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76_conv_standard_1D_dilated_strided__ • conv1d_warp_uniform_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_warp_uniform_base_base
CUDA Speedup (Native) 1.883x
CUDA Speedup (Compile) 8.367x
CUDA Runtime 0.007 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,
    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;

    // Calculate indices with minimal divergence
    int o = idx % out_size;
    int tmp = idx / out_size;
    int oc = tmp % out_channels;
    int b = tmp / out_channels;

    // Pre-calculate start and end positions for the convolution window
    int start_pos = o * stride;
    int end_pos = start_pos + (kernel_size - 1) * dilation;
    
    // Skip computation if entire window is out of bounds
    float sum = 0.0f;
    if (end_pos < in_size) {
        // Main convolution loop - no boundary checks needed
        for (int ic = 0; ic < in_channels; ++ic) {
            const float* x_base = x + b * (in_channels * in_size) + ic * in_size + start_pos;
            const float* w_base = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
            
            // Unroll small kernel sizes for better instruction scheduling
            #pragma unroll 4
            for (int k = 0; k < kernel_size; ++k) {
                sum += x_base[k * dilation] * w_base[k];
            }
        }
    } else {
        // Handle boundary case uniformly for the entire warp
        for (int ic = 0; ic < in_channels; ++ic) {
            const float* x_base = x + b * (in_channels * in_size) + ic * in_size;
            const float* w_base = weight + oc * (in_channels * kernel_size) + ic * kernel_size;
            
            #pragma unroll 4
            for (int k = 0; k < kernel_size; ++k) {
                int input_pos = start_pos + k * dilation;
                // Use multiplication instead of branching
                bool valid = input_pos < in_size;
                sum += valid * x_base[input_pos] * w_base[k];
            }
        }
    }

    // Uniform bias addition across warp
    if (bias != nullptr) {
        sum += bias[oc];
    }
    
    output[b * (out_channels * out_size) + oc * out_size + o] = 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>();

    // Ensure block size is warp-aligned
    int threads = 256;
    int blocks = (B * out_channels * out_size + 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 uniform warp execution");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.098 inst/cycle 0.001 5
Executed Ipc Elapsed 0.630 inst/cycle 0.000 5
Issue Slots Busy 28.328 % 0.398 5
Issued Ipc Active 1.134 inst/cycle 0.001 5
SM Busy 28.328 % 0.398 5
Memory Throughput 11201475349.248 byte/second 16462327042250758.000 5
Mem Busy 8.098 % 0.012 5
Max Bandwidth 5.892 % 0.003 5
L1/TEX Hit Rate 87.020 % 0.000 5
L2 Hit Rate 97.964 % 0.177 5
Mem Pipes Busy 10.964 % 0.020 5
Warp Cycles Per Issued Instruction 16.160 cycle 0.030 5
Warp Cycles Per Executed Instruction 16.702 cycle 0.031 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.180 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 28.860 % 0.063 5
Achieved Active Warps Per SM 18.470 warp 0.025 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (23.2%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
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 (28.7%) 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 557061.26 μs
Device Time 6.40 μs
Self CPU Time 57.70 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 557003.56 μs
Device Time 6.40 μs
Self CPU Time 98.79 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 556777.48 μs
Device Time 0.00 μs
Self CPU Time 105.10 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 555140.24 μs
Device Time 0.00 μs
Self CPU Time 555140.24 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaLaunchKernel
CPU Time 473522.43 μs
Device Time 708.63 μs
Self CPU Time 473522.43 μs
Self Device Time 708.63 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 37961.02 μs
Self CPU Time 0.00 μs
Self Device Time 37961.02 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 17061.28 μs
Device Time 118159.37 μs
Self CPU Time 17061.28 μs
Self Device Time 118159.37 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 65907.70 μs
Device Time 610409.23 μs
Self CPU Time 13176.83 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 52732.11 μs
Device Time 610409.23 μs
Self CPU Time 17576.88 μs
Self Device Time 610409.23 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 610488.40 μs
Self CPU Time 0.00 μs
Self Device Time 610488.40 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 
45300 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/b3_s1_conv1d_warp_uniform_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/b3_s1_conv1d_warp_uniform_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/b3_s1_conv1d_warp_uniform_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/b3_s1_conv1d_warp_uniform_base/base/base.cu:10:5: warning: 2 adjacent parameters of 'conv1d_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
10 | int B,
| ^~~~~~
11 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/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/b3_s1_conv1d_warp_uniform_base/base/base.cu:11:9: note: the last parameter in the range is 'in_channels'
11 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:12:5: warning: 3 adjacent parameters of 'conv1d_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | int in_size,
| ^~~~~~~~~~~~
13 | int out_channels,
| ~~~~~~~~~~~~~~~~~
14 | int kernel_size,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/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/b3_s1_conv1d_warp_uniform_base/base/base.cu:14:9: note: the last parameter in the range is 'kernel_size'
14 | int kernel_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:15:5: warning: 3 adjacent parameters of 'conv1d_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | int out_size,
| ^~~~~~~~~~~~~
16 | int stride,
| ~~~~~~~~~~~
17 | int dilation
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:15:9: note: the first parameter in the range is 'out_size'
15 | int out_size,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:17:9: note: the last parameter in the range is 'dilation'
17 | int dilation
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:19:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:38:35: 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]
38 | const float* x_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:38:69: note: make conversion explicit to silence this warning
4 | const float* x_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:38:69: note: perform multiplication in a wider type
38 | const float* x_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:39:35: 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]
39 | const float* w_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:39:79: note: make conversion explicit to silence this warning
39 | const float* w_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:39:79: note: perform multiplication in a wider type
39 | const float* w_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:44:24: 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 | sum += x_base[k * dilation] * w_base[k];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:44:31: note: make conversion explicit to silence this warning
44 | sum += x_base[k * dilation] * w_base[k];
| ^~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:44:31: note: perform multiplication in a wider type
44 | sum += x_base[k * dilation] * w_base[k];
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:50:35: 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]
50 | const float* x_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:50:69: note: make conversion explicit to silence this warning
50 | const float* x_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:50:69: note: perform multiplication in a wider type
50 | const float* x_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:51:35: 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]
51 | const float* w_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:51:79: note: make conversion explicit to silence this warning
51 | const float* w_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:51:79: note: perform multiplication in a wider type
51 | const float* w_base = 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/b3_s1_conv1d_warp_uniform_base/base/base.cu:58:24: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
58 | sum += valid * x_base[input_pos] * w_base[k];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:72:19: 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]
72 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:73:19: 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]
73 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:93:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
93 | int B = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:94:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:95:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | int in_size = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:96:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | int out_channels = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_76/b3_s1_conv1d_warp_uniform_base/base/base.cu:97:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
97 | int kernel_size = weight.size(2);
| ^