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41_Max_Pooling_1D • modular_device_functions_edit_1_base

Level 1 • Task 41
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    kernel_size: int,
    stride: int,
    padding: int,
    dilation: int,
    return_indices: bool,
) -> torch.Tensor:
    """
    Functional implementation of Max Pooling 1D.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, num_features, sequence_length).
        kernel_size (int): Size of the window to take a max over.
        stride (int): Stride of the window.
        padding (int): Implicit zero padding to be added on both sides.
        dilation (int): Spacing between kernel elements.
        return_indices (bool): Whether to return the indices of the maximum values.

    Returns:
        torch.Tensor: Output tensor with Max Pooling 1D applied.
    """
    return F.max_pool1d(
        x,
        kernel_size=kernel_size,
        stride=stride,
        padding=padding,
        dilation=dilation,
        return_indices=return_indices,
    )


class Model(nn.Module):
    """
    Simple model that performs Max Pooling 1D.
    """

    def __init__(
        self,
        kernel_size: int,
        stride: int,
        padding: int,
        dilation: int,
        return_indices: bool,
    ):
        """
        Initializes the Max Pooling 1D layer.

        Args:
            kernel_size (int): Size of the window to take a max over.
            stride (int): Stride of the window.
            padding (int): Implicit zero padding to be added on both sides.
            dilation (int): Spacing between kernel elements.
            return_indices (bool): Whether to return the indices of the maximum values.
        """
        super(Model, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.return_indices = return_indices

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Applies Max Pooling 1D to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, num_features, sequence_length).
            fn: Function to apply (defaults to module_fn)

        Returns:
            torch.Tensor: Output tensor with Max Pooling 1D applied.
        """
        return fn(
            x,
            self.kernel_size,
            self.stride,
            self.padding,
            self.dilation,
            self.return_indices,
        )


batch_size = 16
features = 64
sequence_length = 128
kernel_size = 4
stride = 2
padding = 2
dilation = 3
return_indices = False


def get_inputs():
    x = torch.randn(batch_size, features, sequence_length)
    return [x]


def get_init_inputs():
    return [kernel_size, stride, padding, dilation, return_indices]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs Max Pooling 1D.
    """
    def __init__(self, kernel_size: int, stride: int = None, padding: int = 0, dilation: int = 1, return_indices: bool = False):
        """
        Initializes the Max Pooling 1D layer.

        Args:
            kernel_size (int): Size of the window to take a max over.
            stride (int, optional): Stride of the window. Defaults to None (same as kernel_size).
            padding (int, optional): Implicit zero padding to be added on both sides. Defaults to 0.
            dilation (int, optional): Spacing between kernel elements. Defaults to 1.
            return_indices (bool, optional): Whether to return the indices of the maximum values. Defaults to False.
        """
        super(Model, self).__init__()
        self.maxpool = nn.MaxPool1d(kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, return_indices=return_indices)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies Max Pooling 1D to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, num_features, sequence_length).

        Returns:
            torch.Tensor: Output tensor with Max Pooling 1D applied, shape (batch_size, num_features, output_sequence_length).
        """
        return self.maxpool(x)

batch_size = 16
features = 64
sequence_length = 128
kernel_size = 4
stride = 2
padding = 2
dilation = 3
return_indices = False

def get_inputs():
    x = torch.randn(batch_size, features, sequence_length)
    return [x]

def get_init_inputs():
    return [kernel_size, stride, padding, dilation, return_indices]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 41_Max_Pooling_1D
Level ID 1
Task ID 41
Kernel Name modular_device_functions_edit_1_base
CUDA Speedup (Native) 1.183x
CUDA Speedup (Compile) 5.007x
CUDA Runtime 0.006 ms
PyTorch Runtime (Native) 0.007 ms
PyTorch Runtime (Compile) 0.030 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 41 • 41_Max_Pooling_1D)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 41_Max_Pooling_1D 0.01 1.18 5.01
🥇 max_pool1d_shared_opt_base 0.01 1.18 5.01
🥇 optimized_workload_distributed_pool1d_base 0.01 1.18 5.01
🥇 max_pool1d_optimized_grid_base 0.01 1.18 5.01
🥇 optimized_max_pool1d_kernel_base 0.01 1.18 5.01
🥇 max_pool1d_kernel_combined_base 0.01 1.18 5.01
🥇 max_pool1d_nosync_base 0.01 1.18 5.01
🥇 coalesced_writes_edit_1 0.01 1.18 5.01
🥇 aligned_memory_access_base 0.01 1.18 5.01
🥇 aligned_memory_access_edit_1 0.01 1.18 5.01
🥇 loop_unrolling_base 0.01 1.18 5.01
🥇 balanced_workload_distribution_base 0.01 1.18 5.01
🥇 balanced_max_pool1d_base 0.01 1.18 5.01
🥇 balanced_workload_distribution_edit_1 0.01 1.18 5.01
🥇 max_pool1d_fused_kernel_base 0.01 1.18 5.01
🥇 coalesced_max_pool1d_kernel_base_base 0.01 1.18 5.01
🥇 experimental_block_size_pool1d_base_base 0.01 1.18 5.01
🥇 max_pool1d_tunable_base 0.01 1.18 5.01
🥇 coalesced_aligned_pooling_base 0.01 1.18 5.01
🥇 modular_device_functions_edit_1_base 0.01 1.18 5.01 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

// Device function to compute the max pooling operation for a given output index
__device__ inline void compute_maxpool1d(
    const float* __restrict__ input_bc,
    const int input_length,
    const int kernel_size,
    const int stride,
    const int padding,
    const int dilation,
    const int out_index,
    float &max_val,
    int &max_idx) {

    const int input_start = out_index * stride - padding;
    max_val = -INFINITY;
    max_idx = -1;
    
    // Loop over the kernel window
    for (int k = 0; k < kernel_size; ++k) {
        int pos = input_start + k * dilation;
        // Check if the position is within the valid range
        if (pos >= 0 && pos < input_length) {
            float val = __ldg(input_bc + pos);
            if (val > max_val) {
                max_val = val;
                max_idx = pos;
            }
        }
    }
}

__global__ void max_pool1d_modular_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t* __restrict__ indices,
    const int batch_size,
    const int num_channels,
    const int input_length,
    const int kernel_size,
    const int stride,
    const int padding,
    const int dilation,
    const int output_length,
    const bool return_indices) {

    int total_elements = batch_size * num_channels * output_length;
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid >= total_elements) return;

    // Decode flattened index into batch, channel, and output index
    int bc = tid / output_length;
    int out_index = tid % output_length;
    int b = bc / num_channels;
    int c = bc % num_channels;

    // Pointer to the start of the input for the current batch and channel
    const float* input_bc = input + (b * num_channels * input_length) + (c * input_length);

    float max_val;
    int max_idx;
    // Use the modular device function to compute the max value and index
    compute_maxpool1d(input_bc, input_length, kernel_size, stride, padding, dilation, out_index, max_val, max_idx);

    // Write the result to the output tensor
    int out_flat_idx = b * num_channels * output_length + c * output_length + out_index;
    output[out_flat_idx] = max_val;
    if (return_indices) {
        indices[out_flat_idx] = max_idx;
    }
}

torch::Tensor forward(
    torch::Tensor x,
    int64_t kernel_size,
    int64_t stride,
    int64_t padding,
    int64_t dilation,
    bool return_indices) {

    TORCH_CHECK(x.dim() == 3, "Input must be 3D");
    TORCH_CHECK(x.is_cuda(), "Input must be on CUDA");
    TORCH_CHECK(x.is_contiguous(), "Input must be contiguous");

    const int batch_size = x.size(0);
    const int num_channels = x.size(1);
    const int input_length = x.size(2);

    const int output_length = ((input_length + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
    TORCH_CHECK(output_length > 0, "Output length must be positive");

    auto options = torch::TensorOptions().dtype(x.dtype()).device(x.device());
    auto output = torch::empty({batch_size, num_channels, output_length}, options);
    torch::Tensor indices;
    if (return_indices) {
        indices = torch::empty({batch_size, num_channels, output_length}, options.dtype(torch::kInt64));
    }

    int total_elements = batch_size * num_channels * output_length;
    int threads = 256;
    int blocks = (total_elements + threads - 1) / threads;

    max_pool1d_modular_kernel<<<blocks, threads>>>(
        x.data_ptr<float>(),
        output.data_ptr<float>(),
        return_indices ? indices.data_ptr<int64_t>() : nullptr,
        batch_size,
        num_channels,
        input_length,
        kernel_size,
        stride,
        padding,
        dilation,
        output_length,
        return_indices
    );

    return return_indices ? torch::cat({output, indices}, -1) : output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "MaxPool1D forward with modular device functions (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.890 inst/cycle 0.000 5
Executed Ipc Elapsed 0.378 inst/cycle 0.000 5
Issue Slots Busy 23.018 % 0.019 5
Issued Ipc Active 0.922 inst/cycle 0.000 5
SM Busy 23.018 % 0.019 5
Memory Throughput 129231537499.480 byte/second 12015123542350800896.000 5
Mem Busy 9.802 % 0.075 5
Max Bandwidth 6.404 % 0.029 5
L1/TEX Hit Rate 68.700 % 0.000 5
L2 Hit Rate 71.946 % 0.150 5
Mem Pipes Busy 5.118 % 0.020 5
Warp Cycles Per Issued Instruction 15.022 cycle 0.362 5
Warp Cycles Per Executed Instruction 15.542 cycle 0.394 5
Avg. Active Threads Per Warp 31.750 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.660 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 10.000 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 21.140 % 0.005 5
Achieved Active Warps Per SM 13.528 warp 0.002 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
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 is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (21.2%) 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
aten::to
CPU Time 748798.09 μs
Device Time 21.09 μs
Self CPU Time 38.61 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::_to_copy
CPU Time 748759.48 μs
Device Time 21.09 μs
Self CPU Time 109.06 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::empty_strided
CPU Time 748483.77 μs
Device Time 0.00 μs
Self CPU Time 87.21 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
cudaDeviceGetStreamPriorityRange
CPU Time 740866.62 μs
Device Time 0.00 μs
Self CPU Time 740866.62 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 473989.91 μs
Device Time 20370.43 μs
Self CPU Time 473989.91 μs
Self Device Time 20370.43 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
max_pool1d_modular_kernel(float const*, float*, long*, int, int, int, int, int, int, int, int, bool)
CPU Time 0.00 μs
Device Time 30450.32 μs
Self CPU Time 0.00 μs
Self Device Time 30450.32 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 17128.19 μs
Device Time 40428.23 μs
Self CPU Time 17128.19 μs
Self Device Time 40428.23 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 61445.92 μs
Device Time 605194.41 μs
Self CPU Time 12665.35 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 48785.15 μs
Device Time 605194.41 μs
Self CPU Time 16460.56 μs
Self Device Time 605194.41 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 605194.41 μs
Self CPU Time 0.00 μs
Self Device Time 605194.41 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 
45295 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/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:8:5 bugprone-easily-swappable-parameters
8 | const int input_length,
| ^~~~~~~~~~~~~~~~~~~~~~~
9 | const int kernel_size,
| ~~~~~~~~~~~~~~~~~~~~~~
10 | const int stride,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:8:15: note: the first parameter in the range is 'input_length'
8 | const int input_length,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:10:15: note: the last parameter in the range is 'stride'
10 | const int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:11:5: warning: 2 adjacent parameters of 'compute_maxpool1d' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
11 | const int padding,
| ^~~~~~~~~~~~~~~~~~
12 | const int dilation,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:11:15: note: the first parameter in the range is 'padding'
11 | const int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:12:15: note: the last parameter in the range is 'dilation'
12 | const int dilation,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:14:5: warning: 2 adjacent parameters of 'compute_maxpool1d' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | float &max_val,
| ^~~~~~~~~~~~~~~
15 | int &max_idx) {
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:14:12: note: the first parameter in the range is 'max_val'
14 | float &max_val,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:15:10: note: the last parameter in the range is 'max_idx'
15 | int &max_idx) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:15:5: note: 'float &' and 'int &' may be implicitly converted: 'float &' (as 'float') -> 'int &' (as 'int'), 'int &' (as 'int') -> 'float &' (as 'float')
15 | int &max_idx) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:45:5: warning: 2 adjacent parameters of 'max_pool1d_modular_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
45 | const int dilation,
| ^~~~~~~~~~~~~~~~~~~
46 | const int output_length,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:45:15: note: the first parameter in the range is 'dilation'
45 | const int dilation,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:46:15: note: the last parameter in the range is 'output_length'
46 | const int output_length,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:50:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
50 | int tid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:60:29: 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]
60 | const float* input_bc = input + (b * num_channels * input_length) + (c * input_length);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:60:74: note: make conversion explicit to silence this warning
4 | const float* input_bc = input + (b * num_channels * input_length) + (c * input_length);
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:60:74: note: perform multiplication in a wider type
60 | const float* input_bc = input + (b * num_channels * input_length) + (c * input_length);
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:76:19: 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]
76 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:87:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | const int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:88:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | const int num_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:89:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
89 | const int input_length = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:91:31: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
91 | const int output_length = ((input_length + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:112:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | kernel_size,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:113:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | stride,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:114:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | padding,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_41/b4_s2_modular_device_functions_edit_1/base/base.cu:115:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | dilation,
| ^