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65_conv_transposed_2D__square_input__asymmetric_kernel • balanced_workload_distribution_edit_1

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 balanced_workload_distribution_edit_1
CUDA Speedup (Native) 0.047x
CUDA Speedup (Compile) 0.055x
CUDA Runtime 3.900 ms
PyTorch Runtime (Native) 0.183 ms
PyTorch Runtime (Compile) 0.215 ms
Correct True
Max Diff (vs. Reference) 0.001000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.00
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 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

// This kernel is designed to distribute workloads evenly across threads and blocks.
// By ensuring that each thread processes a similar amount of work, we can avoid underutilization
// and improve overall performance.

template <typename scalar_t>
__global__ void conv_transpose2d_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
) {
    extern __shared__ char shared_memory[];
    scalar_t* shared_input = reinterpret_cast<scalar_t*>(shared_memory);
    scalar_t* shared_weight = reinterpret_cast<scalar_t*>(shared_memory + sizeof(scalar_t) * blockDim.x);
    
    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;
    
    // Synchronize threads in the block
    __syncthreads();

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

    // Precompute strides to help with 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;

    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];
            }
        }
    }

    output[idx] = val;
}

// The forward function prepares the output tensor and launches the kernel with a one-to-one mapping of threads
// to output elements. Because the output tensor is stored in contiguous order (batch, channel, height, width),
// consecutive threads write to consecutive memory locations, enhancing memory coalescing.

torch::Tensor 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(), "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 (weight shape is [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_cuda", ([&] {
        conv_transpose2d_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", &forward, "Transposed 2D convolution with balanced workload distribution (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 2.460 inst/cycle 0.000 5
Executed Ipc Elapsed 2.450 inst/cycle 0.000 5
Issue Slots Busy 61.420 % 0.000 5
Issued Ipc Active 2.460 inst/cycle 0.000 5
SM Busy 69.288 % 0.000 5
Memory Throughput 18182918701.210 byte/second 23316628911029.617 5
Mem Busy 55.338 % 0.000 5
Max Bandwidth 55.138 % 0.000 5
L1/TEX Hit Rate 81.066 % 0.006 5
L2 Hit Rate 98.942 % 0.322 5
Mem Pipes Busy 51.598 % 0.000 5
Warp Cycles Per Issued Instruction 18.332 cycle 0.000 5
Warp Cycles Per Executed Instruction 18.332 cycle 0.000 5
Avg. Active Threads Per Warp 31.190 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.130 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 70.410 % 0.001 5
Achieved Active Warps Per SM 45.062 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization FMA is the highest-utilized pipeline (40.7%) 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.
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 235953.77 μs
Device Time 3118.48 μs
Self CPU Time 69.62 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 164952.46 μs
Device Time 57303.74 μs
Self CPU Time 4404.22 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 9074431.78 μs
Device Time 252418.43 μs
Self CPU Time 9676.51 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 9064757.40 μs
Device Time 252418.43 μs
Self CPU Time 13269.89 μs
Self Device Time 252418.43 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 9060754.90 μs
Device Time 9167.91 μs
Self CPU Time 9060754.90 μs
Self Device Time 9167.91 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_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 9747933.58 μs
Self CPU Time 0.00 μs
Self Device Time 9747933.58 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 840437.29 μs
Device Time 0.00 μs
Self CPU Time 840437.29 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 195114.69 μs
Self CPU Time 0.00 μs
Self Device Time 195114.69 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/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:12:5 bugprone-easily-swappable-parameters
12 | const scalar_t* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:12:34: note: the first parameter in the range is 'weight'
12 | const scalar_t* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:13:34: note: the last parameter in the range is 'bias'
13 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:15:5: warning: 2 adjacent parameters of 'conv_transpose2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
16 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:15:15: note: the first parameter in the range is 'batch_size'
15 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:16:15: note: the last parameter in the range is 'in_channels'
16 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:18:5: warning: 3 adjacent parameters of 'conv_transpose2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | const int in_width,
| ^~~~~~~~~~~~~~~~~~~
19 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~~
20 | const int kernel_h,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:18:15: note: the first parameter in the range is 'in_width'
18 | const int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:20:15: note: the last parameter in the range is 'kernel_h'
20 | const int kernel_h,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:21:5: warning: 5 adjacent parameters of 'conv_transpose2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | const int kernel_w,
| ^~~~~~~~~~~~~~~~~~~
22 | const int stride,
| ~~~~~~~~~~~~~~~~~
23 | const int padding,
| ~~~~~~~~~~~~~~~~~~
24 | const int output_padding,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
25 | const int groups,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:21:15: note: the first parameter in the range is 'kernel_w'
21 | const int kernel_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:25:15: note: the last parameter in the range is 'groups'
25 | const int groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:26:5: warning: 2 adjacent parameters of 'conv_transpose2d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
26 | const int dilation,
| ^~~~~~~~~~~~~~~~~~~
27 | const int out_height,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:26:15: note: the first parameter in the range is 'dilation'
26 | const int dilation,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:27:15: note: the last parameter in the range is 'out_height'
27 | const int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:35:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | 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/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:107:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
107 | const int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:108:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
108 | const int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:109:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
109 | const int in_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:110:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | const int in_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:113:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | 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/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:114:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | const int kernel_h = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:115:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | const int kernel_w = weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:127:32: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
127 | const int total_elements = output.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_65/b3_s0_balanced_workload_distribution/edit_1/edit_1.cu:131:5: 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]
131 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "conv_transpose2d_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__, \
| ^