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81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__ • unrolled_manual_base

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 unrolled_manual_base
CUDA Speedup (Native) 0.033x
CUDA Speedup (Compile) 0.035x
CUDA Runtime 52.474 ms
PyTorch Runtime (Native) 1.754 ms
PyTorch Runtime (Compile) 1.811 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 1.00
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>

// This kernel performs the 2D transposed convolution operation with manual loop unrolling via #pragma unroll.
// The inner loops (over kernel height and width) are unrolled to reduce loop overhead and improve performance.

__global__ void conv_transpose2d_forward_kernel_unrolled_manual(
    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) {

  // Decode the linear index into output tensor coordinates (b, o, out_h, out_w)
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int total = batch_size * out_channels * out_height * out_width;
  if (index >= total) return;

  int w_out = index % out_width;
  int temp = index / out_width;
  int h_out = temp % out_height;
  temp /= out_height;
  int o = temp % out_channels;
  int b = temp / out_channels;

  // Start with bias for the output channel
  float result = bias[o];

  // Manually unroll the loops for kernel height and width
  #pragma unroll
  for (int c = 0; c < in_channels; c++) {
    #pragma unroll
    for (int p = 0; p < kernel_size; p++) {
      int h_unscaled = h_out + padding - p * dilation;
      if (h_unscaled % stride != 0) continue;
      int h_in = h_unscaled / stride;
      if (h_in < 0 || h_in >= in_height) continue;
      
      #pragma unroll
      for (int q = 0; q < kernel_size; q++) {
        int w_unscaled = w_out + padding - q * dilation;
        if (w_unscaled % stride != 0) continue;
        int w_in = w_unscaled / stride;
        if (w_in < 0 || w_in >= in_width) continue;
        
        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;
        result += input[input_idx] * weight[weight_idx];
      }
    }
  }

  int output_idx = ((b * out_channels + o) * out_height + h_out) * out_width + w_out;
  output[output_idx] = result;
}

// CUDA launcher function
torch::Tensor conv_transpose2d_forward_cuda_unrolled_manual(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor bias,
    int stride,
    int padding,
    int dilation) {
  
  int batch_size = input.size(0);
  int in_channels = input.size(1);
  int in_height = input.size(2);
  int in_width = input.size(3);
  
  int out_channels = weight.size(1);
  int kernel_size = weight.size(2);  // assume square kernel
  
  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());
  
  int total_threads = batch_size * out_channels * out_height * out_width;
  int threads = 1024;
  int blocks = (total_threads + threads - 1) / threads;
  
  conv_transpose2d_forward_kernel_unrolled_manual<<<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_unrolled_manual: %s\n", cudaGetErrorString(err));
  }
  
  return output;
}

// Wrapper function to handle bias being potentially None
torch::Tensor conv_transpose2d_forward_wrapper_unrolled_manual(
    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_unrolled_manual(input, weight, bias, stride, padding, dilation);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &conv_transpose2d_forward_wrapper_unrolled_manual,
        "ConvTranspose2d forward with manually unrolled loops (CUDA)",
        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 3.320 inst/cycle 0.000 5
Executed Ipc Elapsed 3.320 inst/cycle 0.000 5
Issue Slots Busy 83.028 % 0.000 5
Issued Ipc Active 3.320 inst/cycle 0.000 5
SM Busy 83.028 % 0.000 5
Memory Throughput 12788813181.688 byte/second 106721887737764.703 5
Mem Busy 5.880 % 0.000 5
Max Bandwidth 5.830 % 0.000 5
L1/TEX Hit Rate 88.752 % 0.000 5
L2 Hit Rate 99.376 % 0.009 5
Mem Pipes Busy 33.080 % 0.000 5
Warp Cycles Per Issued Instruction 15.068 cycle 0.000 5
Warp Cycles Per Executed Instruction 15.070 cycle 0.000 5
Avg. Active Threads Per Warp 27.640 0.000 5
Avg. Not Predicated Off Threads Per Warp 25.310 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 2.000 block 0.000 5
Block Limit Shared Mem 8.000 block 0.000 5
Block Limit Warps 2.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 78.198 % 0.000 5
Achieved Active Warps Per SM 50.048 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (58.6%) 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 Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (78.2%) 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 343357.12 μs
Device Time 1778.30 μs
Self CPU Time 66.07 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 173808.69 μs
Device Time 50030.93 μs
Self CPU Time 632.85 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 1098024.07 μs
Device Time 65243.21 μs
Self CPU Time 1006.46 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::fill_
CPU Time 1097018.80 μs
Device Time 65243.21 μs
Self CPU Time 1416.10 μs
Self Device Time 65243.21 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 1096554.78 μs
Device Time 828.51 μs
Self CPU Time 1096554.78 μs
Self Device Time 828.51 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 50030.93 μs
Self CPU Time 0.00 μs
Self Device Time 50030.93 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_unrolled_manual(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 10245326.93 μs
Self CPU Time 0.00 μs
Self Device Time 10245326.93 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 9311916.68 μs
Device Time 254.66 μs
Self CPU Time 9311916.68 μs
Self Device Time 254.66 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 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/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:12:31: note: the first parameter in the range is 'weight'
12 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:13:31: note: the last parameter in the range is 'bias'
13 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:15:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_unrolled_manual' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | int batch_size,
| ^~~~~~~~~~~~~~~
16 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:15:9: note: the first parameter in the range is 'batch_size'
15 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:16:9: note: the last parameter in the range is 'in_channels'
16 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:17:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_unrolled_manual' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | int out_channels,
| ^~~~~~~~~~~~~~~~~
18 | int in_height,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:17:9: note: the first parameter in the range is 'out_channels'
17 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:18:9: note: the last parameter in the range is 'in_height'
18 | int in_height,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:19:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_unrolled_manual' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int in_width,
| ^~~~~~~~~~~~~
20 | int kernel_size,
| ~~~~~~~~~~~~~~~~
21 | int out_height,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:19:9: note: the first parameter in the range is 'in_width'
19 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:21:9: note: the last parameter in the range is 'out_height'
21 | int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:22:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_unrolled_manual' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
22 | int out_width,
| ^~~~~~~~~~~~~~
23 | int stride,
| ~~~~~~~~~~~
24 | int padding,
| ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:22:9: note: the first parameter in the range is 'out_width'
22 | int out_width,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:24:9: note: the last parameter in the range is 'padding'
24 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:28:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int index = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:72:19: 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]
72 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:73:19: 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]
73 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:74:19: 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]
74 | torch::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:79:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:80:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:81:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | int in_height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:82:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | int in_width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:84:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
84 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:85:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
85 | int kernel_size = weight.size(2); // assume square kernel
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:124:19: 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]
124 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:125:22: 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]
125 | pybind11::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:130:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
130 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s0_unrolled_manual/base/base.cu:138:56: 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_unrolled_manual(input, weight, bias, stride, padding, dilation);
| ^
| std::move( )