions_base/base/base.cu:16:15 bugprone-narrowing-conversions
16 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:17:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:25:47: warning: 4 adjacent parameters of 'max_pool' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | __device__ float max_pool(const float* input, int height, int width, int in_h_start, int in_w_start, int pool_height, int pool_width) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:25:51: note: the first parameter in the range is 'height'
25 | __device__ float max_pool(const float* input, int height, int width, int in_h_start, int in_w_start, int pool_height, int pool_width) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:25:90: note: the last parameter in the range is 'in_w_start'
25 | __device__ float max_pool(const float* input, int height, int width, int in_h_start, int in_w_start, int pool_height, int pool_width) {
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:25:102: warning: 2 adjacent parameters of 'max_pool' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | __device__ float max_pool(const float* input, int height, int width, int in_h_start, int in_w_start, int pool_height, int pool_width) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:25:106: note: the first parameter in the range is 'pool_height'
25 | __device__ float max_pool(const float* input, int height, int width, int in_h_start, int in_w_start, int pool_height, int pool_width) {
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:25:123: note: the last parameter in the range is 'pool_width'
25 | __device__ float max_pool(const float* input, int height, int width, int in_h_start, int in_w_start, int pool_height, int pool_width) {
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:44:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
44 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:45:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | int stride_threads = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:56:33: 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]
56 | output[idx] = max_pool(&input[((b * channels + c) * height) * width], height, width, in_h_start, in_w_start, pool_height, pool_width);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:56:39: note: make conversion explicit to silence this warning
8 | output[idx] = max_pool(&input[((b * channels + c) * height) * width], height, width, in_h_start, in_w_start, pool_height, pool_width);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:56:39: note: perform multiplication in a wider type
56 | output[idx] = max_pool(&input[((b * channels + c) * height) * width], height, width, in_h_start, in_w_start, pool_height, pool_width);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:61:76: warning: 2 adjacent parameters of 'linear_transform' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
61 | __device__ float linear_transform(const float* input, const float* weight, float bias, int in_features) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:61:82: note: the first parameter in the range is 'bias'
61 | __device__ float linear_transform(const float* input, const float* weight, float bias, int in_features) {
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:61:92: note: the last parameter in the range is 'in_features'
61 | __device__ float linear_transform(const float* input, const float* weight, float bias, int in_features) {
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:61:88: note: 'float' and 'int' may be implicitly converted
61 | __device__ float linear_transform(const float* input, const float* weight, float bias, int in_features) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:75:5: warning: 2 adjacent parameters of 'linear_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
75 | int in_features, int out_features
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:75:9: note: the first parameter in the range is 'in_features'
75 | int in_features, int out_features
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:75:26: note: the last parameter in the range is 'out_features'
75 | int in_features, int out_features
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:77:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
77 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:78:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
78 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:81:48: 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]
81 | output[idx] = linear_transform(input, &weight[idx * in_features], bias[idx], in_features);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:81:55: note: make conversion explicit to silence this warning
81 | output[idx] = linear_transform(input, &weight[idx * in_features], bias[idx], in_features);
| ^~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:81:55: note: perform multiplication in a wider type
81 | output[idx] = linear_transform(input, &weight[idx * in_features], bias[idx], in_features);
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:112:70: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | relu_kernel<<<max_blocks, block_size>>>(conv1.data_ptr<float>(), conv1.numel());
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:117:70: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | relu_kernel<<<max_blocks, block_size>>>(conv2.data_ptr<float>(), conv2.numel());
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:125:68: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | relu_kernel<<<max_blocks, block_size>>>(fc1.data_ptr<float>(), fc1.numel());
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_4/b6_s1_modular_device_functions_base/base/base.cu:129:68: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
129 | relu_kernel<<<max_blocks, block_size>>>(fc2.data_ptr<float>(), fc2.numel());
| ^