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2_ConvTranspose2d_BiasAdd_Clamp_Scaling_Clamp_Divide • ldg_128bit_align_opt_base

Level 2 • Task 2
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    stride: int,
    padding: int,
    output_padding: int,
    scaling_factor: float,
    conv_transpose: torch.Tensor,
    conv_transpose_bias: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """Applies transposed convolution, bias addition, clamping, scaling, clamping and division.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width)
        stride (int): Stride of the convolution
        padding (int): Zero-padding added to both sides of input
        output_padding (int): Additional size added to output shape
        scaling_factor (float): Factor to scale the tensor by
        conv_transpose (torch.Tensor): Transposed convolution weights
        conv_transpose_bias (torch.Tensor): Bias tensor for transposed convolution
        bias (torch.Tensor): Bias tensor to add after convolution

    Returns:
        torch.Tensor: Output tensor after applying operations
    """
    x = F.conv_transpose2d(
        x,
        conv_transpose,
        bias=conv_transpose_bias,
        stride=stride,
        padding=padding,
        output_padding=output_padding,
    )
    x = x + bias
    x = torch.clamp(x, min=0.0, max=1.0)
    x = x * scaling_factor
    x = torch.clamp(x, min=0.0, max=1.0)
    x = x / scaling_factor
    return x


class Model(nn.Module):
    """
    Model that performs a transposed convolution, adds a bias term, clamps, scales, clamps, and divides.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        bias_shape,
        scaling_factor,
    ):
        super(Model, self).__init__()
        conv_transpose = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            padding=padding,
            output_padding=output_padding,
        )
        self.conv_transpose_parameter = nn.Parameter(conv_transpose.weight)
        self.conv_tranpose_bias = nn.Parameter(conv_transpose.bias)
        self.bias_parameter = nn.Parameter(torch.randn(bias_shape) * 0.02)

    def forward(self, x, stride, padding, output_padding, scaling_factor, fn=module_fn):
        return fn(
            x,
            stride,
            padding,
            output_padding,
            scaling_factor,
            self.conv_transpose_parameter,
            self.conv_tranpose_bias,
            self.bias_parameter,
        )


batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
bias_shape = (out_channels, 1, 1)
scaling_factor = 2.0


def get_inputs():
    return [
        torch.randn(batch_size, in_channels, height, width),
        stride,
        padding,
        output_padding,
        scaling_factor,
    ]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        bias_shape,
        scaling_factor,
    ]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Model that performs a transposed convolution, adds a bias term, clamps, scales, clamps, and divides.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        bias_shape,
        scaling_factor,
    ):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
        )
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

        self.scaling_factor = scaling_factor

    def forward(self, x):
        x = self.conv_transpose(x)
        x = x + self.bias
        x = torch.clamp(x, min=0.0, max=1.0)
        x = x * self.scaling_factor
        x = torch.clamp(x, min=0.0, max=1.0)
        x = x / self.scaling_factor
        return x


batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
bias_shape = (out_channels, 1, 1)
scaling_factor = 2.0


def get_inputs():
    return [torch.randn(batch_size, in_channels, height, width)]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        bias_shape,
        scaling_factor,
    ]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 2_ConvTranspose2d_BiasAdd_Clamp_Scaling_Clamp_Divide
Level ID 2
Task ID 2
Kernel Name ldg_128bit_align_opt_base
CUDA Speedup (Native) 1.634x
CUDA Speedup (Compile) 0.770x
CUDA Runtime 0.183 ms
PyTorch Runtime (Native) 0.299 ms
PyTorch Runtime (Compile) 0.141 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 2 • 2_ConvTranspose2d_BiasAdd_Clamp_Scaling_Clamp_Divide)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 shared_mem_sync_opt_base 0.18 1.63 0.77
🥇 ldg_128bit_align_opt_base 0.18 1.63 0.77
🥉 vec_ldg_128_align_base 0.18 1.62 0.77
🥉 combined_opt_base 0.18 1.62 0.77
🥉 modular_device_functions_opt_base 0.18 1.62 0.77
6 optimized_post_process_edit_1 0.18 1.62 0.76
6 modular_optimized_kernel_edit_1 0.18 1.62 0.76
6 coalesced_tiling_unrolled_kernel_base 0.18 1.62 0.76
6 optimized_post_process_base 0.18 1.62 0.76
6 aligned_ldg_128_opt_base 0.18 1.62 0.76
6 optimized_post_process_kernel_edit_1 0.18 1.62 0.76
6 optimized_post_process_kernel_base 0.18 1.62 0.76
6 modular_optimized_kernel_base 0.18 1.62 0.76
14 improved_coalesced_base 0.19 1.61 0.76
14 combined_post_process_base 0.19 1.61 0.76
14 coalesced_tiling_unrolled_kernel_edit_1 0.19 1.61 0.76
17 coalesced_tiling_kernel_edit_1 0.19 1.60 0.75
17 modular_coalesced_tiling_kernel_base 0.19 1.60 0.75
17 memory_coalescing_optimized_post_process_base 0.19 1.60 0.75
17 streamed_memory_transfer_2_convtranspose2d_biasadd_clamp_scaling_clamp_divide_edit_1 0.19 1.60 0.75 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <stdexcept>

#define BLOCK_SIZE 256

// Kernel that optimizes global memory load and store operations by using __ldg() for read-only accesses
// and aligning memory accesses to 128-bit boundaries using float4.
__global__ void ldg_128bit_align_kernel(
    float* __restrict__ output,
    const int total_size,    // total number of floats in the output
    const int height,
    const int width,
    const int channels,
    const float scaling_factor,
    const float* __restrict__ global_bias
) {
    int vec_size = total_size / 4;         // number of float4 chunks
    int remainder = total_size % 4;        // leftover elements

    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int stride = blockDim.x * gridDim.x;

    float4* out_vec = reinterpret_cast<float4*>(output);
    int hw_size = height * width;

    // Process vectorized elements in groups of 4 using a grid-stride loop
    for (int i = idx; i < vec_size; i += stride) {
        // Load 4 floats at once using __ldg for enhanced read-only performance
        float4 data = __ldg(&out_vec[i]);
        int base_index = i * 4;
        float results[4];
        
        // Process each element of the float4
        {
            int index = base_index;
            int c = (index / hw_size) % channels;
            float val = data.x + __ldg(&global_bias[c]);
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            val = val * scaling_factor;
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            results[0] = val / scaling_factor;
        }
        {
            int index = base_index + 1;
            int c = (index / hw_size) % channels;
            float val = data.y + __ldg(&global_bias[c]);
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            val = val * scaling_factor;
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            results[1] = val / scaling_factor;
        }
        {
            int index = base_index + 2;
            int c = (index / hw_size) % channels;
            float val = data.z + __ldg(&global_bias[c]);
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            val = val * scaling_factor;
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            results[2] = val / scaling_factor;
        }
        {
            int index = base_index + 3;
            int c = (index / hw_size) % channels;
            float val = data.w + __ldg(&global_bias[c]);
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            val = val * scaling_factor;
            val = fminf(fmaxf(val, 0.0f), 1.0f);
            results[3] = val / scaling_factor;
        }

        // Write the processed values back in a vectorized manner
        float4 out_val = make_float4(results[0], results[1], results[2], results[3]);
        out_vec[i] = out_val;
    }

    // Process any remaining elements that weren't covered by the vectorized loop
    int rem_start = vec_size * 4;
    for (int i = idx; i < remainder; i += stride) {
        int index = rem_start + i;
        int c = (index / hw_size) % channels;
        float val = __ldg(&output[index]) + __ldg(&global_bias[c]);
        val = fminf(fmaxf(val, 0.0f), 1.0f);
        val = val * scaling_factor;
        val = fminf(fmaxf(val, 0.0f), 1.0f);
        output[index] = val / scaling_factor;
    }
}

// Forward function performs conv_transpose2d followed by the post-processing kernel

torch::Tensor forward(
    torch::Tensor x,
    int64_t stride,
    int64_t padding,
    int64_t output_padding,
    float scaling_factor,
    torch::Tensor conv_transpose,
    torch::Tensor conv_transpose_bias,
    torch::Tensor bias
) {
    // Perform transposed convolution using PyTorch's built-in function
    auto output = torch::conv_transpose2d(
        x, conv_transpose, conv_transpose_bias,
        stride, padding, output_padding
    );

    const int batch_size = output.size(0);
    const int channels = output.size(1);
    const int height = output.size(2);
    const int width = output.size(3);
    const int total_size = batch_size * channels * height * width;

    int threads = BLOCK_SIZE;
    int vec_size = total_size / 4;  // Number of float4 groups
    int blocks = (vec_size + threads - 1) / threads;
    if (blocks == 0) blocks = 1;

    ldg_128bit_align_kernel<<<blocks, threads>>>(
        output.data_ptr<float>(),
        total_size,
        height,
        width,
        channels,
        scaling_factor,
        bias.data_ptr<float>()
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Optimized post-processing kernel with __ldg() and 128-bit alignment (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.760 inst/cycle 0.000 5
Executed Ipc Elapsed 2.462 inst/cycle 0.001 5
Issue Slots Busy 69.212 % 0.007 5
Issued Ipc Active 2.766 inst/cycle 0.000 5
SM Busy 69.212 % 0.007 5
Memory Throughput 1842253282235.698 byte/second 679779132915798507520.000 5
Mem Busy 38.156 % 0.301 5
Max Bandwidth 54.998 % 0.605 5
L1/TEX Hit Rate 55.244 % 0.000 5
L2 Hit Rate 51.306 % 0.016 5
Mem Pipes Busy 15.748 % 0.051 5
Warp Cycles Per Issued Instruction 19.386 cycle 0.000 5
Warp Cycles Per Executed Instruction 19.444 cycle 0.000 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.990 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 84.376 % 0.004 5
Achieved Active Warps Per SM 54.004 warp 0.002 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (56.6%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
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 Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (84.3%) 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::conv_transpose2d
CPU Time 2693340.66 μs
Device Time 2318993.98 μs
Self CPU Time 20894.62 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::convolution
CPU Time 2672446.04 μs
Device Time 2318993.98 μs
Self CPU Time 31099.54 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::_convolution
CPU Time 2641346.50 μs
Device Time 2318993.98 μs
Self CPU Time 66409.65 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::cudnn_convolution_transpose
CPU Time 2269303.25 μs
Device Time 1872447.79 μs
Self CPU Time 448090.10 μs
Self Device Time 1872447.79 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 1663616.19 μs
Device Time 25624.02 μs
Self CPU Time 1663616.19 μs
Self Device Time 25624.02 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 122550.34 μs
Device Time 1142479.80 μs
Self CPU Time 25792.42 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 
45294 warnings generated when compiling for host.
Suppressed 45328 warnings (45281 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/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:13:5 bugprone-easily-swappable-parameters
13 | const int total_size, // total number of floats in the output
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
14 | const int height,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:13:15: note: the first parameter in the range is 'total_size'
13 | const int total_size, // total number of floats in the output
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:14:15: note: the last parameter in the range is 'height'
14 | const int height,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:15:5: warning: 3 adjacent parameters of 'ldg_128bit_align_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | const int width,
| ^~~~~~~~~~~~~~~~
16 | const int channels,
| ~~~~~~~~~~~~~~~~~~~
17 | const float scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:15:15: note: the first parameter in the range is 'width'
15 | const int width,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:17:17: note: the last parameter in the range is 'scaling_factor'
17 | const float scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:17:5: note: 'const int' and 'const float' may be implicitly converted: 'const int' (as 'int') -> 'const float' (as 'float'), 'const float' (as 'float') -> 'const int' (as 'int')
17 | const float scaling_factor,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:23:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:24:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:95:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
95 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:98:5: warning: 2 adjacent parameters of 'forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
98 | int64_t output_padding,
| ^~~~~~~~~~~~~~~~~~~~~~~
99 | float scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:98:13: note: the first parameter in the range is 'output_padding'
98 | int64_t output_padding,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:99:11: note: the last parameter in the range is 'scaling_factor'
99 | float scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:98:5: note:
98 | int64_t output_padding,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:99:5: note: 'int64_t' and 'float' may be implicitly converted: 'int64_t' (as 'long') -> 'float', 'float' -> 'int64_t' (as 'long')
99 | float scaling_factor,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:100:19: warning: the parameter 'conv_transpose' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
100 | torch::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:101:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
101 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
102 | torch::Tensor bias
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:101:19: note: the first parameter in the range is 'conv_transpose_bias'
101 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:102:19: note: the last parameter in the range is 'bias'
102 | torch::Tensor bias
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:102:19: 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]
102 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:110:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | const int batch_size = output.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:111:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | const int channels = output.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:112:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | const int height = output.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_2/b10_s1_ldg_128bit_align_opt/base/base.cu:113:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | const int width = output.size(3);
| ^