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81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__ • input_atomic_transposed_conv_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 input_atomic_transposed_conv_base
CUDA Speedup (Native) 0.082x
CUDA Speedup (Compile) 0.084x
CUDA Runtime 21.477 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 uses an input-centric approach where each thread processes a single input element
// and computes its contributions to the corresponding output patch. Since different input elements
// may contribute to the same output element, atomicAdd is used when updating global memory.
// To reduce the number of atomic operations, the output tensor is preinitialized with the bias
// values so that the atomic updates only add contributions from the input*weight products.

__global__ void conv_transpose2d_forward_kernel_input_atomic(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    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 tid = blockIdx.x * blockDim.x + threadIdx.x;
  int total = batch_size * in_channels * in_height * in_width;
  if (tid >= total)
    return;

  // Decode tid into input indices: (b, c, h_in, w_in)
  int w_in = tid % in_width;
  int tmp = tid / in_width;
  int h_in = tmp % in_height;
  tmp /= in_height;
  int c = tmp % in_channels;
  int b = tmp / in_channels;

  float input_val = input[tid];

  // For each kernel spatial offset
  for (int p = 0; p < kernel_size; ++p) {
    int out_h = h_in * stride - padding + p * dilation;
    if (out_h < 0 || out_h >= out_height)
      continue;
    for (int q = 0; q < kernel_size; ++q) {
      int out_w = w_in * stride - padding + q * dilation;
      if (out_w < 0 || out_w >= out_width)
        continue;
      // For every output channel, accumulate the contribution
      for (int o = 0; o < out_channels; ++o) {
        // Weight is stored as [in_channels, out_channels, kernel_size, kernel_size]
        int weight_idx = ((c * out_channels + o) * kernel_size + p) * kernel_size + q;
        float w_val = weight[weight_idx];
        float contribution = input_val * w_val;
        int out_idx = ((b * out_channels + o) * out_height + out_h) * out_width + out_w;
        atomicAdd(&output[out_idx], contribution);
      }
    }
  }
}

// Launcher for the atomic-based kernel
torch::Tensor conv_transpose2d_forward_cuda_atomic(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor output,
    int stride,
    int padding,
    int dilation) {

  const int batch_size = input.size(0);
  const int in_channels = input.size(1);
  const int in_height = input.size(2);
  const int in_width  = input.size(3);
  const int out_channels = weight.size(1);
  const int kernel_size = weight.size(2);  // assuming square kernel

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

  int total_input = batch_size * in_channels * in_height * in_width;
  int threads = 256;
  int blocks = (total_input + threads - 1) / threads;

  conv_transpose2d_forward_kernel_input_atomic<<<blocks, threads>>>(
      input.data_ptr<float>(),
      weight.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_input_atomic: %s\n", cudaGetErrorString(err));
  }

  return output;
}

// Wrapper function that preinitializes the output tensor with the bias and then calls the atomic kernel
torch::Tensor conv_transpose2d_forward_wrapper_atomic(
    torch::Tensor input,
    torch::Tensor weight,
    pybind11::object bias_obj,
    int stride,
    int padding,
    int dilation) {

  const int batch_size = input.size(0);
  const int in_channels = input.size(1);
  const int in_height = input.size(2);
  const int in_width  = input.size(3);
  const int out_channels = weight.size(1);
  const int kernel_size = weight.size(2);
  const int out_height = (in_height - 1) * stride - 2 * padding + dilation * (kernel_size - 1) + 1;
  const int out_width  = (in_width - 1) * stride - 2 * padding + dilation * (kernel_size - 1) + 1;

  // Create output tensor and preinitialize with bias
  torch::Tensor output = torch::empty({batch_size, out_channels, out_height, out_width}, input.options());
  torch::Tensor bias;
  if (bias_obj.is(pybind11::none())) {
    bias = torch::zeros({out_channels}, weight.options());
  } else {
    bias = bias_obj.cast<torch::Tensor>();
  }
  // Broadcast bias to the output shape
  output.copy_(bias.view({1, out_channels, 1, 1}).expand({batch_size, out_channels, out_height, out_width}));

  return conv_transpose2d_forward_cuda_atomic(input, weight, output, stride, padding, dilation);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &conv_transpose2d_forward_wrapper_atomic,
        "ConvTranspose2d forward using input-driven atomic updates (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 0.120 inst/cycle 0.000 5
Executed Ipc Elapsed 0.120 inst/cycle 0.000 5
Issue Slots Busy 3.078 % 0.000 5
Issued Ipc Active 0.120 inst/cycle 0.000 5
SM Busy 3.602 % 0.000 5
Memory Throughput 1133701417996.830 byte/second 7636252499365165056.000 5
Mem Busy 53.518 % 0.022 5
Max Bandwidth 68.886 % 0.037 5
L1/TEX Hit Rate 4.660 % 0.000 5
L2 Hit Rate 97.424 % 0.001 5
Mem Pipes Busy 3.430 % 0.000 5
Warp Cycles Per Issued Instruction 506.372 cycle 0.424 5
Warp Cycles Per Executed Instruction 506.380 cycle 0.424 5
Avg. Active Threads Per Warp 31.830 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.430 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 97.504 % 0.000 5
Achieved Active Warps Per SM 62.402 warp 0.000 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
INF Occupancy This kernel's theoretical occupancy is not impacted by any block limit.
Operation / Metric Value Unit
aten::to
CPU Time 438857.09 μs
Device Time 1805.24 μs
Self CPU Time 64.32 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::copy_
CPU Time 13414.88 μs
Device Time 229637.27 μs
Self CPU Time 2006.26 μs
Self Device Time 229637.27 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 6287169.64 μs
Device Time 37381.50 μs
Self CPU Time 1892.30 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 6285280.23 μs
Device Time 37381.50 μs
Self CPU Time 2924.60 μs
Self Device Time 37381.50 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 6293836.84 μs
Device Time 4578.60 μs
Self CPU Time 6293836.84 μs
Self Device Time 4578.60 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::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast<at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast<at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1} const&)::{lambda(int)#1})
CPU Time 0.00 μs
Device Time 227832.03 μs
Self CPU Time 0.00 μs
Self Device Time 227832.03 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_input_atomic(float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 9736235.39 μs
Self CPU Time 0.00 μs
Self Device Time 9736235.39 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 3814823.26 μs
Device Time 2.50 μs
Self CPU Time 3814823.26 μs
Self Device Time 2.50 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 
45302 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_s2_input_atomic_transposed_conv/base/base.cu:14:5 bugprone-easily-swappable-parameters
14 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:14:31: note: the first parameter in the range is 'input'
14 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:15:31: note: the last parameter in the range is 'weight'
15 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:18:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_input_atomic' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | int in_channels,
| ^~~~~~~~~~~~~~~~
19 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:18:9: note: the first parameter in the range is 'in_channels'
18 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:19:9: note: the last parameter in the range is 'out_channels'
19 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:21:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_input_atomic' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | int in_width,
| ^~~~~~~~~~~~~
22 | int kernel_size,
| ~~~~~~~~~~~~~~~~
23 | int out_height,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:21:9: note: the first parameter in the range is 'in_width'
21 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:23:9: note: the last parameter in the range is 'out_height'
23 | int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:24:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_input_atomic' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
24 | int out_width,
| ^~~~~~~~~~~~~~
25 | int stride,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:24:9: note: the first parameter in the range is 'out_width'
24 | int out_width,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:25:9: note: the last parameter in the range is 'stride'
25 | int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:29:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | int tid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:68:19: 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]
68 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:69:19: 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]
69 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:75:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
75 | const int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:76:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
76 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:77:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
77 | const int in_height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:78:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
78 | const int in_width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:79:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | const int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:80:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | const 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/b10_s2_input_atomic_transposed_conv/base/base.cu:116:19: 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]
116 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:117:19: 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]
117 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:118:22: 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]
118 | pybind11::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:123:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
123 | const int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:124:13: warning: Value stored to 'in_channels' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
124 | const int in_channels = input.size(1);
| ^~~~~~~~~~~ ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:124:13: note: Value stored to 'in_channels' during its initialization is never read
124 | const int in_channels = input.size(1);
| ^~~~~~~~~~~ ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:124:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:125:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | const int in_height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:126:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | const int in_width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:127:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
127 | const int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b10_s2_input_atomic_transposed_conv/base/base.cu:128:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | const int kernel_size = weight.size(2);
| ^