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76_conv_standard_1D_dilated_strided__ • conv1d_stride_loop_opt_base_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 conv1d_stride_loop_opt_base_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>

__global__ void conv1d_kernel(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    const int B,
    const int in_channels,
    const int in_size,
    const int out_channels,
    const int kernel_size,
    const int out_size,
    const int stride,
    const int dilation
) {
    const int tid = threadIdx.x;
    const int bid = blockIdx.x;
    const int block_size = blockDim.x;
    const int grid_size = gridDim.x;
    
    const int total_elements = B * out_channels * out_size;
    const int elements_per_grid = (total_elements + grid_size - 1) / grid_size;
    const int start_idx = bid * elements_per_grid;
    const int end_idx = min(start_idx + elements_per_grid, total_elements);

    for (int base_idx = start_idx + tid; base_idx < end_idx; base_idx += block_size) {
        const int o = base_idx % out_size;
        const int temp = base_idx / out_size;
        const int oc = temp % out_channels;
        const int b = temp / out_channels;

        const int input_start = o * stride;
        const float* x_batch = x + b * (in_channels * in_size);
        const float* w_oc = weight + oc * (in_channels * kernel_size);
        
        float sum = 0.0f;
        
        #pragma unroll 2
        for (int ic = 0; ic < in_channels; ++ic) {
            const float* x_ic = x_batch + ic * in_size + input_start;
            const float* w_ic = w_oc + ic * kernel_size;
            
            if (input_start + (kernel_size - 1) * dilation < in_size) {
                #pragma unroll 4
                for (int k = 0; k < kernel_size; ++k) {
                    sum += x_ic[k * dilation] * w_ic[k];
                }
            } else {
                #pragma unroll 4
                for (int k = 0; k < kernel_size; ++k) {
                    const int pos = k * dilation;
                    if (input_start + pos < in_size) {
                        sum += x_ic[pos] * w_ic[k];
                    }
                }
            }
        }

        if (bias != nullptr) {
            sum += bias[oc];
        }

        output[base_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 ? bias->data_ptr<float>() : nullptr;
    float* output_data = output.data_ptr<float>();

    const int threads = 256;
    const int max_blocks = 256;
    const int total_elements = B * out_channels * out_size;
    const int blocks = min(max_blocks, (total_elements + threads - 1) / threads);

    conv1d_kernel<<<blocks, threads>>>(
        x_data,
        weight_data,
        bias_data,
        output_data,
        B,
        in_channels,
        in_size,
        out_channels,
        kernel_size,
        out_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 stride loop optimization");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.090 inst/cycle 0.000 5
Executed Ipc Elapsed 0.680 inst/cycle 0.000 5
Issue Slots Busy 27.602 % 0.193 5
Issued Ipc Active 1.104 inst/cycle 0.000 5
SM Busy 27.602 % 0.193 5
Memory Throughput 11250868037.880 byte/second 46436117827926384.000 5
Mem Busy 6.430 % 0.013 5
Max Bandwidth 4.834 % 0.008 5
L1/TEX Hit Rate 90.562 % 0.000 5
L2 Hit Rate 102.478 % 0.377 5
Mem Pipes Busy 8.028 % 0.021 5
Warp Cycles Per Issued Instruction 11.304 cycle 0.026 5
Warp Cycles Per Executed Instruction 11.430 cycle 0.026 5
Avg. Active Threads Per Warp 30.390 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.610 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 6.000 block 0.000 5
Block Limit Shared Mem 32.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 48.000 warp 0.000 5
Theoretical Occupancy 75.000 % 0.000 5
Achieved Occupancy 19.660 % 0.020 5
Achieved Active Warps Per SM 12.578 warp 0.008 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (22.7%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy (75.0%) is limited by the number of required registers. The difference between calculated theoretical (75.0%) and measured achieved occupancy (19.8%) 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 535373.42 μs
Device Time 6.59 μs
Self CPU Time 51.94 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 535321.48 μs
Device Time 6.59 μs
Self CPU Time 100.36 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 535078.61 μs
Device Time 0.00 μs
Self CPU Time 127.28 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
cudaDeviceGetStreamPriorityRange
CPU Time 534639.08 μs
Device Time 0.00 μs
Self CPU Time 534639.08 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 525002.72 μs
Device Time 709.09 μs
Self CPU Time 525002.72 μs
Self Device Time 709.09 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(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 47213.82 μs
Self CPU Time 0.00 μs
Self Device Time 47213.82 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 18395.41 μs
Device Time 126955.52 μs
Self CPU Time 18395.41 μs
Self Device Time 126955.52 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 67640.07 μs
Device Time 653854.07 μs
Self CPU Time 14428.90 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 53213.48 μs
Device Time 653854.07 μs
Self CPU Time 17999.05 μs
Self Device Time 653854.07 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 653854.07 μs
Self CPU Time 0.00 μs
Self Device Time 653854.07 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Failed 
45263 warnings and 1 error generated when compiling for host.
Error while processing /home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu.
Suppressed 45290 warnings (45243 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.
Found compiler error(s).
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:6:5 bugprone-easily-swappable-parameters
6 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:6:31: note: the first parameter in the range is 'x'
6 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:8:31: note: the last parameter in the range is 'bias'
8 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:10:5: warning: 2 adjacent parameters of 'conv1d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
10 | const int B,
| ^~~~~~~~~~~~
11 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:10:15: note: the first parameter in the range is 'B'
10 | const int B,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:11:15: note: the last parameter in the range is 'in_channels'
11 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:12:5: warning: 2 adjacent parameters of 'conv1d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | const int in_size,
| ^~~~~~~~~~~~~~~~~~
13 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:12:15: note: the first parameter in the range is 'in_size'
12 | const int in_size,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:13:15: note: the last parameter in the range is 'out_channels'
13 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:14:5: warning: 3 adjacent parameters of 'conv1d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | const int kernel_size,
| ^~~~~~~~~~~~~~~~~~~~~~
15 | const int out_size,
| ~~~~~~~~~~~~~~~~~~~
16 | const int stride,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:14:15: note: the first parameter in the range is 'kernel_size'
14 | const int kernel_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:16:15: note: the last parameter in the range is 'stride'
16 | const int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:19:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:20:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
20 | const int bid = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:21:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
21 | const int block_size = blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:22:27: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
22 | const int grid_size = gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:36:32: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
36 | const float* x_batch = x + b * (in_channels * in_size);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:36:36: note: make conversion explicit to silence this warning
4 | const float* x_batch = x + b * (in_channels * in_size);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:36:36: note: perform multiplication in a wider type
36 | const float* x_batch = x + b * (in_channels * in_size);
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:37:29: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
37 | const float* w_oc = weight + oc * (in_channels * kernel_size);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:37:38: note: make conversion explicit to silence this warning
37 | const float* w_oc = weight + oc * (in_channels * kernel_size);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:37:38: note: perform multiplication in a wider type
37 | const float* w_oc = weight + oc * (in_channels * kernel_size);
| ^~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:43:33: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
43 | const float* x_ic = x_batch + ic * in_size + input_start;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:43:43: note: make conversion explicit to silence this warning
43 | const float* x_ic = x_batch + ic * in_size + input_start;
| ^~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:43:43: note: perform multiplication in a wider type
43 | const float* x_ic = x_batch + ic * in_size + input_start;
| ^~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:44:33: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
44 | const float* w_ic = w_oc + ic * kernel_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:44:40: note: make conversion explicit to silence this warning
44 | const float* w_ic = w_oc + ic * kernel_size;
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:44:40: note: perform multiplication in a wider type
44 | const float* w_ic = w_oc + ic * kernel_size;
| ^~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:49:28: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
49 | sum += x_ic[k * dilation] * w_ic[k];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:49:33: note: make conversion explicit to silence this warning
49 | sum += x_ic[k * dilation] * w_ic[k];
| ^~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:49:33: note: perform multiplication in a wider type
49 | sum += x_ic[k * dilation] * w_ic[k];
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:71:19: 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]
71 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:72:19: 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]
72 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:92:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
92 | int B = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:93:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
93 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:94:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | int in_size = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:95:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | int out_channels = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:96:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | int kernel_size = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b7_s3_conv1d_stride_loop_opt_base/base/base.cu:112:24: error: no matching function for call to 'min' [clang-diagnostic-error]
112 | const int blocks = min(max_blocks, (total_elements + threads - 1) / threads);
| ^~~
/home/common_modules/clang-tidy/20.0.0git/lib/clang/20/include/__clang_cuda_math.h:201:16: note: candidate function not viable: call to __device__ function from __host__ function
201 | __DEVICE__ int min(int __a, int __b) { return __nv_min(__a, __b); }
| ^
/usr/local/cuda/include/crt/math_functions.hpp:868:38: note: candidate function not viable: call to __device__ function from __host__ function
868 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:873:38: note: candidate function not viable: call to __device__ function from __host__ function
873 | __MATH_FUNCTIONS_DECL__ unsigned int min(const int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:878:38: note: candidate function not viable: call to __device__ function from __host__ function
878 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:883:34: note: candidate function not viable: call to __device__ function from __host__ function
883 | __MATH_FUNCTIONS_DECL__ long int min(const long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:902:43: note: candidate function not viable: call to __device__ function from __host__ function
902 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:919:43: note: candidate function not viable: call to __device__ function from __host__ function
919 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:936:43: note: candidate function not viable: call to __device__ function from __host__ function
936 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:953:39: note: candidate function not viable: call to __device__ function from __host__ function
953 | __MATH_FUNCTIONS_DECL__ long long int min(const long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:958:48: note: candidate function not viable: call to __device__ function from __host__ function
958 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:963:48: note: candidate function not viable: call to __device__ function from __host__ function
963 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:968:48: note: candidate function not viable: call to __device__ function from __host__ function
968 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:973:31: note: candidate function not viable: call to __device__ function from __host__ function
973 | __MATH_FUNCTIONS_DECL__ float min(const float a, const float b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:978:32: note: candidate function not viable: call to __device__ function from __host__ function
978 | __MATH_FUNCTIONS_DECL__ double min(const double a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:983:32: note: candidate function not viable: call to __device__ function from __host__ function
983 | __MATH_FUNCTIONS_DECL__ double min(const float a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:988:32: note: candidate function not viable: call to __device__ function from __host__ function
988 | __MATH_FUNCTIONS_DECL__ double min(const double a, const float b)
| ^