Induced graph sampling cannot be performed when a time attribute is specified, even if pyg-lib has been installed. #10566
Description
🐛 Describe the bug
Induced graph sampling cannot be performed when a time attribute is specified, even if pyg-lib has been installed.
Demo Code:
import torch
from torch_geometric.data import Data
from torch_geometric.loader import NeighborLoader
x = torch.tensor([
[0.1, 0.2, 0.3],
[0.4, 0.5, 0.6],
[0.7, 0.8, 0.9],
[0.2, 0.3, 0.4],
[0.5, 0.6, 0.7]
], dtype=torch.float)
edge_index = torch.tensor([
[0, 1, 1, 2, 2, 3, 3, 4],
[1, 0, 2, 1, 3, 2, 4, 3]
], dtype=torch.long)
edge_time = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=torch.long)
G = Data(x=x, edge_index=edge_index, edge_time=edge_time)
seeds = torch.tensor([0, 4])
node_label_time = torch.tensor([3, 7])
loader = NeighborLoader(
G,
num_neighbors=[2, 1],
input_nodes=seeds,
input_time=node_label_time,
time_attr='edge_time',
temporal_strategy='uniform',
batch_size=2,
shuffle=False,
subgraph_type='induced'
)
print(next(iter(loader))Traceback:
Traceback (most recent call last):
File "/home/stu/projects/demo/test_time_sampling.py", line 87, in <module>
batch = next(iter(loader))
^^^^^^^^^^^^^^^^^^
File "/home/stu/miniconda3/envs/my_env/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "/home/stu/miniconda3/envs/my_env/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 757, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/stu/miniconda3/envs/my_env/lib/python3.12/site-packages/torch/utils/data/_utils/fetch.py", line 55, in fetch
return self.collate_fn(data)
^^^^^^^^^^^^^^^^^^^^^
File "/home/stu/miniconda3/envs/my_env/lib/python3.12/site-packages/torch_geometric/loader/node_loader.py", line 147, in collate_fn
out = self.node_sampler.sample_from_nodes(input_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/stu/miniconda3/envs/my_env/lib/python3.12/site-packages/torch_geometric/sampler/neighbor_sampler.py", line 403, in sample_from_nodes
out = node_sample(inputs, self._sample)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/stu/miniconda3/envs/my_env/lib/python3.12/site-packages/torch_geometric/sampler/neighbor_sampler.py", line 815, in node_sample
out = sample_fn(seed, seed_time)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/stu/miniconda3/envs/my_env/lib/python3.12/site-packages/torch_geometric/sampler/neighbor_sampler.py", line 589, in _sample
raise ValueError("'disjoint' sampling not supported for "
ValueError: 'disjoint' sampling not supported for neighbor sampling via 'torch-sparse'. Please install 'pyg-lib' for improved and optimized sampling routines.
Output of pip list | grep pyg:
pyg_lib 0.4.0+pt25cu121
Versions
PyTorch version: 2.5.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35
Python version: 3.12.12 | packaged by Anaconda, Inc. | (main, Oct 21 2025, 20:16:04) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-161-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090
GPU 2: NVIDIA GeForce RTX 4090
Nvidia driver version: 535.274.02
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A
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): 48
On-line CPU(s) list: 0-47
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Silver 4214 CPU @ 2.20GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 12
Socket(s): 2
Stepping: 7
CPU max MHz: 3200.0000
CPU min MHz: 1000.0000
BogoMIPS: 4400.00
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 pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user
Virtualization: VT-x
L1d cache: 768 KiB (24 instances)
L1i cache: 768 KiB (24 instances)
L2 cache: 24 MiB (24 instances)
L3 cache: 33 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-11,24-35
NUMA node1 CPU(s): 12-23,36-47
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Vulnerability Vmscape: Mitigation; IBPB before exit to userspace
Versions of relevant libraries:
[pip3] numpy==2.3.3
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pytorch-lightning==2.6.0
[pip3] torch==2.5.1+cu121
[pip3] torch_cluster==1.6.3+pt25cu121
[pip3] torch-geometric==2.7.0
[pip3] torch-kmeans==0.2.0
[pip3] torch_scatter==2.1.2+pt25cu121
[pip3] torch_sparse==0.6.18+pt25cu121
[pip3] torch_spline_conv==1.2.2+pt25cu121
[pip3] torchmetrics==1.8.2
[pip3] torchvision==0.20.1+cu121
[pip3] triton==3.1.0
[conda] cuda-cudart 12.9.79 0 nvidia
[conda] cuda-cudart-dev 12.9.79 0 nvidia
[conda] cuda-cudart-dev_linux-64 12.9.79 0 nvidia
[conda] cuda-cudart-static 12.9.79 0 nvidia
[conda] cuda-cudart-static_linux-64 12.9.79 0 nvidia
[conda] cuda-cudart_linux-64 12.9.79 0 nvidia
[conda] cuda-cupti 12.9.79 0 nvidia
[conda] cuda-cupti-dev 12.9.79 0 nvidia
[conda] cuda-libraries 12.6.2 0 nvidia
[conda] cuda-libraries-dev 12.6.2 0 nvidia
[conda] cuda-libraries-static 12.9.1 0 nvidia
[conda] cuda-nvrtc 12.9.86 0 nvidia
[conda] cuda-nvrtc-dev 12.9.86 0 nvidia
[conda] cuda-nvrtc-static 12.9.86 0 nvidia
[conda] cuda-nvtx 12.9.79 0 nvidia
[conda] cuda-opencl 12.9.19 0 nvidia
[conda] cuda-opencl-dev 12.9.19 0 nvidia
[conda] libcublas 12.9.1.4 0 nvidia
[conda] libcublas-dev 12.9.1.4 0 nvidia
[conda] libcublas-static 12.9.1.4 0 nvidia
[conda] libcufft 11.4.1.4 0 nvidia
[conda] libcufft-dev 11.4.1.4 0 nvidia
[conda] libcufft-static 11.4.1.4 0 nvidia
[conda] libcurand 10.3.10.19 0 nvidia
[conda] libcurand-dev 10.3.10.19 0 nvidia
[conda] libcurand-static 10.3.10.19 0 nvidia
[conda] libcusolver 11.7.5.82 0 nvidia
[conda] libcusolver-dev 11.7.5.82 0 nvidia
[conda] libcusolver-static 11.7.5.82 0 nvidia
[conda] libcusparse 12.5.10.65 0 nvidia
[conda] libcusparse-dev 12.5.10.65 0 nvidia
[conda] libcusparse-static 12.5.10.65 0 nvidia
[conda] libnvjitlink 12.9.86 0 nvidia
[conda] libnvjitlink-dev 12.9.86 0 nvidia
[conda] libnvjitlink-static 12.9.86 0 nvidia
[conda] numpy 2.3.3 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.9.86 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi
[conda] pytorch-lightning 2.6.0 pypi_0 pypi
[conda] torch 2.5.1+cu121 pypi_0 pypi
[conda] torch-cluster 1.6.3+pt25cu121 pypi_0 pypi
[conda] torch-geometric 2.7.0 pypi_0 pypi
[conda] torch-kmeans 0.2.0 pypi_0 pypi
[conda] torch-scatter 2.1.2+pt25cu121 pypi_0 pypi
[conda] torch-sparse 0.6.18+pt25cu121 pypi_0 pypi
[conda] torch-spline-conv 1.2.2+pt25cu121 pypi_0 pypi
[conda] torchmetrics 1.8.2 pypi_0 pypi
[conda] torchvision 0.20.1+cu121 pypi_0 pypi
[conda] triton 3.1.0 pypi_0 pypi