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57_conv_transposed_2D__square_input__square_kernel • hybrid_conv_transpose2d_base

Level 1 • Task 57
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,
    padding: int,
    output_padding: int,
    groups: int,
) -> torch.Tensor:
    """
    Performs a transposed 2D convolution with square input and square kernel.

    Args:
        x (torch.Tensor): Input tensor.
        weight (torch.Tensor): Weight tensor.
        bias (torch.Tensor): Bias tensor.
        stride (int): Stride for the convolution.
        padding (int): Padding for the convolution.
        output_padding (int): Additional size added to one side of the output shape.
        groups (int): Number of groups for the convolution.

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


class Model(nn.Module):
    """
    Performs a transposed 2D convolution with square input and square kernel.

    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.
        padding (int): Padding applied to the input.
        output_padding (int): Additional size added to one side of the output shape.
        groups (int): Number of blocked connections from input channels to output channels.
        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,
        padding: int,
        output_padding: int,
        groups: int,
        bias: bool,
    ):
        super(Model, self).__init__()
        conv = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
            groups=groups,
            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.padding = padding
        self.groups = groups
        self.output_padding = output_padding

    def forward(
        self,
        x: torch.Tensor,
        fn=module_fn,
    ) -> torch.Tensor:
        """
        Performs the transposed 2D 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 fn(
            x,
            self.weight,
            self.bias,
            self.stride,
            self.padding,
            self.output_padding,
            self.groups,
        )


# Constants
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = 3
width = 128
height = 128
stride = 1
padding = 0
output_padding = 0
groups = 1
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,
        output_padding,
        groups,
        bias,
    ]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Performs a transposed 2D convolution with square input and square kernel.

    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.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        output_padding (int, optional): Additional size added to one side of the output shape. Defaults to 0.
        groups (int, optional): Number of blocked connections from input channels to output channels. 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,
        padding: int = 0,
        output_padding: int = 0,
        groups: int = 1,
        bias: bool = False,
    ):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
            groups=groups,
            bias=bias,
        )

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


# Test code
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = 3
width = 128
height = 128
stride = 1
padding = 0
output_padding = 0
groups = 1
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,
        output_padding,
        groups,
        bias,
    ]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 57_conv_transposed_2D__square_input__square_kernel
Level ID 1
Task ID 57
Kernel Name hybrid_conv_transpose2d_base
CUDA Speedup (Native) 0.019x
CUDA Speedup (Compile) 0.022x
CUDA Runtime 8.135 ms
PyTorch Runtime (Native) 0.153 ms
PyTorch Runtime (Compile) 0.180 ms
Correct True
Max Diff (vs. Reference) 0.001000
Model o3-mini-2025-01-31
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 57 • 57_conv_transposed_2D__square_input__square_kernel)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 warp_optimized_conv_transpose2d_base 0.15 1.01 1.18
🥇 block_tuned_conv_transpose2d_base_base 0.15 1.01 1.18
🥇 block_size_experiment_conv_transpose2d_base 0.15 1.01 1.18
🥇 mapped_3d_bias_conv_transpose2d_base 0.15 1.01 1.18
🥇 stride_loop_optimized_conv_transpose2d_base 0.15 1.01 1.18
6 atomic_minimized_conv_transpose2d_base_base 0.15 1.00 1.17
6 optimized_conv_transpose2d_base 0.15 1.00 1.17
6 shared_mem_bias_opt_conv_transpose2d_base_base 0.15 1.00 1.17
6 57_conv_transposed_2D__square_input__square_kernel 0.15 1.00 1.17
6 workload_balanced_conv_transpose2d_base_base 0.15 1.00 1.17
11 constant_memory_optimized_conv_transpose2d_base 0.15 0.99 1.17
12 combined_convtranspose_edit_1 2.33 0.07 0.08
13 57_conv_transposed_2d__square_kernel_stream_base 8.13 0.02 0.02
14 hybrid_conv_transpose2d_base 8.13 0.02 0.02
15 conv_transposed2d_coalesced_edit_1 8.32 0.02 0.02
15 conv_transposed2d_coalesced_base 8.32 0.02 0.02
17 conv_transposed2d_uniform_flow_edit_1 8.38 0.02 0.02
17 conv_transposed2d_uniform_flow_base 8.38 0.02 0.02
19 conv_transposed2d_stride_loop_base 8.58 0.02 0.02
19 conv_transposed2d_stride_loop_edit_1 8.58 0.02 0.02 
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <vector>
#include <algorithm>

// Custom CUDA kernel that computes contributions from each input element.
// Each thread processes one input element from a batch chunk and accumulates into the corresponding output region via atomic adds.
__global__ void conv_transpose2d_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    float* __restrict__ output,
    int chunkN,       // number of batches in this chunk
    int C_in, int H, int W,
    int C_out, int K, // square kernel
    int stride,
    int padding,
    int H_out, int W_out) {

    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    int total = chunkN * C_in * H * W;
    if (tid >= total) return;

    // Decode the thread's linear index into (n, c_in, h, w) indices
    int w_idx = tid % W;
    int tmp = tid / W;
    int h_idx = tmp % H;
    tmp = tmp / H;
    int c_in = tmp % C_in;
    int n = tmp / C_in;  // local batch index within the chunk

    float in_val = input[tid];

    // For each kernel element, compute the corresponding output indices
    for (int ki = 0; ki < K; ++ki) {
        for (int kj = 0; kj < K; ++kj) {
            int out_i = h_idx * stride - padding + ki;
            int out_j = w_idx * stride - padding + kj;
            if (out_i < 0 || out_i >= H_out || out_j < 0 || out_j >= W_out) continue;

            // Loop over all output channels
            for (int oc = 0; oc < C_out; ++oc) {
                int weight_idx = c_in * (C_out * K * K) + oc * (K * K) + ki * K + kj;
                float w_val = weight[weight_idx];

                int out_index = n * (C_out * H_out * W_out) + oc * (H_out * W_out) + out_i * W_out + out_j;
                atomicAdd(&output[out_index], in_val * w_val);
            }
        }
    }
}

// Kernel to add bias if provided (each output channel gets its own bias value)
__global__ void add_bias_kernel(
    float* output,
    const float* bias,
    int total_elements,
    int C_out,
    int H_out,
    int W_out) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid >= total_elements) return;

    // Determine which output channel this element belongs to
    int channel = (tid / (H_out * W_out)) % C_out;
    output[tid] += bias[channel];
}

// Hybrid forward function that chooses between a built-in implementation and a custom kernel
// based on the input batch size. For small batches the built-in at::conv_transpose2d is preferred
// (as it may be optimized via cuDNN), while for large batches our custom kernel with multiple streams
// can yield improved GPU occupancy by overlapping work.

torch::Tensor conv_transpose2d_forward(
    torch::Tensor x,
    torch::Tensor weight,
    torch::optional<torch::Tensor> bias,
    int64_t stride,
    int64_t padding,
    int64_t output_padding,
    int64_t groups) {

    // Ensure inputs are CUDA tensors and contiguous
    TORCH_CHECK(x.is_cuda(), "Input tensor must be on CUDA");
    TORCH_CHECK(weight.is_cuda(), "Weight tensor must be on CUDA");
    TORCH_CHECK(x.is_contiguous(), "Input tensor must be contiguous");
    TORCH_CHECK(weight.is_contiguous(), "Weight tensor must be contiguous");
    if (bias.has_value()) {
        auto bias_val = bias.value();
        TORCH_CHECK(bias_val.is_cuda(), "Bias tensor must be on CUDA");
        TORCH_CHECK(bias_val.is_contiguous(), "Bias tensor must be contiguous");
    }

    // Retrieve input dimensions: x is [N, C_in, H, W]
    auto x_sizes = x.sizes();
    int N = x_sizes[0];
    int C_in = x_sizes[1];
    int H = x_sizes[2];
    int W = x_sizes[3];

    // Retrieve weight dimensions: weight is assumed to be [C_in, C_out, K, K]
    auto w_sizes = weight.sizes();
    int C_out = w_sizes[1];
    int K = w_sizes[2];

    // Compute output dimensions using the ConvTranspose2d formula
    int H_out = (H - 1) * stride - 2 * padding + K + output_padding;
    int W_out = (W - 1) * stride - 2 * padding + K + output_padding;

    // For small batches, the built-in implementation (often backed by cuDNN) performs very well
    if (N < 8) {  // threshold parameter; can be tuned
        return at::conv_transpose2d(
            x,
            weight,
            bias,
            {stride, stride},
            {padding, padding},
            {output_padding, output_padding},
            groups
        );
    }

    // Allocate output tensor and initialize to zero
    auto output = torch::zeros({N, C_out, H_out, W_out}, x.options());

    // Use multiple CUDA streams to partition the batch and overlap kernel execution
    int nstreams = 2;
    std::vector<cudaStream_t> streams(nstreams);
    for (int i = 0; i < nstreams; i++) {
        cudaStreamCreate(&streams[i]);
    }

    // Partition the batch dimension among streams
    int chunk = (N + nstreams - 1) / nstreams;
    int block_size = 256;
    const float* weight_ptr = weight.data_ptr<float>();

    for (int i = 0; i < nstreams; i++) {
        int start = i * chunk;
        int end = std::min(N, (i + 1) * chunk);
        int chunkN = end - start;
        if (chunkN <= 0) continue;

        // Number of input elements in this batch chunk
        int num_elements = chunkN * C_in * H * W;
        const float* x_ptr = x.data_ptr<float>() + start * C_in * H * W;
        // Output pointer offset corresponding to this batch chunk
        float* out_ptr = output.data_ptr<float>() + start * C_out * H_out * W_out;

        int grid_size = (num_elements + block_size - 1) / block_size;
        conv_transpose2d_kernel<<<grid_size, block_size, 0, streams[i]>>>(
            x_ptr,
            weight_ptr,
            out_ptr,
            chunkN, C_in, H, W,
            C_out, K,
            stride,
            padding,
            H_out, W_out
        );
    }

    // Synchronize and destroy streams
    for (int i = 0; i < nstreams; i++) {
        cudaStreamSynchronize(streams[i]);
        cudaStreamDestroy(streams[i]);
    }

    // If bias is provided, launch a kernel to add it to the output
    if (bias.has_value()) {
        auto bias_tensor = bias.value();
        int total_output = N * C_out * H_out * W_out;
        int block_bias = 256;
        int grid_bias = (total_output + block_bias - 1) / block_bias;
        add_bias_kernel<<<grid_bias, block_bias>>>(
            output.data_ptr<float>(),
            bias_tensor.data_ptr<float>(),
            total_output, C_out, H_out, W_out
        );
        cudaDeviceSynchronize();
    }

    return output;
}

// Pybind11 module definition
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &conv_transpose2d_forward, "Hybrid ConvTranspose2d forward (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::to
CPU Time 223383.16 μs
Device Time 3238.12 μs
Self CPU Time 69.36 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::_to_copy
CPU Time 223313.80 μs
Device Time 3238.12 μs
Self CPU Time 106.80 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 219677.63 μs
Device Time 0.00 μs
Self CPU Time 132.35 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceGetStreamPriorityRange
CPU Time 218995.90 μs
Device Time 0.00 μs
Self CPU Time 218995.90 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
cudaStreamSynchronize
CPU Time 9976241.37 μs
Device Time 5435.09 μs
Self CPU Time 9976241.37 μs
Self Device Time 5435.09 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zeros
CPU Time 23948.89 μs
Device Time 27637.50 μs
Self CPU Time 2185.27 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 28510.34 μs
Device Time 124288.94 μs
Self CPU Time 5009.31 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 23503.02 μs
Device Time 124288.94 μs
Self CPU Time 6460.65 μs
Self Device Time 124288.94 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv_transpose2d_kernel(float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 10247761.24 μs
Self CPU Time 0.00 μs
Self Device Time 10247761.24 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 96651.44 μs
Self CPU Time 0.00 μs
Self Device Time 96651.44 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 
45313 warnings generated when compiling for host.
Suppressed 45335 warnings (45288 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_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:9:5 bugprone-easily-swappable-parameters
9 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:9:31: note: the first parameter in the range is 'input'
9 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:10:31: note: the last parameter in the range is 'weight'
10 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:13:22: warning: 2 adjacent parameters of 'conv_transpose2d_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | int C_in, int H, int W,
| ^~~~~~
14 | int C_out, int K, // square kernel
| ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:13:26: note: the first parameter in the range is 'W'
13 | int C_in, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:14:9: note: the last parameter in the range is 'C_out'
14 | int C_out, int K, // square kernel
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:14:16: warning: 2 adjacent parameters of 'conv_transpose2d_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | int C_out, int K, // square kernel
| ^~~~~~~~~~~~~~~~~~~~~~~
15 | int stride,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:14:20: note: the first parameter in the range is 'K'
14 | int C_out, int K, // square kernel
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:15:9: note: the last parameter in the range is 'stride'
15 | int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:16:5: warning: 2 adjacent parameters of 'conv_transpose2d_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | int padding,
| ^~~~~~~~~~~~
17 | int H_out, int W_out) {
| ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:16:9: note: the first parameter in the range is 'padding'
16 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:17:9: note: the last parameter in the range is 'H_out'
17 | int H_out, int W_out) {
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:19:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | int tid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:56:5: warning: 2 adjacent parameters of 'add_bias_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
56 | int total_elements,
| ^~~~~~~~~~~~~~~~~~~
57 | int C_out,
| ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:56:9: note: the first parameter in the range is 'total_elements'
56 | int total_elements,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:57:9: note: the last parameter in the range is 'C_out'
57 | int C_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:60:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
60 | int tid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:74:19: 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]
74 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:75:19: 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]
75 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:88:14: warning: the variable 'bias_val' is copy-constructed from a const reference but is only used as const reference; consider making it a const reference [performance-unnecessary-copy-initialization]
88 | auto bias_val = bias.value();
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:95:13: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | int N = x_sizes[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:96:16: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | int C_in = x_sizes[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:97:13: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
97 | int H = x_sizes[2];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:98:13: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | int W = x_sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:102:17: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | int C_out = w_sizes[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:103:13: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | int K = w_sizes[2];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:106:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
106 | int H_out = (H - 1) * stride - 2 * padding + K + output_padding;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:107:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
107 | int W_out = (W - 1) * stride - 2 * padding + K + output_padding;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:145:30: 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]
145 | const float* x_ptr = x.data_ptr<float>() + start * C_in * H * W;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:145:52: note: make conversion explicit to silence this warning
3 | const float* x_ptr = x.data_ptr<float>() + start * C_in * H * W;
| ^~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:145:52: note: perform multiplication in a wider type
145 | const float* x_ptr = x.data_ptr<float>() + start * C_in * H * W;
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:147:26: 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]
147 | float* out_ptr = output.data_ptr<float>() + start * C_out * H_out * W_out;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:147:53: note: make conversion explicit to silence this warning
147 | float* out_ptr = output.data_ptr<float>() + start * C_out * H_out * W_out;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:147:53: note: perform multiplication in a wider type
147 | float* out_ptr = output.data_ptr<float>() + start * C_out * H_out * W_out;
| ^~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:156:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
156 | stride,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:157:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
157 | padding,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_57/b8_s3_hybrid_conv_transpose2d/base/base.cu:170:14: warning: the variable 'bias_tensor' is copy-constructed from a const reference but is only used as const reference; consider making it a const reference [performance-unnecessary-copy-initialization]
170 | auto bias_tensor = bias.value();
| ^
| const &