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63_conv_standard_2D__square_input__square_kernel • balanced_conv2d_cuda_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 balanced_conv2d_cuda_base
CUDA Speedup (Native) 0.159x
CUDA Speedup (Compile) 0.267x
CUDA Runtime 1.443 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 TILE_SIZE 4
#define KERNEL_SIZE 3
#define WARPS_PER_BLOCK 8
#define SHARED_SIZE ((BLOCK_SIZE * TILE_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[SHARED_SIZE][SHARED_SIZE];
    __shared__ float shared_weight[KERNEL_SIZE][KERNEL_SIZE];
    
    const int tx = threadIdx.x % BLOCK_SIZE;
    const int ty = threadIdx.y % BLOCK_SIZE;
    const int tid = threadIdx.y * blockDim.x + threadIdx.x;
    const int warp_id = tid / 32;
    
    const int bx = blockIdx.x * (BLOCK_SIZE * TILE_SIZE);
    const int by = blockIdx.y * (BLOCK_SIZE * TILE_SIZE);
    const int b = blockIdx.z / ((out_channels + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);
    const int oc_block = (blockIdx.z % ((out_channels + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK)) * WARPS_PER_BLOCK;
    
    float partial_sums[TILE_SIZE][TILE_SIZE] = {0.0f};
    
    for (int oc_offset = 0; oc_offset < WARPS_PER_BLOCK && (oc_block + oc_offset) < out_channels; ++oc_offset) {
        const int oc = oc_block + oc_offset;
        
        for (int ic = 0; ic < in_channels; ++ic) {
            if (tid < KERNEL_SIZE * KERNEL_SIZE) {
                int kid = tid;
                int kh = kid / KERNEL_SIZE;
                int kw = kid % KERNEL_SIZE;
                shared_weight[kh][kw] = weight[((oc * in_channels + ic) * KERNEL_SIZE + kh) * KERNEL_SIZE + kw];
            }
            __syncthreads();
            
            #pragma unroll
            for (int i = 0; i < SHARED_SIZE; i += blockDim.y) {
                #pragma unroll
                for (int j = 0; j < SHARED_SIZE; j += blockDim.x) {
                    int load_y = i + ty;
                    int load_x = j + tx;
                    if (load_y < SHARED_SIZE && load_x < SHARED_SIZE) {
                        int ih = by + load_y - padding;
                        int iw = bx + load_x - padding;
                        
                        float val = 0.0f;
                        if (ih >= 0 && ih < input_height && iw >= 0 && iw < input_width) {
                            val = input[((b * in_channels + ic) * input_height + ih) * input_width + iw];
                        }
                        shared_input[load_y][load_x] = val;
                    }
                }
            }
            __syncthreads();
            
            #pragma unroll
            for (int ti = 0; ti < TILE_SIZE; ++ti) {
                #pragma unroll
                for (int tj = 0; tj < TILE_SIZE; ++tj) {
                    float sum = 0.0f;
                    
                    #pragma unroll
                    for (int kh = 0; kh < KERNEL_SIZE; ++kh) {
                        #pragma unroll
                        for (int kw = 0; kw < KERNEL_SIZE; ++kw) {
                            int sy = ty * TILE_SIZE + ti * stride + kh;
                            int sx = tx * TILE_SIZE + tj * stride + kw;
                            sum += shared_input[sy][sx] * shared_weight[kh][kw];
                        }
                    }
                    partial_sums[ti][tj] += sum;
                }
            }
            __syncthreads();
        }
        
        #pragma unroll
        for (int ti = 0; ti < TILE_SIZE; ++ti) {
            #pragma unroll
            for (int tj = 0; tj < TILE_SIZE; ++tj) {
                int out_y = by + ty * TILE_SIZE + ti;
                int out_x = bx + tx * TILE_SIZE + tj;
                
                if (out_y < output_height && out_x < output_width) {
                    output[((b * out_channels + oc) * output_height + out_y) * output_width + out_x] = partial_sums[ti][tj];
                }
            }
        }
        
        #pragma unroll
        for (int ti = 0; ti < TILE_SIZE; ++ti) {
            #pragma unroll
            for (int tj = 0; tj < TILE_SIZE; ++tj) {
                partial_sums[ti][tj] = 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() && x.is_contiguous(), "Input must be a contiguous CUDA tensor");
    TORCH_CHECK(weight.is_cuda() && weight.is_contiguous(), "Weight must be a contiguous CUDA tensor");
    
    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 * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
        (output_height + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
        batch_size * ((out_channels + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK)
    );
    
    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, "Balanced CUDA conv2d implementation");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.270 inst/cycle 0.000 5
Executed Ipc Elapsed 1.224 inst/cycle 0.000 5
Issue Slots Busy 31.850 % 0.000 5
Issued Ipc Active 1.270 inst/cycle 0.000 5
SM Busy 31.850 % 0.000 5
Memory Throughput 146193803852.770 byte/second 38514176958810368.000 5
Mem Busy 75.612 % 0.008 5
Max Bandwidth 24.914 % 0.001 5
L1/TEX Hit Rate 37.906 % 0.002 5
L2 Hit Rate 98.154 % 0.008 5
Mem Pipes Busy 24.914 % 0.001 5
Warp Cycles Per Issued Instruction 23.494 cycle 0.000 5
Warp Cycles Per Executed Instruction 23.498 cycle 0.000 5
Avg. Active Threads Per Warp 29.780 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.880 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 4.000 block 0.000 5
Block Limit Shared Mem 7.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 32.000 warp 0.000 5
Theoretical Occupancy 50.000 % 0.000 5
Achieved Occupancy 46.764 % 0.000 5
Achieved Active Warps Per SM 29.928 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (24.3%) 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.
WRN Occupancy This kernel's theoretical occupancy (50.0%) 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 345586.27 μs
Device Time 1209.56 μs
Self CPU Time 65.28 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 4416617.48 μs
Device Time 8329.95 μs
Self CPU Time 4416617.48 μs
Self Device Time 8329.95 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 4687413.79 μs
Self CPU Time 0.00 μs
Self Device Time 4687413.79 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 399051.76 μs
Device Time 78.85 μs
Self CPU Time 399051.76 μs
Self Device Time 78.85 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 9612.66 μs
Device Time 16494.30 μs
Self CPU Time 9612.66 μs
Self Device Time 16494.30 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 4283024.15 μs
Device Time 255216.91 μs
Self CPU Time 10299.66 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 4272726.83 μs
Device Time 255216.91 μs
Self CPU Time 16826.64 μs
Self Device Time 255216.91 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 255216.91 μs
Self CPU Time 0.00 μs
Self Device Time 255216.91 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 
45310 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/b9_s2_balanced_conv2d_cuda/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/b9_s2_balanced_conv2d_cuda/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/b9_s2_balanced_conv2d_cuda/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/b9_s2_balanced_conv2d_cuda/base/base.cu:15:5: warning: 3 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
16 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
17 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:15:15: note: the first parameter in the range is 'batch_size'
15 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:17:15: note: the last parameter in the range is 'out_channels'
17 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/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 input_width,
| ^~~~~~~~~~~~~~~~~~~~~~
20 | const int output_height,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:19:15: note: the first parameter in the range is 'input_width'
19 | const int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:20:15: note: the last parameter in the range is 'output_height'
20 | const int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:21:5: warning: 3 adjacent parameters of 'conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | const int output_width,
| ^~~~~~~~~~~~~~~~~~~~~~~
22 | const int stride,
| ~~~~~~~~~~~~~~~~~
23 | const int padding) {
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:21:15: note: the first parameter in the range is 'output_width'
21 | const int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:23:15: note: the last parameter in the range is 'padding'
23 | const int padding) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:28:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | const int tx = threadIdx.x % BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:29:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | const int ty = threadIdx.y % BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:30:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | const int tid = threadIdx.y * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:31:15: warning: Value stored to 'warp_id' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
31 | const int warp_id = tid / 32;
| ^~~~~~~ ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:31:15: note: Value stored to 'warp_id' during its initialization is never read
31 | const int warp_id = tid / 32;
| ^~~~~~~ ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:33:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
33 | const int bx = blockIdx.x * (BLOCK_SIZE * TILE_SIZE);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:34:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
34 | const int by = blockIdx.y * (BLOCK_SIZE * TILE_SIZE);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:35:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | const int b = blockIdx.z / ((out_channels + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:36:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | const int oc_block = (blockIdx.z % ((out_channels + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK)) * WARPS_PER_BLOCK;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:53:51: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
53 | for (int i = 0; i < SHARED_SIZE; i += blockDim.y) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:55:55: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
55 | for (int j = 0; j < SHARED_SIZE; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:117:19: 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]
117 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:118:19: 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]
118 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:121:5: warning: 3 adjacent parameters of 'forward' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
121 | int padding,
| ^~~~~~~~~~~~
122 | int dilation,
| ~~~~~~~~~~~~~
123 | int groups) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:121:9: note: the first parameter in the range is 'padding'
121 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:123:9: note: the last parameter in the range is 'groups'
123 | int groups) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:134:42: 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]
134 | 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/b9_s2_balanced_conv2d_cuda/base/base.cu:134:42: 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/b9_s2_balanced_conv2d_cuda/base/base.cu:134:42: note: perform multiplication in a wider type
134 | 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/b9_s2_balanced_conv2d_cuda/base/base.cu:135:40: 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]
135 | 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/b9_s2_balanced_conv2d_cuda/base/base.cu:135:40: note: make conversion explicit to silence this warning
135 | 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/b9_s2_balanced_conv2d_cuda/base/base.cu:135:40: note: perform multiplication in a wider type
135 | 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/b9_s2_balanced_conv2d_cuda/base/base.cu:142:25: 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]
142 | (output_width + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:142:25: note: make conversion explicit to silence this warning
142 | (output_width + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:142:25: note: perform multiplication in a wider type
142 | (output_width + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:142:56: 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]
142 | (output_width + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:142:56: note: make conversion explicit to silence this warning
142 | (output_width + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:142:56: note: perform multiplication in a wider type
142 | (output_width + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:143:26: 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]
143 | (output_height + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:143:26: note: make conversion explicit to silence this warning
143 | (output_height + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:143:26: note: perform multiplication in a wider type
143 | (output_height + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:143:57: 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]
143 | (output_height + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:143:57: note: make conversion explicit to silence this warning
143 | (output_height + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:143:57: note: perform multiplication in a wider type
143 | (output_height + BLOCK_SIZE * TILE_SIZE - 1) / (BLOCK_SIZE * TILE_SIZE),
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:151:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
151 | batch_size,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:152:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
152 | in_channels,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:153:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
153 | out_channels,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:154:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
154 | input_height,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:155:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
155 | input_width,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:156:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
156 | output_height,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s2_balanced_conv2d_cuda/base/base.cu:157:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
157 | output_width,
| ^