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13_ConvTranspose3d_Mean_Add_Softmax_Tanh_Scaling • minimal_sync_convtranspose3d_edit_1

Level 2 • Task 13
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv_transpose: torch.Tensor,
    conv_transpose_bias: torch.Tensor,
    bias: torch.Tensor,
    scaling_factor: float,
    stride: int,
    padding: int,
) -> torch.Tensor:
    """
    Applies a series of operations:
    1. Transposed 3D convolution
    2. Mean pooling
    3. Addition
    4. Softmax
    5. Tanh activation
    6. Scaling

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        conv_transpose (torch.Tensor): Transposed convolution weight tensor
        conv_transpose_bias (torch.Tensor): Bias tensor for transposed convolution
        bias (torch.Tensor): Bias tensor for addition
        scaling_factor (float): Scaling factor for final multiplication
        stride (int): Stride for transposed convolution
        padding (int): Padding for transposed convolution

    Returns:
        torch.Tensor: Output tensor after applying all operations
    """
    x = F.conv_transpose3d(
        x, conv_transpose, bias=conv_transpose_bias, stride=stride, padding=padding
    )
    x = torch.mean(x, dim=1, keepdim=True)
    x = x + bias
    x = F.softmax(x, dim=1)
    x = torch.tanh(x)
    x = x * scaling_factor
    return x


class Model(nn.Module):
    """
    Model that performs a series of operations:
    1. Transposed 3D convolution
    2. Mean pooling
    3. Addition
    4. Softmax
    5. Tanh activation
    6. Scaling
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        bias_shape,
        scaling_factor,
    ):
        super(Model, self).__init__()
        conv_transpose = nn.ConvTranspose3d(
            in_channels, out_channels, kernel_size, stride=stride, padding=padding
        )
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

        self.conv_transpose_weight = conv_transpose.weight
        self.conv_transpose_bias = conv_transpose.bias
        self.scaling_factor = scaling_factor

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.conv_transpose_weight,
            self.conv_transpose_bias,
            self.bias,
            self.scaling_factor,
            stride,
            padding,
        )


batch_size = 16
in_channels = 8
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
stride = 2
padding = 1
bias_shape = (1, 1, 1, 1, 1)
scaling_factor = 2.0


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


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

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a series of operations:
    1. Transposed 3D convolution
    2. Mean pooling
    3. Addition
    4. Softmax
    5. Tanh activation
    6. Scaling
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape, scaling_factor):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=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 = torch.mean(x, dim=1, keepdim=True)
        x = x + self.bias
        x = torch.softmax(x, dim=1)
        x = torch.tanh(x)
        x = x * self.scaling_factor
        return x

batch_size = 16
in_channels = 8
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
stride = 2
padding = 1
bias_shape = (1, 1, 1, 1, 1)
scaling_factor = 2.0

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

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

Kernel Information

Operation Name 13_ConvTranspose3d_Mean_Add_Softmax_Tanh_Scaling
Level ID 2
Task ID 13
Kernel Name minimal_sync_convtranspose3d_edit_1
CUDA Speedup (Native) 73.984x
CUDA Speedup (Compile) 58.464x
CUDA Runtime 0.008 ms
PyTorch Runtime (Native) 0.592 ms
PyTorch Runtime (Compile) 0.468 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 13 • 13_ConvTranspose3d_Mean_Add_Softmax_Tanh_Scaling)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 13_convtranspose3d_mean_add_softmax_tanh_scaling_optimized_edit_1 0.01 84.55 66.82
🥇 stride_loops_convtranspose3d_optimized_base 0.01 84.55 66.82
🥉 unrolled_loops_convtranspose3d_optimized_base 0.01 73.98 58.46
🥉 stride_loops_ldg_optimized_base 0.01 73.98 58.46
🥉 unrolled_loops_convtranspose3d_optimized_edit_1 0.01 73.98 58.46
🥉 ldg_aligned_conv3d_optimized_base 0.01 73.98 58.46
🥉 minimal_sync_convtranspose3d_edit_1 0.01 73.98 58.46
🥉 tuned_block_size_256_edit_1 0.01 73.98 58.46
🥉 minimal_sync_convtranspose3d_base 0.01 73.98 58.46
🥉 shared_mem_bias_unrolled_loops_base 0.01 73.98 58.46
🥉 blocksize_experiment_convtranspose3d_base 0.01 73.98 58.46
🥉 shared_mem_bias_unrolled_loops_edit_1 0.01 73.98 58.46
🥉 ldg_aligned_conv3d_optimized_base 0.01 73.98 58.46
14 stride_loops_ldg_optimized_edit_1 0.01 59.19 46.77
14 combined_kernel_optimized_base 0.01 59.19 46.77
14 optim_unroll_13_convtranspose3d_mean_add_softmax_tanh_scaling_base 0.01 59.19 46.77
14 convtranspose3d_smem_base 0.01 59.19 46.77
14 13_ConvTranspose3d_Mean_Add_Softmax_Tanh_Scaling 0.01 59.19 46.77
14 tuned_block_size_256_base 0.01 59.19 46.77
14 constant_mem_conv3d_optimized_base 0.01 59.19 46.77 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <algorithm>

// Optimized kernel with minimal use of __syncthreads() for shared memory consistency
__global__ void fused_operations_kernel(
    const float* __restrict__ input,
    const float* __restrict__ conv_weight,
    const float* __restrict__ conv_bias,
    const float* __restrict__ spatial_bias,
    float scaling_factor,
    int stride,
    int padding,
    int batch_size,
    int in_channels,
    int in_depth,
    int in_height,
    int in_width,
    int out_channels,
    int kernel_d,
    int kernel_h,
    int kernel_w,
    int out_depth,
    int out_height,
    int out_width,
    float* __restrict__ output
) {
    // Load conv_bias into shared memory for faster repeated access
    extern __shared__ float s_conv_bias[];

    for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
        s_conv_bias[i] = conv_bias[i];
    }
    // One barrier is necessary to ensure all threads have loaded the bias
    __syncthreads();

    int total_elements = batch_size * out_depth * out_height * out_width;
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= total_elements) return;

    // Compute output indices
    int w = idx % out_width;
    int h = (idx / out_width) % out_height;
    int d = (idx / (out_width * out_height)) % out_depth;
    int b = idx / (out_width * out_height * out_depth);

    float total = 0.0f;
    int dpad = d + padding;
    int hpad = h + padding;
    int wpad = w + padding;
    // Iterate over output channels
    for (int oc = 0; oc < out_channels; ++oc) {
        float channel_val = s_conv_bias[oc];
        for (int kd = 0; kd < kernel_d; ++kd) {
            int d_offset = dpad - kd;
            bool valid_d = ((d_offset) % stride == 0);
            int in_d_unclamped = d_offset / stride;
            bool in_bounds_d = (in_d_unclamped >= 0 && in_d_unclamped < in_depth);
            for (int kh = 0; kh < kernel_h; ++kh) {
                int h_offset = hpad - kh;
                bool valid_h = ((h_offset) % stride == 0);
                int in_h_unclamped = h_offset / stride;
                bool in_bounds_h = (in_h_unclamped >= 0 && in_h_unclamped < in_height);
                for (int kw = 0; kw < kernel_w; ++kw) {
                    int w_offset = wpad - kw;
                    bool valid_w = ((w_offset) % stride == 0);
                    int in_w_unclamped = w_offset / stride;
                    bool in_bounds_w = (in_w_unclamped >= 0 && in_w_unclamped < in_width);

                    bool stride_valid = valid_d && valid_h && valid_w;
                    bool in_bounds = in_bounds_d && in_bounds_h && in_bounds_w;
                    float valid = (stride_valid && in_bounds) ? 1.0f : 0.0f;
                    int in_d = max(0, min(in_depth - 1, in_d_unclamped));
                    int in_h = max(0, min(in_height - 1, in_h_unclamped));
                    int in_w = max(0, min(in_width - 1, in_w_unclamped));

                    for (int ic = 0; ic < in_channels; ++ic) {
                        int input_idx = (((b * in_channels + ic) * in_depth + in_d)
                                          * in_height + in_h) * in_width + in_w;
                        int weight_idx = (((ic * out_channels + oc) * kernel_d + kd)
                                           * kernel_h + kh) * kernel_w + kw;
                        channel_val += input[input_idx] * conv_weight[weight_idx] * valid;
                    }
                }
            }
        }
        total += channel_val;
    }

    float mean_val = total / out_channels;
    int spatial_idx = d * out_height * out_width + h * out_width + w;
    float biased = mean_val + spatial_bias[spatial_idx];

    // The softmax reduces to a constant, so we use tanh on 1.0f and apply scaling
    output[idx] = tanhf(1.0f) * scaling_factor;
}


torch::Tensor forward_cuda(
    const torch::Tensor& input,
    const torch::Tensor& conv_weight,
    const torch::Tensor& conv_bias,
    const torch::Tensor& spatial_bias,
    float scaling_factor,
    int stride,
    int padding
) {
    TORCH_CHECK(input.dim() == 5, "Input must be 5D tensor");
    TORCH_CHECK(conv_weight.dim() == 5, "Conv weight must be 5D tensor");

    int batch_size = input.size(0);
    int in_channels = input.size(1);
    int in_depth = input.size(2);
    int in_height = input.size(3);
    int in_width = input.size(4);

    int out_channels = conv_weight.size(1);
    int kernel_d = conv_weight.size(2);
    int kernel_h = conv_weight.size(3);
    int kernel_w = conv_weight.size(4);

    int out_depth = (in_depth - 1) * stride + kernel_d - 2 * padding;
    int out_height = (in_height - 1) * stride + kernel_h - 2 * padding;
    int out_width = (in_width - 1) * stride + kernel_w - 2 * padding;

    auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());
    torch::Tensor output = torch::empty({batch_size, 1, out_depth, out_height, out_width}, options);

    int total_elements = batch_size * out_depth * out_height * out_width;
    const int blockSize = 512;
    int grid = (total_elements + blockSize - 1) / blockSize;
    // Allocate shared memory for conv_bias
    int sharedMemSize = out_channels * sizeof(float);

    fused_operations_kernel<<<grid, blockSize, sharedMemSize>>>(
        input.data_ptr<float>(),
        conv_weight.data_ptr<float>(),
        conv_bias.data_ptr<float>(),
        spatial_bias.data_ptr<float>(),
        scaling_factor,
        stride,
        padding,
        batch_size,
        in_channels,
        in_depth,
        in_height,
        in_width,
        out_channels,
        kernel_d,
        kernel_h,
        kernel_w,
        out_depth,
        out_height,
        out_width,
        output.data_ptr<float>()
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward_cuda, "Fused Transposed Conv3D Operations with Minimal Synchronization (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.910 inst/cycle 0.000 5
Executed Ipc Elapsed 1.262 inst/cycle 0.000 5
Issue Slots Busy 49.602 % 0.217 5
Issued Ipc Active 1.982 inst/cycle 0.000 5
SM Busy 49.602 % 0.217 5
Memory Throughput 463455300.640 byte/second 426774235678897.188 5
Mem Busy 17.434 % 0.014 5
Max Bandwidth 31.426 % 0.047 5
L1/TEX Hit Rate 2.930 % 0.000 5
L2 Hit Rate 100.554 % 0.277 5
Mem Pipes Busy 49.672 % 0.130 5
Warp Cycles Per Issued Instruction 22.774 cycle 1.837 5
Warp Cycles Per Executed Instruction 23.684 cycle 1.990 5
Avg. Active Threads Per Warp 31.440 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.180 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 14.000 block 0.000 5
Block Limit Warps 4.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 70.412 % 0.053 5
Achieved Active Warps Per SM 45.064 warp 0.021 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.
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 (70.6%) 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 177994.77 μs
Device Time 699.77 μs
Self CPU Time 53.06 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_to_copy
CPU Time 177941.70 μs
Device Time 699.77 μs
Self CPU Time 103.21 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::empty_strided
CPU Time 176891.74 μs
Device Time 0.00 μs
Self CPU Time 98.91 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
cudaDeviceGetStreamPriorityRange
CPU Time 176410.91 μs
Device Time 0.00 μs
Self CPU Time 176410.91 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
cudaLaunchKernel
CPU Time 466654.41 μs
Device Time 16848.68 μs
Self CPU Time 466654.41 μs
Self Device Time 16848.68 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
fused_operations_kernel(float const*, float const*, float const*, float const*, float, int, int, int, int, int, int, int, int, int, int, int, int, int, int, float*)
CPU Time 0.00 μs
Device Time 42186.63 μs
Self CPU Time 0.00 μs
Self Device Time 42186.63 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 21371.81 μs
Device Time 32477.35 μs
Self CPU Time 21371.81 μs
Self Device Time 32477.35 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 65027.77 μs
Device Time 607290.60 μs
Self CPU Time 15383.15 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 49645.98 μs
Device Time 607290.60 μs
Self CPU Time 15483.66 μs
Self Device Time 607290.60 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::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 607368.61 μs
Self CPU Time 0.00 μs
Self Device Time 607368.61 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 45327 warnings (45280 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_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:9:5 bugprone-easily-swappable-parameters
9 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const float* __restrict__ conv_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ spatial_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:9:31: note: the first parameter in the range is 'conv_weight'
9 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:11:31: note: the last parameter in the range is 'spatial_bias'
11 | const float* __restrict__ spatial_bias,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:12:5: warning: 5 adjacent parameters of 'fused_operations_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | float scaling_factor,
| ^~~~~~~~~~~~~~~~~~~~~
13 | int stride,
| ~~~~~~~~~~~
14 | int padding,
| ~~~~~~~~~~~~
15 | int batch_size,
| ~~~~~~~~~~~~~~~
16 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:12:11: note: the first parameter in the range is 'scaling_factor'
12 | float scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:16:9: note: the last parameter in the range is 'in_channels'
16 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:13:5: note: 'float' and 'int' may be implicitly converted
13 | int stride,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:19:5: warning: 2 adjacent parameters of 'fused_operations_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int in_width,
| ^~~~~~~~~~~~~
20 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:19:9: note: the first parameter in the range is 'in_width'
19 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:20:9: note: the last 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_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:23:5: warning: 2 adjacent parameters of 'fused_operations_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | int kernel_w,
| ^~~~~~~~~~~~~
24 | int out_depth,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:23:9: note: the first parameter in the range is 'kernel_w'
23 | int kernel_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:24:9: note: the last parameter in the range is 'out_depth'
24 | int out_depth,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:32:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:32:54: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:39:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:91:30: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
91 | float mean_val = total / out_channels;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:93:11: warning: Value stored to 'biased' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
93 | float biased = mean_val + spatial_bias[spatial_idx];
| ^~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:93:11: note: Value stored to 'biased' during its initialization is never read
93 | float biased = mean_val + spatial_bias[spatial_idx];
| ^~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:112:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:113:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:114:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | int in_depth = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:115:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | int in_height = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:116:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | int in_width = input.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:118:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
118 | int out_channels = conv_weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:119:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | int kernel_d = conv_weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:120:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | int kernel_h = conv_weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:121:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | int kernel_w = conv_weight.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b3_s1_minimal_sync_convtranspose3d/edit_1/edit_1.cu:134:25: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
134 | int sharedMemSize = out_channels * sizeof(float);
| ^