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81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__ • coalesced_memory_access_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]
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Kernel Information

Operation Name 81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__
Level ID 1
Task ID 81
Kernel Name coalesced_memory_access_base
CUDA Speedup (Native) 0.034x
CUDA Speedup (Compile) 0.035x
CUDA Runtime 51.695 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>

// CUDA kernel with 2D thread blocks and 3D grid arrangement to ensure memory coalescing for output accesses
// Each thread computes one output element with index mapping arranged so that threads in a warp access consecutive output columns.
__global__ void conv_transpose2d_forward_kernel_coalesced(
    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) {

  // Determine the (out_w, out_h) coordinate within an output feature map
  int out_w = blockIdx.x * blockDim.x + threadIdx.x;
  int out_h = blockIdx.y * blockDim.y + threadIdx.y;
  
  // blockIdx.z encodes both batch index and output channel: z = b * out_channels + o
  int bo_idx = blockIdx.z;
  
  if (out_w >= out_width || out_h >= out_height)
    return;

  int o = bo_idx % out_channels;
  int b = bo_idx / out_channels;
  
  // Initialize with bias
  float out_val = bias[o];
  
  // Loop over input channels and kernel elements
  for (int c = 0; c < in_channels; ++c) {
    for (int p = 0; p < kernel_size; ++p) {
      int h_unscaled = out_h + padding - p * dilation;
      if ((h_unscaled % stride) != 0)
        continue;
      int h_in = h_unscaled / stride;
      if (h_in < 0 || h_in >= in_height)
        continue;
      
      for (int q = 0; q < kernel_size; ++q) {
        int w_unscaled = out_w + 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;
        out_val += input[input_idx] * weight[weight_idx];
      }
    }
  }

  int output_idx = ((b * out_channels + o) * out_height + out_h) * out_width + out_w;
  output[output_idx] = out_val;
}

// CUDA launcher function
torch::Tensor conv_transpose2d_forward_cuda_coalesced(
    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);  // assuming square kernel
  
  // Calculate 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());
  
  // Configure 2D thread block along (w, h) and use grid.z for batch and channel combination
  dim3 block(32, 8, 1);
  dim3 grid((out_width + block.x - 1) / block.x,
            (out_height + block.y - 1) / block.y,
            batch_size * out_channels);
  
  conv_transpose2d_forward_kernel_coalesced<<<grid, block>>>(
      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_coalesced: %s\n", cudaGetErrorString(err));
  }
  
  return output;
}

// Wrapper to handle the possibility of a None bias
torch::Tensor conv_transpose2d_forward_wrapper_coalesced(
    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_coalesced(input, weight, bias, stride, padding, dilation);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &conv_transpose2d_forward_wrapper_coalesced,
        "ConvTranspose2d forward with coalesced memory accesses (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 3.350 inst/cycle 0.000 5
Executed Ipc Elapsed 3.350 inst/cycle 0.000 5
Issue Slots Busy 83.710 % 0.000 5
Issued Ipc Active 3.350 inst/cycle 0.000 5
SM Busy 83.710 % 0.000 5
Memory Throughput 12979968654.768 byte/second 11528509551233.879 5
Mem Busy 5.780 % 0.000 5
Max Bandwidth 5.752 % 0.000 5
L1/TEX Hit Rate 87.140 % 0.000 5
L2 Hit Rate 99.432 % 0.001 5
Mem Pipes Busy 33.162 % 0.000 5
Warp Cycles Per Issued Instruction 15.422 cycle 0.000 5
Warp Cycles Per Executed Instruction 15.422 cycle 0.000 5
Avg. Active Threads Per Warp 28.030 0.000 5
Avg. Not Predicated Off Threads Per Warp 25.740 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 80.720 % 0.001 5
Achieved Active Warps Per SM 51.662 warp 0.001 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (57.9%) 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 (80.7%) 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 472902.50 μs
Device Time 1673.41 μs
Self CPU Time 55.09 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 15300.18 μs
Device Time 50483.36 μs
Self CPU Time 526.06 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 1042185.07 μs
Device Time 65811.78 μs
Self CPU Time 881.62 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::fill_
CPU Time 1041305.83 μs
Device Time 65811.78 μs
Self CPU Time 1242.60 μs
Self Device Time 65811.78 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 1040938.95 μs
Device Time 829.02 μs
Self CPU Time 1040938.95 μs
Self Device Time 829.02 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 50483.36 μs
Self CPU Time 0.00 μs
Self Device Time 50483.36 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_coalesced(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 10198676.27 μs
Self CPU Time 0.00 μs
Self Device Time 10198676.27 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 9176743.75 μs
Device Time 254.43 μs
Self CPU Time 9176743.75 μs
Self Device Time 254.43 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/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:11:5 bugprone-easily-swappable-parameters
11 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:11:31: note: the first parameter in the range is 'weight'
11 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:12:31: note: the last parameter in the range is 'bias'
12 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:14:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_coalesced' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | int batch_size,
| ^~~~~~~~~~~~~~~
15 | int in_channels,
| ~~~~~~~~~~~~~~~~
16 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:14:9: note: the first parameter in the range is 'batch_size'
14 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:16:9: note: the last parameter in the range is 'out_channels'
16 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:18:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_coalesced' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | int in_width,
| ^~~~~~~~~~~~~
19 | int kernel_size,
| ~~~~~~~~~~~~~~~~
20 | int out_height,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:18:9: note: the first parameter in the range is 'in_width'
18 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:20:9: note: the last parameter in the range is 'out_height'
20 | int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:21:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_coalesced' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | int out_width,
| ^~~~~~~~~~~~~~
22 | int stride,
| ~~~~~~~~~~~
23 | int padding,
| ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:21:9: note: the first parameter in the range is 'out_width'
21 | int out_width,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:23:9: note: the last parameter in the range is 'padding'
23 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:27:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int out_w = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:28:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int out_h = blockIdx.y * blockDim.y + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:31:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int bo_idx = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:73:19: 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]
73 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:74:19: 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]
74 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:75:19: 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]
75 | torch::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:80:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:81:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:82:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | int in_height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:83:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | int in_width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:85:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
85 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:86:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
86 | int kernel_size = weight.size(2); // assuming square kernel
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:128:19: 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]
128 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:129:22: 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]
129 | pybind11::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:134:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
134 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b2_s3_coalesced_memory_access/base/base.cu:142:50: 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_coalesced(input, weight, bias, stride, padding, dilation);
| ^
| std::move( )