`torch.linalg.svd` is significantly slower than expected on a Max Series GPU

[ogrisel]

Describe the bug

I tried torch.linalg.svd a Max Series GPU using the Intel Devcloud and packages from the intel conda channel, and while I cannot reproduce the segfault, the performance on XPU is lower than on CPU:

>>> import dpctl
>>> dpctl.get_devices()
[<dpctl.SyclDevice [backend_type.opencl, device_type.cpu,  Intel(R) Xeon(R) Platinum 8480+] at 0x15040cdd6bf0>,
 <dpctl.SyclDevice [backend_type.opencl, device_type.accelerator,  Intel(R) FPGA Emulation Device] at 0x15040cdd6db0>,
 <dpctl.SyclDevice [backend_type.level_zero, device_type.gpu,  Intel(R) Data Center GPU Max 1100] at 0x15040cdd6df0>]

>>> import intel_extension_for_pytorch
>>> import torch
>>> intel_extension_for_pytorch.__version__
'2.0.110+xpu'
>>> torch.__version__
'2.0.1a0+cxx11.abi'

>>> data_cpu = torch.randn(4096, 2046)
>>> %time _ = torch.linalg.svd(data_cpu, full_matrices=False)
CPU times: user 1min 1s, sys: 2.3 s, total: 1min 3s
Wall time: 783 ms

>>> data_xpu = data_cpu.to("xpu")
>>> %time _ = torch.linalg.svd(data_xpu, full_matrices=False)
CPU times: user 3min 52s, sys: 1.14 s, total: 3min 53s
Wall time: 2.31 s

Also note that I checked that a GEMM on a Max Series GPU is approximately 4 to 5x faster on XPU than on CPU:

>>> A_cpu, B_cpu = torch.randn(4096, 4096), torch.randn(4096, 4096)
>>> A_xpu, B_xpu = A_cpu.to("xpu"), B_cpu.to("xpu")
>>> %timeit (A_cpu @ B_cpu)[0, 0].item()
29 ms ± 363 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
>>> %timeit (A_xpu @ B_xpu)[0, 0].item()
6.91 ms ± 25.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

I would have expected a similar 4-5x speed-up for SVD on XPU instead of 3x slowdown.

Versions

``` PyTorch version: 2.0.1a0+cxx11.abi PyTorch CXX11 ABI: Yes IPEX version: 2.0.110+xpu IPEX commit: ba7f6c127 Build type: Release OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: N/A IGC version: N/A CMake version: version 3.22.1 Libc version: glibc-2.35 Python version: 3.10.12 (main, Jul 20 2023, 00:27:19) [GCC 13.1.0] (64-bit runtime) Python platform: Linux-5.15.0-83-generic-x86_64-with-glibc2.35 Is XPU available: True DPCPP runtime version: N/A MKL version: N/A GPU models and configuration: [0] _DeviceProperties(name='Intel(R) Data Center GPU Max 1100', platform_name='Intel(R) Level-Zero', dev_type='gpu, support_fp64=1, total_memory=49152MB, max_compute_units=448, gpu_eu_count=448) Intel OpenCL ICD version: 23.22.26516.25-682~22.04 Level Zero version: 1.3.26516.25-682~22.04 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 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] intel-extension-for-pytorch==2.0.110+xpu [pip3] numpy==1.24.3 [pip3] torch==2.0.1a0+cxx11.abi [conda] intel-extension-for-pytorch 2.0.110 py310_xpu_0 intel [conda] mkl 2023.2.0 intel_49495 intel [conda] mkl-dpcpp 2023.2.0 intel_49495 intel [conda] mkl-service 2.4.0 py310hae59892_35 intel [conda] mkl_fft 1.3.6 py310h173b8ae_56 intel [conda] mkl_random 1.2.2 py310h1595b48_76 intel [conda] mkl_umath 0.1.1 py310hd987cd3_86 intel [conda] numpy 1.24.3 py310hed7eef7_0 intel [conda] numpy-base 1.24.3 py310he88ecf9_0 intel [conda] pytorch 2.0.1 py310_xpu_0 intel ```

[jingxu10]

@gujinghui @tye1