NotImplementedError: Could not run 'aten::_upsample_bicubic2d_aa.out' with arguments from the 'XPU' backend.

[Nuullll]

Describe the bug

import torch
import intel_extension_for_pytorch as ipex

input = torch.randn(1,3,512,512,device='xpu')
torch.nn.functional.interpolate(input, size=(512,512), mode='bicubic', antialias=True)
torch.nn.functional.interpolate(input, size=(512,512), mode='bilinear', antialias=True)

File "D:\ComfyUI-Arc\python\lib\site-packages\torch\nn\functional.py", line 4027, in interpolate
    return torch._C._nn._upsample_bicubic2d_aa(input, output_size, align_corners, scale_factors)
NotImplementedError: Could not run 'aten::_upsample_bicubic2d_aa.out' with arguments from the 'XPU' 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::_upsample_bicubic2d_aa.out' is only available for these backends: [CPU, Meta, 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, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

CPU: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\build\aten\src\ATen\RegisterCPU.cpp:31188 [kernel]
Meta: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\build\aten\src\ATen\RegisterMeta.cpp:26829 [kernel]
BackendSelect: fallthrough registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\core\BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\core\PythonFallbackKernel.cpp:153 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\functorch\DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\build\aten\src\ATen\RegisterFunctionalization_0.cpp:21905 [kernel]
Named: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\core\NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\native\NegateFallback.cpp:19 [backend fallback]
ZeroTensor: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\ADInplaceOrViewType_0.cpp:4733 [kernel]
AutogradOther: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradCPU: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradCUDA: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradHIP: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradXLA: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradMPS: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradIPU: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradXPU: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradHPU: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradVE: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradLazy: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradMTIA: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradPrivateUse1: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradPrivateUse2: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradPrivateUse3: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
AutogradMeta: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
\torch\csrc\autograd\generated\VariableType_2.cpp:18610 [autograd kernel]
Tracer: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\torch\csrc\autograd\generated\TraceType_0.cpp:16725 [kernel]
AutocastCPU: fallthrough registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\autocast_mode.cpp:382 [backend fallback]
AutocastXPU: fallthrough registered at C:/Jenkins/workspace/IPEX-GPU-ARC770-Windows-Build/frameworks.ai.pytorch.ipex-gpu/csrc/gpu/aten/amp/autocast_mode.cpp:45 [backend fallback]
AutocastCUDA: fallthrough registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\autocast_mode.cpp:249 [backend fallback]
FuncTorchBatched: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\functorch\LegacyBatchingRegistrations.cpp:710 [backend fallback]
FuncTorchVmapMode: fallthrough registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\functorch\VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\functorch\TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\core\PythonFallbackKernel.cpp:161 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\functorch\DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\core\PythonFallbackKernel.cpp:165 [backend fallback]
PythonDispatcher: registered at C:\Jenkins\workspace\IPEX-GPU-ARC770-Windows-Build\frameworks.ai.pytorch.private-gpu\aten\src\ATen\core\PythonFallbackKernel.cpp:157 [backend fallback]

Versions

PyTorch version: 2.1.0.post3+cxx11.abi
PyTorch CXX11 ABI: No
IPEX version: 2.1.40+xpu
IPEX commit: 80ed47655
Build type: Release

OS: Microsoft Windows 11 专业版
GCC version: (GCC) 13.1.0
Clang version: N/A
IGC version: N/A
CMake version: version 3.28.1
Libc version: N/A

Python version: 3.10.11 (tags/v3.10.11:7d4cc5a, Apr  5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22631-SP0
Is XPU available: True
DPCPP runtime version: N/A
MKL version: N/A
GPU models and configuration:
[0] _DeviceProperties(name='Intel(R) Arc(TM) A770 Graphics', platform_name='Intel(R) Level-Zero', dev_type='gpu', driver_version='1.3.30398', has_fp64=0, total_memory=15930MB, max_compute_units=512, gpu_eu_count=512)
[1] _DeviceProperties(name='Intel(R) Arc(TM) A750 Graphics', platform_name='Intel(R) Level-Zero', dev_type='gpu', driver_version='1.3.30398', has_fp64=0, total_memory=7934MB, max_compute_units=448, gpu_eu_count=448)
Intel OpenCL ICD version: N/A
Level Zero version: N/A

CPU:
Architecture=9
CurrentClockSpeed=2000
DeviceID=CPU0
Family=207
L2CacheSize=32768
L2CacheSpeed=
Manufacturer=GenuineIntel
MaxClockSpeed=2000
Name=13th Gen Intel(R) Core(TM) i9-13900
ProcessorType=3
Revision=

Versions of relevant libraries:
[pip3] intel_extension_for_pytorch==2.1.40+xpu
[pip3] numpy==1.26.2
[pip3] open-clip-torch==2.20.0
[pip3] pytorch-lightning==1.9.4
[pip3] torch==2.1.0.post3+cxx11.abi
[pip3] torchaudio==2.1.0.post3+cxx11.abi
[pip3] torchdiffeq==0.2.3
[pip3] torchmetrics==1.2.1
[pip3] torchsde==0.2.6
[pip3] torchvision==0.16.0.post3+cxx11.abi