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78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__conv_trans_unroll_optimized_base

Level 1 • Task 78
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: tuple,
    padding: tuple,
) -> torch.Tensor:
    """
    Performs a 2D transposed convolution operation with asymmetric input and kernel, with optional padding.

    Args:
        x (torch.Tensor): Input tensor
        stride (tuple): Stride of convolution
        padding (tuple): Padding to apply
        weight (torch.Tensor): Convolution weights
        bias (torch.Tensor): Bias tensor (optional)

    Returns:
        torch.Tensor: Output tensor
    """
    return F.conv_transpose2d(x, weight, bias=bias, stride=stride, padding=padding)


class Model(nn.Module):
    """
    Performs a 2D transposed convolution operation with asymmetric input and kernel, with optional padding.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Size of the convolution kernel (height, width).
        stride (tuple): Stride of the convolution (height, width).
        padding (tuple): Padding applied to the input (height, width).
        bias (bool): If `True`, adds a learnable bias to the output.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple,
        stride: tuple,
        padding: tuple,
        bias: bool,
    ):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            bias=bias,
        )

        # Copy the initialized parameters
        self.weight = nn.Parameter(self.conv_transpose2d.weight.clone())
        self.bias = nn.Parameter(self.conv_transpose2d.bias.clone()) if bias else None

        self.stride = stride
        self.padding = padding

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Performs the 2D transposed convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).
            fn: Function to use for forward pass

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


# Constants
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = (3, 5)
height = 128
width = 256
stride = (1, 1)
padding = (1, 2)
bias = False


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, bias]
import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Performs a 2D transposed convolution operation with asymmetric input and kernel, with optional padding.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Size of the convolution kernel (height, width).
        stride (tuple, optional): Stride of the convolution (height, width). Defaults to (1, 1).
        padding (tuple, optional): Padding applied to the input (height, width). Defaults to (0, 0).
        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: tuple,
        stride: tuple = (1, 1),
        padding: tuple = (0, 0),
        bias: bool = False,
    ):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 2D transposed convolution.

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

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv_transpose2d(x)


# Constants
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = (3, 5)
height = 128
width = 256
stride = (1, 1)
padding = (1, 2)
bias = False


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


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

Kernel Information

Related Kernels (Level 1, Task 78 • 78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__)

#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <pybind11/pybind11.h>
#include <vector>

namespace py = pybind11;

// Declare constant memory for weights (maximum 64KB)
__constant__ float c_weight[16384]; // 64KB / 4 bytes = 16384 floats

// Kernel with manual loop unrolling for inner loops
// Merges the kH and kW loops into one and unrolls them using #pragma unroll

template <int BLOCK_SIZE = 256>
__global__ void conv_transpose2d_forward_kernel(
    const float* __restrict__ input,
    const float* __restrict__ bias,
    float* __restrict__ output,
    const int N,
    const int C_in,
    const int H_in,
    const int W_in,
    const int C_out,
    const int H_out,
    const int W_out,
    const int kH,
    const int kW,
    const int sH,
    const int sW,
    const int pH,
    const int pW
) {
    const int tid = threadIdx.x;
    const int bid = blockIdx.x;
    const int index = bid * BLOCK_SIZE + tid;
    if (index >= N * C_out * H_out * W_out) return;

    // Decode output indices
    const int ow = index % W_out;
    const int oh = (index / W_out) % H_out;
    const int oc = (index / (W_out * H_out)) % C_out;
    const int n  = index / (W_out * H_out * C_out);

    float sum = 0.0f;

    // Unroll loop over input channels
    #pragma unroll
    for (int ic = 0; ic < C_in; ++ic) {
        // Merge kernel height and width loops into a single loop
        #pragma unroll
        for (int k = 0; k < kH * kW; ++k) {
            int kh = k / kW;
            int kw = k % kW;
            
            int i_val = oh + pH - kh;
            int j_val = ow + pW - kw;
            
            if ((i_val % sH) == 0 && (j_val % sW) == 0) {
                int i_in = i_val / sH;
                int j_in = j_val / sW;
                if (i_in >= 0 && i_in < H_in && j_in >= 0 && j_in < W_in) {
                    int input_index = ((n * C_in + ic) * H_in + i_in) * W_in + j_in;
                    int weight_index = ((ic * C_out + oc) * kH + kh) * kW + kw;
                    sum += input[input_index] * c_weight[weight_index];
                }
            }
        }
    }

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

    output[index] = sum;
}

// Hybrid conv_transpose2d forward function with constant memory and manual loop unrolling

torch::Tensor conv_transpose2d_forward(
    torch::Tensor x,
    torch::Tensor weight,
    py::object bias_obj,
    std::vector<int64_t> stride,
    std::vector<int64_t> padding
) {
    // Check if weight size fits in constant memory
    int weight_numel = weight.numel();
    int weight_size = weight_numel * sizeof(float);
    const int max_const_size = 64 * 1024; // 64KB
    if (weight_size > max_const_size) {
        c10::optional<torch::Tensor> bias = c10::nullopt;
        if (!bias_obj.is_none()) {
            bias = bias_obj.cast<torch::Tensor>();
        }
        return at::conv_transpose2d(x, weight, bias, stride, padding);
    }
    
    // Copy weight data to constant memory
    cudaMemcpyToSymbol(c_weight, weight.data_ptr<float>(), weight_size);

    torch::Tensor bias;
    const float* bias_ptr = nullptr;
    if (!bias_obj.is_none()) {
        bias = bias_obj.cast<torch::Tensor>();
        bias_ptr = bias.data_ptr<float>();
    }

    const int N = x.size(0);
    const int C_in = x.size(1);
    const int H_in = x.size(2);
    const int W_in = x.size(3);
    
    const int kH = weight.size(2);
    const int kW = weight.size(3);
    const int C_out = weight.size(1);

    const int sH = stride[0];
    const int sW = stride[1];
    const int pH = padding[0];
    const int pW = padding[1];

    // Compute output dimensions for conv_transpose2d
    int H_out = (H_in - 1) * sH - 2 * pH + kH;
    int W_out = (W_in - 1) * sW - 2 * pW + kW;

    auto output = torch::zeros({N, C_out, H_out, W_out}, x.options());

    const int total_elements = N * C_out * H_out * W_out;
    constexpr int BLOCK_SIZE = 256;
    int num_blocks = (total_elements + BLOCK_SIZE - 1) / BLOCK_SIZE;

    conv_transpose2d_forward_kernel<BLOCK_SIZE><<<num_blocks, BLOCK_SIZE>>>(
        x.data_ptr<float>(),
        bias_ptr,
        output.data_ptr<float>(),
        N, C_in, H_in, W_in,
        C_out, H_out, W_out,
        kH, kW,
        sH, sW,
        pH, pW
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &conv_transpose2d_forward, "Conv Transpose 2D forward with manual loop unrolling",
          py::arg("x"),
          py::arg("weight"),
          py::arg("bias") = py::none(),
          py::arg("stride"),
          py::arg("padding"));
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv_transpose2d
CPU Time 1871642.58 μs
Device Time 1486999.59 μs
Self CPU Time 7626.56 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::convolution
CPU Time 1864016.01 μs
Device Time 1486999.59 μs
Self CPU Time 8507.55 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::_convolution
CPU Time 1855508.46 μs
Device Time 1486999.59 μs
Self CPU Time 11727.02 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::cudnn_convolution_transpose
CPU Time 1843781.45 μs
Device Time 1486999.59 μs
Self CPU Time 358404.04 μs
Self Device Time 1486999.59 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 1029766.83 μs
Device Time 0.00 μs
Self CPU Time 1029766.83 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
sm90_xmma_dgrad_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize128x64x32_warpgroupsize1x1x1_g1_execute_segment_k_off_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 770554.58 μs
Self CPU Time 0.00 μs
Self Device Time 770554.58 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
45302 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_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:17:5 bugprone-easily-swappable-parameters
17 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
18 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:17:31: note: the first parameter in the range is 'input'
17 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:18:31: note: the last parameter in the range is 'bias'
18 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:20:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | const int N,
| ^~~~~~~~~~~~
21 | const int C_in,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:20:15: note: the first parameter in the range is 'N'
20 | const int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:21:15: note: the last parameter in the range is 'C_in'
21 | const int C_in,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:23:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | const int W_in,
| ^~~~~~~~~~~~~~~
24 | const int C_out,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:23:15: note: the first parameter in the range is 'W_in'
23 | const int W_in,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:24:15: note: the last parameter in the range is 'C_out'
24 | const int C_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:26:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
26 | const int W_out,
| ^~~~~~~~~~~~~~~~
27 | const int kH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:26:15: note: the first parameter in the range is 'W_out'
26 | const int W_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:27:15: note: the last parameter in the range is 'kH'
27 | const int kH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:28:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
28 | const int kW,
| ^~~~~~~~~~~~~
29 | const int sH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:28:15: note: the first parameter in the range is 'kW'
28 | const int kW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:29:15: note: the last parameter in the range is 'sH'
29 | const int sH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:30:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
30 | const int sW,
| ^~~~~~~~~~~~~
31 | const int pH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:30:15: note: the first parameter in the range is 'sW'
30 | const int sW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:31:15: note: the last parameter in the range is 'pH'
31 | const int pH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:34:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
34 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:35:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | const int bid = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:81:19: 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]
81 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:82:19: 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]
82 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:83:16: warning: the parameter 'bias_obj' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
83 | py::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:88:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | int weight_numel = weight.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:89:23: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
89 | int weight_size = weight_numel * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:109:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
109 | const int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_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 | const int C_in = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:111:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | const int H_in = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:112:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | const int W_in = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:114:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | const int kH = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:115:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | const int kW = weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:116:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | const int C_out = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:118:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
118 | const int sH = stride[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:119:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | const int sW = stride[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:120:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | const int pH = padding[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s3_conv_trans_unroll_optimized/base/base.cu:121:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | const int pW = padding[1];
| ^