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63_conv_standard_2D__square_input__square_kernel • conv2d_shared_atomic_minimized_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_atomic_minimized_base
CUDA Speedup (Native) 0.166x
CUDA Speedup (Compile) 0.278x
CUDA Runtime 1.385 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 CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)

// This kernel uses shared memory to load the weights for a given output channel once per block.
// Each block is mapped to a tile of output pixels for a specific (batch, out_channel) pair.
// Each thread computes one output element by iterating over all input channels and the kernel window.
// Since each thread writes to a unique output element, no atomic operations on global memory are used.
// Atomic operations would only be used if multiple threads needed to update the same global variable, but here that race is avoided.

__global__ void conv2d_shared_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    float* __restrict__ output,
    int N,
    int Cin,
    int H,
    int W,
    int Cout,
    int K,
    int outH,
    int outW,
    int stride,
    int padding) {

    // Decode blockIdx.z into batch index and output channel
    int b_oc = blockIdx.z; // ranges over N * Cout
    int n = b_oc / Cout;
    int oc = b_oc % Cout;

    // Compute output tile offsets
    int ox0 = blockIdx.x * blockDim.x;
    int oy0 = blockIdx.y * blockDim.y;
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    int ox = ox0 + tx;
    int oy = oy0 + ty;

    // Allocate shared memory for the weight for this output channel
    // Size: Cin * K * K
    extern __shared__ float shared_weight[];
    int total_weight = Cin * K * K;
    int tid = ty * blockDim.x + tx;
    for (int i = tid; i < total_weight; i += blockDim.x * blockDim.y) {
        shared_weight[i] = weight[((oc * Cin) * K * K) + i];
    }
    __syncthreads();

    float sum = 0.0f;
    if (ox < outW && oy < outH) {
        // Loop over all input channels and kernel elements
        for (int c = 0; c < Cin; ++c) {
            for (int kh = 0; kh < K; ++kh) {
                for (int kw = 0; kw < K; ++kw) {
                    int in_y = oy * stride - padding + kh;
                    int in_x = ox * stride - padding + kw;
                    if (in_y >= 0 && in_y < H && in_x >= 0 && in_x < W) {
                        int input_idx = ((n * Cin + c) * H + in_y) * W + in_x;
                        int w_idx = (c * K + kh) * K + kw;
                        sum += input[input_idx] * shared_weight[w_idx];
                    }
                }
            }
        }
    }

    if (ox < outW && oy < outH) {
        int output_idx = ((n * Cout + oc) * outH + oy) * outW + ox;
        output[output_idx] = sum;
    }
}


// Host function forwarding the convolution operation
// Assumes square input and square kernel. Dilation is not used in this kernel.

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor weight,
    torch::optional<torch::Tensor> bias,
    int stride,
    int padding,
    int dilation, // dilation is not applied in this kernel
    int groups) {

    CHECK_INPUT(x);
    CHECK_INPUT(weight);
    if (bias.has_value()) {
        CHECK_INPUT(bias.value());
    }
    TORCH_CHECK(groups == 1, "groups != 1 is not supported by this kernel");

    int N = x.size(0);
    int Cin = x.size(1);
    int H = x.size(2);
    int W = x.size(3);
    int Cout = weight.size(0);
    int K = weight.size(2); // assuming square kernel (K x K)

    int outH = (H + 2 * padding - K) / stride + 1;
    int outW = (W + 2 * padding - K) / stride + 1;

    auto output = torch::empty({N, Cout, outH, outW}, x.options());

    // Define block dimensions; each thread computes one output element
    dim3 blockDim(16, 16);
    dim3 gridDim((outW + blockDim.x - 1) / blockDim.x,
                 (outH + blockDim.y - 1) / blockDim.y,
                 N * Cout);

    // Allocate shared memory for weights: size is Cin * K * K floats per block
    size_t shared_mem_size = sizeof(float) * Cin * K * K;

    conv2d_shared_kernel<<<gridDim, blockDim, shared_mem_size>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        output.data_ptr<float>(),
        N, Cin, H, W, Cout, K, outH, outW, 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 shared memory for weights");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.530 inst/cycle 0.000 5
Executed Ipc Elapsed 3.520 inst/cycle 0.000 5
Issue Slots Busy 88.194 % 0.000 5
Issued Ipc Active 3.530 inst/cycle 0.000 5
SM Busy 88.194 % 0.000 5
Memory Throughput 151389686940.316 byte/second 18230194542411916.000 5
Mem Busy 52.794 % 0.000 5
Max Bandwidth 34.766 % 0.000 5
L1/TEX Hit Rate 84.590 % 0.000 5
L2 Hit Rate 98.034 % 0.001 5
Mem Pipes Busy 48.524 % 0.000 5
Warp Cycles Per Issued Instruction 15.670 cycle 0.000 5
Warp Cycles Per Executed Instruction 15.670 cycle 0.000 5
Avg. Active Threads Per Warp 31.750 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.720 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 28.000 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 86.540 % 0.000 5
Achieved Active Warps Per SM 55.386 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (55.4%) 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 is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (86.5%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. 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 239704.91 μs
Device Time 1283.25 μs
Self CPU Time 54.98 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 239649.93 μs
Device Time 1283.25 μs
Self CPU Time 100.64 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::empty_strided
CPU Time 237998.82 μs
Device Time 0.00 μs
Self CPU Time 116.32 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 237252.45 μs
Device Time 0.00 μs
Self CPU Time 237252.45 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 34041.73 μs
Device Time 24540.66 μs
Self CPU Time 34041.73 μs
Self Device Time 24540.66 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv2d_shared_kernel(float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 4644804.41 μs
Self CPU Time 0.00 μs
Self Device Time 4644804.41 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 4869419.59 μs
Device Time 547.48 μs
Self CPU Time 4869419.59 μs
Self Device Time 547.48 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 38000.84 μs
Device Time 260569.75 μs
Self CPU Time 6755.13 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 31247.73 μs
Device Time 260569.75 μs
Self CPU Time 10515.55 μs
Self Device Time 260569.75 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 260569.75 μs
Self CPU Time 0.00 μs
Self Device Time 260569.75 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/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:5:35 bugprone-macro-parentheses
5 | #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
| ^
| ()
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:6:41: warning: macro argument should be enclosed in parentheses [bugprone-macro-parentheses]
6 | #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
| ^
| ()
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:16:5: warning: 2 adjacent parameters of 'conv2d_shared_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
17 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:16:31: note: the first parameter in the range is 'input'
16 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:17:31: note: the last parameter in the range is 'weight'
17 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:19:5: warning: 2 adjacent parameters of 'conv2d_shared_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int N,
| ^~~~~~
20 | int Cin,
| ~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:19:9: note: the first parameter in the range is 'N'
19 | int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:20:9: note: the last parameter in the range is 'Cin'
20 | int Cin,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:22:5: warning: 3 adjacent parameters of 'conv2d_shared_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
22 | int W,
| ^~~~~~
23 | int Cout,
| ~~~~~~~~~
24 | int K,
| ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:22:9: note: the first parameter in the range is 'W'
22 | int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:24:9: note: the last parameter in the range is 'K'
24 | int K,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:26:5: warning: 2 adjacent parameters of 'conv2d_shared_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
26 | int outW,
| ^~~~~~~~~
27 | int stride,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:26:9: note: the first parameter in the range is 'outW'
26 | int outW,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:27:9: note: the last parameter in the range is 'stride'
27 | int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:31:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int b_oc = blockIdx.z; // ranges over N * Cout
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:36:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | int ox0 = blockIdx.x * blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:37:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
37 | int oy0 = blockIdx.y * blockDim.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:38:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:39:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:47:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
47 | int tid = ty * blockDim.x + tx;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:48:46: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
48 | for (int i = tid; i < total_weight; i += blockDim.x * blockDim.y) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:82:19: 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]
82 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:83:19: 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]
83 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:86:5: warning: 3 adjacent parameters of 'forward' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
86 | int padding,
| ^~~~~~~~~~~~
87 | int dilation, // dilation is not applied in this kernel
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
88 | int groups) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:86:9: note: the first parameter in the range is 'padding'
86 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:88:9: note: the last parameter in the range is 'groups'
88 | int groups) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:97:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
97 | int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:98:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | int Cin = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:99:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | int H = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:100:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | int W = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:101:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | int Cout = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b6_s2_conv2d_shared_atomic_minimized/base/base.cu:102:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | int K = weight.size(2); // assuming square kernel (K x K)
| ^