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13_ConvTranspose3d_Mean_Add_Softmax_Tanh_Scaling • combined_kernel_optimized_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 combined_kernel_optimized_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>

// Combined optimized kernel for fused transposed conv3D operations
// This kernel merges loop unrolling from kernel1 with streamlined stride checks from kernel2.

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

    // Compute 4D coordinates [b, d, h, w] from the flattened index
    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;

    // Loop over output channels with loop unrolling
    #pragma unroll
    for (int oc = 0; oc < out_channels; ++oc) {
        float channel_val = conv_bias[oc];

        // Iterate over kernel dimensions (depth, height, width)
        #pragma unroll
        for (int kd = 0; kd < kernel_d; ++kd) {
            #pragma unroll
            for (int kh = 0; kh < kernel_h; ++kh) {
                #pragma unroll
                for (int kw = 0; kw < kernel_w; ++kw) {
                    // Compute the offset coordinates with padding and kernel index
                    int d_offset = d - kd + padding;
                    int h_offset = h - kh + padding;
                    int w_offset = w - kw + padding;

                    // Consolidated stride alignment check
                    if ((d_offset % stride == 0) && (h_offset % stride == 0) && (w_offset % stride == 0)) {
                        int in_d = d_offset / stride;
                        int in_h = h_offset / stride;
                        int in_w = w_offset / stride;

                        // Check if the computed input indices are within bounds
                        if (in_d >= 0 && in_d < in_depth &&
                            in_h >= 0 && in_h < in_height &&
                            in_w >= 0 && in_w < in_width) {

                            // Loop over input channels with unrolling
                            #pragma unroll
                            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;
    }

    // Compute mean pooling over the output 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];

    // Apply a constant softmax shortcut (since softmax with one element is 1.0) followed by tanh activation and scaling
    output[idx] = tanhf(1.0f) * scaling_factor;
}

// CUDA forward wrapper for the combined kernel
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 a 5D tensor");
    TORCH_CHECK(conv_weight.dim() == 5, "Conv weight must be a 5D tensor");

    // Extract input dimensions
    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);

    // Extract convolution kernel and output channel dimensions
    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);

    // Calculate output dimensions for a transposed convolution operation
    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 total_elements = batch_size * out_depth * out_height * out_width;
    const int threads = 256;
    const int blocks = (total_elements + threads - 1) / threads;

    fused_operations_kernel_combined<<<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, "Combined Optimized Fused Transposed Conv3D Operations (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.222 inst/cycle 0.000 5
Executed Ipc Elapsed 0.788 inst/cycle 0.000 5
Issue Slots Busy 32.144 % 0.311 5
Issued Ipc Active 1.284 inst/cycle 0.001 5
SM Busy 32.144 % 0.311 5
Memory Throughput 374646114.394 byte/second 2660861578824350.000 5
Mem Busy 15.034 % 0.342 5
Max Bandwidth 26.314 % 0.005 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 100.728 % 1.735 5
Mem Pipes Busy 32.434 % 0.010 5
Warp Cycles Per Issued Instruction 14.868 cycle 0.283 5
Warp Cycles Per Executed Instruction 15.676 cycle 0.318 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 16.000 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 31.828 % 1.359 5
Achieved Active Warps Per SM 20.368 warp 0.561 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.
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 (31.4%) 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 285077.38 μs
Device Time 792.89 μs
Self CPU Time 68.96 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 285008.42 μs
Device Time 792.89 μs
Self CPU Time 128.10 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 283767.87 μs
Device Time 0.00 μs
Self CPU Time 121.84 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 279696.30 μs
Device Time 0.00 μs
Self CPU Time 279696.30 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
cudaLaunchKernel
CPU Time 524408.37 μs
Device Time 22210.06 μs
Self CPU Time 524408.37 μs
Self Device Time 22210.06 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_combined(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 53138.30 μs
Self CPU Time 0.00 μs
Self Device Time 53138.30 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 19716.75 μs
Device Time 44103.45 μs
Self CPU Time 19716.75 μs
Self Device Time 44103.45 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 66001.99 μs
Device Time 659024.63 μs
Self CPU Time 13654.40 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 52349.08 μs
Device Time 659024.63 μs
Self CPU Time 16584.48 μs
Self Device Time 659024.63 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 659024.63 μs
Self CPU Time 0.00 μs
Self Device Time 659024.63 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 
45296 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/b4_s1_combined_kernel_optimized/base/base.cu:10:5 bugprone-easily-swappable-parameters
10 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ conv_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const float* __restrict__ spatial_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:10:31: note: the first parameter in the range is 'conv_weight'
10 | const float* __restrict__ conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:12:31: note: the last parameter in the range is 'spatial_bias'
12 | const float* __restrict__ spatial_bias,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:13:5: warning: 5 adjacent parameters of 'fused_operations_kernel_combined' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | float scaling_factor,
| ^~~~~~~~~~~~~~~~~~~~~
14 | int stride,
| ~~~~~~~~~~~
15 | int padding,
| ~~~~~~~~~~~~
16 | int batch_size,
| ~~~~~~~~~~~~~~~
17 | int in_channels,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:13:11: note: the first parameter in the range is 'scaling_factor'
13 | float scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:17:9: note: the last parameter in the range is 'in_channels'
17 | int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:14:5: note: 'float' and 'int' may be implicitly converted
14 | int stride,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:20:5: warning: 2 adjacent parameters of 'fused_operations_kernel_combined' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | int in_width,
| ^~~~~~~~~~~~~
21 | int out_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:20:9: note: the first parameter in the range is 'in_width'
20 | int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:21:9: note: the last parameter in the range is 'out_channels'
21 | int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:24:5: warning: 2 adjacent parameters of 'fused_operations_kernel_combined' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
24 | int kernel_w,
| ^~~~~~~~~~~~~
25 | int out_depth,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:24:9: note: the first parameter in the range is 'kernel_w'
24 | int kernel_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:25:9: note: the last parameter in the range is 'out_depth'
25 | int out_depth,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:30:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | 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/b4_s1_combined_kernel_optimized/base/base.cu:86:30: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
86 | float mean_val = total / out_channels;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:90:11: warning: Value stored to 'biased' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
90 | 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/b4_s1_combined_kernel_optimized/base/base.cu:90:11: note: Value stored to 'biased' during its initialization is never read
90 | 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/b4_s1_combined_kernel_optimized/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 = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:111:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:112:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | const int in_depth = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:113:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | const int in_height = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:114:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | const int in_width = input.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:117:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | const int out_channels = conv_weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:118:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
118 | const int kernel_d = conv_weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:119:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | const int kernel_h = conv_weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_13/b4_s1_combined_kernel_optimized/base/base.cu:120:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | const int kernel_w = conv_weight.size(4);
| ^