[Evaluated] Issue of unit test cases for test_autograd_xpu.py

PenghuiCheng commented 3 months ago

🐛 Describe the bug

RuntimeError: PyTorch was compiled without CUDA support----fixed cases:
```
TestAutograd.test_checkpointing_non_reentrant_autocast_gpu
```

module 'torch._C' has no attribute '_scatter' cases:

TestAutograd.test_checkpointing_without_reentrant_dataparallel,
TestMultithreadAutograd.test_dataparallel_saved_tensors_hooks

AttributeError: module 'torch.xpu' has no attribute cases:

TestAutograd.test_graph_save_on_cpu_cuda,
TestAutograd.test_checkpointing_without_reentrant_memory_savings,
TestAutogradDeviceTypeXPU.test_pin_memory_xpu

NotImplementedError: Could not run 'aten::_sparse_coo_tensor_with_dims_and_tensors' with arguments from the 'SparseXPU' backend. cases:

test_sparse_mask_autograd_xpu
test_sparse_ctor_getter_backward_xpu_float64
test_sparse_ctor_getter_backward_xpu_complex128
test_sparse_backward_xpu_float64
test_sparse_backward_xpu_complex128

c10::NotImplementedError cases:

TestAutogradMultipleDispatchXPU::test_autograd_composite_implicit_and_dispatch_registration_xpu
TestAutogradMultipleDispatchXPU::test_autograd_multiple_dispatch_registrations_xpu

segment fault ---fixed cases:

TestAutograd::test_custom_function_cycle
TestAutograd::test_custom_function_forward_mode_wrong_formula
TestAutograd::test_custom_function_non_tensor_inputs_outputs
TestAutograd::test_custom_function_exception
TestAutograd::test_custom_function_save_for_forward
TestAutograd::test_custom_function_saved_tensors
TestAutograd::test_custom_function_setup_context_multi_input
TestAutograd::test_custom_function_setup_context_multi_output
TestAutogradDeviceTypeXPU::test_resize_version_bump_xpu
TestAutogradDeviceTypeXPU::test_resize_version_bump

Command:

PYTORCH_ENABLE_XPU_FALLBACK=1 PYTORCH_TEST_WITH_SLOW=1 pytest -v test/xpu/test_autograd_xpu.py

Versions

PyTorch version: 2.4.0a0+gitf87fbfd Is debug build: False CUDA used to build PyTorch: None 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 3.26.4 Libc version: glibc-2.35

Python version: 3.9.19 (main, May 6 2024, 19:43:03) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.35 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, 57 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) Gold 6342 CPU @ 2.80GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 1 Stepping: 6 CPU max MHz: 3500.0000 CPU min MHz: 800.0000 BogoMIPS: 5600.00 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 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 hwp hwp_act_window hwp_epp hwp_pkg_req 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 Virtualization: VT-x L1d cache: 1.1 MiB (24 instances) L1i cache: 768 KiB (24 instances) L2 cache: 30 MiB (24 instances) L3 cache: 36 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-47 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 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 Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] optree==0.11.0 [pip3] torch==2.4.0a0+gitf87fbfd [conda] numpy 1.26.4 pypi_0 pypi [conda] optree 0.11.0 pypi_0 pypi [conda] torch 2.4.0a0+gitf87fbfd dev_0

PenghuiCheng commented 3 months ago

autograd.log