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57_conv_transposed_2D__square_input__square_kernel • conv_transposed2d_coalesced_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 conv_transposed2d_coalesced_base
CUDA Speedup (Native) 0.018x
CUDA Speedup (Compile) 0.022x
CUDA Runtime 8.321 ms
PyTorch Runtime (Native) 0.153 ms
PyTorch Runtime (Compile) 0.180 ms
Correct True
Max Diff (vs. Reference) 0.001000
Model openrouter-deepseek-reasoner
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.h>
#include <cuda_runtime.h>

__device__ inline int d_min(int a, int b) { return a < b ? a : b; }
__device__ inline int d_max(int a, int b) { return a > b ? a : b; }

__global__ void convTranspose2dCoalescedKernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int batch,
    int in_channels,
    int out_channels,
    int height_in,
    int width_in,
    int kernel_size,
    int stride,
    int padding,
    int height_out,
    int width_out,
    int groups,
    bool bias_present
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total = batch * out_channels * height_out * width_out;
    if (idx >= total) return;

    // Coalesced thread mapping: [out_ch, w, h, b]
    int out_ch = idx % out_channels;
    int tmp = idx / out_channels;
    int w = tmp % width_out;
    tmp /= width_out;
    int h = tmp % height_out;
    int b = tmp / height_out;

    float out_val = 0.0f;

    int out_channels_per_group = out_channels / groups;
    int in_channels_per_group = in_channels / groups;
    int group = out_ch / out_channels_per_group;
    int out_ch_mod = out_ch % out_channels_per_group;

    int h_temp = h + padding;
    int w_temp = w + padding;

    // Optimized bounds checking
    int p0 = h_temp % stride;
    int p_min = d_max(p0, h_temp - (height_in - 1) * stride);
    int p_max = d_min(kernel_size - 1, h_temp);
    int p_start = p_min + ((p0 - (p_min % stride) + stride) % stride);

    int q0 = w_temp % stride;
    int q_min = d_max(q0, w_temp - (width_in - 1) * stride);
    int q_max = d_min(kernel_size - 1, w_temp);
    int q_start = q_min + ((q0 - (q_min % stride) + stride) % stride);

    int in_ch_start = group * in_channels_per_group;
    int in_ch_end = in_ch_start + in_channels_per_group;

    for (int in_ch = in_ch_start; in_ch < in_ch_end; in_ch++) {
        for (int p = p_start; p <= p_max; p += stride) {
            int i_in = (h_temp - p) / stride;
            for (int q = q_start; q <= q_max; q += stride) {
                int j_in = (w_temp - q) / stride;
                
                // Coalesced weight access using out_ch_mod
                int weight_idx = ((in_ch * out_channels_per_group + out_ch_mod) * kernel_size + p) * kernel_size + q;
                int input_idx = ((b * in_channels + in_ch) * height_in + i_in) * width_in + j_in;
                
                out_val += input[input_idx] * weight[weight_idx];
            }
        }
    }

    if (bias_present) out_val += bias[out_ch];

    // Coalesced write to output
    int output_idx = ((b * out_channels + out_ch) * height_out + h) * width_out + w;
    output[output_idx] = out_val;
}

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
) {
    TORCH_CHECK(x.is_cuda() && weight.is_cuda(), "Inputs must be on CUDA");
    TORCH_CHECK(x.is_contiguous() && weight.is_contiguous(), "Inputs must be contiguous");

    int batch = x.size(0);
    int in_channels = x.size(1);
    int height_in = x.size(2);
    int width_in = x.size(3);
    int kernel_size = weight.size(2);
    int out_channels = weight.size(1) * groups;

    int height_out = (height_in - 1) * stride - 2 * padding + kernel_size + output_padding;
    int width_out = (width_in - 1) * stride - 2 * padding + kernel_size + output_padding;

    auto output = torch::zeros({batch, out_channels, height_out, width_out}, x.options());

    int total_threads = batch * out_channels * height_out * width_out;
    int block_size = 256;
    int grid_size = (total_threads + block_size - 1) / block_size;

    convTranspose2dCoalescedKernel<<<grid_size, block_size>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.has_value() ? bias->data_ptr<float>() : nullptr,
        output.data_ptr<float>(),
        batch, in_channels, out_channels,
        height_in, width_in,
        kernel_size, stride, padding,
        height_out, width_out,
        groups, bias.has_value()
    );

    TORCH_CHECK(cudaGetLastError() == cudaSuccess, "Kernel launch failed");
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &conv_transpose2d_forward, "ConvTranspose2d with coalesced memory access");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.040 inst/cycle 0.000 5
Executed Ipc Elapsed 3.030 inst/cycle 0.000 5
Issue Slots Busy 75.910 % 0.000 5
Issued Ipc Active 3.040 inst/cycle 0.000 5
SM Busy 75.910 % 0.000 5
Memory Throughput 8217459046.320 byte/second 356105226889423.750 5
Mem Busy 14.330 % 0.000 5
Max Bandwidth 14.040 % 0.000 5
L1/TEX Hit Rate 99.680 % 0.000 5
L2 Hit Rate 96.626 % 0.019 5
Mem Pipes Busy 37.828 % 0.000 5
Warp Cycles Per Issued Instruction 15.664 cycle 0.000 5
Warp Cycles Per Executed Instruction 15.666 cycle 0.000 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.220 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 6.000 block 0.000 5
Block Limit Shared Mem 32.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 48.000 warp 0.000 5
Theoretical Occupancy 75.000 % 0.000 5
Achieved Occupancy 74.322 % 0.000 5
Achieved Active Warps Per SM 47.570 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (57.0%) 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.
WRN Occupancy This kernel's theoretical occupancy (75.0%) is limited by the number of required registers. 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 238705.79 μs
Device Time 3377.61 μs
Self CPU Time 51.30 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::zeros
CPU Time 67559.46 μs
Device Time 26988.30 μs
Self CPU Time 2874.46 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 8184338.61 μs
Device Time 120187.82 μs
Self CPU Time 5684.70 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::fill_
CPU Time 8178655.86 μs
Device Time 120187.82 μs
Self CPU Time 8249.41 μs
Self Device Time 120187.82 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 8175737.85 μs
Device Time 3230.99 μs
Self CPU Time 8175737.85 μs
Self Device Time 3230.99 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
convTranspose2dCoalescedKernel(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int, int, bool)
CPU Time 0.00 μs
Device Time 9925314.16 μs
Self CPU Time 0.00 μs
Self Device Time 9925314.16 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 1773766.76 μs
Device Time 101.47 μs
Self CPU Time 1773766.76 μs
Self Device Time 101.47 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 93199.52 μs
Self CPU Time 0.00 μs
Self Device Time 93199.52 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 
45301 warnings generated when compiling for host.
Suppressed 45327 warnings (45280 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/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:10:5 bugprone-easily-swappable-parameters
10 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:10:31: note: the first parameter in the range is 'weight'
10 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:11:31: note: the last parameter in the range is 'bias'
11 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:13:5: warning: 2 adjacent parameters of 'convTranspose2dCoalescedKernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | int batch,
| ^~~~~~~~~~
14 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:13:9: note: the first parameter in the range is 'batch'
13 | int batch,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:14:9: note: the last parameter in the range is 'in_channels'
14 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:15:5: warning: 2 adjacent parameters of 'convTranspose2dCoalescedKernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | int out_channels,
| ^~~~~~~~~~~~~~~~~
16 | int height_in,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:15:9: note: the first parameter in the range is 'out_channels'
15 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:16:9: note: the last parameter in the range is 'height_in'
16 | int height_in,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:17:5: warning: 2 adjacent parameters of 'convTranspose2dCoalescedKernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | int width_in,
| ^~~~~~~~~~~~~
18 | int kernel_size,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:17:9: note: the first parameter in the range is 'width_in'
17 | int width_in,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:18:9: note: the last parameter in the range is 'kernel_size'
18 | int kernel_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:19:5: warning: 3 adjacent parameters of 'convTranspose2dCoalescedKernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int stride,
| ^~~~~~~~~~~
20 | int padding,
| ~~~~~~~~~~~~
21 | int height_out,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:19:9: note: the first parameter in the range is 'stride'
19 | int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:21:9: note: the last parameter in the range is 'height_out'
21 | int height_out,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:22:5: warning: 2 adjacent parameters of 'convTranspose2dCoalescedKernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
22 | int width_out,
| ^~~~~~~~~~~~~~
23 | int groups,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:22:9: note: the first parameter in the range is 'width_out'
22 | int width_out,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:23:9: note: the last parameter in the range is 'groups'
23 | int groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:26:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:85:19: 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]
85 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:86:19: 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]
86 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:90:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
90 | int64_t output_padding,
| ^~~~~~~~~~~~~~~~~~~~~~~
91 | int64_t groups
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:90:13: note: the first parameter in the range is 'output_padding'
90 | int64_t output_padding,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:91:13: note: the last parameter in the range is 'groups'
91 | int64_t groups
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:96:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | int batch = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/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 in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:98:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | int height_in = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:99:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | int width_in = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:100:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | int kernel_size = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:101:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | int out_channels = weight.size(1) * groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:103:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | int height_out = (height_in - 1) * stride - 2 * padding + kernel_size + output_padding;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:104:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
104 | int width_out = (width_in - 1) * stride - 2 * padding + kernel_size + output_padding;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:119:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | kernel_size, stride, padding,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:119:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | kernel_size, stride, padding,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_1/task_57/b4_s3_conv_transposed2d_coalesced/base/base.cu:121:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | groups, bias.has_value()
| ^