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63_conv_standard_2D__square_input__square_kernel • conv2d_coalesced_coalescing_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_coalesced_coalescing_base
CUDA Speedup (Native) 0.269x
CUDA Speedup (Compile) 0.452x
CUDA Runtime 0.853 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>

#define BLOCK_SIZE 16
#define KERNEL_SIZE 3

// This kernel assumes stride == 1, dilation == 1, groups == 1 and a 3x3 kernel.
// It loads input tiles and weight tiles into shared memory using a 1D loop to ensure global memory coalescing.

__global__ void conv2d_coalesced_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    float* __restrict__ output,
    int batch_size,
    int in_channels,
    int out_channels,
    int input_height,
    int input_width,
    int output_height,
    int output_width,
    int stride,
    int padding) {

    // For stride == 1, the shared tile dimension is BLOCK_SIZE + KERNEL_SIZE - 1.
    const int tile_dim = BLOCK_SIZE + KERNEL_SIZE - 1; 
    __shared__ float shared_input[tile_dim * tile_dim];
    __shared__ float shared_weight[KERNEL_SIZE * KERNEL_SIZE];

    // Compute output spatial coordinates for this thread
    int out_x = blockIdx.x * BLOCK_SIZE + threadIdx.x;
    int out_y = blockIdx.y * BLOCK_SIZE + threadIdx.y;
    int b = blockIdx.z;

    // Compute the top-left coordinate of the input tile
    int in_x_origin = blockIdx.x * BLOCK_SIZE - padding;
    int in_y_origin = blockIdx.y * BLOCK_SIZE - padding;

    // Flatten thread index within the block for coalesced loads
    int t_idx = threadIdx.y * blockDim.x + threadIdx.x;
    int threads_per_block = blockDim.x * blockDim.y;

    // Loop over output channels
    for (int oc = 0; oc < out_channels; ++oc) {
        float result = 0.0f;
        // Sum over input channels
        for (int ic = 0; ic < in_channels; ++ic) {
            // Load the input tile for the current (b, ic) channel into shared memory.
            int total_tile = tile_dim * tile_dim;
            for (int index = t_idx; index < total_tile; index += threads_per_block) {
                int i = index / tile_dim;
                int j = index % tile_dim;
                int in_i = in_y_origin + i;
                int in_j = in_x_origin + j;
                if (in_i >= 0 && in_i < input_height && in_j >= 0 && in_j < input_width) {
                    shared_input[index] = input[((b * in_channels + ic) * input_height + in_i) * input_width + in_j];
                } else {
                    shared_input[index] = 0.0f;
                }
            }
            
            // Load the 3x3 kernel for the current (oc, ic) pair into shared memory
            int total_weight = KERNEL_SIZE * KERNEL_SIZE;
            for (int index = t_idx; index < total_weight; index += threads_per_block) {
                shared_weight[index] = weight[((oc * in_channels + ic) * total_weight) + index];
            }
            
            __syncthreads();
            
            // Each thread computes the convolution for its output pixel if within bounds
            if (out_y < output_height && out_x < output_width) {
                int local_y = threadIdx.y;
                int local_x = threadIdx.x;
                float sum = 0.0f;
                #pragma unroll
                for (int ki = 0; ki < KERNEL_SIZE; ++ki) {
                    #pragma unroll
                    for (int kj = 0; kj < KERNEL_SIZE; ++kj) {
                        int shared_index = (local_y + ki) * tile_dim + (local_x + kj);
                        sum += shared_input[shared_index] * shared_weight[ki * KERNEL_SIZE + kj];
                    }
                }
                result += sum;
            }
            __syncthreads();
        }
        // Write the result to global memory in a coalesced manner
        if (out_y < output_height && out_x < output_width) {
            output[((b * out_channels + oc) * output_height + out_y) * output_width + out_x] = result;
        }
        __syncthreads();
    }
}


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");
    // This kernel only supports stride==1, dilation==1, groups==1 and 3x3 kernels
    TORCH_CHECK(stride == 1, "Only stride==1 is supported in conv2d_coalesced_kernel");
    TORCH_CHECK(dilation == 1, "Only dilation==1 is supported in conv2d_coalesced_kernel");
    TORCH_CHECK(groups == 1, "Only groups==1 is supported in conv2d_coalesced_kernel");
    TORCH_CHECK(weight.size(2) == KERNEL_SIZE && weight.size(3) == KERNEL_SIZE, "Only 3x3 kernel supported");

    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 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());

    dim3 threads(BLOCK_SIZE, BLOCK_SIZE);
    dim3 blocks((output_width + BLOCK_SIZE - 1) / BLOCK_SIZE,
                (output_height + BLOCK_SIZE - 1) / BLOCK_SIZE,
                batch_size);

    conv2d_coalesced_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,
        output_height,
        output_width,
        stride,
        padding
    );

    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 conv2d with memory coalescing");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.530 inst/cycle 0.000 5
Executed Ipc Elapsed 2.456 inst/cycle 0.000 5
Issue Slots Busy 63.374 % 0.002 5
Issued Ipc Active 2.534 inst/cycle 0.000 5
SM Busy 63.374 % 0.002 5
Memory Throughput 256413316518.146 byte/second 454203505404347648.000 5
Mem Busy 80.236 % 0.025 5
Max Bandwidth 44.494 % 0.009 5
L1/TEX Hit Rate 77.152 % 0.003 5
L2 Hit Rate 94.458 % 0.094 5
Mem Pipes Busy 43.706 % 0.008 5
Warp Cycles Per Issued Instruction 23.354 cycle 0.000 5
Warp Cycles Per Executed Instruction 23.408 cycle 0.000 5
Avg. Active Threads Per Warp 30.370 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.830 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 26.000 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 92.486 % 0.002 5
Achieved Active Warps Per SM 59.190 warp 0.001 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (31.9%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
INF Occupancy This kernel's theoretical occupancy is not impacted by any block limit.
Operation / Metric Value Unit
aten::to
CPU Time 252212.27 μs
Device Time 1146.24 μs
Self CPU Time 53.20 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 252159.08 μs
Device Time 1146.24 μs
Self CPU Time 114.90 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 3669652.44 μs
Device Time 11234.02 μs
Self CPU Time 3669652.44 μs
Self Device Time 11234.02 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv2d_coalesced_kernel(float const*, float const*, float*, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 3677829.28 μs
Self CPU Time 0.00 μs
Self Device Time 3677829.28 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 9928.40 μs
Device Time 22236.35 μs
Self CPU Time 9928.40 μs
Self Device Time 22236.35 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 3434953.36 μs
Device Time 339142.65 μs
Self CPU Time 9000.25 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 3425955.21 μs
Device Time 339142.65 μs
Self CPU Time 12230.36 μs
Self Device Time 339142.65 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 339142.65 μs
Self CPU Time 0.00 μs
Self Device Time 339142.65 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 
45301 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/b10_s3_conv2d_coalesced_coalescing/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:12:31: note: the first parameter in the range is 'input'
12 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:13:31: note: the last parameter in the range is 'weight'
13 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:15:5: warning: 3 adjacent parameters of 'conv2d_coalesced_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | int batch_size,
| ^~~~~~~~~~~~~~~
16 | int in_channels,
| ~~~~~~~~~~~~~~~~
17 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:15:9: note: the first parameter in the range is 'batch_size'
15 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:17:9: note: the last parameter in the range is 'out_channels'
17 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:19:5: warning: 2 adjacent parameters of 'conv2d_coalesced_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int input_width,
| ^~~~~~~~~~~~~~~~
20 | int output_height,
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:19:9: note: the first parameter in the range is 'input_width'
19 | int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:20:9: note: the last parameter in the range is 'output_height'
20 | int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:21:5: warning: 3 adjacent parameters of 'conv2d_coalesced_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | int output_width,
| ^~~~~~~~~~~~~~~~~
22 | int stride,
| ~~~~~~~~~~~
23 | int padding) {
| ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:21:9: note: the first parameter in the range is 'output_width'
21 | int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:23:9: note: the last parameter in the range is 'padding'
23 | int padding) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:31:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int out_x = blockIdx.x * BLOCK_SIZE + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:32:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | int out_y = blockIdx.y * BLOCK_SIZE + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:33:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
33 | int b = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:36:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | int in_x_origin = blockIdx.x * BLOCK_SIZE - padding;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:37:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
37 | int in_y_origin = blockIdx.y * BLOCK_SIZE - padding;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:40:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
40 | int t_idx = threadIdx.y * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:41:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
41 | int threads_per_block = blockDim.x * blockDim.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:72:31: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
72 | int local_y = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:73:31: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
73 | int local_x = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:97:19: 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]
97 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:98:19: 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]
98 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:101:5: warning: 3 adjacent parameters of 'forward' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
101 | int padding,
| ^~~~~~~~~~~~
102 | int dilation,
| ~~~~~~~~~~~~~
103 | int groups) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:101:9: note: the first parameter in the range is 'padding'
101 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:103:9: note: the last parameter in the range is 'groups'
103 | int groups) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:113:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:114:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:115:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | int input_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:116:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | int input_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s3_conv2d_coalesced_coalescing/base/base.cu:117:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int out_channels = weight.size(0);
| ^