daisyden
commented
2 months ago Fixed some issues in index_put, https://github.com/intel/torch-xpu-ops/pull/514
test_fn_grad_bernoulli_xpu_float64: The analytical grad is right, numerical grad is not. The issue could in forward pass. could depending on the fix of uniform issue that is owned by @xytintel . TestTorchDeviceType.test_discontiguous_out_cumsum: According to @xytintel cumsum implementation should align with cuda, or wait for the structure element feature.
fengyuan14
chunhuanMeng
gradchecker failed: first compare with cpu, if align with cpu it is low priority.
Runtime error
grad reentrant issue - Low priority
🐛 Describe the bug
Error #2 in TestBwdGradientsXPU , totally 8 , torch.autograd.gradcheck.GradcheckError: Jacobian mismatch for output 0 with respect to input 0,
"test_fn_grad_bernoulli_xpu_float64", - Fixed "test_fn_grad_linalg_norm_xpu_complex128", "test_fn_grad_linalg_vector_norm_xpu_complex128", "test_fn_grad_nn_functional_rrelu_xpu_float64", "test_fn_grad_norm_inf_xpu_complex128", "test_fn_gradgrad_nn_functional_rrelu_xpu_float64", "test_inplace_grad_nn_functional_rrelu_xpu_float64", "test_inplace_gradgrad_nn_functional_rrelu_xpu_float64",
Error #3 in TestBwdGradientsXPU , totally 8 , torch.autograd.gradcheck.GradcheckError: While considering the imaginary part of complex outputs only, Jacobian mismatch for output 0 with respect to input 0, - Only test_fn_gradgrad_norm_inf_xpu_complex128 failed.
"test_fn_grad_masked_normalize_xpu_complex128", "test_fn_grad_renorm_xpu_complex128", "test_fn_gradgrad_linalg_vector_norm_xpu_complex128", "test_fn_gradgrad_masked_normalize_xpu_complex128", "test_fn_gradgrad_norm_inf_xpu_complex128", - Failed "test_fn_gradgrad_renorm_xpu_complex128", "test_inplace_grad_renorm_xpu_complex128", "test_inplace_gradgrad_renorm_xpu_complex128",
Error #5 in TestBwdGradientsXPU , totally 2 , RuntimeError: input tensor must have at least one element, but got input_sizes = [1, 0, 1], see also https://github.com/intel/torch-xpu-ops/issues/249
"test_fn_grad_nn_functional_group_norm_xpu_float64", "test_fn_gradgrad_nn_functional_group_norm_xpu_float64",
Error #6 in TestBwdGradientsXPU , totally 5 , torch.autograd.gradcheck.GradcheckError: Backward is not reentrant, i.e., running backward with same input and grad_output multiple times gives different values, although analytical gradient matches numerical gradient.The tolerance for nondeterminism was 0.0.
"test_fn_grad_nn_functional_max_pool2d_xpu_float64", "test_fn_gradgrad_index_reduce_mean_xpu_float64", "test_fn_gradgrad_index_reduce_prod_xpu_float64", "test_inplace_gradgrad_index_reduce_mean_xpu_float64", "test_inplace_gradgrad_index_reduce_prod_xpu_float64",
Error #7 in TestBwdGradientsXPU , totally 2 , NotImplementedError: Could not run 'aten::_sparse_coo_tensor_with_dims_and_tensors' with arguments from the 'SparseXPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_sparse_coo_tensor_with_dims_and_tensors' is only available for these backends: [XPU, Meta, SparseCPU, SparseMeta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastXPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
see also https://github.com/intel/torch-xpu-ops/issues/240 - Move to #357
"test_fn_grad_to_sparse_xpu_float64", "test_fn_gradgrad_to_sparse_xpu_float64",
Error #8 in TestBwdGradientsXPU , totally 2 , RuntimeError: DispatchStub: unsupported device typexpu, see also https://github.com/intel/torch-xpu-ops/issues/249 - Fixed
"test_inplace_grad_conj_physical_xpu_complex128", "test_inplace_gradgrad_conj_physical_xpu_complex128",
Versions
Collecting environment information... PyTorch version: N/A Is debug build: N/A CUDA used to build PyTorch: N/A ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: 14.0.0-1ubuntu1.1 CMake version: version 3.26.4 Libc version: glibc-2.35
Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-106-generic-x86_64-with-glibc2.35 Is CUDA available: N/A CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: N/A GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: N/A
CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 224 On-line CPU(s) list: 0-223 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8480+ CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 56 Socket(s): 2 Stepping: 8 CPU max MHz: 3800.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 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 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 5.3 MiB (112 instances) L1i cache: 3.5 MiB (112 instances) L2 cache: 224 MiB (112 instances) L3 cache: 210 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-55,112-167 NUMA node1 CPU(s): 56-111,168-223 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 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 BHI_DIS_S 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+git5fb11cd [conda] mkl-include 2024.1.0 intel_691 intel [conda] mkl-static 2024.1.0 intel_691 intel [conda] numpy 1.26.4 pypi_0 pypi [conda] optree 0.11.0 pypi_0 pypi [conda] torch 2.4.0a0+git5fb11cd pypi_0 pypi