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76_conv_standard_1D_dilated_strided__ • conv1d_ldg_optimized_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_ldg_optimized_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 azure-gpt-4o-2024-08-06
Temperature 0.00
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>

// Device function that performs the convolution for one output element.
// This function encapsulates boundary checking and loop unrolling.
__device__ __forceinline__ float compute_conv1d(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    int b,
    int oc,
    int o,
    int in_channels,
    int in_size,
    int kernel_size,
    int stride,
    int dilation) {
  float sum = 0.0f;
  int start_pos = o * stride;
  int end_pos = start_pos + (kernel_size - 1) * dilation;

  // If the convolution window is fully in bounds, avoid per-element boundary checks
  if (end_pos < in_size) {
    for (int ic = 0; ic < in_channels; ++ic) {
      const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size + start_pos;
      const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
      #pragma unroll
      for (int k = 0; k < kernel_size; ++k) {
        sum += __ldg(&x_ptr[k * dilation]) * __ldg(&w_ptr[k]);
      }
    }
  } else {
    // In boundary cases, check each position
    for (int ic = 0; ic < in_channels; ++ic) {
      const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size;
      const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
      #pragma unroll
      for (int k = 0; k < kernel_size; ++k) {
        int pos = start_pos + k * dilation;
        if (pos < in_size) {
          sum += __ldg(&x_ptr[pos]) * __ldg(&w_ptr[k]);
        }
      }
    }
  }
  return sum;
}

// CUDA kernel that computes the 1D convolution using the modular device function above
__global__ void conv1d_ldg_kernel(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int B,
    int in_channels,
    int in_size,
    int out_channels,
    int kernel_size,
    int out_size,
    int stride,
    int dilation) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  int total_elements = B * out_channels * out_size;
  if (idx >= total_elements) return;

  // Decode the linear index into (b, oc, o)
  int o = idx % out_size;
  int tmp = idx / out_size;
  int oc = tmp % out_channels;
  int b = tmp / out_channels;

  // Compute the convolution using the modular device function
  float sum = compute_conv1d(x, weight, b, oc, o, in_channels, in_size, kernel_size, stride, dilation);

  // Add bias if provided
  if (bias != nullptr) {
    sum += __ldg(&bias[oc]);
  }

  int out_idx = b * (out_channels * out_size) + oc * out_size + o;
  output[out_idx] = sum;
}

// Forward function exposed via pybind11
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.value().device().is_cuda(), "bias must be a CUDA tensor");
    TORCH_CHECK(bias.value().is_contiguous(), "bias must be contiguous");
    TORCH_CHECK(bias.value().dim() == 1, "bias must be 1D");
    TORCH_CHECK(bias.value().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>();

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

  conv1d_ldg_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, "CUDA kernel launch error: ", cudaGetErrorString(err));

  return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &forward, "Optimized 1D convolution forward (CUDA) with __ldg");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.154 inst/cycle 0.000 5
Executed Ipc Elapsed 0.682 inst/cycle 0.000 5
Issue Slots Busy 29.672 % 0.130 5
Issued Ipc Active 1.188 inst/cycle 0.000 5
SM Busy 29.672 % 0.130 5
Memory Throughput 10752300450.196 byte/second 61803692559009808.000 5
Mem Busy 7.678 % 0.032 5
Max Bandwidth 5.590 % 0.015 5
L1/TEX Hit Rate 87.020 % 0.000 5
L2 Hit Rate 98.134 % 0.016 5
Mem Pipes Busy 10.436 % 0.055 5
Warp Cycles Per Issued Instruction 15.200 cycle 0.009 5
Warp Cycles Per Executed Instruction 15.656 cycle 0.009 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.190 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 32.000 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 28.730 % 0.150 5
Achieved Active Warps Per SM 18.388 warp 0.062 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (28.0%) 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 is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (29.1%) 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 752866.71 μs
Device Time 6.50 μs
Self CPU Time 50.23 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 752816.48 μs
Device Time 6.50 μs
Self CPU Time 102.56 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 752588.18 μs
Device Time 0.00 μs
Self CPU Time 105.66 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceGetStreamPriorityRange
CPU Time 751801.65 μs
Device Time 0.00 μs
Self CPU Time 751801.65 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 500574.26 μs
Device Time 707.42 μs
Self CPU Time 500574.26 μs
Self Device Time 707.42 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv1d_ldg_kernel(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 40500.39 μs
Self CPU Time 0.00 μs
Self Device Time 40500.39 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 17793.17 μs
Device Time 122400.29 μs
Self CPU Time 17793.17 μs
Self Device Time 122400.29 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 64529.49 μs
Device Time 631146.09 μs
Self CPU Time 13123.12 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 51410.74 μs
Device Time 631146.09 μs
Self CPU Time 17045.06 μs
Self Device Time 631146.09 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 631146.09 μs
Self CPU Time 0.00 μs
Self Device Time 631146.09 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 
45303 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/b10_s1_conv1d_ldg_optimized/base/base.cu:8:5 bugprone-easily-swappable-parameters
8 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
9 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:8:31: note: the first parameter in the range is 'x'
8 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:9:31: note: the last parameter in the range is 'weight'
9 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:10:5: warning: 2 adjacent parameters of 'compute_conv1d' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
10 | int b,
| ^~~~~~
11 | int oc,
| ~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:10:9: note: the first parameter in the range is 'b'
10 | int b,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:11:9: note: the last parameter in the range is 'oc'
11 | int oc,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:12:5: warning: 2 adjacent parameters of 'compute_conv1d' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | int o,
| ^~~~~~
13 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:12:9: note: the first parameter in the range is 'o'
12 | int o,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:13:9: note: the last parameter in the range is 'in_channels'
13 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:14:5: warning: 3 adjacent parameters of 'compute_conv1d' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | int in_size,
| ^~~~~~~~~~~~
15 | int kernel_size,
| ~~~~~~~~~~~~~~~~
16 | int stride,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:14:9: note: the first parameter in the range is 'in_size'
14 | int in_size,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:16:9: note: the last parameter in the range is 'stride'
16 | int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:25:28: 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]
25 | const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size + start_pos;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:25:62: note: make conversion explicit to silence this warning
4 | const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size + start_pos;
| ^~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:25:62: note: perform multiplication in a wider type
25 | const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size + start_pos;
| ^~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:26:28: 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]
26 | const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:26:72: note: make conversion explicit to silence this warning
26 | const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:26:72: note: perform multiplication in a wider type
26 | const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
| ^~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:29:23: 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]
29 | sum += __ldg(&x_ptr[k * dilation]) * __ldg(&w_ptr[k]);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:29:29: note: make conversion explicit to silence this warning
29 | sum += __ldg(&x_ptr[k * dilation]) * __ldg(&w_ptr[k]);
| ^~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:29:29: note: perform multiplication in a wider type
29 | sum += __ldg(&x_ptr[k * dilation]) * __ldg(&w_ptr[k]);
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:35:28: 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]
35 | const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:35:62: note: make conversion explicit to silence this warning
35 | const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size;
| ^~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:35:62: note: perform multiplication in a wider type
35 | const float* x_ptr = x + b * (in_channels * in_size) + ic * in_size;
| ^~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:36:28: 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]
36 | const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:36:72: note: make conversion explicit to silence this warning
36 | const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:36:72: note: perform multiplication in a wider type
36 | const float* w_ptr = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
| ^~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:52:5: warning: 2 adjacent parameters of 'conv1d_ldg_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
52 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
53 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:52:31: note: the first parameter in the range is 'weight'
52 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:53:31: note: the last parameter in the range is 'bias'
53 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:55:5: warning: 2 adjacent parameters of 'conv1d_ldg_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
55 | int B,
| ^~~~~~
56 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:55:9: note: the first parameter in the range is 'B'
55 | int B,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:56:9: note: the last parameter in the range is 'in_channels'
56 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:57:5: warning: 2 adjacent parameters of 'conv1d_ldg_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
57 | int in_size,
| ^~~~~~~~~~~~
58 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:57:9: note: the first parameter in the range is 'in_size'
57 | int in_size,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:58:9: note: the last parameter in the range is 'out_channels'
58 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:59:5: warning: 2 adjacent parameters of 'conv1d_ldg_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
59 | int kernel_size,
| ^~~~~~~~~~~~~~~~
60 | int out_size,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:59:9: note: the first parameter in the range is 'kernel_size'
59 | int kernel_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:60:9: note: the last parameter in the range is 'out_size'
60 | int out_size,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:63:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:87:19: 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]
87 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:88:19: 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]
88 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:107:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
107 | int B = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:108:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
108 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:109:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
109 | int in_size = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:110:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | int out_channels = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b10_s1_conv1d_ldg_optimized/base/base.cu:111:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | int kernel_size = weight.size(2);
| ^