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13_ConvTranspose3d_Mean_Add_Softmax_Tanh_Scaling • tuned_block_size_256_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 tuned_block_size_256_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 gpt-4o-mini-2024-07-18
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>

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

    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;

    for (int oc = 0; oc < out_channels; ++oc) {
        float channel_val = conv_bias[oc];
        
        for (int kd = 0; kd < kernel_d; ++kd) {
            for (int kh = 0; kh < kernel_h; ++kh) {
                for (int kw = 0; kw < kernel_w; ++kw) {
                    const int in_d_unclamped = (d - kd + padding) / stride;
                    const int in_h_unclamped = (h - kh + padding) / stride;
                    const int in_w_unclamped = (w - kw + padding) / stride;

                    const bool stride_valid = 
                        ((d - kd + padding) % stride == 0) &&
                        ((h - kh + padding) % stride == 0) &&
                        ((w - kw + padding) % stride == 0);

                    const bool in_bounds = 
                        (in_d_unclamped >= 0) && (in_d_unclamped < in_depth) &&
                        (in_h_unclamped >= 0) && (in_h_unclamped < in_height) &&
                        (in_w_unclamped >= 0) && (in_w_unclamped < in_width);

                    const float valid = (stride_valid && in_bounds) ? 1.0f : 0.0f;

                    const int in_d = max(0, min(in_depth - 1, in_d_unclamped));
                    const int in_h = max(0, min(in_height - 1, in_h_unclamped));
                    const int in_w = max(0, min(in_width - 1, in_w_unclamped));

                    for (int ic = 0; ic < in_channels; ++ic) {
                        const int input_idx = (((b * in_channels + ic) * in_depth + in_d)
                                            * in_height + in_h) * in_width + in_w;
                        const 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;
    }

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

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

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

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

    const int threads = 512;  // Increased threads for better occupancy
    const int total_elements = batch_size * out_depth * out_height * out_width;
    const int blocks = (total_elements + threads - 1) / threads;

    fused_operations_kernel<<<blocks, threads>>>(
        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 Tuned Block Size (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.886 inst/cycle 0.000 5
Executed Ipc Elapsed 1.118 inst/cycle 0.000 5
Issue Slots Busy 49.690 % 0.059 5
Issued Ipc Active 1.988 inst/cycle 0.000 5
SM Busy 49.690 % 0.059 5
Memory Throughput 501912800.904 byte/second 1599589159994033.250 5
Mem Busy 21.048 % 0.050 5
Max Bandwidth 37.308 % 0.024 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 99.518 % 0.062 5
Mem Pipes Busy 45.982 % 0.037 5
Warp Cycles Per Issued Instruction 18.102 cycle 0.175 5
Warp Cycles Per Executed Instruction 19.092 cycle 0.193 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.400 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 16.000 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 58.642 % 0.008 5
Achieved Active Warps Per SM 37.532 warp 0.003 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 (58.7%) 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 171417.46 μs
Device Time 861.75 μs
Self CPU Time 78.37 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_to_copy
CPU Time 171339.09 μs
Device Time 861.75 μs
Self CPU Time 126.90 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 170024.52 μs
Device Time 0.00 μs
Self CPU Time 141.85 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 169338.90 μs
Device Time 0.00 μs
Self CPU Time 169338.90 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 109763.82 μs
Device Time 2908.69 μs
Self CPU Time 109763.82 μs
Self Device Time 2908.69 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 6593.98 μs
Self CPU Time 0.00 μs
Self Device Time 6593.98 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 2841.72 μs
Device Time 5580.35 μs
Self CPU Time 2841.72 μs
Self Device Time 5580.35 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 46537.26 μs
Device Time 106937.27 μs
Self CPU Time 2737.30 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 43801.73 μs
Device Time 106937.27 μs
Self CPU Time 2738.02 μs
Self Device Time 106937.27 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 106937.27 μs
Self CPU Time 0.00 μs
Self Device Time 106937.27 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 
45297 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/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:7:5 bugprone-easily-swappable-parameters
7 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 | const float* __restrict__ conv_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
9 | const float* __restrict__ spatial_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:7:31: note: the first parameter in the range is 'conv_weight'
7 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:9:31: note: the last parameter in the range is 'spatial_bias'
9 | const float* __restrict__ spatial_bias,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:10:5: warning: 2 adjacent parameters of 'fused_operations_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
10 | float scaling_factor,
| ^~~~~~~~~~~~~~~~~~~~~
11 | int stride,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:10:11: note: the first parameter in the range is 'scaling_factor'
10 | float scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:11:9: note: the last parameter in the range is 'stride'
11 | int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:11:5: note: 'float' and 'int' may be implicitly converted
11 | int stride,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:12:5: warning: 3 adjacent parameters of 'fused_operations_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | int padding,
| ^~~~~~~~~~~~
13 | int batch_size,
| ~~~~~~~~~~~~~~~
14 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:12:9: note: the first parameter in the range is 'padding'
12 | int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:14:9: note: the last parameter in the range is 'in_channels'
14 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:17:5: warning: 2 adjacent parameters of 'fused_operations_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | int in_width,
| ^~~~~~~~~~~~~
18 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:17:9: note: the first parameter in the range is 'in_width'
17 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:18:9: note: the last parameter in the range is 'out_channels'
18 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:21:5: warning: 2 adjacent parameters of 'fused_operations_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | int kernel_w,
| ^~~~~~~~~~~~~
22 | int out_depth,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:21:9: note: the first parameter in the range is 'kernel_w'
21 | int kernel_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:22:9: note: the last parameter in the range is 'out_depth'
22 | int out_depth,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:27:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | const 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/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:77:36: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
77 | const float mean_val = total / out_channels;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:79:17: warning: Value stored to 'biased' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
79 | const 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/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:79:17: note: Value stored to 'biased' during its initialization is never read
79 | const 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/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:95:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | const int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:96:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:97:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
97 | const int in_depth = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:98:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | const int in_height = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:99:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | const int in_width = input.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:101:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | const int out_channels = conv_weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:102:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | const int kernel_d = conv_weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:103:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | const int kernel_h = conv_weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_13/b5_s2_tuned_block_size_256/edit_1/edit_1.cu:104:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
104 | const int kernel_w = conv_weight.size(4);
| ^