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63_conv_standard_2D__square_input__square_kernel • conv2d_grid_stride_base

Level 1 • Task 63
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor,
    stride: int,
    padding: int,
    dilation: int,
    groups: int,
) -> torch.Tensor:
    """
    Performs a standard 2D convolution operation with a square input and square kernel.

    Args:
        x (torch.Tensor): Input tensor.
        weight (torch.Tensor): Weight tensor.
        bias (torch.Tensor): Bias tensor.
        stride (int): Stride of the convolution.
        padding (int): Padding applied to the input.
        dilation (int): Dilation of the convolution.
        groups (int): Number of blocked connections from input channels to output channels.

    Returns:
        torch.Tensor: Output tensor.
    """
    return F.conv2d(
        x,
        weight,
        bias,
        stride=stride,
        padding=padding,
        dilation=dilation,
        groups=groups,
    )


class Model(nn.Module):
    """
    Performs a standard 2D convolution operation with a square input and square kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int): Stride of the convolution.
        padding (int): Padding applied to the input.
        dilation (int): Spacing between kernel elements.
        groups (int): Number of blocked connections from input channels to output channels.
        bias (bool): If `True`, adds a learnable bias to the output.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int,
        padding: int,
        dilation: int,
        groups: int,
        bias: bool,
    ):
        super(Model, self).__init__()
        # Create a Conv2d layer to get the same initialization
        conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )
        # Copy the initialized parameters
        self.weight = nn.Parameter(conv.weight.clone())
        self.bias = nn.Parameter(conv.bias.clone()) if bias else None

        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups

    def forward(
        self,
        x: torch.Tensor,
        fn=module_fn,
    ) -> torch.Tensor:
        """
        Performs the 2D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return fn(
            x,
            self.weight,
            self.bias,
            self.stride,
            self.padding,
            self.dilation,
            self.groups,
        )


# Constants
batch_size = 16
in_channels = 3
out_channels = 64
kernel_size = 3
width = 256
height = 256
stride = 1
padding = 0
dilation = 1
groups = 1
bias = False


def get_inputs():
    x = torch.randn(batch_size, in_channels, height, width)
    return [x]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        groups,
        bias,
    ]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Performs a standard 2D convolution operation with a square input and square kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        dilation (int, optional): Spacing between kernel elements. Defaults to 1.
        groups (int, optional): Number of blocked connections from input channels to output channels. Defaults to 1.
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        padding: int = 0,
        dilation: int = 1,
        groups: int = 1,
        bias: bool = False,
    ):
        super(Model, self).__init__()
        self.conv2d = nn.Conv2d(
            in_channels,
            out_channels,
            (kernel_size, kernel_size),
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 2D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv2d(x)


# Test code
batch_size = 16
in_channels = 3
out_channels = 64
kernel_size = 3
width = 256
height = 256
stride = 1
padding = 0
dilation = 1
groups = 1
bias = False


def get_inputs():
    x = torch.randn(batch_size, in_channels, height, width)
    return [x]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        groups,
        bias,
    ]  # Provide in_channels, out_channels, kernel_size for initialization
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 63_conv_standard_2D__square_input__square_kernel
Level ID 1
Task ID 63
Kernel Name conv2d_grid_stride_base
CUDA Speedup (Native) 0.163x
CUDA Speedup (Compile) 0.273x
CUDA Runtime 1.410 ms
PyTorch Runtime (Native) 0.230 ms
PyTorch Runtime (Compile) 0.385 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 63 • 63_conv_standard_2D__square_input__square_kernel)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 63_conv_standard_2D__square_input__square_kernel 0.23 1.00 1.68
🥇 adaptive_conv2d_cuda_base 0.23 1.00 1.68
🥇 conv2d_minimized_warp_divergence_base 0.23 1.00 1.68
🥇 adaptive_conv2d_cuda_base 0.23 1.00 1.68
5 conv2d_shared_mem_optimized_base 0.43 0.54 0.90
6 conv2d_coalesced_coalescing_base 0.85 0.27 0.45
7 conv2d_shared_mem_optimized_base 1.10 0.21 0.35
8 conv2d_shared_mem_optimized_base 1.10 0.21 0.35
8 conv2d_shared_mem_opt_base_base 1.10 0.21 0.35
10 63_conv_warp_optimized_base 1.18 0.19 0.33
11 mod_conv2d_kernel_modular_base 1.20 0.19 0.32
12 conv2d_unrolled_shared_base 1.22 0.19 0.32
13 63_conv_optimized_thread_mapping_base 1.34 0.17 0.29
14 constant_memory_optim_conv2d_edit_1 1.35 0.17 0.28
15 conv2d_shared_atomic_minimized_base 1.39 0.17 0.28
16 conv2d_grid_stride_base 1.41 0.16 0.27
17 atomic_minimized_conv2d_base_base 1.42 0.16 0.27
18 balanced_conv2d_cuda_base 1.44 0.16 0.27
19 block_size_optimization_conv2d_base 1.45 0.16 0.27
20 block_size_optimization_conv2d_edit_1 1.47 0.16 0.26 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

// This kernel uses grid-stride loops to handle workloads larger than the number of active threads.
// Each thread processes multiple output elements by iterating through a flattened output index space.

__global__ void conv2d_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    float* __restrict__ output,
    const int batch_size,
    const int in_channels,
    const int out_channels,
    const int input_height,
    const int input_width,
    const int kernel_size,
    const int output_height,
    const int output_width,
    const int stride,
    const int padding) {

    // Total number of output elements in 4D tensor: (N, out_channels, output_height, output_width)
    int total = batch_size * out_channels * output_height * output_width;
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int step = blockDim.x * gridDim.x;

    // Grid-stride loop: Each thread processes multiple elements if necessary
    for (int index = idx; index < total; index += step) {
        int temp = index;
        int ox = temp % output_width;
        temp /= output_width;
        int oy = temp % output_height;
        temp /= output_height;
        int oc = temp % out_channels;
        int n = temp / out_channels;

        float sum = 0.0f;
        // Convolution accumulation
        for (int ic = 0; ic < in_channels; ++ic) {
            for (int kh = 0; kh < kernel_size; ++kh) {
                int in_y = oy * stride - padding + kh;
                if (in_y < 0 || in_y >= input_height)
                    continue;
                for (int kw = 0; kw < kernel_size; ++kw) {
                    int in_x = ox * stride - padding + kw;
                    if (in_x < 0 || in_x >= input_width)
                        continue;
                    int input_idx = ((n * in_channels + ic) * input_height + in_y) * input_width + in_x;
                    int weight_idx = ((oc * in_channels + ic) * kernel_size + kh) * kernel_size + kw;
                    sum += input[input_idx] * weight[weight_idx];
                }
            }
        }
        int output_idx = ((n * out_channels + oc) * output_height + oy) * output_width + ox;
        output[output_idx] = sum;
    }
}


// Host function to launch the kernel
// This implementation supports square kernels and does not support dilation or groups other than 1.

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor weight,
    torch::optional<torch::Tensor> bias,
    int stride,
    int padding,
    int dilation,
    int groups) {

    TORCH_CHECK(x.is_cuda(), "Input must be a CUDA tensor");
    TORCH_CHECK(weight.is_cuda(), "Weight must be a CUDA tensor");
    TORCH_CHECK(dilation == 1, "Dilation other than 1 is not supported in this kernel");
    TORCH_CHECK(groups == 1, "Groups other than 1 are not supported in this kernel");

    int batch_size = x.size(0);
    int in_channels = x.size(1);
    int input_height = x.size(2);
    int input_width = x.size(3);
    int out_channels = weight.size(0);
    int kernel_size = weight.size(2); // assuming square kernel

    int output_height = (input_height + 2 * padding - kernel_size) / stride + 1;
    int output_width = (input_width + 2 * padding - kernel_size) / stride + 1;

    auto output = torch::empty({batch_size, out_channels, output_height, output_width}, x.options());
    
    int total_elements = batch_size * out_channels * output_height * output_width;
    int threads = 256;
    int blocks = (total_elements + threads - 1) / threads;

    conv2d_kernel<<<blocks, threads>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        output.data_ptr<float>(),
        batch_size,
        in_channels,
        out_channels,
        input_height,
        input_width,
        kernel_size,
        output_height,
        output_width,
        stride,
        padding);

    cudaDeviceSynchronize();

    if (bias.has_value()) {
        output.add_(bias.value().view({1, -1, 1, 1}));
    }

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "CUDA forward function for 2D convolution using grid-stride loops");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.440 inst/cycle 0.000 5
Executed Ipc Elapsed 3.430 inst/cycle 0.000 5
Issue Slots Busy 85.994 % 0.000 5
Issued Ipc Active 3.440 inst/cycle 0.000 5
SM Busy 85.994 % 0.000 5
Memory Throughput 147319453999.938 byte/second 28328722267525996.000 5
Mem Busy 35.312 % 0.000 5
Max Bandwidth 32.734 % 0.000 5
L1/TEX Hit Rate 79.764 % 0.000 5
L2 Hit Rate 98.802 % 0.007 5
Mem Pipes Busy 31.828 % 0.000 5
Warp Cycles Per Issued Instruction 17.580 cycle 0.000 5
Warp Cycles Per Executed Instruction 17.580 cycle 0.000 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.350 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 8.000 block 0.000 5
Block Limit Shared Mem 32.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 94.656 % 0.000 5
Achieved Active Warps Per SM 60.582 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (57.5%) 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 Occupancy This kernel's theoretical occupancy is not impacted by any block limit.
Operation / Metric Value Unit
aten::to
CPU Time 256527.46 μs
Device Time 1244.82 μs
Self CPU Time 53.96 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::_to_copy
CPU Time 256473.50 μs
Device Time 1244.82 μs
Self CPU Time 99.73 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 254881.32 μs
Device Time 0.00 μs
Self CPU Time 104.82 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 254409.86 μs
Device Time 0.00 μs
Self CPU Time 254409.86 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
cudaLaunchKernel
CPU Time 36866.71 μs
Device Time 27635.71 μs
Self CPU Time 36866.71 μs
Self Device Time 27635.71 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv2d_kernel(float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 5276812.23 μs
Self CPU Time 0.00 μs
Self Device Time 5276812.23 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 5526421.09 μs
Device Time 392.16 μs
Self CPU Time 5526421.09 μs
Self Device Time 392.16 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 41044.36 μs
Device Time 290806.80 μs
Self CPU Time 8119.63 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 32926.67 μs
Device Time 290806.80 μs
Self CPU Time 10891.61 μs
Self Device Time 290806.80 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 290806.80 μs
Self CPU Time 0.00 μs
Self Device Time 290806.80 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 45327 warnings (45280 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/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
13 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:12:15: note: the first parameter in the range is 'batch_size'
12 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:13:15: note: the last parameter in the range is 'in_channels'
13 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:14:5: warning: 2 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | const int out_channels,
| ^~~~~~~~~~~~~~~~~~~~~~~
15 | const int input_height,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:14:15: note: the first parameter in the range is 'out_channels'
14 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:15:15: note: the last parameter in the range is 'input_height'
15 | const int input_height,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:16:5: warning: 3 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | const int input_width,
| ^~~~~~~~~~~~~~~~~~~~~~
17 | const int kernel_size,
| ~~~~~~~~~~~~~~~~~~~~~~
18 | const int output_height,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:16:15: note: the first parameter in the range is 'input_width'
16 | const int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:18:15: note: the last parameter in the range is 'output_height'
18 | const int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:19:5: warning: 2 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | const int output_width,
| ^~~~~~~~~~~~~~~~~~~~~~~
20 | const int stride,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:19:15: note: the first parameter in the range is 'output_width'
19 | const int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:20:15: note: the last parameter in the range is 'stride'
20 | const int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:25:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:26:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int step = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:65:19: 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]
65 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:66:19: warning: the parameter 'weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
66 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:69:5: warning: 3 adjacent parameters of 'forward' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
69 | int padding,
| ^~~~~~~~~~~~
70 | int dilation,
| ~~~~~~~~~~~~~
71 | int groups) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:69:9: note: the first parameter in the range is 'padding'
69 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:71:9: note: the last parameter in the range is 'groups'
71 | int groups) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:78:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
78 | int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:79:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:80:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | int input_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:81:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | int input_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:82:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | int out_channels = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s2_conv2d_grid_stride/base/base.cu:83:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | int kernel_size = weight.size(2); // assuming square kernel
| ^