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81_conv_transposed_2D_asymmetric_input_square_kernel___dilated____padded____strided__ • tiled_atomic_free_base_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 tiled_atomic_free_base_base
CUDA Speedup (Native) 0.058x
CUDA Speedup (Compile) 0.060x
CUDA Runtime 30.061 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 0.50
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>

__global__ void conv_transpose2d_forward_kernel_tiled(
    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) {
    
    // 2D thread blocks for output coordinates
    const int out_w = blockIdx.x * blockDim.x + threadIdx.x;
    const int out_h = blockIdx.y * blockDim.y + threadIdx.y;
    const int bo_idx = blockIdx.z;
    
    // Early exit if out of bounds
    if (out_w >= out_width || out_h >= out_height)
        return;
    
    // Extract batch and output channel indices
    const int o = bo_idx % out_channels;
    const int b = bo_idx / out_channels;
    
    // Initialize output with bias
    float result = bias[o];
    
    // Process input channels in tiles
    const int TILE_SIZE = 4;  // Process multiple input channels at once
    #pragma unroll
    for (int c_base = 0; c_base < in_channels; c_base += TILE_SIZE) {
        // Register array for temporary results
        float temp_results[TILE_SIZE] = {0.0f};
        
        // Process kernel elements for this channel tile
        #pragma unroll
        for (int p = 0; p < kernel_size; p++) {
            const int h_unscaled = out_h + padding - p * dilation;
            if (h_unscaled % stride != 0)
                continue;
                
            const 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++) {
                const int w_unscaled = out_w + padding - q * dilation;
                if (w_unscaled % stride != 0)
                    continue;
                    
                const int w_in = w_unscaled / stride;
                if (w_in < 0 || w_in >= in_width)
                    continue;
                
                // Process multiple channels in the tile
                #pragma unroll
                for (int c_offset = 0; c_offset < TILE_SIZE && c_base + c_offset < in_channels; c_offset++) {
                    const int c = c_base + c_offset;
                    const int input_idx = ((b * in_channels + c) * in_height + h_in) * in_width + w_in;
                    const int weight_idx = ((c * out_channels + o) * kernel_size + p) * kernel_size + q;
                    temp_results[c_offset] += input[input_idx] * weight[weight_idx];
                }
            }
        }
        
        // Accumulate results from the tile
        #pragma unroll
        for (int i = 0; i < TILE_SIZE && c_base + i < in_channels; i++) {
            result += temp_results[i];
        }
    }
    
    // Write final result to output
    const int output_idx = ((b * out_channels + o) * out_height + out_h) * out_width + out_w;
    output[output_idx] = result;
}

torch::Tensor conv_transpose2d_forward_cuda_tiled(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor bias,
    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;
    
    auto output = torch::zeros({batch_size, out_channels, out_height, out_width}, input.options());
    
    // Use 16x16 thread blocks for better occupancy
    const dim3 threads(16, 16);
    const dim3 blocks(
        (out_width + threads.x - 1) / threads.x,
        (out_height + threads.y - 1) / threads.y,
        batch_size * out_channels
    );
    
    conv_transpose2d_forward_kernel_tiled<<<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);
    
    return output;
}

torch::Tensor conv_transpose2d_forward_wrapper_tiled(
    torch::Tensor input,
    torch::Tensor weight,
    pybind11::object bias_obj,
    int stride,
    int padding,
    int dilation) {
    
    const 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_tiled(input, weight, bias, stride, padding, dilation);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &conv_transpose2d_forward_wrapper_tiled,
          "ConvTranspose2d forward tiled (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 3.080 inst/cycle 0.000 5
Executed Ipc Elapsed 3.080 inst/cycle 0.000 5
Issue Slots Busy 77.202 % 0.000 5
Issued Ipc Active 3.090 inst/cycle 0.000 5
SM Busy 77.202 % 0.000 5
Memory Throughput 22359735602.780 byte/second 425799325038959.812 5
Mem Busy 19.700 % 0.000 5
Max Bandwidth 19.528 % 0.000 5
L1/TEX Hit Rate 89.990 % 0.000 5
L2 Hit Rate 99.586 % 0.001 5
Mem Pipes Busy 40.386 % 0.000 5
Warp Cycles Per Issued Instruction 16.342 cycle 0.000 5
Warp Cycles Per Executed Instruction 16.384 cycle 0.000 5
Avg. Active Threads Per Warp 15.790 0.000 5
Avg. Not Predicated Off Threads Per Warp 14.490 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 78.884 % 0.001 5
Achieved Active Warps Per SM 50.484 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (56.3%) 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.
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.
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 15.8 threads being active per cycle. This is further reduced to 14.5 threads per warp due to predication. The compiler may use predication to avoid an actual branch. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Try to avoid different execution paths within a warp when possible. In addition, ensure your kernel makes use of Independent Thread Scheduling, which allows a warp to reconverge after a data-dependent conditional block by explicitly calling __syncwarp().
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (78.9%) 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 232930.06 μs
Device Time 1865.75 μs
Self CPU Time 66.17 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 32081.00 μs
Device Time 85072.36 μs
Self CPU Time 1305.98 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 4659637.35 μs
Device Time 111051.55 μs
Self CPU Time 2036.52 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 4657603.31 μs
Device Time 111051.55 μs
Self CPU Time 2916.52 μs
Self Device Time 111051.55 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 4656294.14 μs
Device Time 79.01 μs
Self CPU Time 4656294.14 μs
Self Device Time 79.01 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 85072.36 μs
Self CPU Time 0.00 μs
Self Device Time 85072.36 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_tiled(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 9950455.39 μs
Self CPU Time 0.00 μs
Self Device Time 9950455.39 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 5348830.50 μs
Device Time 0.00 μs
Self CPU Time 5348830.50 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
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/b5_s2_tiled_atomic_free_base/base/base.cu:9:5 bugprone-easily-swappable-parameters
9 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:9:31: note: the first parameter in the range is 'weight'
9 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:10:31: note: the last parameter in the range is 'bias'
10 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:12:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_tiled' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | int batch_size,
| ^~~~~~~~~~~~~~~
13 | int in_channels,
| ~~~~~~~~~~~~~~~~
14 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:12:9: note: the first parameter in the range is 'batch_size'
12 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:14:9: note: the last parameter in the range is 'out_channels'
14 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:16:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_tiled' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | int in_width,
| ^~~~~~~~~~~~~
17 | int kernel_size,
| ~~~~~~~~~~~~~~~~
18 | int out_height,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:16:9: note: the first parameter in the range is 'in_width'
16 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:18:9: note: the last parameter in the range is 'out_height'
18 | int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:19:5: warning: 3 adjacent parameters of 'conv_transpose2d_forward_kernel_tiled' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int out_width,
| ^~~~~~~~~~~~~~
20 | int stride,
| ~~~~~~~~~~~
21 | int padding,
| ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:19:9: note: the first parameter in the range is 'out_width'
19 | int out_width,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:21:9: note: the last parameter in the range is 'padding'
21 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:25:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | const 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/b5_s2_tiled_atomic_free_base/base/base.cu:26:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | const 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/b5_s2_tiled_atomic_free_base/base/base.cu:27:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | const int bo_idx = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:92:19: 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]
92 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:93:19: 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]
93 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:94:19: 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]
94 | torch::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:99:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | const int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:100:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:101:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | const int in_height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:102:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | const int in_width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:104:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
104 | const int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:105:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
105 | const int kernel_size = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:142:19: 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]
142 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:143:22: 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]
143 | pybind11::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:148:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
148 | const int out_channels = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_81/b5_s2_tiled_atomic_free_base/base/base.cu:156:48: 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_tiled(input, weight, bias, stride, padding, dilation);
| ^
| std::move( )