Description
🐛 Describe the bug
I have an IterableDataset that is being passed in the data loader. The Dataset yields a sparse_csr_tensor. This is what is currently Exception being thrown. I have a duplicate MapDataset that is identical in logic to the IterableDataset but instead returns the in getitem of course. I have set pin_memory to false and get the same issue. I can include more of my code if needed if there is reason to believe this is my own implementation issue. Everything is being ran on one GPU.
train_data = DataLoader(
train_set,
worker_init_fn=worker_init_fn,
num_workers=world_size,
shuffle=False,
pin_memory=True, # pin_memory_device not set so default is cpu
prefetch_factor=1,
persistent_workers=True,
)
Traceback (most recent call last):
File "/home/denhofja/D-MMVAE/src/main.py", line 160, in <module>
main(args, world_size=world_size)
File "/home/denhofja/D-MMVAE/src/main.py", line 152, in main
trainer.train(args.total_epochs)
File "/home/denhofja/D-MMVAE/src/Trainer.py", line 48, in train
self._run_epoch(epoch)
File "/home/denhofja/D-MMVAE/src/Trainer.py", line 55, in _run_epoch
for source in self._loader:
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
return self._process_data(data)
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/_utils.py", line 694, in reraise
raise exception
NotImplementedError: Caught NotImplementedError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 42, in fetch
return self.collate_fn(data)
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 265, in default_collate
return collate(batch, collate_fn_map=default_collate_fn_map)
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 119, in collate
return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map)
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 160, in collate_tensor_fn
storage = elem._typed_storage()._new_shared(numel, device=elem.device)
File "/cm/shared/venv/python/3.9.18/ml-python39/lib64/python3.9/site-packages/torch/_tensor.py", line 242, in _typed_storage
untyped_storage = self.untyped_storage()
NotImplementedError: Cannot access storage of SparseCsrTensorImplVersions
PyTorch version: 2.1.1+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: Red Hat Enterprise Linux 9.2 (Plow) (x86_64)
GCC version: (GCC) 11.2.0
Clang version: Could not collect
CMake version: version 3.26.5
Libc version: glibc-2.34
Python version: 3.9.18 (main, Sep 7 2023, 00:00:00) [GCC 11.4.1 20230605 (Red Hat 11.4.1-2)] (64-bit runtime)
Python platform: Linux-5.14.0-284.18.1.el9_2.x86_64-x86_64-with-glibc2.34
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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: Intel(R) Xeon(R) Gold 6226 CPU @ 2.70GHz
CPU family: 6
Model: 85
Thread(s) per core: 1
Core(s) per socket: 12
Socket(s): 2
Stepping: 7
CPU max MHz: 3700.0000
CPU min MHz: 1200.0000
BogoMIPS: 5400.00
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 monitor ds_cpl 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 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
L1d cache: 768 KiB (24 instances)
L1i cache: 768 KiB (24 instances)
L2 cache: 24 MiB (24 instances)
L3 cache: 38.5 MiB (2 instances)
NUMA node(s): 4
NUMA node0 CPU(s): 0,4,8,12,16,20
NUMA node1 CPU(s): 1,5,9,13,17,21
NUMA node2 CPU(s): 2,6,10,14,18,22
NUMA node3 CPU(s): 3,7,11,15,19,23
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Retbleed: Mitigation; Enhanced IBRS
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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Versions of relevant libraries:
[pip3] numpy==1.26.2
[pip3] torch==2.1.1+cu118
[pip3] torchaudio==2.1.1+cu118
[pip3] torchvision==0.16.1+cu118
[pip3] triton==2.1.0
[conda] Could not collect
cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer @jcaip @ssnl @VitalyFedyunin @ejguan @dzhulgakov