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13_ConvTranspose3d_Mean_Add_Softmax_Tanh_Scaling • convtranspose3d_smem_base

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 convtranspose3d_smem_base
CUDA Speedup (Native) 59.188x
CUDA Speedup (Compile) 46.771x
CUDA Runtime 0.010 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 1.00
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 <math.h>

// This kernel caches the conv_bias in shared memory to reduce global memory accesses.
// __syncthreads() is used only once after loading the shared memory to ensure 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,
    // Input dimensions
    int batch_size,
    int in_channels,
    int in_depth,
    int in_height,
    int in_width,
    // Conv parameters
    int out_channels,
    int kernel_d,
    int kernel_h,
    int kernel_w,
    // Output dimensions
    int out_depth,
    int out_height,
    int out_width,
    float* __restrict__ output
) {
    // Allocate shared memory for conv_bias
    extern __shared__ float s_conv_bias[];
    // Each thread loads parts of conv_bias to shared memory
    for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
        s_conv_bias[i] = conv_bias[i];
    }
    // Synchronize only once to ensure the shared memory is ready
    __syncthreads();

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

    // Compute output coordinates from the linear index
    const int w = idx % out_width;
    const int h = (idx / out_width) % out_height;
    const int d = (idx / (out_width * out_height)) % out_depth;
    const int b = idx / (out_width * out_height * out_depth);

    float total = 0.0f;
    // Loop over output channels
    for (int oc = 0; oc < out_channels; ++oc) {
        float channel_val = s_conv_bias[oc];
        
        // Loop over kernel dimensions for transposed convolution
        for (int kd = 0; kd < kernel_d; ++kd) {
            for (int kh = 0; kh < kernel_h; ++kh) {
                for (int kw = 0; kw < kernel_w; ++kw) {
                    int out_d_offset = d - kd + padding;
                    int out_h_offset = h - kh + padding;
                    int out_w_offset = w - kw + padding;
                    
                    // Check stride alignment
                    if (out_d_offset % stride != 0) continue;
                    if (out_h_offset % stride != 0) continue;
                    if (out_w_offset % stride != 0) continue;
                    
                    int in_d = out_d_offset / stride;
                    int in_h = out_h_offset / stride;
                    int in_w = out_w_offset / stride;
                    
                    // Check boundaries
                    if (in_d < 0 || in_d >= in_depth) continue;
                    if (in_h < 0 || in_h >= in_height) continue;
                    if (in_w < 0 || in_w >= in_width) continue;
                    
                    // Accumulate contributions over input channels
                    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];
                    }
                }
            }
        }
        total += channel_val;
    }

    // Mean pooling over channels
    float mean_val = total / out_channels;
    
    // Add spatial bias
    int spatial_idx = d * out_height * out_width + h * out_width + w;
    float biased = mean_val + spatial_bias[spatial_idx];

    // Softmax over a single element yields 1.0, then apply tanh and scaling (as in the reference)
    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 threads = 256;
    int total_elements = batch_size * out_depth * out_height * out_width;
    int blocks = (total_elements + threads - 1) / threads;

    // Allocate shared memory size for conv_bias
    size_t shared_memory_size = out_channels * sizeof(float);

    fused_operations_kernel<<<blocks, threads, shared_memory_size>>>(
        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 Shared Memory Optimization (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.536 inst/cycle 0.000 5
Executed Ipc Elapsed 1.070 inst/cycle 0.000 5
Issue Slots Busy 39.830 % 0.146 5
Issued Ipc Active 1.592 inst/cycle 0.000 5
SM Busy 39.830 % 0.146 5
Memory Throughput 400669456.066 byte/second 893288127001228.625 5
Mem Busy 14.472 % 0.003 5
Max Bandwidth 25.738 % 0.002 5
L1/TEX Hit Rate 5.780 % 0.000 5
L2 Hit Rate 99.930 % 0.213 5
Mem Pipes Busy 42.198 % 0.007 5
Warp Cycles Per Issued Instruction 18.124 cycle 0.330 5
Warp Cycles Per Executed Instruction 18.804 cycle 0.354 5
Avg. Active Threads Per Warp 30.920 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.620 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 28.000 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 46.432 % 0.290 5
Achieved Active Warps Per SM 29.716 warp 0.119 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 (47.5%) 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.
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.
Operation / Metric Value Unit
aten::to
CPU Time 527763.05 μs
Device Time 805.63 μs
Self CPU Time 71.17 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::_to_copy
CPU Time 527691.87 μs
Device Time 805.63 μs
Self CPU Time 153.05 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 526416.72 μs
Device Time 0.00 μs
Self CPU Time 160.21 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
cudaDeviceGetStreamPriorityRange
CPU Time 525872.14 μs
Device Time 0.00 μs
Self CPU Time 525872.14 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 425080.53 μs
Device Time 18221.11 μs
Self CPU Time 425080.53 μs
Self Device Time 18221.11 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 44983.38 μs
Self CPU Time 0.00 μs
Self Device Time 44983.38 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 16441.95 μs
Device Time 36055.63 μs
Self CPU Time 16441.95 μs
Self Device Time 36055.63 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 58026.48 μs
Device Time 541946.51 μs
Self CPU Time 11257.41 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 46770.90 μs
Device Time 541946.51 μs
Self CPU Time 15420.98 μs
Self Device Time 541946.51 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 541946.51 μs
Self CPU Time 0.00 μs
Self Device Time 541946.51 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 
45298 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/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:11:5 bugprone-easily-swappable-parameters
11 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const float* __restrict__ conv_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const float* __restrict__ spatial_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:11:31: note: the first parameter in the range is 'conv_weight'
11 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:13:31: note: the last parameter in the range is 'spatial_bias'
13 | const float* __restrict__ spatial_bias,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:14:5: warning: 5 adjacent parameters of 'fused_operations_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | float scaling_factor,
| ^~~~~~~~~~~~~~~~~~~~~
15 | int stride,
| ~~~~~~~~~~~
16 | int padding,
| ~~~~~~~~~~~~
17 | // Input dimensions
| ~~~~~~~~~~~~~~~~~~~
18 | int batch_size,
| ~~~~~~~~~~~~~~~
19 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:14:11: note: the first parameter in the range is 'scaling_factor'
14 | float scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/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/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:15:5: note: 'float' and 'int' may be implicitly converted
15 | int stride,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:22:5: warning: 2 adjacent parameters of 'fused_operations_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
22 | int in_width,
| ^~~~~~~~~~~~~
23 | // Conv parameters
| ~~~~~~~~~~~~~~~~~~
24 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/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/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:24:9: note: the last parameter in the range is 'out_channels'
24 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:27:5: warning: 2 adjacent parameters of 'fused_operations_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
27 | int kernel_w,
| ^~~~~~~~~~~~~
28 | // Output dimensions
| ~~~~~~~~~~~~~~~~~~~~
29 | int out_depth,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:27:9: note: the first parameter in the range is 'kernel_w'
27 | int kernel_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:29:9: note: the last parameter in the range is 'out_depth'
29 | int out_depth,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:37:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
37 | for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:37:54: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
37 | for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:43:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
43 | const int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:94:30: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
94 | float mean_val = total / out_channels;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:98:11: warning: Value stored to 'biased' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
98 | float biased = mean_val + spatial_bias[spatial_idx];
| ^~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:98:11: note: Value stored to 'biased' during its initialization is never read
98 | float biased = mean_val + spatial_bias[spatial_idx];
| ^~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:117:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:118:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
118 | int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:119:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | int in_depth = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:120:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | int in_height = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:121:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | int in_width = input.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:123:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
123 | int out_channels = conv_weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:124:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | int kernel_d = conv_weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:125:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | int kernel_h = conv_weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b1_s3_convtranspose3d_smem/base/base.cu:126:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | int kernel_w = conv_weight.size(4);
| ^