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65_conv_transposed_2D__square_input__asymmetric_kernel • conv_transpose2d_optimized_base

Level 1 • Task 65
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 a square input and an asymmetric kernel.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).
        weight (torch.Tensor): Weight tensor of shape (out_channels, in_channels // groups, kernel_height, kernel_width).
        bias (torch.Tensor): Bias tensor of shape (out_channels).
        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.

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
    """
    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 a square input and an asymmetric kernel.

    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 (int): Stride of the convolution.
        padding (int or tuple): Padding applied to the input.
        output_padding (int or tuple): 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: tuple,
        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 for default arguments
stride = 1
padding = 0
output_padding = 0
groups = 1
bias = False

# Test code
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = (3, 5)  # Asymmetric kernel
width = 128
height = 128


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 a square input and an asymmetric kernel.

    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 (int, optional): Stride of the convolution. Defaults to 1.
        padding (int or tuple, optional): Padding applied to the input. Defaults to 0.
        output_padding (int or tuple, 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: tuple,
        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)


# Constants for default arguments
stride = 1
padding = 0
output_padding = 0
groups = 1
bias = False

# Test code
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = (3, 5)  # Asymmetric kernel
width = 128
height = 128


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 65_conv_transposed_2D__square_input__asymmetric_kernel
Level ID 1
Task ID 65
Kernel Name conv_transpose2d_optimized_base
CUDA Speedup (Native) 0.049x
CUDA Speedup (Compile) 0.058x
CUDA Runtime 3.712 ms
PyTorch Runtime (Native) 0.183 ms
PyTorch Runtime (Compile) 0.215 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 65 • 65_conv_transposed_2D__square_input__asymmetric_kernel)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 atomic_operations_minimization_base 3.71 0.05 0.06
🥈 mem_coalesce_v1_base 3.71 0.05 0.06
🥈 conv_transpose2d_optimized_base 3.71 0.05 0.06
4 constant_memory_conv_transposed_2d_optimized_edit_1 3.83 0.05 0.06
4 constant_memory_conv_transposed_2d_optimized_base 3.83 0.05 0.06
6 distributed_workload_conv_transposed_2d_base 3.83 0.05 0.06
6 constant_memory_conv_transposed_2d_base 3.83 0.05 0.06
8 optimized_block_size_conv_transposed_2d_edit_1 3.83 0.05 0.06
8 optimized_block_size_conv_transposed_2d_base 3.83 0.05 0.06
10 modular_conv_transposed_2d_base 3.83 0.05 0.06
11 minimize_warp_divergence_edit_1 3.87 0.05 0.06
12 gather_fused_unroll_base 3.87 0.05 0.06
13 gather_fused_base 3.88 0.05 0.06
14 balanced_workload_distribution_base 3.90 0.05 0.06
15 warp_level_optimization_edit_1 3.90 0.05 0.06
16 balanced_workload_distribution_edit_1 3.90 0.05 0.06
16 modular_conv_transposed_2d_edit_1 3.90 0.05 0.06
18 gather_fused_optimized_block_base 3.91 0.05 0.05
19 optimized_conv_transpose2d_kernel_edit_1 3.92 0.05 0.05
20 gather_unified_base 3.93 0.05 0.05 
// Includes
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

// Optimized CUDA kernel for ConvTranspose2D with specialized branch for stride==1 for improved memory coalescing

template <typename scalar_t>
__global__ void conv_transpose2d_optimized_kernel(
    const scalar_t* __restrict__ input,
    const scalar_t* __restrict__ weight,
    const scalar_t* __restrict__ bias,
    scalar_t* __restrict__ output,
    const int batch_size,
    const int in_channels,
    const int in_height,
    const int in_width,
    const int out_channels,
    const int kernel_h,
    const int kernel_w,
    const int stride,
    const int padding,
    const int output_padding,
    const int groups,
    const int dilation,
    const int out_height,
    const int out_width
) {
    // Calculate the global thread index
    const int total_elements = batch_size * out_channels * out_height * out_width;
    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= total_elements) return;

    // Unravel linear index into (b, oc, oh, ow) assuming contiguous layout: (batch, channel, height, width)
    int n = idx;
    const int ow = n % out_width;
    n /= out_width;
    const int oh = n % out_height;
    n /= out_height;
    const int oc = n % out_channels;
    n /= out_channels;
    const int b = n;

    // Group and channel indexing
    const int out_channels_per_group = out_channels / groups;
    const int g = oc / out_channels_per_group;
    const int oc_group = oc % out_channels_per_group;
    const int in_channels_per_group = in_channels / groups;
    const int ic_start = g * in_channels_per_group;

    // Initialize the accumulator with bias if available
    scalar_t val = (bias != nullptr) ? bias[oc] : static_cast<scalar_t>(0);

    // Precompute common strides for memory addressing
    const int input_b_stride = in_channels * in_height * in_width;
    const int input_c_stride = in_height * in_width;
    const int weight_channel_stride = kernel_h * kernel_w;

    if (stride == 1) {
        // Optimized branch for stride == 1 eliminates division overhead
        for (int kh = 0; kh < kernel_h; ++kh) {
            int h_in = oh - kh * dilation + padding;  // No division since stride==1
            if (h_in < 0 || h_in >= in_height) continue;
            for (int kw = 0; kw < kernel_w; ++kw) {
                int w_in = ow - kw * dilation + padding;
                if (w_in < 0 || w_in >= in_width) continue;
                // Loop over each input channel in the current group
                for (int ic = 0; ic < in_channels_per_group; ++ic) {
                    int input_idx = b * input_b_stride + (ic_start + ic) * input_c_stride + h_in * in_width + w_in;
                    int weight_idx = (ic_start + ic) * (out_channels_per_group * kernel_h * kernel_w) +
                                     oc_group * weight_channel_stride +
                                     kh * kernel_w + kw;
                    val += input[input_idx] * weight[weight_idx];
                }
            }
        }
    } else {
        // General case for stride != 1
        for (int kh = 0; kh < kernel_h; ++kh) {
            int h_in_base = oh - kh * dilation + padding;
            if (h_in_base % stride != 0) continue;
            int h_in = h_in_base / stride;
            if (h_in < 0 || h_in >= in_height) continue;
            for (int kw = 0; kw < kernel_w; ++kw) {
                int w_in_base = ow - kw * dilation + padding;
                if (w_in_base % stride != 0) continue;
                int w_in = w_in_base / stride;
                if (w_in < 0 || w_in >= in_width) continue;
                for (int ic = 0; ic < in_channels_per_group; ++ic) {
                    int input_idx = b * input_b_stride + (ic_start + ic) * input_c_stride + h_in * in_width + w_in;
                    int weight_idx = (ic_start + ic) * (out_channels_per_group * kernel_h * kernel_w) +
                                     oc_group * weight_channel_stride +
                                     kh * kernel_w + kw;
                    val += input[input_idx] * weight[weight_idx];
                }
            }
        }
    }

    // Write the computed value to the output tensor
    output[idx] = val;
}

// Forward function that sets up the kernel launch
torch::Tensor conv_transpose2d_optimized_forward(
    torch::Tensor x,
    torch::Tensor weight,
    torch::optional<torch::Tensor> bias,
    int stride,
    int padding,
    int output_padding,
    int groups,
    int dilation = 1
) {
    TORCH_CHECK(x.device().is_cuda(), "Input tensor x must be a CUDA tensor");
    TORCH_CHECK(x.dim() == 4, "Input must be 4D");
    TORCH_CHECK(weight.dim() == 4, "Weight must be 4D");

    const int batch_size = x.size(0);
    const int in_channels = x.size(1);
    const int in_height = x.size(2);
    const int in_width = x.size(3);

    // out_channels is defined as weight.size(1) * groups (with weight shape: [in_channels, out_channels/groups, kH, kW])
    const int out_channels = weight.size(1) * groups;
    const int kernel_h = weight.size(2);
    const int kernel_w = weight.size(3);

    if (bias.has_value() && bias->defined()) {
        TORCH_CHECK(bias->numel() == out_channels, "Bias must have out_channels elements");
        TORCH_CHECK(bias->device().is_cuda(), "Bias must be a CUDA tensor");
    }

    const int out_height = (in_height - 1) * stride - 2 * padding + dilation * (kernel_h - 1) + output_padding + 1;
    const int out_width = (in_width - 1) * stride - 2 * padding + dilation * (kernel_w - 1) + output_padding + 1;

    auto output = torch::zeros({batch_size, out_channels, out_height, out_width}, x.options());
    const int total_elements = output.numel();
    const int threads = 256;
    const int blocks = (total_elements + threads - 1) / threads;

    AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "conv_transpose2d_optimized_cuda", ([&] {
        conv_transpose2d_optimized_kernel<scalar_t><<<blocks, threads>>>(
            x.data_ptr<scalar_t>(),
            weight.data_ptr<scalar_t>(),
            (bias.has_value() && bias->defined()) ? bias->data_ptr<scalar_t>() : nullptr,
            output.data_ptr<scalar_t>(),
            batch_size,
            in_channels,
            in_height,
            in_width,
            out_channels,
            kernel_h,
            kernel_w,
            stride,
            padding,
            output_padding,
            groups,
            dilation,
            out_height,
            out_width
        );
    }));

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &conv_transpose2d_optimized_forward, "Optimized ConvTranspose2D (CUDA)",
          py::arg("x"), py::arg("weight"), py::arg("bias") = py::none(),
          py::arg("stride"), py::arg("padding"), py::arg("output_padding"),
          py::arg("groups"), py::arg("dilation") = 1);
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.420 inst/cycle 0.000 5
Executed Ipc Elapsed 2.410 inst/cycle 0.000 5
Issue Slots Busy 60.462 % 0.000 5
Issued Ipc Active 2.420 inst/cycle 0.000 5
SM Busy 63.754 % 0.000 5
Memory Throughput 18978843113.264 byte/second 945793708185140.000 5
Mem Busy 59.484 % 0.001 5
Max Bandwidth 59.278 % 0.001 5
L1/TEX Hit Rate 73.574 % 0.012 5
L2 Hit Rate 99.386 % 0.366 5
Mem Pipes Busy 54.326 % 0.000 5
Warp Cycles Per Issued Instruction 24.232 cycle 0.000 5
Warp Cycles Per Executed Instruction 24.232 cycle 0.000 5
Avg. Active Threads Per Warp 31.070 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.460 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 91.630 % 0.001 5
Achieved Active Warps Per SM 58.644 warp 0.001 5
Analysis Rules
Rule Description
INF HighPipeUtilization FMA is the highest-utilized pipeline (38.4%) based on active cycles, taking into account the rates of its different instructions. It executes 32-bit floating point (FADD, FMUL, FMAD, ...) and integer (IMUL, IMAD) operations. It is well-utilized, but should not be a bottleneck.
INF Occupancy This kernel's theoretical occupancy is not impacted by any block limit.
Operation / Metric Value Unit
aten::to
CPU Time 384134.62 μs
Device Time 3342.49 μs
Self CPU Time 53.19 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 172898.22 μs
Device Time 60383.08 μs
Self CPU Time 5164.72 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 9099723.66 μs
Device Time 264514.49 μs
Self CPU Time 10957.77 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 9088768.38 μs
Device Time 264514.49 μs
Self CPU Time 14400.03 μs
Self Device Time 264514.49 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 9084368.77 μs
Device Time 9672.98 μs
Self CPU Time 9084368.77 μs
Self Device Time 9672.98 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void conv_transpose2d_optimized_kernel<float>(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 9726934.30 μs
Self CPU Time 0.00 μs
Self Device Time 9726934.30 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 801096.59 μs
Device Time 0.00 μs
Self CPU Time 801096.59 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
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 204131.41 μs
Self CPU Time 0.00 μs
Self Device Time 204131.41 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 
45298 warnings generated when compiling for host.
Suppressed 45328 warnings (45281 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/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:11:5 bugprone-easily-swappable-parameters
11 | const scalar_t* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:11:34: note: the first parameter in the range is 'weight'
11 | const scalar_t* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:12:34: note: the last parameter in the range is 'bias'
12 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:14:5: warning: 2 adjacent parameters of 'conv_transpose2d_optimized_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
15 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:14:15: note: the first parameter in the range is 'batch_size'
14 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:15:15: note: the last parameter in the range is 'in_channels'
15 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:17:5: warning: 3 adjacent parameters of 'conv_transpose2d_optimized_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | const int in_width,
| ^~~~~~~~~~~~~~~~~~~
18 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~~
19 | const int kernel_h,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:17:15: note: the first parameter in the range is 'in_width'
17 | const int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:19:15: note: the last parameter in the range is 'kernel_h'
19 | const int kernel_h,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:20:5: warning: 5 adjacent parameters of 'conv_transpose2d_optimized_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | const int kernel_w,
| ^~~~~~~~~~~~~~~~~~~
21 | const int stride,
| ~~~~~~~~~~~~~~~~~
22 | const int padding,
| ~~~~~~~~~~~~~~~~~~
23 | const int output_padding,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
24 | const int groups,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:20:15: note: the first parameter in the range is 'kernel_w'
20 | const int kernel_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:24:15: note: the last parameter in the range is 'groups'
24 | const int groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:25:5: warning: 2 adjacent parameters of 'conv_transpose2d_optimized_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | const int dilation,
| ^~~~~~~~~~~~~~~~~~~
26 | const int out_height,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:25:15: note: the first parameter in the range is 'dilation'
25 | const int dilation,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:26:15: note: the last parameter in the range is 'out_height'
26 | const int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:31:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | const int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:119:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | const int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:120:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | const int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:121:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | const int in_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:122:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
122 | const int in_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:125:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | const int out_channels = weight.size(1) * groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:126:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | const int kernel_h = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:127:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
127 | const int kernel_w = weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:138:32: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
138 | const int total_elements = output.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b4_s3_conv_transpose2d_optimized/base/base.cu:142:5: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
142 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "conv_transpose2d_optimized_cuda", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^