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intel / intel-extension-for-pytorch

A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
Apache License 2.0
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NotImplementedError: Could not run 'aten::_upsample_bicubic2d_aa.out' with arguments from the 'XPU' backend. #705

Open Nuullll opened 1 month ago

Nuullll commented 1 month ago

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
fengyuan14 commented 2 weeks ago

Hi, @Nuullll, according to our priority, so far, the operator is not on the plan of PT2.6. Please clarify your real usage, like which model you get the failure on. We will evaluate the customer impact, and adjust the priority.