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RuntimeError: false INTERNAL ASSERT FAILED at "../aten/src/ATen/native/BatchLinearAlgebra.cpp":498 #131319

Open KnightGOKU opened 4 months ago

KnightGOKU commented 4 months ago

šŸ› Describe the bug

I encountered a bug in PyTorch when I created two random tensors of shape [3, 3, 5, 5] with data type float64, converted it to sparse CSC format, and used torch.triangular_solve to solve a linear system involving triangular matrices.

import torch
tensor1 = torch.rand([3, 3, 5, 5], dtype=torch.float64).to_sparse_csc()
tensor2 = torch.rand([3, 3, 5, 5], dtype=torch.float64).to_sparse_csc()
res = torch.triangular_solve(tensor1, tensor2, upper=False)

The error message was as follows:

Traceback (most recent call last):
  File "/data/torch.triangular_solve-0_csc.py", line 13, in <module>
    res = torch.triangular_solve(tensor1, tensor2, upper=False)
RuntimeError: false INTERNAL ASSERT FAILED at "../aten/src/ATen/native/BatchLinearAlgebra.cpp":498, please report a bug to PyTorch. triangular_solve: Got an unexpected layout.

I have confirmed that above code would crash on 2.5.0.dev20240720+cu121 (nightly-build). Also, I provided that a colab notebook to reproduce the error.

Versions

PyTorch version: 2.5.0.dev20240720+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.3 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.16.3 Libc version: glibc-2.31

Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-107-generic-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 535.183.01 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 Byte Order: Little Endian Address sizes: 46 bits physical, 57 bits virtual CPU(s): 64 On-line CPU(s) list: 0-63 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 106 Model name: Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz Stepping: 6 CPU MHz: 881.623 CPU max MHz: 3500.0000 CPU min MHz: 800.0000 BogoMIPS: 5800.00 Virtualization: VT-x L1d cache: 1.5 MiB L1i cache: 1 MiB L2 cache: 40 MiB L3 cache: 48 MiB NUMA node0 CPU(s): 0-15,32-47 NUMA node1 CPU(s): 16-31,48-63 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Not affected 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 IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected 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 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] optree==0.12.1 [pip3] pytorch-triton==3.0.0+dedb7bdf33 [pip3] torch==2.5.0.dev20240720+cu121 [pip3] torchaudio==2.4.0.dev20240721+cu121 [pip3] torchvision==0.20.0.dev20240721+cu121 [pip3] triton==2.3.1 [conda] numpy 1.26.4 pypi_0 pypi [conda] optree 0.12.1 pypi_0 pypi [conda] pytorch-triton 3.0.0+dedb7bdf33 pypi_0 pypi [conda] torch 2.5.0.dev20240720+cu121 pypi_0 pypi [conda] torchaudio 2.4.0.dev20240721+cu121 pypi_0 pypi [conda] torchvision 0.20.0.dev20240721+cu121 pypi_0 pypi [conda] triton 2.3.1 pypi_0 pypi

cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer @jcaip @malfet

malfet commented 4 months ago

It looks like this function is simply unimplemented for sparse CSC layout. And easy fix would be to raise "unsupported sparse layout error"