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67_Conv2d_GELU_GlobalAvgPool • modular_fused_conv_gelu_pool_base

Level 2 • Task 67
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,
) -> torch.Tensor:
    """
    Applies convolution, GELU activation, and global average pooling.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width)
        conv_weight (torch.Tensor): Convolution weight tensor of shape
            (out_channels, in_channels, kernel_size, kernel_size)
        conv_bias (torch.Tensor): Convolution bias tensor of shape (out_channels)

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, out_channels)
    """
    x = F.conv2d(x, conv_weight, bias=conv_bias)
    x = F.gelu(x)
    x = F.adaptive_avg_pool2d(x, 1)
    x = x.squeeze(-1).squeeze(-1)
    return x


class Model(nn.Module):
    """
    Simple model that performs a convolution, applies GELU, and then performs global average pooling.
    """

    def __init__(self, in_channels, out_channels, kernel_size):
        super(Model, self).__init__()
        conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv_weight = nn.Parameter(conv.weight)
        self.conv_bias = nn.Parameter(conv.bias)

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


batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3


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


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

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a convolution, applies GELU, and then performs global average pooling.
    """
    def __init__(self, in_channels, out_channels, kernel_size):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)

    def forward(self, x):
        """
        Args:
            x: Input tensor of shape (batch_size, in_channels, height, width)
        Returns:
            Output tensor of shape (batch_size, out_channels)
        """
        x = self.conv(x)
        x = torch.nn.functional.gelu(x)
        x = torch.nn.functional.adaptive_avg_pool2d(x, 1)
        x = x.squeeze(-1).squeeze(-1)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3

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

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

Operation Name 67_Conv2d_GELU_GlobalAvgPool
Level ID 2
Task ID 67
Kernel Name modular_fused_conv_gelu_pool_base
CUDA Speedup (Native) 1.371x
CUDA Speedup (Compile) 1.915x
CUDA Runtime 0.031 ms
PyTorch Runtime (Native) 0.042 ms
PyTorch Runtime (Compile) 0.059 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 67 • 67_Conv2d_GELU_GlobalAvgPool)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_reduction_shared_base_base 0.03 1.63 2.28
🥇 unrolled_fused_conv_gelu_pool_base 0.03 1.63 2.28
🥇 balanced_workload_distribution_base 0.03 1.63 2.28
4 coalesced_fused_conv_gelu_pool_base 0.03 1.52 2.12
5 balanced_workload_fused_conv_gelu_pool_base 0.03 1.47 2.05
5 fused_shared_base 0.03 1.47 2.05
7 optimized_reduction_conv_gelu_pool_base 0.03 1.37 1.91
7 blocksize_fused_conv_gelu_pool_base 0.03 1.37 1.91
7 minimal_sync_conv_gelu_pool_base_base 0.03 1.37 1.91
7 fused_conv_gelu_pool_base 0.03 1.37 1.91
7 modular_fused_conv_gelu_pool_base 0.03 1.37 1.91
7 modular_fused_conv_gelu_pool_base 0.03 1.37 1.91
7 sync_optimized_conv_gelu_pool_base 0.03 1.37 1.91
14 manual_unroll_fused_conv2d_gelu_pool_base 0.03 1.33 1.86
14 streamed_fused_conv_gelu_pool_base 0.03 1.33 1.86
14 unrolled_conv2d_gelu_pool_base 0.03 1.33 1.86
17 warp_optimized_conv2d_gelu_pool_base 0.03 1.29 1.80
18 warp_reduction_conv2d_gelu_pool_base 0.03 1.25 1.75
19 optimized_thread_mapping_conv_gelu_pool_base 0.04 1.06 1.48
19 conv_gelu_avgpool_unroll_edit_1 0.04 1.06 1.48 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>

#define KERNEL_SIZE 3
#define WARP_SIZE 32

// Modular device function: GELU activation
__device__ inline float gelu_activate(float x) {
    return 0.5f * x * (1.f + erff(x / 1.41421356f));
}

// Modular device function: Compute 3x3 convolution for one output pixel
__device__ float compute_conv3x3(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const int in_channels,
    const int in_h,
    const int in_w,
    const int n,
    const int c_out,
    const int row,
    const int col
) {
    float sum = 0.0f;
    
    // Loop over input channels
    for (int c = 0; c < in_channels; c++) {
        // Calculate base indices
        int input_base = ((n * in_channels + c) * in_h);
        int weight_base = ((c_out * in_channels + c) * KERNEL_SIZE * KERNEL_SIZE);
        
        // Unrolled 3x3 convolution
        int idx = (input_base + row) * in_w + col;
        sum += input[idx] * weight[weight_base + 0];
        sum += input[idx + 1] * weight[weight_base + 1];
        sum += input[idx + 2] * weight[weight_base + 2];
        
        idx += in_w;
        sum += input[idx] * weight[weight_base + 3];
        sum += input[idx + 1] * weight[weight_base + 4];
        sum += input[idx + 2] * weight[weight_base + 5];
        
        idx += in_w;
        sum += input[idx] * weight[weight_base + 6];
        sum += input[idx + 1] * weight[weight_base + 7];
        sum += input[idx + 2] * weight[weight_base + 8];
    }
    return sum;
}

// Modular device function: Process one pixel by applying convolution, bias addition, and GELU activation
__device__ float process_pixel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    const int in_channels,
    const int in_h,
    const int in_w,
    const int n,
    const int c_out,
    const int row,
    const int col
) {
    float conv_val = compute_conv3x3(input, weight, in_channels, in_h, in_w, n, c_out, row, col);
    conv_val += bias[c_out];
    return gelu_activate(conv_val);
}

// Modular device function: Warp-level reduction using shuffle
__device__ float warp_reduce_sum(float val) {
    for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
        val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

// Fused kernel: performs convolution, activation, and global average pooling in one pass
extern "C" __global__ void conv2d_gelu_pool_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    const int N,
    const int in_channels,
    const int in_h,
    const int in_w,
    const int out_channels,
    const int out_h,
    const int out_w
) {
    // Each block handles one (n, c_out) pair
    int n = blockIdx.y;
    int c_out = blockIdx.x;
    
    int total_pixels = out_h * out_w;
    float local_sum = 0.0f;
    
    // Grid-stride loop over spatial dimensions
    for (int idx = threadIdx.x; idx < total_pixels; idx += blockDim.x) {
        int row = idx / out_w;
        int col = idx % out_w;
        local_sum += process_pixel(input, weight, bias, in_channels, in_h, in_w, n, c_out, row, col);
    }
    
    // Warp-level reduction
    local_sum = warp_reduce_sum(local_sum);
    
    // Shared memory reduction across warps
    __shared__ float shared[32];  // Assumes a maximum of 32 warps per block
    int lane = threadIdx.x & (WARP_SIZE - 1);
    int warp_id = threadIdx.x / WARP_SIZE;
    if (lane == 0) {
        shared[warp_id] = local_sum;
    }
    __syncthreads();
    
    float block_sum = 0.0f;
    int num_warps = (blockDim.x + WARP_SIZE - 1) / WARP_SIZE;
    if (threadIdx.x < num_warps) {
        block_sum = shared[threadIdx.x];
    }
    block_sum = warp_reduce_sum(block_sum);
    
    // First thread writes the final result (global average pooling)
    if (threadIdx.x == 0) {
        output[n * out_channels + c_out] = block_sum / float(total_pixels);
    }
}

// Host function to launch the fused kernel
torch::Tensor forward(
    torch::Tensor input,
    torch::Tensor conv_weight,
    torch::Tensor conv_bias
) {
    TORCH_CHECK(input.is_cuda(), "input 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");
    
    int N = input.size(0);
    int in_channels = input.size(1);
    int in_h = input.size(2);
    int in_w = input.size(3);
    int out_channels = conv_weight.size(0);
    int out_h = in_h - 2;
    int out_w = in_w - 2;
    
    auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());
    auto final_output = torch::empty({N, out_channels}, options);
    
    // Launch kernel with grid dimensions (out_channels, N) and 256 threads per block
    dim3 grid(out_channels, N);
    int threads = 256;
    
    conv2d_gelu_pool_kernel<<<grid, threads>>>(
        input.data_ptr<float>(),
        conv_weight.data_ptr<float>(),
        conv_bias.data_ptr<float>(),
        final_output.data_ptr<float>(),
        N, in_channels, in_h, in_w,
        out_channels, out_h, out_w
    );
    
    return final_output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Modular Fused Conv2d + GELU + GlobalAvgPool");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.834 inst/cycle 0.000 5
Executed Ipc Elapsed 1.620 inst/cycle 0.000 5
Issue Slots Busy 46.116 % 0.041 5
Issued Ipc Active 1.844 inst/cycle 0.000 5
SM Busy 46.116 % 0.041 5
Memory Throughput 44863439608.830 byte/second 42828868709921784.000 5
Mem Busy 68.278 % 0.066 5
Max Bandwidth 52.134 % 0.052 5
L1/TEX Hit Rate 91.764 % 0.001 5
L2 Hit Rate 84.940 % 0.240 5
Mem Pipes Busy 46.970 % 0.032 5
Warp Cycles Per Issued Instruction 29.520 cycle 0.007 5
Warp Cycles Per Executed Instruction 29.630 cycle 0.007 5
Avg. Active Threads Per Warp 31.070 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.080 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 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 85.306 % 0.071 5
Achieved Active Warps Per SM 54.594 warp 0.029 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (21.1%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
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 (85.2%) 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 343499.31 μs
Device Time 78.82 μs
Self CPU Time 45.30 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::_to_copy
CPU Time 343454.01 μs
Device Time 78.82 μs
Self CPU Time 99.74 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 343024.46 μs
Device Time 0.00 μs
Self CPU Time 98.25 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 342541.14 μs
Device Time 0.00 μs
Self CPU Time 342541.14 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 514382.86 μs
Device Time 18184.94 μs
Self CPU Time 514382.86 μs
Self Device Time 18184.94 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv2d_gelu_pool_kernel
CPU Time 0.00 μs
Device Time 176291.64 μs
Self CPU Time 0.00 μs
Self Device Time 176291.64 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 37428.73 μs
Device Time 33454.47 μs
Self CPU Time 37428.73 μs
Self Device Time 33454.47 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 146426.20 μs
Device Time 503738.53 μs
Self CPU Time 10909.95 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 135517.95 μs
Device Time 503738.53 μs
Self CPU Time 12783.04 μs
Self Device Time 503738.53 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 503816.96 μs
Self CPU Time 0.00 μs
Self Device Time 503816.96 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 45323 warnings (45276 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_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:20:5 bugprone-easily-swappable-parameters
20 | const int in_w,
| ^~~~~~~~~~~~~~~
21 | const int n,
| ~~~~~~~~~~~~
22 | const int c_out,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:20:15: note: the first parameter in the range is 'in_w'
20 | const int in_w,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:22:15: note: the last parameter in the range is 'c_out'
22 | const int c_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:56:5: warning: 2 adjacent parameters of 'process_pixel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
56 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
57 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:56:31: note: the first parameter in the range is 'weight'
56 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:57:31: note: the last parameter in the range is 'bias'
57 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:85:5: warning: 2 adjacent parameters of 'conv2d_gelu_pool_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
85 | const int N,
| ^~~~~~~~~~~~
86 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:85:15: note: the first parameter in the range is 'N'
85 | const int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:86:15: note: the last parameter in the range is 'in_channels'
86 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:88:5: warning: 3 adjacent parameters of 'conv2d_gelu_pool_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
88 | const int in_w,
| ^~~~~~~~~~~~~~~
89 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~~
90 | const int out_h,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:88:15: note: the first parameter in the range is 'in_w'
88 | const int in_w,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:90:15: note: the last parameter in the range is 'out_h'
90 | const int out_h,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:94:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | int n = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:95:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | int c_out = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:101:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | for (int idx = threadIdx.x; idx < total_pixels; idx += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:101:60: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | for (int idx = threadIdx.x; idx < total_pixels; idx += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:112:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | int lane = threadIdx.x & (WARP_SIZE - 1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:113:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | int warp_id = threadIdx.x / WARP_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:120:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | int num_warps = (blockDim.x + WARP_SIZE - 1) / WARP_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:134:19: warning: the parameter 'input' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
134 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:135:19: 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]
135 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:136:19: warning: the parameter 'conv_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
136 | torch::Tensor conv_bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:142:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
142 | int N = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:143:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
143 | int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:144:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
144 | int in_h = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:145:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
145 | int in_w = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b6_s1_modular_fused_conv_gelu_pool/base/base.cu:146:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
146 | int out_channels = conv_weight.size(0);
| ^