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63_conv_standard_2D__square_input__square_kernel • conv2d_unrolled_shared_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_unrolled_shared_base
CUDA Speedup (Native) 0.188x
CUDA Speedup (Compile) 0.315x
CUDA Runtime 1.222 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  // Assumed fixed 3x3 kernel for loop unrolling
#define SHARED_SIZE (BLOCK_SIZE + KERNEL_SIZE - 1)

// This kernel uses shared memory to load input tiles and kernel weights, and employs
// #pragma unroll to unroll the inner loops for the kernel window, reducing loop overhead.
// It assumes square input, square kernel (3x3), stride and padding, with groups == 1.

__global__ void conv2d_unroll_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) {

    // Each block is mapped to a tile of the output for a specific (batch, out_channel) pair.
    int b_oc = blockIdx.z; // blockIdx.z ranges over (batch_size * out_channels)
    int b = b_oc / out_channels;
    int oc = b_oc % out_channels;

    int bx = blockIdx.x * BLOCK_SIZE;
    int by = blockIdx.y * BLOCK_SIZE;

    int tx = threadIdx.x;
    int ty = threadIdx.y;

    int x = bx + tx; // output column index
    int y = by + ty; // output row index

    float sum = 0.0f;

    // Shared memory for input tile and kernel weights
    __shared__ float shared_input[SHARED_SIZE][SHARED_SIZE];
    __shared__ float shared_weight[KERNEL_SIZE][KERNEL_SIZE];

    // Loop over all input channels
    for (int ic = 0; ic < in_channels; ++ic) {
        // Load kernel weights for current (oc, ic) into shared memory
        if (tx < KERNEL_SIZE && ty < KERNEL_SIZE) {
            // Weight layout: [out_channels, in_channels, KERNEL_SIZE, KERNEL_SIZE]
            int w_idx = ((oc * in_channels + ic) * KERNEL_SIZE + ty) * KERNEL_SIZE + tx;
            shared_weight[ty][tx] = weight[w_idx];
        }

        // Load input tile for current (b, ic) into shared memory
        // Each block loads a tile of size SHARED_SIZE x SHARED_SIZE
        for (int i = ty; i < SHARED_SIZE; i += BLOCK_SIZE) {
            for (int j = tx; j < SHARED_SIZE; j += BLOCK_SIZE) {
                int in_y = by + i - padding;
                int in_x = bx + j - padding;
                if (in_y >= 0 && in_y < input_height && in_x >= 0 && in_x < input_width) {
                    shared_input[i][j] = input[((b * in_channels + ic) * input_height + in_y) * input_width + in_x];
                } else {
                    shared_input[i][j] = 0.0f;
                }
            }
        }
        __syncthreads();

        // Compute convolution for this input channel using unrolled loops
        if (x < output_width && y < output_height) {
            #pragma unroll
            for (int ky = 0; ky < KERNEL_SIZE; ++ky) {
                #pragma unroll
                for (int kx = 0; kx < KERNEL_SIZE; ++kx) {
                    sum += shared_input[ty + ky][tx + kx] * shared_weight[ky][kx];
                }
            }
        }
        __syncthreads();
    }

    // Write the computed sum to the output tensor
    if (x < output_width && y < output_height) {
        int o_idx = ((b * out_channels + oc) * output_height + y) * output_width + x;
        output[o_idx] = sum;
    }
}


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(weight.size(2) == KERNEL_SIZE && weight.size(3) == KERNEL_SIZE,
                "This implementation assumes a 3x3 kernel");
    TORCH_CHECK(groups == 1, "groups != 1 is not supported by 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 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 * out_channels);

    conv2d_unroll_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);

    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 with loop unrolling and shared memory");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.770 inst/cycle 0.000 5
Executed Ipc Elapsed 2.760 inst/cycle 0.000 5
Issue Slots Busy 69.138 % 0.000 5
Issued Ipc Active 2.770 inst/cycle 0.000 5
SM Busy 69.138 % 0.000 5
Memory Throughput 174596648689.476 byte/second 2987860471759319.000 5
Mem Busy 73.528 % 0.000 5
Max Bandwidth 40.592 % 0.000 5
L1/TEX Hit Rate 13.434 % 0.000 5
L2 Hit Rate 97.958 % 0.001 5
Mem Pipes Busy 45.022 % 0.000 5
Warp Cycles Per Issued Instruction 22.476 cycle 0.000 5
Warp Cycles Per Executed Instruction 22.476 cycle 0.000 5
Avg. Active Threads Per Warp 28.100 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.150 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 97.308 % 0.000 5
Achieved Active Warps Per SM 62.280 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (31.7%) 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 454645.26 μs
Device Time 1287.90 μs
Self CPU Time 56.57 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 454588.69 μs
Device Time 1287.90 μs
Self CPU Time 112.18 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 452926.76 μs
Device Time 0.00 μs
Self CPU Time 124.88 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 452413.79 μs
Device Time 0.00 μs
Self CPU Time 452413.79 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 37857.02 μs
Device Time 27299.33 μs
Self CPU Time 37857.02 μs
Self Device Time 27299.33 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv2d_unroll_kernel(float const*, float const*, float*, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 4477927.68 μs
Self CPU Time 0.00 μs
Self Device Time 4477927.68 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 4722433.30 μs
Device Time 392.22 μs
Self CPU Time 4722433.30 μs
Self Device Time 392.22 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 41513.96 μs
Device Time 287523.93 μs
Self CPU Time 8137.93 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 33377.93 μs
Device Time 287523.93 μs
Self CPU Time 10421.00 μs
Self Device Time 287523.93 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 287523.93 μs
Self CPU Time 0.00 μs
Self Device Time 287523.93 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 
45297 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_s3_conv2d_unrolled_shared/base/base.cu:14:5 bugprone-easily-swappable-parameters
14 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:14:31: note: the first parameter in the range is 'input'
14 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:15:31: note: the last parameter in the range is 'weight'
15 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:17:5: warning: 3 adjacent parameters of 'conv2d_unroll_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
18 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
19 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:17:15: note: the first parameter in the range is 'batch_size'
17 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:19:15: note: the last parameter in the range is 'out_channels'
19 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:21:5: warning: 2 adjacent parameters of 'conv2d_unroll_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | const int input_width,
| ^~~~~~~~~~~~~~~~~~~~~~
22 | const int output_height,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:21:15: note: the first parameter in the range is 'input_width'
21 | const int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:22:15: note: the last parameter in the range is 'output_height'
22 | const int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:23:5: warning: 3 adjacent parameters of 'conv2d_unroll_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | const int output_width,
| ^~~~~~~~~~~~~~~~~~~~~~~
24 | const int stride,
| ~~~~~~~~~~~~~~~~~
25 | const int padding) {
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:23:15: note: the first parameter in the range is 'output_width'
23 | const int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:25:15: note: the last parameter in the range is 'padding'
25 | const int padding) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:28:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int b_oc = blockIdx.z; // blockIdx.z ranges over (batch_size * out_channels)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:32:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | int bx = blockIdx.x * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:33:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
33 | int by = blockIdx.y * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:35:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:36:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:93:19: 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]
93 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:94:19: 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]
94 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:97:5: warning: 3 adjacent parameters of 'forward' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
97 | int padding,
| ^~~~~~~~~~~~
98 | int dilation,
| ~~~~~~~~~~~~~
99 | int groups) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:97:9: note: the first parameter in the range is 'padding'
97 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:99:9: note: the last parameter in the range is 'groups'
99 | int groups) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:107:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
107 | int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:108:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
108 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:109:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
109 | int input_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:110:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | int input_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b7_s3_conv2d_unrolled_shared/base/base.cu:111:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | int out_channels = weight.size(0);
| ^