← Back to Leaderboard

The AI CUDA Engineer 👷

81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__ • conv_transpose2d_thread_block_map_edit_1

Level 1 • Task 81
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,
    dilation: int,
) -> torch.Tensor:
    """
    Performs a 2D transposed convolution operation with asymmetric input and square kernel, supporting dilation, padding, and stride.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height_in, width_in).
        weight (torch.Tensor): Weight tensor of shape (in_channels, out_channels, kernel_size, kernel_size).
        bias (torch.Tensor): Bias tensor of shape (out_channels).
        stride (int): Stride of the convolution.
        padding (int): Padding applied to the input.
        dilation (int): Dilation rate.

    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, dilation=dilation
    )


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

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

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


# Constants
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = 3
height_in = 64
width_in = 128
stride = 5
padding = 1
dilation = 2
bias = False


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


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

import torch
import torch.nn as nn


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

    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 convolution kernel (square, e.g., 3 for a 3x3 kernel).
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        dilation (int, optional): Spacing between kernel elements. 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,
        dilation: int = 1,
        bias: bool = False,
    ):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            bias=bias,
        )

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

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height_in, width_in).

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


# Constants
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = 3
height_in = 64
width_in = 128
stride = 5
padding = 1
dilation = 2
bias = False


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, dilation, bias]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__
Level ID 1
Task ID 81
Kernel Name conv_transpose2d_thread_block_map_edit_1
CUDA Speedup (Native) 0.160x
CUDA Speedup (Compile) 0.166x
CUDA Runtime 10.934 ms
PyTorch Runtime (Native) 1.754 ms
PyTorch Runtime (Compile) 1.811 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model gpt-4o-mini-2024-07-18
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 1, Task 81 • 81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 conv_transpose2d_thread_block_map_edit_1 10.93 0.16 0.17
🥈 conv_transpose2d_no_divergence_edit_1 11.96 0.15 0.15
🥉 conv_transpose2d_constant_mem_base 12.07 0.15 0.15
4 conv_transpose2d_constant_mem_edit_1 12.13 0.14 0.15
5 conv_transpose2d_no_divergence_base 12.34 0.14 0.15
6 conv_transpose2d_thread_block_map_base 15.41 0.11 0.12
7 input_atomic_transposed_conv_base 21.48 0.08 0.08
8 aligned_ldg_memory_access_optimized_base 29.92 0.06 0.06
9 aligned_ldg_memory_access_base_base 29.93 0.06 0.06
10 tiled_atomic_free_base_base 30.06 0.06 0.06
11 shared_mem_weight_coalesced_base 51.16 0.03 0.04
12 unrolled_shared_mem_coalesced_base_base 51.16 0.03 0.04
13 double_buffered_conv_transpose2d_base 51.34 0.03 0.04
14 coalesced_memory_access_base 51.70 0.03 0.04
15 spatial_tile_indexing_base 51.76 0.03 0.03
16 coalesced_aligned_base 52.02 0.03 0.03
17 balanced_thread_block_distribution_base 52.04 0.03 0.03
18 81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__ 52.47 0.03 0.03
19 unroll_loops_for_critical_sections_base 52.47 0.03 0.03
19 unrolled_manual_base 52.47 0.03 0.03 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cstdio>
#include <pybind11/pybind11.h>

// Define a maximum kernel size assumed (adjust if necessary)
#define MAX_KERNEL_SIZE 16

// Optimized CUDA kernel for 2D transposed convolution that maps threads efficiently
// across a 3D grid covering batch size, output height, and output width. This aims to
// balance load across multi-dimensional spaces for better parallel utilization.

__global__ void conv_transpose2d_forward_kernel_thread_block_map(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int batch_size,
    int in_channels,
    int out_channels,
    int in_height,
    int in_width,
    int kernel_size,
    int out_height,
    int out_width,
    int stride,
    int padding,
    int dilation) {

  int w_out = blockIdx.x * blockDim.x + threadIdx.x;
  int h_out = blockIdx.y * blockDim.y + threadIdx.y;
  int b = blockIdx.z;
  if (h_out >= out_height || w_out >= out_width || b >= batch_size)
    return;

  for (int o = 0; o < out_channels; ++o) {  // Each block computes for a batch output, parallel across channels
    // Precompute base indices for output location
    int base_h = h_out + padding;
    int base_w = w_out + padding;

    // Precompute valid kernel indices for the h dimension
    int valid_p_count = 0;
    int valid_p[MAX_KERNEL_SIZE];        // stores the valid p index
    int h_in_list[MAX_KERNEL_SIZE];        // stores corresponding h_in
    for (int p = 0; p < kernel_size; p++) {
      int p_dilated = p * dilation;
      if (base_h >= p_dilated && ((base_h - p_dilated) % stride) == 0) {
        int h_in = (base_h - p_dilated) / stride;
        if (h_in < in_height) {
          valid_p[valid_p_count] = p;
          h_in_list[valid_p_count] = h_in;
          valid_p_count++;
        }
      }
    }

    // Precompute valid kernel indices for the w dimension
    int valid_q_count = 0;
    int valid_q[MAX_KERNEL_SIZE];        // stores the valid q index
    int w_in_list[MAX_KERNEL_SIZE];        // stores corresponding w_in
    for (int q = 0; q < kernel_size; q++) {
      int q_dilated = q * dilation;
      if (base_w >= q_dilated && ((base_w - q_dilated) % stride) == 0) {
        int w_in = (base_w - q_dilated) / stride;
        if (w_in < in_width) {
          valid_q[valid_q_count] = q;
          w_in_list[valid_q_count] = w_in;
          valid_q_count++;
        }
      }
    }

    // Initialize the output value with the bias for channel o using read-only cache
    float out_val = __ldg(&bias[o]);

    // Iterate over input channels
    for (int c = 0; c < in_channels; ++c) {
      // Loop over precomputed valid p positions
      for (int i = 0; i < valid_p_count; i++) {
        int p = valid_p[i];
        int h_in = h_in_list[i];
        // Loop over precomputed valid q positions
        for (int j = 0; j < valid_q_count; j++) {
          int q = valid_q[j];
          int w_in = w_in_list[j];
          
          // Compute flat indices for input and weight tensors
          int input_idx = (((b * in_channels + c) * in_height) + h_in) * in_width + w_in;
          int weight_idx = (((c * out_channels + o) * kernel_size + p) * kernel_size) + q;
          
          // Accumulate contributions using read-only loads
          out_val += __ldg(&input[input_idx]) * __ldg(&weight[weight_idx]);
        }
      }
    }

    // Write the computed result to the output
    int output_idx = (((b * out_channels) + o) * out_height + h_out) * out_width + w_out;
    output[output_idx] = out_val;
  }
}

// CUDA forward function using efficient 3D thread and block mapping
torch::Tensor conv_transpose2d_forward_cuda_thread_block_map(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor bias,
    int stride,
    int padding,
    int dilation) {
  // Get dimensions from input and weight tensors
  int batch_size = input.size(0);
  int in_channels = input.size(1);
  int in_height = input.size(2);
  int in_width = input.size(3);

  // Weight tensor has shape: [in_channels, out_channels, kernel_size, kernel_size]
  int out_channels = weight.size(1);
  int kernel_size = weight.size(2);  // assume square kernel

  // Compute output dimensions
  int out_height = (in_height - 1) * stride - 2 * padding + dilation * (kernel_size - 1) + 1;
  int out_width  = (in_width - 1) * stride - 2 * padding + dilation * (kernel_size - 1) + 1;

  auto output = torch::zeros({batch_size, out_channels, out_height, out_width}, input.options());

  dim3 threads(32, 8, 1);  // 32x8 threads per block
  dim3 blocks((out_width + threads.x - 1) / threads.x, (out_height + threads.y - 1) / threads.y, batch_size);

  conv_transpose2d_forward_kernel_thread_block_map<<<blocks, threads>>>(
      input.data_ptr<float>(),
      weight.data_ptr<float>(),
      bias.data_ptr<float>(),
      output.data_ptr<float>(),
      batch_size,
      in_channels,
      out_channels,
      in_height,
      in_width,
      kernel_size,
      out_height,
      out_width,
      stride,
      padding,
      dilation);

  cudaError_t err = cudaGetLastError();
  if (err != cudaSuccess) {
    printf("Error in conv_transpose2d_forward_kernel_thread_block_map: %s\n", cudaGetErrorString(err));
  }

  return output;
}

// Wrapper function to support bias being None (creates a zero bias tensor if needed)
torch::Tensor conv_transpose2d_forward_wrapper_thread_block_map(
    torch::Tensor input,
    torch::Tensor weight,
    pybind11::object bias_obj,
    int stride,
    int padding,
    int dilation) {
  int out_channels = weight.size(1);
  torch::Tensor bias;
  if (bias_obj.is(pybind11::none())) {
    bias = torch::zeros({out_channels}, weight.options());
  } else {
    bias = bias_obj.cast<torch::Tensor>();
  }
  return conv_transpose2d_forward_cuda_thread_block_map(input, weight, bias, stride, padding, dilation);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &conv_transpose2d_forward_wrapper_thread_block_map,
        "ConvTranspose2d forward (CUDA) with optimal 3D mapping",
        pybind11::arg("input"),
        pybind11::arg("weight"),
        pybind11::arg("bias"),
        pybind11::arg("stride"),
        pybind11::arg("padding"),
        pybind11::arg("dilation"));
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.446 inst/cycle 0.000 5
Executed Ipc Elapsed 3.382 inst/cycle 0.000 5
Issue Slots Busy 86.144 % 0.003 5
Issued Ipc Active 3.446 inst/cycle 0.000 5
SM Busy 86.144 % 0.003 5
Memory Throughput 75842037720.462 byte/second 28236942506261412.000 5
Mem Busy 39.174 % 0.004 5
Max Bandwidth 36.190 % 0.003 5
L1/TEX Hit Rate 97.404 % 0.000 5
L2 Hit Rate 98.938 % 0.001 5
Mem Pipes Busy 70.880 % 0.013 5
Warp Cycles Per Issued Instruction 14.292 cycle 0.000 5
Warp Cycles Per Executed Instruction 14.292 cycle 0.000 5
Avg. Active Threads Per Warp 24.550 0.000 5
Avg. Not Predicated Off Threads Per Warp 22.530 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 76.904 % 0.027 5
Achieved Active Warps Per SM 49.220 warp 0.010 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (44.6%) 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 ThreadDivergence Instructions are executed in warps, which are groups of 32 threads. Optimal instruction throughput is achieved if all 32 threads of a warp execute the same instruction. The chosen launch configuration, early thread completion, and divergent flow control can significantly lower the number of active threads in a warp per cycle. This kernel achieves an average of 24.5 threads being active per cycle. This is further reduced to 22.5 threads per warp due to predication. The compiler may use predication to avoid an actual branch. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Try to avoid different execution paths within a warp when possible. In addition, ensure your kernel makes use of Independent Thread Scheduling, which allows a warp to reconverge after a data-dependent conditional block by explicitly calling __syncwarp().
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (76.8%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. 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 717006.26 μs
Device Time 1696.22 μs
Self CPU Time 48.26 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 101648.27 μs
Device Time 150885.95 μs
Self CPU Time 2039.64 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 4468788.15 μs
Device Time 197822.99 μs
Self CPU Time 3231.76 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 4465557.81 μs
Device Time 197822.99 μs
Self CPU Time 4496.28 μs
Self Device Time 197822.99 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 4463631.83 μs
Device Time 3512.95 μs
Self CPU Time 4463631.83 μs
Self Device Time 3512.95 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<float>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<float>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 150885.95 μs
Self CPU Time 0.00 μs
Self Device Time 150885.95 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_forward_kernel_thread_block_map(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 6307407.26 μs
Self CPU Time 0.00 μs
Self Device Time 6307407.26 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 1949083.50 μs
Device Time 254.91 μs
Self CPU Time 1949083.50 μs
Self Device Time 254.91 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 
45299 warnings generated when compiling for host.
Suppressed 45326 warnings (45279 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/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:16:5 bugprone-easily-swappable-parameters
16 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
17 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:16:31: note: the first parameter in the range is 'weight'
16 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:17:31: note: the last parameter in the range is 'bias'
17 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:19:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_thread_block_map' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int batch_size,
| ^~~~~~~~~~~~~~~
20 | int in_channels,
| ~~~~~~~~~~~~~~~~
21 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:19:9: note: the first parameter in the range is 'batch_size'
19 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:21:9: note: the last parameter in the range is 'out_channels'
21 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:23:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_thread_block_map' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | int in_width,
| ^~~~~~~~~~~~~
24 | int kernel_size,
| ~~~~~~~~~~~~~~~~
25 | int out_height,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:23:9: note: the first parameter in the range is 'in_width'
23 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:25:9: note: the last parameter in the range is 'out_height'
25 | int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:26:5: warning: 4 adjacent parameters of 'conv_transpose2d_forward_kernel_thread_block_map' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
26 | int out_width,
| ^~~~~~~~~~~~~~
27 | int stride,
| ~~~~~~~~~~~
28 | int padding,
| ~~~~~~~~~~~~
29 | int dilation) {
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:26:9: note: the first parameter in the range is 'out_width'
26 | int out_width,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:29:9: note: the last parameter in the range is 'dilation'
29 | int dilation) {
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:31:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int w_out = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:32:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | int h_out = blockIdx.y * blockDim.y + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:33:11: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
33 | int b = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:106:19: warning: the parameter 'input' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
106 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:107:19: 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]
107 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:108:19: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
108 | torch::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:113:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:114:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:115:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | int in_height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:116:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | int in_width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:119:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:120:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | int kernel_size = weight.size(2); // assume square kernel
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:159:19: 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]
159 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:160:22: warning: the parameter 'bias_obj' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
160 | pybind11::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:164:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
164 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s0_conv_transpose2d_thread_block_map/edit_1/edit_1.cu:171:57: warning: parameter 'input' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
5 | return conv_transpose2d_forward_cuda_thread_block_map(input, weight, bias, stride, padding, dilation);
| ^
| std::move( )