Link Neighborhood Loader on Heteregenous Graph

[hcagri]

🐛 Describe the bug

Hi,

I am using the Link Neighborhood Loader on a large multigraph dataset with 31M edges and 2M nodes. Since the graph contains parallel edges, I assign unique IDs to each edge to track which edges are sampled. This is done as follows:

data['node', 'to', 'node'].edge_attr = torch.cat( [torch.arange(data['node', 'to', 'node'].edge_attr.shape[0]).view(-1, 1), data['node', 'to', 'node'].edge_attr], dim=1 )

However, I noticed that during sampling, the same edge can appear multiple times in the sampled batch. This issue did not occur with smaller graphs but seems to arise with this large dataset.

Is there a way to ensure that sampled batches do not contain duplicate edges? Or is this behavior expected when working with large multigraphs?

replace parameter is set to False.

Versions

PyTorch version: 2.2.2
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.20 | packaged by conda-forge | (main, Sep 30 2024, 17:49:10)  [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-6.5.0-26-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 550.54.15
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             24
On-line CPU(s) list:                0-23
Vendor ID:                          GenuineIntel
Model name:                         13th Gen Intel(R) Core(TM) i7-13700
CPU family:                         6
Model:                              183
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          1
Stepping:                           1
CPU max MHz:                        5200,0000
CPU min MHz:                        800,0000
BogoMIPS:                           4224.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          640 KiB (16 instances)
L1i cache:                          768 KiB (16 instances)
L2 cache:                           24 MiB (10 instances)
L3 cache:                           30 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-23
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.2.2
[pip3] torch_cluster==1.6.3+pt22cu118
[pip3] torch_geometric==2.5.2
[pip3] torch_scatter==2.1.2+pt22cu118
[pip3] torch_sparse==0.6.18+pt22cu118
[pip3] torch_spline_conv==1.2.2+pt22cu118
[pip3] torchmetrics==1.5.2
[pip3] torchvision==0.17.2
[pip3] triton==2.2.0
[conda] blas                      1.0                         mkl    conda-forge
[conda] cuda-cudart               11.8.89                       0    nvidia
[conda] cuda-cupti                11.8.87                       0    nvidia
[conda] cuda-libraries            11.8.0                        0    nvidia
[conda] cuda-nvrtc                11.8.89                       0    nvidia
[conda] cuda-nvtx                 11.8.86                       0    nvidia
[conda] cuda-runtime              11.8.0                        0    nvidia
[conda] cudatoolkit               11.8.0              h4ba93d1_13    conda-forge
[conda] libblas                   3.9.0            16_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            16_linux64_mkl    conda-forge
[conda] libcublas                 11.11.3.6                     0    nvidia
[conda] libcufft                  10.9.0.58                     0    nvidia
[conda] libcurand                 10.3.5.147                    0    nvidia
[conda] libcusolver               11.4.1.48                     0    nvidia
[conda] libcusparse               11.7.5.86                     0    nvidia
[conda] liblapack                 3.9.0            16_linux64_mkl    conda-forge
[conda] libopenvino-pytorch-frontend 2024.4.0             h5888daf_0    conda-forge
[conda] mkl                       2022.1.0           hc2b9512_224  
[conda] numpy                     1.26.4           py39h474f0d3_0    conda-forge
[conda] pyg                       2.5.2           py39_torch_2.2.0_cu118    pyg
[conda] pytorch                   2.2.2           py3.9_cuda11.8_cudnn8.7.0_0    pytorch
[conda] pytorch-cuda              11.8                 h7e8668a_6    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch-cluster             1.6.3+pt22cu118          pypi_0    pypi
[conda] torch-scatter             2.1.2+pt22cu118          pypi_0    pypi
[conda] torch-sparse              0.6.18+pt22cu118          pypi_0    pypi
[conda] torch-spline-conv         1.2.2+pt22cu118          pypi_0    pypi
[conda] torchmetrics              1.5.2              pyhd8ed1ab_1    conda-forge
[conda] torchtriton               2.2.0                      py39    pytorch
[conda] torchvision               0.17.2               py39_cu118    pytorch