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90_Conv3d_LeakyReLU_Sum_Clamp_GELU • aligned_vectorized_ldg_90_conv3d_edit_1

Level 2 • Task 90
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv_weight: torch.Tensor,
    conv_bias: torch.Tensor,
    sum_tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Applies 3D convolution, LeakyReLU, tensor addition, clamping and GELU activation.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        conv_weight (torch.Tensor): 3D convolution weight tensor of shape
            (out_channels, in_channels, kernel_size, kernel_size, kernel_size)
        conv_bias (torch.Tensor): Bias tensor for 3D convolution of shape (out_channels)
        sum_tensor (torch.Tensor): Tensor to add of shape (out_channels, 1, 1, 1)

    Returns:
        torch.Tensor: Output tensor after applying convolution, LeakyReLU, addition,
            clamping and GELU activation
    """
    x = F.conv3d(x, conv_weight, bias=conv_bias)
    x = F.leaky_relu(x, negative_slope=0.2)
    x = x + sum_tensor
    x = torch.clamp(x, min=-1.0, max=1.0)
    x = F.gelu(x)
    return x


class Model(nn.Module):
    """
    Model that performs a 3D convolution, applies LeakyReLU, sums with a tensor, clamps, and applies GELU activation.
    """

    def __init__(self, in_channels, out_channels, kernel_size, sum_tensor_shape):
        super(Model, self).__init__()
        conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.conv_weight = conv.weight
        self.conv_bias = conv.bias
        self.sum_tensor = nn.Parameter(torch.randn(sum_tensor_shape) * 0.02)

    def forward(self, x, fn=module_fn):
        return fn(x, self.conv_weight, self.conv_bias, self.sum_tensor)


batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
sum_tensor_shape = (out_channels, 1, 1, 1)


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, sum_tensor_shape]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D convolution, applies LeakyReLU, sums with a tensor, clamps, and applies GELU activation.
    """
    def __init__(self, in_channels, out_channels, kernel_size, sum_tensor_shape):
        super(Model, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.sum_tensor = nn.Parameter(torch.randn(sum_tensor_shape)*0.02)

    def forward(self, x):
        x = self.conv(x)
        x = torch.nn.functional.leaky_relu(x, negative_slope=0.2)
        x = x + self.sum_tensor
        x = torch.clamp(x, min=-1.0, max=1.0)
        x = torch.nn.functional.gelu(x)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
sum_tensor_shape = (out_channels, 1, 1, 1)

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, sum_tensor_shape]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 90_Conv3d_LeakyReLU_Sum_Clamp_GELU
Level ID 2
Task ID 90
Kernel Name aligned_vectorized_ldg_90_conv3d_edit_1
CUDA Speedup (Native) 1.246x
CUDA Speedup (Compile) 0.665x
CUDA Runtime 0.794 ms
PyTorch Runtime (Native) 0.990 ms
PyTorch Runtime (Compile) 0.528 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 90 • 90_Conv3d_LeakyReLU_Sum_Clamp_GELU)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 aligned_vectorized_ldg_90_conv3d_edit_1 0.79 1.25 0.66
🥈 aligned_vectorized_ldg_90_conv3d_base 0.80 1.24 0.66
🥉 modular_device_functions_base 0.81 1.21 0.65
🥉 load_balanced_kernel_base_base 0.81 1.21 0.65
5 constant_memory_optimization_base 0.82 1.21 0.65
6 coalesced_mem_access_opt_base 0.82 1.20 0.64
7 atomic_minimal_usage_kernel_opt_base 0.83 1.20 0.64
7 atomic_minimal_usage_kernel_opt_edit_1 0.83 1.20 0.64
9 modular_device_functions_v2_base 0.83 1.20 0.64
10 balanced_workload_distribution_base 0.83 1.19 0.64
11 warp_primitives_based_kernel_edit_1 0.83 1.19 0.63
12 optimized_strided_loop_base_base 0.83 1.19 0.63
13 gridstride_const_base 0.84 1.18 0.63
13 block_size_256_kernel_base 0.84 1.18 0.63
15 warp_divergence_free_base 0.84 1.18 0.63
15 modular_device_functions_base 0.84 1.18 0.63
17 optimized_kernel_combination_base 0.84 1.18 0.63
18 multidim_indexed_kernel_base 0.84 1.18 0.63
18 const_mem_conv3d_leakyrelu_sumclamp_gelu_base 0.84 1.18 0.63
20 90_Conv3d_LeakyReLU_Sum_Clamp_GELU 0.84 1.17 0.63 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <cuda_runtime.h>
#include <vector>
#include <cmath>
#include <cstdint>

// This kernel processes the main part of the tensor in groups of 4 elements using 128-bit aligned loads/stores via float4.
__global__ void my_kernel_vectorized(
    const float* __restrict__ input,
    const float* __restrict__ sum_tensor,
    float* __restrict__ output,
    const int64_t num_vectorized,
    const int64_t width,
    const int64_t height,
    const int64_t depth,
    const int64_t channels) {

    int id = blockIdx.x * blockDim.x + threadIdx.x;
    if (id < num_vectorized) {
        int64_t base = id * 4;
        // Cast input to float4 pointer and use __ldg() for a 128-bit aligned read
        const float4* input4 = reinterpret_cast<const float4*>(input);
        float4 in_val = __ldg(&input4[id]);
        float4 res;

        // Process each element in the vector
        #pragma unroll
        for (int i = 0; i < 4; i++) {
            int64_t idx = base + i;
            // Compute the 5D tensor indices given the flattened index idx
            int64_t w = idx % width;
            int64_t h = (idx / width) % height;
            int64_t d = (idx / (width * height)) % depth;
            int64_t c = (idx / (width * height * depth)) % channels;
            
            float x;
            if (i == 0) x = in_val.x;
            else if (i == 1) x = in_val.y;
            else if (i == 2) x = in_val.z;
            else x = in_val.w;
            
            // Use branchless LeakyReLU
            float y = fmaxf(x, 0.2f * x);
            // Add bias from sum_tensor using __ldg() for read-only access
            y += __ldg(&sum_tensor[c]);
            // Clamp the value to [-1, 1]
            y = fmaxf(fminf(y, 1.0f), -1.0f);
            // Apply GELU activation
            float cdf = 0.5f * (1.0f + tanhf(0.7978845608f * (y + 0.044715f * y * y * y)));
            y = y * cdf;
            
            // Assign the computed value to the corresponding component
            if (i == 0) res.x = y;
            else if (i == 1) res.y = y;
            else if (i == 2) res.z = y;
            else res.w = y;
        }
        
        // Write back the result using a 128-bit aligned store
        float4* output4 = reinterpret_cast<float4*>(output);
        output4[id] = res;
    }
}

// This kernel processes any remaining elements that do not fit into a group of 4
__global__ void my_kernel_remainder(
    const float* __restrict__ input,
    const float* __restrict__ sum_tensor,
    float* __restrict__ output,
    const int64_t start,
    const int64_t num_elements,
    const int64_t width,
    const int64_t height,
    const int64_t depth,
    const int64_t channels) {
    
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int64_t global_idx = start + idx;
    if (global_idx < num_elements) {
        float x = __ldg(&input[global_idx]);
        float y = fmaxf(x, 0.2f * x);
        int64_t w = global_idx % width;
        int64_t h = (global_idx / width) % height;
        int64_t d = (global_idx / (width * height)) % depth;
        int64_t c = (global_idx / (width * height * depth)) % channels;
        y += __ldg(&sum_tensor[c]);
        y = fmaxf(fminf(y, 1.0f), -1.0f);
        float cdf = 0.5f * (1.0f + tanhf(0.7978845608f * (y + 0.044715f * y * y * y)));
        output[global_idx] = y * cdf;
    }
}

// Launcher that selects between vectorized and remainder kernels
void my_kernel_launcher(
    torch::Tensor& x,
    torch::Tensor& sum_tensor) {
    
    const int64_t num_elements = x.numel();
    const int64_t batch_size = x.size(0);
    const int64_t channels = x.size(1);
    const int64_t depth = x.size(2);
    const int64_t height = x.size(3);
    const int64_t width = x.size(4);
    
    // Calculate the number of groups of 4 elements and remainder
    int64_t num_vectorized = num_elements / 4;
    int64_t remainder = num_elements % 4;
    
    // Launch the vectorized kernel with 128 threads per block for better occupancy
    int threads = 128;
    int blocks = (num_vectorized + threads - 1) / threads;
    my_kernel_vectorized<<<blocks, threads>>>(
        x.data_ptr<float>(),
        sum_tensor.data_ptr<float>(),
        x.data_ptr<float>(),
        num_vectorized,
        width,
        height,
        depth,
        channels
    );
    
    // Launch the remainder kernel if there are leftover elements
    if (remainder > 0) {
        // Use fixed block size of 128 threads for better efficiency
        int threads_rem = 128;
        int blocks_rem = (remainder + threads_rem - 1) / threads_rem;
        int64_t start = num_vectorized * 4;
        my_kernel_remainder<<<blocks_rem, threads_rem>>>(
            x.data_ptr<float>(),
            sum_tensor.data_ptr<float>(),
            x.data_ptr<float>(),
            start,
            num_elements,
            width,
            height,
            depth,
            channels
        );
    }
    
    cudaDeviceSynchronize();
}

// Forward function that performs the 3D convolution and applies the custom CUDA kernel
torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor conv_weight,
    torch::Tensor conv_bias,
    torch::Tensor sum_tensor) {

    TORCH_CHECK(x.is_cuda(), "x must be a CUDA tensor");
    TORCH_CHECK(conv_weight.is_cuda(), "conv_weight must be a CUDA tensor");
    TORCH_CHECK(conv_bias.is_cuda(), "conv_bias must be a CUDA tensor");
    TORCH_CHECK(sum_tensor.is_cuda(), "sum_tensor must be a CUDA tensor");
    TORCH_CHECK(x.scalar_type() == at::kFloat, "x must be of type float32");

    // Perform 3D convolution
    auto x_conv = at::conv3d(x, conv_weight, conv_bias);

    // Ensure output is contiguous
    auto output = x_conv.contiguous();

    // Apply the optimized kernel with aligned 128-bit memory accesses using __ldg()
    my_kernel_launcher(output, sum_tensor);

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Custom forward function (CUDA) with aligned 128-bit vectorized loads/stores");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv3d
CPU Time 340047.79 μs
Device Time 4084475.57 μs
Self CPU Time 10572.76 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::convolution
CPU Time 329475.03 μs
Device Time 4084475.57 μs
Self CPU Time 13800.97 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_convolution
CPU Time 315674.06 μs
Device Time 4084475.57 μs
Self CPU Time 28507.80 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::cudnn_convolution
CPU Time 220756.42 μs
Device Time 3545900.29 μs
Self CPU Time 158393.77 μs
Self Device Time 3545900.29 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_fprop_implicit_gemm_indexed_f32f32_f32f32_f32_nchwkcrs_nchw_tilesize32x32x8_stage3_warpsize1x2x1_g1_ffma_aligna4_alignc4_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 3545898.79 μs
Self CPU Time 0.00 μs
Self Device Time 3545898.79 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 4853687.87 μs
Device Time 76968.09 μs
Self CPU Time 4853687.87 μs
Self Device Time 76968.09 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 
45295 warnings generated when compiling for host.
Suppressed 45324 warnings (45277 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/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:10:5 bugprone-easily-swappable-parameters
10 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ sum_tensor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:10:31: note: the first parameter in the range is 'input'
10 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:11:31: note: the last parameter in the range is 'sum_tensor'
11 | const float* __restrict__ sum_tensor,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:13:5: warning: 2 adjacent parameters of 'my_kernel_vectorized' of similar type ('const int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | const int64_t num_vectorized,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
14 | const int64_t width,
| ~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:13:19: note: the first parameter in the range is 'num_vectorized'
13 | const int64_t num_vectorized,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:14:19: note: the last parameter in the range is 'width'
14 | const int64_t width,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:19:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | int id = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:21:24: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
21 | int64_t base = id * 4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:21:24: note: make conversion explicit to silence this warning
4 | int64_t base = id * 4;
| ^~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:21:24: note: perform multiplication in a wider type
21 | int64_t base = id * 4;
| ^~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:32:21: warning: Value stored to 'w' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
32 | int64_t w = idx % width;
| ^ ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:32:21: note: Value stored to 'w' during its initialization is never read
32 | int64_t w = idx % width;
| ^ ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:33:21: warning: Value stored to 'h' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
33 | int64_t h = (idx / width) % height;
| ^ ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:33:21: note: Value stored to 'h' during its initialization is never read
33 | int64_t h = (idx / width) % height;
| ^ ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:34:21: warning: Value stored to 'd' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
34 | int64_t d = (idx / (width * height)) % depth;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:34:21: note: Value stored to 'd' during its initialization is never read
34 | int64_t d = (idx / (width * height)) % depth;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:68:5: warning: 2 adjacent parameters of 'my_kernel_remainder' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
68 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
69 | const float* __restrict__ sum_tensor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:68:31: note: the first parameter in the range is 'input'
68 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:69:31: note: the last parameter in the range is 'sum_tensor'
69 | const float* __restrict__ sum_tensor,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:71:5: warning: 3 adjacent parameters of 'my_kernel_remainder' of similar type ('const int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
71 | const int64_t start,
| ^~~~~~~~~~~~~~~~~~~~
72 | const int64_t num_elements,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~
73 | const int64_t width,
| ~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:71:19: note: the first parameter in the range is 'start'
71 | const int64_t start,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:73:19: note: the last parameter in the range is 'width'
73 | const int64_t width,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:78:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
78 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:83:17: warning: Value stored to 'w' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
83 | int64_t w = global_idx % width;
| ^ ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:83:17: note: Value stored to 'w' during its initialization is never read
83 | int64_t w = global_idx % width;
| ^ ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:84:17: warning: Value stored to 'h' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
84 | int64_t h = (global_idx / width) % height;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:84:17: note: Value stored to 'h' during its initialization is never read
84 | int64_t h = (global_idx / width) % height;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:85:17: warning: Value stored to 'd' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
85 | int64_t d = (global_idx / (width * height)) % depth;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:85:17: note: Value stored to 'd' during its initialization is never read
85 | int64_t d = (global_idx / (width * height)) % depth;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:100:19: warning: Value stored to 'batch_size' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
100 | const int64_t batch_size = x.size(0);
| ^~~~~~~~~~ ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:100:19: note: Value stored to 'batch_size' during its initialization is never read
100 | const int64_t batch_size = x.size(0);
| ^~~~~~~~~~ ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:112:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | int blocks = (num_vectorized + threads - 1) / threads;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:128:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | int blocks_rem = (remainder + threads_rem - 1) / threads_rem;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:148:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
148 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_90/b5_s3_aligned_vectorized_ldg_90_conv3d/edit_1/edit_1.cu:149:19: warning: the parameter 'conv_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
149 | torch::Tensor conv_weight,
| ^
| const &