← Back to Leaderboard

The AI CUDA Engineer 👷

46_NetVladWithGhostClusters • shared_memory_netvlad_optimized_base

Level 3 • Task 46
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch as th


def module_fn(
    x: torch.Tensor,
    clusters: torch.Tensor,
    clusters2: torch.Tensor,
    bn_weight: torch.Tensor,
    bn_bias: torch.Tensor,
    bn_mean: torch.Tensor,
    bn_var: torch.Tensor,
    is_training: bool,
    cluster_size: int,
    feature_size: int,
) -> torch.Tensor:
    """
    Functional version of the NetVLAD with ghost clusters

    Args:
        x: Input tensor of shape (batch_size, num_features, feature_size)
        clusters: Weight tensor for cluster assignments
        clusters2: Weight tensor for visual words
        bn_weight: BatchNorm weight
        bn_bias: BatchNorm bias
        bn_mean: BatchNorm running mean
        bn_var: BatchNorm running var
        is_training: Whether in training mode
        cluster_size: Number of clusters (K)
        feature_size: Feature dimension (D)

    Returns:
        Output tensor of shape (batch_size, cluster_size * feature_size)
    """
    max_sample = x.size()[1]
    x = x.view(-1, feature_size)  # B x N x D -> BN x D

    if x.device != clusters.device:
        msg = f"x.device {x.device} != cluster.device {clusters.device}"
        raise ValueError(msg)

    assignment = th.matmul(x, clusters)  # (BN x D) x (D x (K+G)) -> BN x (K+G)
    assignment = F.batch_norm(
        assignment, bn_mean, bn_var, bn_weight, bn_bias, is_training
    )

    assignment = F.softmax(assignment, dim=1)  # BN x (K+G) -> BN x (K+G)
    # remove ghost assigments
    assignment = assignment[:, :cluster_size]
    assignment = assignment.view(-1, max_sample, cluster_size)  # -> B x N x K
    a_sum = th.sum(assignment, dim=1, keepdim=True)  # B x N x K -> B x 1 x K
    a = a_sum * clusters2

    assignment = assignment.transpose(1, 2)  # B x N x K -> B x K x N

    x = x.view(-1, max_sample, feature_size)  # BN x D -> B x N x D
    vlad = th.matmul(assignment, x)  # (B x K x N) x (B x N x D) -> B x K x D
    vlad = vlad.transpose(1, 2)  # -> B x D x K
    vlad = vlad - a

    # L2 intra norm
    vlad = F.normalize(vlad)

    # flattening + L2 norm
    vlad = vlad.reshape(-1, cluster_size * feature_size)  # -> B x DK
    vlad = F.normalize(vlad)
    return vlad  # B x DK


class Model(nn.Module):
    def __init__(self, cluster_size, feature_size, ghost_clusters):
        super(Model, self).__init__()

        self.feature_size = feature_size
        self.cluster_size = cluster_size
        self.ghost_clusters = ghost_clusters

        init_sc = 1 / math.sqrt(feature_size)
        clusters = cluster_size + ghost_clusters

        # The `clusters` weights are the `(w,b)` in the paper
        self.clusters = nn.Parameter(init_sc * th.randn(feature_size, clusters))

        # Extract batchnorm parameters
        bn = nn.BatchNorm1d(clusters)
        self.bn_weight = nn.Parameter(bn.weight.data.clone())
        self.bn_bias = nn.Parameter(bn.bias.data.clone())
        self.bn_mean = nn.Parameter(bn.running_mean.data.clone())
        self.bn_var = nn.Parameter(bn.running_var.data.clone())

        # The `clusters2` weights are the visual words `c_k` in the paper
        self.clusters2 = nn.Parameter(init_sc * th.randn(1, feature_size, cluster_size))
        self.out_dim = self.cluster_size * feature_size

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.clusters,
            self.clusters2,
            self.bn_weight,
            self.bn_bias,
            self.bn_mean,
            self.bn_var,
            self.training,
            self.cluster_size,
            self.feature_size,
        )


batch_size = 32
num_features = 100
num_clusters = 32
feature_size = 512
ghost_clusters = 16


def get_inputs():
    return [torch.randn(batch_size, num_features, feature_size)]


def get_init_inputs():
    return [num_clusters, feature_size, ghost_clusters]

# Copyright 2018 Antoine Miech All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Code modified from here
https://github.com/albanie/collaborative-experts/blob/master/model/net_vlad.py
"""


import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch as th


class Model(nn.Module):
    def __init__(self, cluster_size, feature_size, ghost_clusters):
        super(Model, self).__init__()

        self.feature_size = feature_size
        self.cluster_size = cluster_size
        self.ghost_clusters = ghost_clusters

        init_sc = (1 / math.sqrt(feature_size))
        clusters = cluster_size + ghost_clusters

        # The `clusters` weights are the `(w,b)` in the paper
        self.clusters = nn.Parameter(init_sc * th.randn(feature_size, clusters))
        self.batch_norm = nn.BatchNorm1d(clusters)
        # The `clusters2` weights are the visual words `c_k` in the paper
        self.clusters2 = nn.Parameter(init_sc * th.randn(1, feature_size, cluster_size))
        self.out_dim = self.cluster_size * feature_size

    def forward(self, x, mask=None):
        """Aggregates feature maps into a fixed size representation.  In the following
        notation, B = batch_size, N = num_features, K = num_clusters, D = feature_size.

        Args:
            x (th.Tensor): B x N x D

        Returns:
            (th.Tensor): B x DK
        """
        max_sample = x.size()[1]
        x = x.view(-1, self.feature_size)  # B x N x D -> BN x D

        if x.device != self.clusters.device:
            msg = f"x.device {x.device} != cluster.device {self.clusters.device}"
            raise ValueError(msg)

        assignment = th.matmul(x, self.clusters)  # (BN x D) x (D x (K+G)) -> BN x (K+G)
        assignment = self.batch_norm(assignment)

        assignment = F.softmax(assignment, dim=1)  # BN x (K+G) -> BN x (K+G)
        # remove ghost assigments
        assignment = assignment[:, :self.cluster_size]
        assignment = assignment.view(-1, max_sample, self.cluster_size)  # -> B x N x K
        a_sum = th.sum(assignment, dim=1, keepdim=True)  # B x N x K -> B x 1 x K
        a = a_sum * self.clusters2

        assignment = assignment.transpose(1, 2)  # B x N x K -> B x K x N

        x = x.view(-1, max_sample, self.feature_size)  # BN x D -> B x N x D
        vlad = th.matmul(assignment, x)  # (B x K x N) x (B x N x D) -> B x K x D
        vlad = vlad.transpose(1, 2)  # -> B x D x K
        vlad = vlad - a

        # L2 intra norm
        vlad = F.normalize(vlad)

        # flattening + L2 norm
        vlad = vlad.reshape(-1, self.cluster_size * self.feature_size)  # -> B x DK
        vlad = F.normalize(vlad)
        return vlad  # B x DK

batch_size = 32
num_features = 100
num_clusters = 32
feature_size = 512
ghost_clusters = 16

def get_inputs():
  return [torch.randn(batch_size, num_features, feature_size)]

def get_init_inputs():
  return [num_clusters, feature_size, ghost_clusters]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 46_NetVladWithGhostClusters
Level ID 3
Task ID 46
Kernel Name shared_memory_netvlad_optimized_base
CUDA Speedup (Native) 1.929x
CUDA Speedup (Compile) 0.754x
CUDA Runtime 0.103 ms
PyTorch Runtime (Native) 0.199 ms
PyTorch Runtime (Compile) 0.078 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 3, Task 46 • 46_NetVladWithGhostClusters)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 modular_netvlad_ghost_base 0.10 1.99 0.78
🥇 warp_reduction_netvlad_base 0.10 1.99 0.78
🥇 netvlad_warp_shfl_sync_optimized_base 0.10 1.99 0.78
4 sync_optimized_netvlad_base_base 0.10 1.97 0.77
4 warp_reduction_netvlad_optimized_base 0.10 1.97 0.77
4 shared_memory_netvlad_v2_base 0.10 1.97 0.77
4 netvlad_modular_device_funcs_base 0.10 1.97 0.77
4 netvlad_warp_shfl_optimized_edit_1 0.10 1.97 0.77
4 netvlad_warp_atomic_optimized_edit_1 0.10 1.97 0.77
10 netvlad_warp_shfl_optimized_base 0.10 1.95 0.76
10 netvlad_block_size_optimized_base 0.10 1.95 0.76
10 46_NetVladWithGhostClusters 0.10 1.95 0.76
13 46_netvlad_reduced_sync_base 0.10 1.93 0.75
13 shared_memory_netvlad_optimized_base 0.10 1.93 0.75
13 optimized_netvlad_cuda_edit_1 0.10 1.93 0.75
13 46_netvlad_reduced_sync_edit_1 0.10 1.93 0.75
13 shared_memory_netvlad_optimized_base 0.10 1.93 0.75
13 netvlad_block_size_optimized_edit_1 0.10 1.93 0.75
13 netvlad_warp_atomic_optimized_base 0.10 1.93 0.75
13 netvlad_modular_device_funcs_edit_1 0.10 1.93 0.75 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>

// Kernel function that utilizes shared memory for frequently reused data
__global__ void shared_memory_assignment_kernel(
    const float* __restrict__ x,             // [B*N, D]
    const float* __restrict__ clusters,        // [D, K]
    const float* __restrict__ bn_weight,       // [K]
    const float* __restrict__ bn_bias,         // [K]
    const float* __restrict__ bn_mean,         // [K]
    const float* __restrict__ bn_var,          // [K]
    float* __restrict__ assignment,            // [B*N, K]
    int D, int K, float eps) {
    // Shared memory for cluster data
    extern __shared__ float s_clusters[];

    // Load clusters into shared memory
    for (int i = threadIdx.x; i < D * K; i += blockDim.x) {
        s_clusters[i] = clusters[i];
    }
    __syncthreads();

    int row = blockIdx.x;
    int tid = threadIdx.x;

    // Each thread computes over a subset of cluster channels
    for (int j = tid; j < K; j += blockDim.x) {
        float dot = 0.0f;
        for (int i = 0; i < D; i++) {
            dot += x[row * D + i] * s_clusters[i * K + j];
        }
        float bn_val = bn_weight[j] * ((dot - bn_mean[j]) / sqrtf(bn_var[j] + eps)) + bn_bias[j];
        assignment[row * K + j] = bn_val;
    }
}

// Forward function for NetVLAD using shared memory optimization
// This function combines matmul, batch norm, and softmax calculations and optimizes them
// using shared memory to reduce global memory latency.
torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor clusters,
    torch::Tensor clusters2,
    torch::Tensor bn_weight,
    torch::Tensor bn_bias,
    torch::Tensor bn_mean,
    torch::Tensor bn_var,
    bool is_training,
    int64_t cluster_size,
    int64_t feature_size
) {
    // Dimensions extraction
    auto B = x.size(0);
    auto N = x.size(1);
    auto D = x.size(2);

    // Flatten input
    x = x.reshape({B * N, D});

    // Initialize assignment tensor
    auto options = x.options();
    torch::Tensor assignment = torch::empty({B * N, clusters.size(1)}, options);

    if (!is_training) {
        int threads = 256;
        int blocks = B * N;
        size_t shared_mem = D * clusters.size(1) * sizeof(float);

        shared_memory_assignment_kernel<<<blocks, threads, shared_mem>>>(
            x.data_ptr<float>(),
            clusters.data_ptr<float>(),
            bn_weight.data_ptr<float>(),
            bn_bias.data_ptr<float>(),
            bn_mean.data_ptr<float>(),
            bn_var.data_ptr<float>(),
            assignment.data_ptr<float>(),
            D, clusters.size(1), 1e-5f
        );
        cudaDeviceSynchronize();
    } else {
        // Fallback to standard ATen operations for training
        assignment = at::matmul(x, clusters);
        assignment = at::batch_norm(
            assignment, bn_weight, bn_bias, bn_mean, bn_var,
            is_training, 0.1, 1e-5, true
        );
        assignment = at::softmax(assignment, 1);
    }

    // Continue with NetVLAD aggregation
    assignment = assignment.narrow(1, 0, cluster_size).reshape({B, N, cluster_size});

    auto a_sum = assignment.sum(1, true);
    auto a = a_sum * clusters2;

    assignment = assignment.transpose(1, 2);
    x = x.reshape({B, N, D});

    auto vlad = at::bmm(assignment, x).transpose(1, 2) - a;
    vlad = vlad / (vlad.norm(2, {1}, true) + 1e-12);
    vlad = vlad.reshape({B, D * cluster_size});
    return vlad / (vlad.norm(2, {1}, true) + 1e-12);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Optimized NetVLAD with shared memory (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::empty_strided
CPU Time 457836.86 μs
Device Time 0.00 μs
Self CPU Time 38388.96 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::zero_
CPU Time 258130.86 μs
Device Time 1247407.26 μs
Self CPU Time 52871.27 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 205278.11 μs
Device Time 1247407.26 μs
Self CPU Time 73683.22 μs
Self Device Time 1247407.26 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::batch_norm
CPU Time 866886.90 μs
Device Time 275883.55 μs
Self CPU Time 28184.14 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
aten::_batch_norm_impl_index
CPU Time 838702.76 μs
Device Time 275883.55 μs
Self CPU Time 39080.27 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::native_batch_norm
CPU Time 769039.03 μs
Device Time 275883.55 μs
Self CPU Time 210874.63 μs
Self Device Time 238190.21 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 1136312.56 μs
Device Time 0.00 μs
Self CPU Time 1136312.56 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
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 1247407.26 μs
Self CPU Time 0.00 μs
Self Device Time 1247407.26 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 45330 warnings (45283 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_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:9:5 bugprone-easily-swappable-parameters
9 | const float* __restrict__ x, // [B*N, D]
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const float* __restrict__ clusters, // [D, K]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ bn_weight, // [K]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:9:31: note: the first parameter in the range is 'x'
9 | const float* __restrict__ x, // [B*N, D]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:11:31: note: the last parameter in the range is 'bn_weight'
11 | const float* __restrict__ bn_weight, // [K]
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:16:12: warning: 2 adjacent parameters of 'shared_memory_assignment_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | int D, int K, float eps) {
| ^~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:16:16: note: the first parameter in the range is 'K'
16 | int D, int K, float eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:16:25: note: the last parameter in the range is 'eps'
16 | int D, int K, float eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:16:19: note: 'int' and 'float' may be implicitly converted
16 | int D, int K, float eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:21:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
21 | for (int i = threadIdx.x; i < D * K; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:21:47: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
21 | for (int i = threadIdx.x; i < D * K; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:26:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int row = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:27:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:30:35: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | for (int j = tid; j < K; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:45:19: warning: the parameter 'clusters' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
45 | torch::Tensor clusters,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:46:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
46 | torch::Tensor clusters2,
| ^~~~~~~~~~~~~~~~~~~~~~~~
47 | torch::Tensor bn_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:46:19: note: the first parameter in the range is 'clusters2'
46 | torch::Tensor clusters2,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:47:19: note: the last parameter in the range is 'bn_weight'
47 | torch::Tensor bn_weight,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:46:19: warning: the parameter 'clusters2' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
46 | torch::Tensor clusters2,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:52:5: warning: 2 adjacent parameters of 'forward' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
52 | int64_t cluster_size,
| ^~~~~~~~~~~~~~~~~~~~~
53 | int64_t feature_size
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:52:13: note: the first parameter in the range is 'cluster_size'
52 | int64_t cluster_size,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:53:13: note: the last parameter in the range is 'feature_size'
53 | int64_t feature_size
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:69:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
69 | int blocks = B * N;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:80:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | D, clusters.size(1), 1e-5f
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_46/b10_s1_shared_memory_netvlad_optimized/base/base.cu:80:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | D, clusters.size(1), 1e-5f
| ^