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63_conv_standard_2D__square_input__square_kernel • mod_conv2d_kernel_modular_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 mod_conv2d_kernel_modular_base
CUDA Speedup (Native) 0.191x
CUDA Speedup (Compile) 0.321x
CUDA Runtime 1.202 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
#define SHARED_SIZE (BLOCK_SIZE + KERNEL_SIZE - 1)

// Device function: Load a 3x3 tile from the weight tensor into shared memory
__device__ inline void load_weight_tile(const float* weight, int oc, int ic, int in_channels, float shared_weight[KERNEL_SIZE][KERNEL_SIZE]) {
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    if (tx < KERNEL_SIZE && ty < KERNEL_SIZE) {
        int weight_idx = ((oc * in_channels + ic) * KERNEL_SIZE + ty) * KERNEL_SIZE + tx;
        shared_weight[ty][tx] = weight[weight_idx];
    }
}

// Device function: Load a tile of the input channel into shared memory
// Template parameter BS is the block size
template <int BS>
__device__ inline void load_input_tile(const float* input, int b, int ic, int in_channels,
                                         int input_height, int input_width, int padding,
                                         int block_y, int block_x,
                                         float shared_input[SHARED_SIZE][SHARED_SIZE]) {
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    for (int i = ty; i < SHARED_SIZE; i += BS) {
        for (int j = tx; j < SHARED_SIZE; j += BS) {
            int ih = block_y + i - padding;
            int iw = block_x + 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;
            }
        }
    }
}

// Device function: Compute convolution for one output element using data in shared memory
__device__ inline float compute_convolution(const float shared_input[SHARED_SIZE][SHARED_SIZE],
                                              const float shared_weight[KERNEL_SIZE][KERNEL_SIZE],
                                              int tx, int ty, int stride) {
    float sum = 0.0f;
    #pragma unroll
    for (int i = 0; i < KERNEL_SIZE; ++i) {
        #pragma unroll
        for (int j = 0; j < KERNEL_SIZE; ++j) {
            sum += shared_input[ty * stride + i][tx * stride + j] * shared_weight[i][j];
        }
    }
    return sum;
}

// Modular convolution kernel that leverages shared memory and device functions
__global__ void mod_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];

    int bx = blockIdx.x * BLOCK_SIZE;
    int by = blockIdx.y * BLOCK_SIZE;
    int b = blockIdx.z;
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    int x_out = bx + tx;
    int y_out = by + ty;

    // Loop over each output channel
    for (int oc = 0; oc < out_channels; ++oc) {
        float sum = 0.0f;
        // Accumulate contributions from all input channels
        for (int ic = 0; ic < in_channels; ++ic) {
            // Load weight tile for current oc and ic
            load_weight_tile(weight, oc, ic, in_channels, shared_weight);
            __syncthreads();
            
            // Load corresponding input tile for current channel into shared memory
            load_input_tile<BLOCK_SIZE>(input, b, ic, in_channels, input_height, input_width, padding, by, bx, shared_input);
            __syncthreads();
            
            // If within output bounds, compute convolution using shared memory
            if (x_out < output_width && y_out < output_height) {
                sum += compute_convolution(shared_input, shared_weight, tx, ty, stride);
            }
            __syncthreads();
        }
        // Write the accumulated result to the output tensor
        if (x_out < output_width && y_out < output_height) {
            int out_idx = ((b * out_channels + oc) * output_height + y_out) * output_width + x_out;
            output[out_idx] = sum;
        }
        __syncthreads();
    }
}

// PyTorch binding function
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, "Kernel size must be 3x3.");

    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);

    mod_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, "Modular adaptive CUDA conv2d implementation");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.520 inst/cycle 0.000 5
Executed Ipc Elapsed 2.458 inst/cycle 0.000 5
Issue Slots Busy 63.234 % 0.000 5
Issued Ipc Active 2.530 inst/cycle 0.000 5
SM Busy 63.234 % 0.000 5
Memory Throughput 180131104431.756 byte/second 55081762440383920.000 5
Mem Busy 83.172 % 0.014 5
Max Bandwidth 47.920 % 0.004 5
L1/TEX Hit Rate 75.246 % 0.003 5
L2 Hit Rate 94.194 % 0.012 5
Mem Pipes Busy 47.920 % 0.004 5
Warp Cycles Per Issued Instruction 23.488 cycle 0.000 5
Warp Cycles Per Executed Instruction 23.548 cycle 0.000 5
Avg. Active Threads Per Warp 27.600 0.000 5
Avg. Not Predicated Off Threads Per Warp 25.550 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 92.816 % 0.001 5
Achieved Active Warps Per SM 59.402 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (22.2%) 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 214304.51 μs
Device Time 1223.49 μs
Self CPU Time 51.24 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 4716390.89 μs
Device Time 10442.65 μs
Self CPU Time 4716390.89 μs
Self Device Time 10442.65 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
mod_conv2d_kernel(float const*, float const*, float*, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 4845694.63 μs
Self CPU Time 0.00 μs
Self Device Time 4845694.63 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 336399.06 μs
Device Time 78.46 μs
Self CPU Time 336399.06 μs
Self Device Time 78.46 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 12298.68 μs
Device Time 20653.48 μs
Self CPU Time 12298.68 μs
Self Device Time 20653.48 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 4507240.22 μs
Device Time 316334.15 μs
Self CPU Time 9176.64 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 4498068.36 μs
Device Time 316334.15 μs
Self CPU Time 12442.23 μs
Self Device Time 316334.15 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 316334.15 μs
Self CPU Time 0.00 μs
Self Device Time 316334.15 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 
45302 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_s3_mod_conv2d_kernel_modular/base/base.cu:11:14 bugprone-narrowing-conversions
11 | int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:12:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
12 | int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:23:60: warning: 2 adjacent parameters of 'load_input_tile' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | int input_height, int input_width, int padding,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:23:64: note: the first parameter in the range is 'input_width'
23 | int input_height, int input_width, int padding,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:23:81: note: the last parameter in the range is 'padding'
23 | int input_height, int input_width, int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:26:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:27:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:57:35: warning: 2 adjacent parameters of 'mod_conv2d_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
57 | __global__ void mod_conv2d_kernel(const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
58 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:57:61: note: the first parameter in the range is 'input'
57 | __global__ void mod_conv2d_kernel(const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:58:63: note: the last parameter in the range is 'weight'
58 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:60:37: warning: 3 adjacent parameters of 'mod_conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
60 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
61 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
62 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:60:47: note: the first parameter in the range is 'batch_size'
60 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:62:47: note: the last parameter in the range is 'out_channels'
62 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:64:37: warning: 2 adjacent parameters of 'mod_conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
64 | const int input_width,
| ^~~~~~~~~~~~~~~~~~~~~~
65 | const int output_height,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:64:47: note: the first parameter in the range is 'input_width'
64 | const int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:65:47: note: the last parameter in the range is 'output_height'
65 | const int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:66:37: warning: 3 adjacent parameters of 'mod_conv2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
66 | const int output_width,
| ^~~~~~~~~~~~~~~~~~~~~~~
67 | const int stride,
| ~~~~~~~~~~~~~~~~~
68 | const int padding) {
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:66:47: note: the first parameter in the range is 'output_width'
66 | const int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:68:47: note: the last parameter in the range is 'padding'
68 | const int padding) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:72:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
72 | int bx = blockIdx.x * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:73:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
73 | int by = blockIdx.y * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:74:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
74 | int b = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:75:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
75 | int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:76:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
76 | int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:109:37: 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]
109 | torch::Tensor forward(torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:110:37: 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]
110 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:113:23: warning: 3 adjacent parameters of 'forward' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
113 | int padding,
| ^~~~~~~~~~~~
114 | int dilation,
| ~~~~~~~~~~~~~
115 | int groups) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:113:27: note: the first parameter in the range is 'padding'
113 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:115:27: note: the last parameter in the range is 'groups'
115 | int groups) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:120:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:121:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:122:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
122 | int input_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:123:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
123 | int input_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b9_s3_mod_conv2d_kernel_modular/base/base.cu:124:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | int out_channels = weight.size(0);
| ^