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67_Conv2d_GELU_GlobalAvgPool • fused_shared_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 fused_shared_base
CUDA Speedup (Native) 1.465x
CUDA Speedup (Compile) 2.047x
CUDA Runtime 0.029 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

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

// Warp-level reduction using shuffle intrinsics
__device__ inline float warp_reduce_sum(float val) {
    // Assuming warp size is 32
    for (int offset = 16; offset > 0; offset /= 2)
        val += __shfl_down_sync(0xffffffff, val, offset);
    return val;
}

// Compute convolution for a fixed 3x3 kernel using unrolling and shared weight
__device__ inline float compute_conv_unrolled_shared(
    const float* __restrict__ input,
    const float* __restrict__ sweight,
    const int n,
    const int in_channels,
    const int in_h,
    const int in_w,
    const int row,
    const int col
) {
    float sum = 0.0f;
    // Loop over input channels with unrolling for a 3x3 kernel
    #pragma unroll
    for (int c = 0; c < in_channels; c++) {
        int input_channel_offset = n * in_channels * in_h * in_w + c * in_h * in_w;
        int weight_channel_offset = c * (KERNEL_SIZE * KERNEL_SIZE);
        // Unrolled 3x3 convolution
        sum += __ldg(&input[input_channel_offset + (row + 0) * in_w + (col + 0)]) * sweight[weight_channel_offset + 0];
        sum += __ldg(&input[input_channel_offset + (row + 0) * in_w + (col + 1)]) * sweight[weight_channel_offset + 1];
        sum += __ldg(&input[input_channel_offset + (row + 0) * in_w + (col + 2)]) * sweight[weight_channel_offset + 2];

        sum += __ldg(&input[input_channel_offset + (row + 1) * in_w + (col + 0)]) * sweight[weight_channel_offset + 3];
        sum += __ldg(&input[input_channel_offset + (row + 1) * in_w + (col + 1)]) * sweight[weight_channel_offset + 4];
        sum += __ldg(&input[input_channel_offset + (row + 1) * in_w + (col + 2)]) * sweight[weight_channel_offset + 5];

        sum += __ldg(&input[input_channel_offset + (row + 2) * in_w + (col + 0)]) * sweight[weight_channel_offset + 6];
        sum += __ldg(&input[input_channel_offset + (row + 2) * in_w + (col + 1)]) * sweight[weight_channel_offset + 7];
        sum += __ldg(&input[input_channel_offset + (row + 2) * in_w + (col + 2)]) * sweight[weight_channel_offset + 8];
    }
    return sum;
}

// Fused kernel: convolution, bias addition, GELU activation, and global average pooling
// Each block computes one (n, c_out) pair over the convolution output spatial dimensions (out_h x out_w).
extern "C" __global__ void fused_conv_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
) {
    // Block assignment: blockIdx.y -> batch index (n), blockIdx.x -> output channel (c_out)
    const int n = blockIdx.y;
    const int c_out = blockIdx.x;
    const int total_pixels = out_h * out_w;

    // Shared memory for loading the filter weights for the current output channel.
    // Size: in_channels * KERNEL_SIZE * KERNEL_SIZE floats.
    extern __shared__ float sweight[];
    const int numWeights = in_channels * KERNEL_SIZE * KERNEL_SIZE;

    // Load weights from global memory into shared memory once per block
    for (int i = threadIdx.x; i < numWeights; i += blockDim.x) {
        sweight[i] = weight[c_out * numWeights + i];
    }
    __syncthreads();

    float local_sum = 0.0f;

    // Each thread processes multiple output pixels using a grid-stride loop
    for (int idx = threadIdx.x; idx < total_pixels; idx += blockDim.x) {
        int row = idx / out_w;
        int col = idx % out_w;
        float conv_val = compute_conv_unrolled_shared(input, sweight, n, in_channels, in_h, in_w, row, col);
        conv_val += bias[c_out];
        conv_val = gelu_activate(conv_val);
        local_sum += conv_val;
    }

    // Warp-level reduction
    local_sum = warp_reduce_sum(local_sum);

    // Shared memory for block-level reduction among warps
    __shared__ float shared_sum[32]; // Supports up to 32 warps per block
    int lane = threadIdx.x & 31;
    int warpId = threadIdx.x >> 5;
    if (lane == 0) {
        shared_sum[warpId] = local_sum;
    }
    __syncthreads();

    float block_sum = 0.0f;
    int numWarps = (blockDim.x + 31) >> 5;
    if (threadIdx.x < numWarps) {
        block_sum = shared_sum[threadIdx.x];
    }
    block_sum = warp_reduce_sum(block_sum);

    // Thread 0 writes the final averaged result to global memory
    if (threadIdx.x == 0) {
        output[n * out_channels + c_out] = block_sum / float(total_pixels);
    }
}

// Host function that launches 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");

    const int N = input.size(0);
    const int in_channels = input.size(1);
    const int in_h = input.size(2);
    const int in_w = input.size(3);
    const int out_channels = conv_weight.size(0);
    // For a 3x3 filter, output dimensions are (in_h - 2, in_w - 2)
    const int out_h = in_h - 2;
    const int out_w = in_w - 2;

    auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());
    // Final output has shape (N, out_channels) after global average pooling
    auto final_output = torch::empty({N, out_channels}, options);

    // Launch the fused kernel with a 2D grid: (out_channels, N)
    dim3 grid(out_channels, N);
    const int threads = 256;
    // Shared memory size: in_channels * 3 * 3 * sizeof(float)
    size_t shared_mem = in_channels * KERNEL_SIZE * KERNEL_SIZE * sizeof(float);

    fused_conv_gelu_pool_kernel<<<grid, threads, shared_mem>>>(
        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, "Fused Conv2d + GELU + GlobalAvgPool with Shared Weight Tiling");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.366 inst/cycle 0.000 5
Executed Ipc Elapsed 2.116 inst/cycle 0.000 5
Issue Slots Busy 59.262 % 0.046 5
Issued Ipc Active 2.368 inst/cycle 0.000 5
SM Busy 59.262 % 0.046 5
Memory Throughput 50826967940.462 byte/second 38562498585386344.000 5
Mem Busy 67.174 % 0.072 5
Max Bandwidth 45.006 % 0.033 5
L1/TEX Hit Rate 90.094 % 0.000 5
L2 Hit Rate 86.082 % 0.362 5
Mem Pipes Busy 42.592 % 0.029 5
Warp Cycles Per Issued Instruction 22.428 cycle 0.001 5
Warp Cycles Per Executed Instruction 22.484 cycle 0.001 5
Avg. Active Threads Per Warp 31.050 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.900 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 25.000 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 83.224 % 0.159 5
Achieved Active Warps Per SM 53.260 warp 0.064 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (38.1%) 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 (83.2%) 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 407940.31 μs
Device Time 81.18 μs
Self CPU Time 45.18 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 407895.13 μs
Device Time 81.18 μs
Self CPU Time 96.99 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 407447.73 μs
Device Time 0.00 μs
Self CPU Time 116.38 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 406973.89 μs
Device Time 0.00 μs
Self CPU Time 406973.89 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 574083.73 μs
Device Time 18491.46 μs
Self CPU Time 574083.73 μs
Self Device Time 18491.46 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
fused_conv_gelu_pool_kernel
CPU Time 0.00 μs
Device Time 179166.38 μs
Self CPU Time 0.00 μs
Self Device Time 179166.38 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 15629.20 μs
Device Time 36752.11 μs
Self CPU Time 15629.20 μs
Self Device Time 36752.11 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 149223.58 μs
Device Time 550869.71 μs
Self CPU Time 11804.02 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 137421.35 μs
Device Time 550869.71 μs
Self CPU Time 14096.08 μs
Self Device Time 550869.71 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 550869.71 μs
Self CPU Time 0.00 μs
Self Device Time 550869.71 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 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/b8_s3_fused_shared/base/base.cu:57:5 bugprone-easily-swappable-parameters
57 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
58 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
59 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:57:31: note: the first parameter in the range is 'input'
57 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:59:31: note: the last parameter in the range is 'bias'
59 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:61:5: warning: 2 adjacent parameters of 'fused_conv_gelu_pool_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
61 | const int N,
| ^~~~~~~~~~~~
62 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:61:15: note: the first parameter in the range is 'N'
61 | const int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:62:15: note: the last parameter in the range is 'in_channels'
62 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:64:5: warning: 3 adjacent parameters of 'fused_conv_gelu_pool_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
64 | const int in_w,
| ^~~~~~~~~~~~~~~
65 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~~
66 | const int out_h,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:64:15: note: the first parameter in the range is 'in_w'
64 | const int in_w,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:66:15: note: the last parameter in the range is 'out_h'
66 | const int out_h,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:70:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
70 | const int n = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:71:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
71 | const int c_out = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:80:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | for (int i = threadIdx.x; i < numWeights; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:80:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | for (int i = threadIdx.x; i < numWeights; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:88:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | 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/b8_s3_fused_shared/base/base.cu:88:60: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | 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/b8_s3_fused_shared/base/base.cu:102:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | int lane = threadIdx.x & 31;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:103:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | int warpId = threadIdx.x >> 5;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:110:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | int numWarps = (blockDim.x + 31) >> 5;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:124:19: 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]
124 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:125:19: 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]
125 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:126:19: 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]
126 | torch::Tensor conv_bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:132:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
132 | const int N = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:133:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
133 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:134:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
134 | const int in_h = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:135:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
135 | const int in_w = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:136:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
136 | const int out_channels = conv_weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:149:25: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
149 | size_t shared_mem = in_channels * KERNEL_SIZE * KERNEL_SIZE * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:149:25: note: make conversion explicit to silence this warning
5 | size_t shared_mem = in_channels * KERNEL_SIZE * KERNEL_SIZE * sizeof(float);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<unsigned long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b8_s3_fused_shared/base/base.cu:149:25: note: perform multiplication in a wider type
149 | size_t shared_mem = in_channels * KERNEL_SIZE * KERNEL_SIZE * sizeof(float);
| ^~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<long>( )