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76_conv_standard_1D_dilated_strided__ • hybrid_conv1d_kernel_base

Level 1 • Task 76
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,
    dilation: int,
) -> torch.Tensor:
    """
    Performs a standard 1D convolution operation with asymmetric input and a square kernel, potentially dilated and strided.


    Args:
        x (torch.Tensor): Input tensor.
        weight (torch.Tensor): Weight tensor.
        bias (torch.Tensor): Bias tensor.
        stride (int): Stride of the convolution.
        dilation (int): Dilation of the convolution.

    Returns:
        torch.Tensor: Output tensor.
    """
    return F.conv1d(x, weight, bias=bias, stride=stride, dilation=dilation)


class Model(nn.Module):
    """
    Performs a standard 1D convolution operation with asymmetric input and a square kernel, potentially dilated and strided.

    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.
        dilation (int): Spacing between kernel elements.
        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,
        dilation: int,
        bias: bool,
    ):
        super(Model, self).__init__()
        conv = nn.Conv1d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            dilation=dilation,
            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.dilation = dilation

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Performs the 1D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, length).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, length_out).
        """
        return fn(x, self.weight, self.bias, self.stride, self.dilation)


# Constants
batch_size = 16
in_channels = 3
out_channels = 64
kernel_size = 3
length = 256
stride = 3
dilation = 4
bias = False


def get_inputs():
    x = torch.randn(batch_size, in_channels, length)
    return [x]


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, dilation, bias]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Performs a standard 1D convolution operation with asymmetric input and a square kernel, potentially dilated and strided.

    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.
        dilation (int, optional): Spacing between kernel elements. 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,
        dilation: int = 1,
        bias: bool = False,
    ):
        super(Model, self).__init__()
        self.conv1d = nn.Conv1d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            dilation=dilation,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 1D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, length).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, length_out).
        """
        return self.conv1d(x)


# Constants
batch_size = 16
in_channels = 3
out_channels = 64
kernel_size = 3
length = 256
stride = 3
dilation = 4
bias = False


def get_inputs():
    x = torch.randn(batch_size, in_channels, length)
    return [x]


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, dilation, bias]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 76_conv_standard_1D_dilated_strided__
Level ID 1
Task ID 76
Kernel Name hybrid_conv1d_kernel_base
CUDA Speedup (Native) 1.647x
CUDA Speedup (Compile) 7.321x
CUDA Runtime 0.008 ms
PyTorch Runtime (Native) 0.013 ms
PyTorch Runtime (Compile) 0.059 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.50
View Experiment Progress Details

Related Kernels (Level 1, Task 76 • 76_conv_standard_1D_dilated_strided__)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 conv1d_warp_uniform_base_base 0.01 1.88 8.37
🥇 optimized_conv1d_base 0.01 1.88 8.37
🥇 76_conv_1d_branchless_base 0.01 1.88 8.37
🥇 76_conv_1d_branchless_edit_1 0.01 1.88 8.37
🥇 modular_conv1d_base 0.01 1.88 8.37
6 conv1d_grid_stride_base 0.01 1.65 7.32
6 conv1d_unrolled_base 0.01 1.65 7.32
6 76_conv_standard_1D_dilated_strided_optimal_block_base 0.01 1.65 7.32
6 conv1d_ldg_align_opt_base 0.01 1.65 7.32
6 76_conv_standard_1D_dilated_strided__ 0.01 1.65 7.32
6 conv1d_blocksize_512_base 0.01 1.65 7.32
6 conv1d_shared_opt_base 0.01 1.65 7.32
6 conv1d_stride_loop_opt_base 0.01 1.65 7.32
6 grid_strided_conv1d_base 0.01 1.65 7.32
6 conv1d_memory_coalesce_base 0.01 1.65 7.32
6 conv1d_stride_loop_opt_base_base 0.01 1.65 7.32
6 hybrid_conv1d_kernel_base 0.01 1.65 7.32
6 conv1d_optimized_base 0.01 1.65 7.32
6 conv1d_ldg_optimized_base 0.01 1.65 7.32
6 conv1d_strided_loop_opt_base 0.01 1.65 7.32 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

// Hybrid kernel that combines shared memory for weights and loop unrolling
__global__ void conv1d_kernel_hybrid(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int in_channels,
    int in_size,
    int out_size,
    int kernel_size,
    int stride,
    int dilation
) {
    int b = blockIdx.x;
    int oc = blockIdx.y;
    
    // Shared memory for weights
    extern __shared__ float sweight[];
    int filter_size = in_channels * kernel_size;
    
    // Cooperatively load weights into shared memory
    #pragma unroll 4
    for (int i = threadIdx.x; i < filter_size; i += blockDim.x) {
        sweight[i] = weight[oc * filter_size + i];
    }
    __syncthreads();
    
    float bias_val = (bias != nullptr) ? bias[oc] : 0.0f;
    
    // Grid-stride loop over output positions
    for (int o = threadIdx.x; o < out_size; o += blockDim.x) {
        float sum = 0.0f;
        
        // Loop over input channels
        for (int ic = 0; ic < in_channels; ++ic) {
            // Unroll kernel loop for better instruction-level parallelism
            #pragma unroll
            for (int k = 0; k < kernel_size; ++k) {
                int input_pos = o * stride + k * dilation;
                if (input_pos < in_size) {
                    int x_idx = b * (in_channels * in_size) + ic * in_size + input_pos;
                    sum += x[x_idx] * sweight[ic * kernel_size + k];
                }
            }
        }
        
        sum += bias_val;
        int out_idx = b * (gridDim.y * out_size) + oc * out_size + o;
        output[out_idx] = sum;
    }
}

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor weight,
    torch::optional<torch::Tensor> bias,
    int stride,
    int dilation
) {
    TORCH_CHECK(x.device().is_cuda(), "x must be a CUDA tensor");
    TORCH_CHECK(weight.device().is_cuda(), "weight must be a CUDA tensor");
    TORCH_CHECK(x.is_contiguous(), "x must be contiguous");
    TORCH_CHECK(weight.is_contiguous(), "weight must be contiguous");
    TORCH_CHECK(x.dim() == 3, "x must be 3D");
    TORCH_CHECK(weight.dim() == 3, "weight must be 3D");
    TORCH_CHECK(weight.size(1) == x.size(1), "Input channels mismatch");

    if (bias.has_value()) {
        TORCH_CHECK(bias->device().is_cuda(), "bias must be a CUDA tensor");
        TORCH_CHECK(bias->is_contiguous(), "bias must be contiguous");
        TORCH_CHECK(bias->dim() == 1, "bias must be 1D");
        TORCH_CHECK(bias->size(0) == weight.size(0), "Bias size mismatch");
    }

    int B = x.size(0);
    int in_channels = x.size(1);
    int in_size = x.size(2);
    int out_channels = weight.size(0);
    int kernel_size = weight.size(2);

    int out_size = (in_size - dilation * (kernel_size - 1) - 1) / stride + 1;
    TORCH_CHECK(out_size > 0, "Invalid output size");

    auto output = torch::empty({B, out_channels, out_size}, x.options());
    if (output.numel() == 0) return output;

    const float* x_data = x.data_ptr<float>();
    const float* weight_data = weight.data_ptr<float>();
    const float* bias_data = bias.has_value() ? bias.value().data_ptr<float>() : nullptr;
    float* output_data = output.data_ptr<float>();

    dim3 blocks(B, out_channels);
    int threads = 256;
    int shared_mem_size = in_channels * kernel_size * sizeof(float);

    conv1d_kernel_hybrid<<<blocks, threads, shared_mem_size>>>(
        x_data,
        weight_data,
        bias_data,
        output_data,
        in_channels,
        in_size,
        out_size,
        kernel_size,
        stride,
        dilation
    );

    cudaError_t err = cudaGetLastError();
    TORCH_CHECK(err == cudaSuccess, "Kernel launch error: ", cudaGetErrorString(err));

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "1D convolution forward (CUDA) with hybrid optimization");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.072 inst/cycle 0.001 5
Executed Ipc Elapsed 0.618 inst/cycle 0.000 5
Issue Slots Busy 28.462 % 0.694 5
Issued Ipc Active 1.138 inst/cycle 0.001 5
SM Busy 28.462 % 0.694 5
Memory Throughput 9684066121.200 byte/second 7902431820488384.000 5
Mem Busy 7.202 % 0.008 5
Max Bandwidth 5.716 % 0.004 5
L1/TEX Hit Rate 80.620 % 0.000 5
L2 Hit Rate 98.880 % 0.158 5
Mem Pipes Busy 17.560 % 0.017 5
Warp Cycles Per Issued Instruction 27.418 cycle 0.306 5
Warp Cycles Per Executed Instruction 29.108 cycle 0.361 5
Avg. Active Threads Per Warp 28.190 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.050 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 49.722 % 1.306 5
Achieved Active Warps Per SM 31.822 warp 0.534 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
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 (49.0%) 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.
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.
Operation / Metric Value Unit
aten::to
CPU Time 588755.62 μs
Device Time 6.46 μs
Self CPU Time 62.27 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 588693.35 μs
Device Time 6.46 μs
Self CPU Time 112.45 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
aten::empty_strided
CPU Time 588434.26 μs
Device Time 0.00 μs
Self CPU Time 111.85 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 587151.81 μs
Device Time 0.00 μs
Self CPU Time 587151.81 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 160665.79 μs
Device Time 235.17 μs
Self CPU Time 160665.79 μs
Self Device Time 235.17 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv1d_kernel_hybrid(float const*, float const*, float const*, float*, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 1916.31 μs
Self CPU Time 0.00 μs
Self Device Time 1916.31 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 775.12 μs
Device Time 2202.03 μs
Self CPU Time 775.12 μs
Self Device Time 2202.03 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 145329.99 μs
Device Time 27777.58 μs
Self CPU Time 573.35 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 144761.43 μs
Device Time 27777.58 μs
Self CPU Time 711.07 μs
Self Device Time 27777.58 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 27777.58 μs
Self CPU Time 0.00 μs
Self Device Time 27777.58 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 
45296 warnings generated when compiling for host.
Suppressed 45325 warnings (45278 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_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:7:5 bugprone-easily-swappable-parameters
7 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
9 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:7:31: note: the first parameter in the range is 'x'
7 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:9:31: note: the last parameter in the range is 'bias'
9 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:12:5: warning: 4 adjacent parameters of 'conv1d_kernel_hybrid' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | int in_size,
| ^~~~~~~~~~~~
13 | int out_size,
| ~~~~~~~~~~~~~
14 | int kernel_size,
| ~~~~~~~~~~~~~~~~
15 | int stride,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:12:9: note: the first parameter in the range is 'in_size'
12 | int in_size,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:15:9: note: the last parameter in the range is 'stride'
15 | int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:18:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | int b = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:19:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | int oc = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:27:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | for (int i = threadIdx.x; i < filter_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:27:53: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | for (int i = threadIdx.x; i < filter_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:35:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | for (int o = threadIdx.x; o < out_size; o += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:35:50: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | for (int o = threadIdx.x; o < out_size; o += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:52:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
52 | int out_idx = b * (gridDim.y * out_size) + oc * out_size + o;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:58:19: 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]
58 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:59:19: 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]
59 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:79:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | int B = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:80:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:81:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | int in_size = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:82:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | int out_channels = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:83:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | int kernel_size = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:98:27: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
98 | int shared_mem_size = in_channels * kernel_size * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:98:27: note: make conversion explicit to silence this warning
4 | int shared_mem_size = in_channels * kernel_size * sizeof(float);
| ^~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<unsigned long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:98:27: note: perform multiplication in a wider type
98 | int shared_mem_size = in_channels * kernel_size * sizeof(float);
| ^~~~~~~~~~~
| static_cast<long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b4_s2_hybrid_conv1d_kernel/base/base.cu:98:27: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | int shared_mem_size = in_channels * kernel_size * sizeof(float);
| ^