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63_conv_standard_2D__square_input__square_kernel • conv2d_shared_mem_optimized_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_shared_mem_optimized_base
CUDA Speedup (Native) 0.536x
CUDA Speedup (Compile) 0.900x
CUDA Runtime 0.428 ms
PyTorch Runtime (Native) 0.230 ms
PyTorch Runtime (Compile) 0.385 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
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
#define FEATURES_PER_BLOCK 4
#define TILE_SIZE (BLOCK_SIZE + KERNEL_SIZE - 1)

__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 output_height,
    const int output_width,
    const int stride,
    const int padding) {
    
    __shared__ float shared_input[TILE_SIZE][TILE_SIZE];
    __shared__ float shared_weights[FEATURES_PER_BLOCK][KERNEL_SIZE][KERNEL_SIZE];
    
    const int tx = threadIdx.x;
    const int ty = threadIdx.y;
    const int bx = blockIdx.x * BLOCK_SIZE;
    const int by = blockIdx.y * BLOCK_SIZE;
    const int b = blockIdx.z;
    
    const int x_out = bx + tx;
    const int y_out = by + ty;
    
    float partial_sums[FEATURES_PER_BLOCK] = {0.0f};
    
    for (int ocb = 0; ocb < out_channels; ocb += FEATURES_PER_BLOCK) {
        for (int ic = 0; ic < in_channels; ++ic) {
            if (tx < KERNEL_SIZE && ty < KERNEL_SIZE) {
                for (int f = 0; f < FEATURES_PER_BLOCK && (ocb + f) < out_channels; ++f) {
                    shared_weights[f][ty][tx] = weight[((ocb + f) * in_channels + ic) * KERNEL_SIZE * KERNEL_SIZE + ty * KERNEL_SIZE + tx];
                }
            }
            
            for (int i = ty; i < TILE_SIZE; i += BLOCK_SIZE) {
                for (int j = tx; j < TILE_SIZE; j += BLOCK_SIZE) {
                    int ih = by + i - padding;
                    int iw = bx + j - padding;
                    if (ih >= 0 && ih < input_height && iw >= 0 && iw < input_width) {
                        shared_input[i][j] = input[((b * in_channels + ic) * input_height + ih) * input_width + iw];
                    } else {
                        shared_input[i][j] = 0.0f;
                    }
                }
            }
            __syncthreads();
            
            if (x_out < output_width && y_out < output_height) {
                for (int f = 0; f < FEATURES_PER_BLOCK && (ocb + f) < out_channels; ++f) {
                    float sum = 0.0f;
                    #pragma unroll
                    for (int ky = 0; ky < KERNEL_SIZE; ++ky) {
                        #pragma unroll
                        for (int kx = 0; kx < KERNEL_SIZE; ++kx) {
                            sum += shared_input[ty * stride + ky][tx * stride + kx] * 
                                   shared_weights[f][ky][kx];
                        }
                    }
                    partial_sums[f] += sum;
                }
            }
            __syncthreads();
        }
        
        if (x_out < output_width && y_out < output_height) {
            for (int f = 0; f < FEATURES_PER_BLOCK && (ocb + f) < out_channels; ++f) {
                output[((b * out_channels + (ocb + f)) * output_height + y_out) * output_width + x_out] = 
                    partial_sums[f];
                partial_sums[f] = 0.0f;
            }
        }
    }
}

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(x.is_contiguous(), "Input must be contiguous");
    TORCH_CHECK(weight.is_contiguous(), "Weight must be contiguous");
    
    auto batch_size = x.size(0);
    auto in_channels = x.size(1);
    auto input_height = x.size(2);
    auto input_width = x.size(3);
    auto out_channels = weight.size(0);
    
    auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
    auto 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_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, "Shared memory optimized CUDA conv2d implementation");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.750 inst/cycle 0.000 5
Executed Ipc Elapsed 2.666 inst/cycle 0.000 5
Issue Slots Busy 68.754 % 0.001 5
Issued Ipc Active 2.750 inst/cycle 0.000 5
SM Busy 68.754 % 0.001 5
Memory Throughput 504378107388.478 byte/second 893109096258582784.000 5
Mem Busy 69.540 % 0.013 5
Max Bandwidth 43.194 % 0.005 5
L1/TEX Hit Rate 46.942 % 0.000 5
L2 Hit Rate 94.436 % 0.009 5
Mem Pipes Busy 36.810 % 0.004 5
Warp Cycles Per Issued Instruction 16.408 cycle 0.000 5
Warp Cycles Per Executed Instruction 16.416 cycle 0.000 5
Avg. Active Threads Per Warp 27.890 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.120 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 6.000 block 0.000 5
Block Limit Shared Mem 12.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 48.000 warp 0.000 5
Theoretical Occupancy 75.000 % 0.000 5
Achieved Occupancy 70.502 % 0.001 5
Achieved Active Warps Per SM 45.124 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (35.5%) 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 CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy (75.0%) is limited by the number of required registers. 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 269266.48 μs
Device Time 1209.91 μs
Self CPU Time 59.58 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 269206.90 μs
Device Time 1209.91 μs
Self CPU Time 112.70 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 2440591.14 μs
Device Time 13897.36 μs
Self CPU Time 2440591.14 μs
Self Device Time 13897.36 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)
CPU Time 0.00 μs
Device Time 2271671.76 μs
Self CPU Time 0.00 μs
Self Device Time 2271671.76 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 11944.48 μs
Device Time 27722.99 μs
Self CPU Time 11944.48 μs
Self Device Time 27722.99 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 2131871.28 μs
Device Time 418007.64 μs
Self CPU Time 11683.71 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 2120192.09 μs
Device Time 418007.64 μs
Self CPU Time 12924.37 μs
Self Device Time 418007.64 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 418007.64 μs
Self CPU Time 0.00 μs
Self Device Time 418007.64 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_s2_conv2d_shared_mem_optimized/base/base.cu:11:5 bugprone-easily-swappable-parameters
11 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:11:31: note: the first parameter in the range is 'input'
11 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:12:31: note: the last parameter in the range is 'weight'
12 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:14:5: warning: 3 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
15 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
16 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:14:15: note: the first parameter in the range is 'batch_size'
14 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:16:15: note: the last parameter in the range is 'out_channels'
16 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:18:5: warning: 2 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | const int input_width,
| ^~~~~~~~~~~~~~~~~~~~~~
19 | const int output_height,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:18:15: note: the first parameter in the range is 'input_width'
18 | const int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:19:15: note: the last parameter in the range is 'output_height'
19 | const int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:20:5: warning: 3 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | const int output_width,
| ^~~~~~~~~~~~~~~~~~~~~~~
21 | const int stride,
| ~~~~~~~~~~~~~~~~~
22 | const int padding) {
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:20:15: note: the first parameter in the range is 'output_width'
20 | const int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:22:15: note: the last parameter in the range is 'padding'
22 | const int padding) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:27:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | const int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:28:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | const int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:29:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | const int bx = blockIdx.x * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:30:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | const int by = blockIdx.y * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:31:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | const int b = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:87:19: 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]
87 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:88:19: 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]
88 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:91:5: warning: 3 adjacent parameters of 'forward' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
91 | int padding,
| ^~~~~~~~~~~~
92 | int dilation,
| ~~~~~~~~~~~~~
93 | int groups) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:91:9: note: the first parameter in the range is 'padding'
91 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:93:9: note: the last parameter in the range is 'groups'
93 | int groups) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:106:42: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
106 | auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:106:42: note: make conversion explicit to silence this warning
4 | auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:106:42: note: perform multiplication in a wider type
106 | auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:107:40: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
107 | auto output_width = (input_width + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:107:40: note: make conversion explicit to silence this warning
107 | auto output_width = (input_width + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:107:40: note: perform multiplication in a wider type
107 | auto output_width = (input_width + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:121:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | batch_size,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:122:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
122 | in_channels,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:123:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
123 | out_channels,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:124:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | input_height,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:125:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | input_width,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:126:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | output_height,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b10_s2_conv2d_shared_mem_optimized/base/base.cu:127:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
127 | output_width,
| ^