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81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__ • conv_transpose2d_constant_mem_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 conv_transpose2d_constant_mem_base
CUDA Speedup (Native) 0.145x
CUDA Speedup (Compile) 0.150x
CUDA Runtime 12.066 ms
PyTorch Runtime (Native) 1.754 ms
PyTorch Runtime (Compile) 1.811 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
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 MAX_KERNEL_SIZE 16
#define MAX_CONSTANT_WEIGHTS 16384  // 64KB / sizeof(float)

// Constant memory for weights - 64KB on H100
__constant__ float const_weight[MAX_CONSTANT_WEIGHTS];

__global__ void conv_transpose2d_forward_kernel_constant(
    const float* __restrict__ input,
    const float* __restrict__ weight,  // Fallback for large weights
    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,
    bool use_const_mem) {

  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int total = batch_size * out_channels * out_height * out_width;
  if (index >= total)
    return;

  // Decode index
  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;

  // Precompute base indices
  int base_h = h_out + padding;
  int base_w = w_out + padding;

  // Precompute valid kernel indices for h dimension
  int valid_p_count = 0;
  int valid_p[MAX_KERNEL_SIZE];
  int h_in_list[MAX_KERNEL_SIZE];
  
  #pragma unroll
  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 w dimension
  int valid_q_count = 0;
  int valid_q[MAX_KERNEL_SIZE];
  int w_in_list[MAX_KERNEL_SIZE];
  
  #pragma unroll
  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++;
      }
    }
  }

  float out_val = __ldg(&bias[o]);

  for (int c = 0; c < in_channels; ++c) {
    #pragma unroll
    for (int i = 0; i < valid_p_count; i++) {
      int p = valid_p[i];
      int h_in = h_in_list[i];
      
      #pragma unroll
      for (int j = 0; j < valid_q_count; j++) {
        int q = valid_q[j];
        int w_in = w_in_list[j];
        
        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;
        
        float weight_val;
        if (use_const_mem) {
          weight_val = const_weight[weight_idx];
        } else {
          weight_val = __ldg(&weight[weight_idx]);
        }
        
        out_val += __ldg(&input[input_idx]) * weight_val;
      }
    }
  }

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

torch::Tensor conv_transpose2d_forward_cuda_constant(
    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);
  
  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());

  // Determine if weight tensor fits in constant memory
  int weight_size = weight.numel() * sizeof(float);
  bool use_const_mem = weight_size <= (MAX_CONSTANT_WEIGHTS * sizeof(float));
  
  if (use_const_mem) {
    // Copy weights to constant memory
    cudaMemcpyToSymbol(const_weight, weight.data_ptr<float>(), weight_size);
  }

  int total_threads = batch_size * out_channels * out_height * out_width;
  int threads = 128;
  int blocks = (total_threads + threads - 1) / threads;

  conv_transpose2d_forward_kernel_constant<<<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,
      use_const_mem);

  return output;
}

torch::Tensor conv_transpose2d_forward_wrapper_constant(
    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_constant(input, weight, bias, stride, padding, dilation);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &conv_transpose2d_forward_wrapper_constant,
        "ConvTranspose2d forward (CUDA) with constant memory optimization",
        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 3.690 inst/cycle 0.000 5
Executed Ipc Elapsed 3.690 inst/cycle 0.000 5
Issue Slots Busy 92.318 % 0.000 5
Issued Ipc Active 3.690 inst/cycle 0.000 5
SM Busy 92.318 % 0.000 5
Memory Throughput 58188400715.550 byte/second 733023570344982.375 5
Mem Busy 36.734 % 0.000 5
Max Bandwidth 34.082 % 0.000 5
L1/TEX Hit Rate 95.510 % 0.000 5
L2 Hit Rate 99.610 % 0.000 5
Mem Pipes Busy 68.060 % 0.000 5
Warp Cycles Per Issued Instruction 15.500 cycle 0.000 5
Warp Cycles Per Executed Instruction 15.504 cycle 0.000 5
Avg. Active Threads Per Warp 24.430 0.000 5
Avg. Not Predicated Off Threads Per Warp 22.080 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 16.000 block 0.000 5
Block Limit Shared Mem 32.000 block 0.000 5
Block Limit Warps 16.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 89.484 % 0.000 5
Achieved Active Warps Per SM 57.268 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (49.4%) 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 24.4 threads being active per cycle. This is further reduced to 22.1 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 (89.5%) 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 548431.93 μs
Device Time 1730.14 μs
Self CPU Time 45.83 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 70468.14 μs
Device Time 208462.07 μs
Self CPU Time 2974.29 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 7649257.76 μs
Device Time 272467.12 μs
Self CPU Time 4806.37 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 7644455.32 μs
Device Time 272467.12 μs
Self CPU Time 6881.10 μs
Self Device Time 272467.12 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 7644169.76 μs
Device Time 10089.88 μs
Self CPU Time 7644169.76 μs
Self Device Time 10089.88 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 208462.07 μs
Self CPU Time 0.00 μs
Self Device Time 208462.07 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_constant(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int, int, int, int, bool)
CPU Time 0.00 μs
Device Time 9705458.39 μs
Self CPU Time 0.00 μs
Self Device Time 9705458.39 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 2160115.29 μs
Device Time 2.21 μs
Self CPU Time 2160115.29 μs
Self Device Time 2.21 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_s2_conv_transpose2d_constant_mem/base/base.cu:14:5 bugprone-easily-swappable-parameters
14 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ weight, // Fallback for large weights
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/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/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:16:31: note: the last parameter in the range is 'bias'
16 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:18:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_constant' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | int batch_size,
| ^~~~~~~~~~~~~~~
19 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:18:9: note: the first parameter in the range is 'batch_size'
18 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:19:9: note: the last parameter in the range is 'in_channels'
19 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:20:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_constant' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | int out_channels,
| ^~~~~~~~~~~~~~~~~
21 | int in_height,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:20:9: note: the first parameter in the range is 'out_channels'
20 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:21:9: note: the last parameter in the range is 'in_height'
21 | int in_height,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:22:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_constant' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
22 | int in_width,
| ^~~~~~~~~~~~~
23 | int kernel_size,
| ~~~~~~~~~~~~~~~~
24 | int out_height,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:22:9: note: the first parameter in the range is 'in_width'
22 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:24:9: note: the last parameter in the range is 'out_height'
24 | int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:25:5: warning: 4 adjacent parameters of 'conv_transpose2d_forward_kernel_constant' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | int out_width,
| ^~~~~~~~~~~~~~
26 | int stride,
| ~~~~~~~~~~~
27 | int padding,
| ~~~~~~~~~~~~
28 | int dilation,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:25:9: note: the first parameter in the range is 'out_width'
25 | int out_width,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:28:9: note: the last parameter in the range is 'dilation'
28 | int dilation,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:31:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int index = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/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/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/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/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:118:19: 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]
118 | torch::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:123:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
123 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:124:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:125:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | int in_height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:126:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | int in_width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:128:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:129:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
129 | int kernel_size = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:137:21: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
137 | int weight_size = weight.numel() * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:172:19: 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]
172 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:173:22: 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]
173 | pybind11::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:178:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_81/b5_s2_conv_transpose2d_constant_mem/base/base.cu:186:49: 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_constant(input, weight, bias, stride, padding, dilation);
| ^
| std::move( )