Releases

2.5.10+xpu

Intel® Extension for PyTorch* v2.5.10+xpu is the new release which supports Intel® GPU platforms (Intel® Data Center GPU Max Series, Intel® Arc™ Graphics family, Intel® Core™ Ultra Processors with Intel® Arc™ Graphics, Intel® Core™ Ultra Series 2 with Intel® Arc™ Graphics and Intel® Data Center GPU Flex Series) based on PyTorch* 2.5.1.

Highlights

Intel® oneDNN v3.6 integration
Intel® oneAPI Base Toolkit 2025.0.1 compatibility
Intel® Arc™ B-series Graphics support on Windows (prototype)
Large Language Model (LLM) optimization

Intel® Extension for PyTorch* enhances KV Cache management to cover both Dynamic Cache and Static Cache methods defined by Hugging Face, which helps reduce computation time and improve response rates so as to optimize the performance of models in various generative tasks. Intel® Extension for PyTorch* also supports new LLM features including speculative decoding which optimizes inference by making educated guesses about future tokens while generating the current token, sliding window attention which uses a fixed-size window to limit the attention span of each token thus significantly improves processing speed and efficiency for long documents, and multi-round conversations for supporting a natural human conversation where information is exchanged in multiple turns back and forth.

Besides that, Intel® Extension for PyTorch* optimizes more LLM models for inference and finetuning. A full list of optimized models can be found at LLM Optimizations Overview.
Serving framework support

Typical LLM serving frameworks including vLLM and TGI can co-work with Intel® Extension for PyTorch* on Intel® GPU platforms on Linux (intensively verified on Intel® Data Center GPU Max Series). The support to low precision such as INT4 Weight Only Quantization, which based on Generalized Post-Training Quantization (GPTQ) algorithm, is enhanced in this release.
Beta support of full fine-tuning and LoRA PEFT with mixed precision

Intel® Extension for PyTorch* enhances this feature for optimizing typical LLM models and makes it reach Beta quality.
Kineto Profiler Support

Intel® Extension for PyTorch* removes this redundant feature as the support of Kineto Profiler based on PTI on Intel® GPU platforms is available in PyTorch* 2.5.
Hybrid ATen operator implementation

Intel® Extension for PyTorch* uses ATen operators available in Torch XPU Operators as much as possible and overrides very limited operators for better performance and broad data type support.

Breaking Changes

Block format support: oneDNN Block format integration support has been removed since v2.5.10+xpu.

Known Issues

Please refer to Known Issues webpage.

2.3.110+xpu

Intel® Extension for PyTorch* v2.3.110+xpu is the new release which supports Intel® GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics) based on PyTorch* 2.3.1.

Highlights

Intel® oneDNN v3.5.3 integration
Intel® oneAPI Base Toolkit 2024.2.1 compatibility
Large Language Model (LLM) optimization

Intel® Extension for PyTorch* provides a new dedicated module, ipex.llm, to host for Large Language Models (LLMs) specific APIs. With ipex.llm, Intel® Extension for PyTorch* provides comprehensive LLM optimization on FP16 and INT4 datatypes. Specifically for low precision, Weight-Only Quantization is supported for various scenarios. And user can also run Intel® Extension for PyTorch* with Tensor Parallel to fit in the multiple ranks or multiple nodes scenarios to get even better performance.

A typical API under this new module is ipex.llm.optimize, which is designed to optimize transformer-based models within frontend Python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. ipex.llm.optimize is an upgrade API to replace previous ipex.optimize_transformers, which will bring you more consistent LLM experience and performance. Below shows a simple example of ipex.llm.optimize for fp16 inference:
```
  import torch
  import intel_extension_for_pytorch as ipex
  import transformers

  model= transformers.AutoModelForCausalLM.from_pretrained(model_name_or_path).eval()

  dtype = torch.float16
  model = ipex.llm.optimize(model, dtype=dtype, device="xpu")

  model.generate(YOUR_GENERATION_PARAMS)
```
More examples of this API can be found at LLM optimization API.

Besides that, we optimized more LLM inference models. A full list of optimized models can be found at LLM Optimizations Overview.
Serving framework support

Typical LLM serving frameworks including vLLM and TGI can co-work with Intel® Extension for PyTorch* on Intel® GPU platforms (Intel® Data Center GPU Max 1550 and Intel® Arc™ A-Series Graphics). Besides the integration of LLM serving frameworks with ipex.llm module level APIs, we enhanced the performance and quality of underneath Intel® Extension for PyTorch* operators such as paged attention and flash attention for better end to end model performance.
Prototype support of full fine-tuning and LoRA PEFT with mixed precision

Intel® Extension for PyTorch* also provides new capability for supporting popular recipes with both full fine-tuning and LoRA PEFT for mixed precision with BF16 and FP32. We optimized many typical LLM models including Llama 2 (7B and 70B), Llama 3 8B, Phi-3-Mini 3.8B model families and Chinese model Qwen-7B, on both single GPU and Multi-GPU (distributed fine-tuning based on PyTorch FSDP) use cases.

Breaking Changes

Block format support: oneDNN Block format integration support is being deprecated and will no longer be available starting from the release after v2.3.110+xpu.

Known Issues

Please refer to Known Issues webpage.

2.1.40+xpu

Intel® Extension for PyTorch* v2.1.40+xpu is a minor release which supports Intel® GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series，Intel® Arc™ A-Series Graphics and Intel® Core™ Ultra Processors with Intel® Arc™ Graphics) based on PyTorch* 2.1.0.

Highlights

Intel® oneAPI Base Toolkit 2024.2.1 compatibility
Intel® oneDNN v3.5 integration
Intel® oneCCL 2021.13.1 integration
Intel® Core™ Ultra Processors with Intel® Arc™ Graphics (MTL-H) support on Windows (Prototype)
Bug fixing and other optimization
- Fix host memory leak #4280
- Fix LayerNorm issue for undefined grad_input #4317
- Replace FP64 device check method #4354
- Fix online doc search issue #4358
- Fix pdist unit test failure on client GPUs #4361
- Remove primitive cache from conv fwd #4429
- Fix sdp bwd page fault with no grad bias #4439
- Fix implicit data conversion #4463
- Fix compiler version parsing issue #4468
- Fix irfft invalid descriptor #4480
- Change condition order to fix out-of-bound access in index #4495
- Add parameter check in embedding bag #4504
- Add the backward implementation for rms norm #4527
- Fix attn_mask for sdpa beam_search #4557
- Use data_ptr template instead of force data conversion #4558
- Workaround windows AOT image size over 2GB issue on Intel® Core™ Ultra Processors with Intel® Arc™ Graphics #4407 #4450

Known Issues

Please refer to Known Issues webpage.

2.1.30+xpu

Intel® Extension for PyTorch* v2.1.30+xpu is an update release which supports Intel® GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics) based on PyTorch* 2.1.0.

Highlights

Intel® oneDNN v3.4.1 integration
Intel® oneAPI Base Toolkit 2024.1 compatibility
Large Language Model (LLM) optimizations for FP16 inference on Intel® Data Center GPU Max Series (Beta): Intel® Extension for PyTorch* provides a lot of specific optimizations for LLM workloads in this release on Intel® Data Center GPU Max Series. In operator level, we provide highly efficient GEMM kernel to speed up Linear layer and customized fused operators to reduce HBM access/kernel launch overhead. To reduce memory footprint, we define a segment KV Cache policy to save device memory and improve the throughput. Such optimizations are added in this release to enhance existing optimized LLM FP16 models and more Chinese LLM models such as Baichuan2-13B, ChatGLM3-6B and Qwen-7B.
LLM optimizations for INT4 inference on Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics (Prototype): Intel® Extension for PyTorch* shows remarkable performance when executing LLM models on Intel® GPU. However, deploying such models on GPUs with limited resources is challenging due to their high computational and memory requirements. To achieve a better trade-off, a low-precision solution, e.g., weight-only-quantization for INT4 is enabled to allow Llama 2-7B, GPT-J-6B and Qwen-7B to be executed efficiently on Intel® Arc™ A-Series Graphics. The same optimization makes INT4 models achieve 1.5x speeded up in total latency performance compared with FP16 models with the same configuration and parameters on Intel® Data Center GPU Max Series.
Opt-in collective performance optimization with oneCCL Bindings for Pytorch*: This opt-in feature can be enabled by setting TORCH_LLM_ALLREDUCE=1 to provide better scale-up performance by enabling optimized collectives such as allreduce, allgather, reducescatter algorithms in Intel® oneCCL. This feature requires XeLink enabled for cross-cards communication.

Known Issues

Please refer to Known Issues webpage.

2.1.20+xpu

Intel® Extension for PyTorch* v2.1.20+xpu is a minor release which supports Intel® GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics) based on PyTorch* 2.1.0.

Highlights

Intel® oneAPI Base Toolkit 2024.1 compatibility
Intel® oneDNN v3.4 integration
LLM inference scaling optimization based on Intel® oneCCL 2021.12 (Prototype)
Bug fixing and other optimization
- Uplift XeTLA to v0.3.4.1 #3696
- [SDP] Fallback unsupported bias size to native impl #3706
- Error handling enhancement #3788, #3841
- Fix beam search accuracy issue in workgroup reduce #3796
- Support int32 index tensor in index operator #3808
- Add deepspeed in LLM dockerfile #3829
- Fix batch norm accuracy issue #3882
- Prebuilt wheel dockerfile update #3887, #3970
- Fix windows build failure with Intel® oneMKL 2024.1 in torch_patches #18
- Fix FFT core dump issue with Intel® oneMKL 2024.1 in torch_patches #20, #21

Known Issues

Please refer to Known Issues webpage.

2.1.10+xpu

Intel® Extension for PyTorch* v2.1.10+xpu is the new Intel® Extension for PyTorch* release supports both CPU platforms and GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics) based on PyTorch* 2.1.0. It extends PyTorch* 2.1.0 with up-to-date features and optimizations on xpu for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

Highlights

This release provides the following features:

Large Language Model (LLM) optimizations for FP16 inference on Intel® Data Center GPU Max Series (Prototype): Intel® Extension for PyTorch* provides a lot of specific optimizations for LLM workloads on Intel® Data Center GPU Max Series in this release. In operator level, we provide highly efficient GEMM kernel to speedup Linear layer and customized fused operators to reduce HBM access and kernel launch overhead. To reduce memory footprint, we define a segment KV Cache policy to save device memory and improve the throughput. To better trade-off the performance and accuracy, low-precision solution e.g., weight-only-quantization for INT4 is enabled. Besides, tensor parallel can also be adopted to get lower latency for LLMs.
- A new API function, ipex.optimize_transformers, is designed to optimize transformer-based models within frontend Python modules, with a particular focus on LLMs. It provides optimizations for both model-wise and content-generation-wise. You just need to invoke the ipex.optimize_transformers API instead of the ipex.optimize API to apply all optimizations transparently. More detailed information can be found at Large Language Model optimizations overview.
- A typical usage of this new feature is quite simple as below:
```
import torch
import intel_extension_for_pytorch as ipex
...
model = ipex.optimize_transformers(model, dtype=dtype)
```
Torch.compile functionality on Intel® Data Center GPU Max Series (Beta): Extends Intel® Extension for PyTorch* capabilities to support torch.compile APIs on Intel® Data Center GPU Max Series. And provides Intel GPU support on top of Triton* compiler to reach competitive performance speed-up over eager mode by default “inductor” backend of Intel® Extension for PyTorch*.
Intel® Arc™ A-Series Graphics on WSL2, native Windows and native Linux are officially supported in this release. Intel® Arc™ A770 Graphic card has been used as primary verification vehicle for product level test.
Other features are listed as following, more detailed information can be found in public documentation:
- FP8 datatype support (Prototype): Add basic data type and FP8 Linear operator support based on emulation kernel.
- Kineto Profiling (Prototype): An extension of PyTorch* profiler for profiling operators on Intel® GPU devices.
- Fully Sharded Data Parallel (FSDP): Support new PyTorch* FSDP API which provides an industry-grade solution for large-scale model training.
- Asymmetric INT8 quantization: Support asymmetric quantization to align with stock PyTorch* and provide better accuracy in INT8.
CPU support has been merged in this release. CPU features and optimizations are equivalent to what has been released in Intel® Extension for PyTorch* v2.1.0+cpu release that was made publicly available in Oct 2023. For customers who would like to evaluate workloads on both GPU and CPU, they can use this package. For customers who are focusing on CPU only, we still recommend them to use Intel® Extension for PyTorch* v2.1.0+cpu release for smaller footprint, less dependencies and broader OS support.

Known Issues

Please refer to Known Issues webpage.

2.0.110+xpu

Intel® Extension for PyTorch* v2.0.110+xpu is the new Intel® Extension for PyTorch* release supports both CPU platforms and GPU platforms (Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series) based on PyTorch* 2.0.1. It extends PyTorch* 2.0.1 with up-to-date features and optimizations on xpu for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

Highlights

This release introduces specific XPU solution optimizations on Intel discrete GPUs which include Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series. Optimized operators and kernels are implemented and registered through PyTorch* dispatching mechanism for the xpu device. These operators and kernels are accelerated on Intel GPU hardware from the corresponding native vectorization and matrix calculation features. In graph mode, additional operator fusions are supported to reduce operator/kernel invocation overheads, and thus increase performance.

This release provides the following features:

oneDNN 3.3 API integration and adoption
Libtorch support
ARC support on Windows, WSL2 and Ubuntu (Prototype)
OOB models improvement
- More fusion patterns enabled for optimizing OOB models
CPU support is merged in this release:
- CPU features and optimizations are equivalent to what has been released in Intel® Extension for PyTorch* v2.0.100+cpu release that was made publicly available in May 2023. For customers who would like to evaluate workloads on both GPU and CPU, they can use this package. For customers who are focusing on CPU only, we still recommend them to use Intel® Extension for PyTorch* v2.0.100+cpu release for smaller footprint, less dependencies and broader OS support.

This release adds the following fusion patterns in PyTorch* JIT mode for Intel GPU:

add + softmax
add + view + softmax

Known Issues

Please refer to Known Issues webpage.

1.13.120+xpu

Intel® Extension for PyTorch* v1.13.120+xpu is the updated Intel® Extension for PyTorch* release supports both CPU platforms and GPU platforms (Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series) based on PyTorch* 1.13.1. It extends PyTorch* 1.13.1 with up-to-date features and optimizations on xpu for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

Highlights

This release introduces specific XPU solution optimizations on Intel discrete GPUs which include Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series. Optimized operators and kernels are implemented and registered through PyTorch* dispatching mechanism for the xpu device. These operators and kernels are accelerated on Intel GPU hardware from the corresponding native vectorization and matrix calculation features. In graph mode, additional operator fusions are supported to reduce operator/kernel invocation overheads, and thus increase performance.

This release provides the following features:

oneDNN 3.1 API integration and adoption
OOB models improvement
- More fusion patterns enabled for optimizing OOB models
CPU support is merged in this release:
- CPU features and optimizations are equivalent to what has been released in Intel® Extension for PyTorch* v1.13.100+cpu release that was made publicly available in Feb 2023. For customers who would like to evaluate workloads on both GPU and CPU, they can use this package. For customers who are focusing on CPU only, we still recommend them to use Intel® Extension for PyTorch* v1.13.100+cpu release for smaller footprint, less dependencies and broader OS support.

This release adds the following fusion patterns in PyTorch* JIT mode for Intel GPU:

Matmul + UnaryOp(abs, sqrt, square, exp, log, round, Log_Sigmoid, Hardswish, HardSigmoid, Pow, ELU, SiLU, hardtanh, Leaky_relu)
Conv2d + BinaryOp(add, sub, mul, div, max, min, eq, ne, ge, gt, le, lt)
Linear + BinaryOp(add, sub, mul, div, max, min)
Conv2d + mul + add
Conv2d + mul + add + relu
Conv2d + sigmoid + mul + add
Conv2d + sigmoid + mul + add + relu

Known Issues

Please refer to Known Issues webpage.

1.13.10+xpu

Intel® Extension for PyTorch* v1.13.10+xpu is the first Intel® Extension for PyTorch* release supports both CPU platforms and GPU platforms (Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series) based on PyTorch* 1.13. It extends PyTorch* 1.13 with up-to-date features and optimizations on xpu for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

Highlights

This release introduces specific XPU solution optimizations on Intel discrete GPUs which include Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series. Optimized operators and kernels are implemented and registered through PyTorch* dispatching mechanism for the xpu device. These operators and kernels are accelerated on Intel GPU hardware from the corresponding native vectorization and matrix calculation features. In graph mode, additional operator fusions are supported to reduce operator/kernel invocation overheads, and thus increase performance.

This release provides the following features:

Distributed Training on GPU:
- support of distributed training with DistributedDataParallel (DDP) on Intel GPU hardware
- support of distributed training with Horovod (prototype feature) on Intel GPU hardware
Automatic channels last format conversion on GPU:
- Automatic channels last format conversion is enabled. Models using torch.xpu.optimize API running on Intel® Data Center GPU Max Series will be converted to channels last memory format, while models running on Intel® Data Center GPU Flex Series will choose oneDNN block format.
CPU support is merged in this release:
- CPU features and optimizations are equivalent to what has been released in Intel® Extension for PyTorch* v1.13.0+cpu release that was made publicly available in Nov 2022. For customers who would like to evaluate workloads on both GPU and CPU, they can use this package. For customers who are focusing on CPU only, we still recommend them to use Intel® Extension for PyTorch* v1.13.0+cpu release for smaller footprint, less dependencies and broader OS support.

This release adds the following fusion patterns in PyTorch* JIT mode for Intel GPU:

Conv2D + UnaryOp(abs, sqrt, square, exp, log, round, GeLU, Log_Sigmoid, Hardswish, Mish, HardSigmoid, Tanh, Pow, ELU, hardtanh)
Linear + UnaryOp(abs, sqrt, square, exp, log, round, Log_Sigmoid, Hardswish, HardSigmoid, Pow, ELU, SiLU, hardtanh, Leaky_relu)

Known Issues

Please refer to Known Issues webpage.

1.10.200+gpu

Intel® Extension for PyTorch* v1.10.200+gpu extends PyTorch* 1.10 with up-to-date features and optimizations on XPU for an extra performance boost on Intel Graphics cards. XPU is a user visible device that is a counterpart of the well-known CPU and CUDA in the PyTorch* community. XPU represents an Intel-specific kernel and graph optimizations for various “concrete” devices. The XPU runtime will choose the actual device when executing AI workloads on the XPU device. The default selected device is Intel GPU. XPU kernels from Intel® Extension for PyTorch* are written in DPC++ that supports SYCL language and also a number of DPC++ extensions.

Highlights

This release introduces specific XPU solution optimizations on Intel® Data Center GPU Flex Series 170. Optimized operators and kernels are implemented and registered through PyTorch* dispatching mechanism for the XPU device. These operators and kernels are accelerated on Intel GPU hardware from the corresponding native vectorization and matrix calculation features. In graph mode, additional operator fusions are supported to reduce operator/kernel invocation overheads, and thus increase performance.

This release provides the following features:

Auto Mixed Precision (AMP)
- support of AMP with BFloat16 and Float16 optimization of GPU operators
Channels Last
- support of channels_last (NHWC) memory format for most key GPU operators
DPC++ Extension
- mechanism to create PyTorch* operators with custom DPC++ kernels running on the XPU device
Optimized Fusion
- support of SGD/AdamW fusion for both FP32 and BF16 precision

This release supports the following fusion patterns in PyTorch* JIT mode:

Conv2D + ReLU
Conv2D + Sum
Conv2D + Sum + ReLU
Pad + Conv2d
Conv2D + SiLu
Permute + Contiguous
Conv3D + ReLU
Conv3D + Sum
Conv3D + Sum + ReLU
Linear + ReLU
Linear + Sigmoid
Linear + Div(scalar)
Linear + GeLu
Linear + GeLu_
T + Addmm
T + Addmm + ReLu
T + Addmm + Sigmoid
T + Addmm + Dropout
T + Matmul
T + Matmul + Add
T + Matmul + Add + GeLu
T + Matmul + Add + Dropout
Transpose + Matmul
Transpose + Matmul + Div
Transpose + Matmul + Div + Add
MatMul + Add
MatMul + Div
Dequantize + PixelShuffle
Dequantize + PixelShuffle + Quantize
Mul + Add
Add + ReLU
Conv2D + Leaky_relu
Conv2D + Leaky_relu_
Conv2D + Sigmoid
Conv2D + Dequantize
Softplus + Tanh
Softplus + Tanh + Mul
Conv2D + Dequantize + Softplus + Tanh + Mul
Conv2D + Dequantize + Softplus + Tanh + Mul + Quantize
Conv2D + Dequantize + Softplus + Tanh + Mul + Quantize + Add

Known Issues

[FATAL ERROR] Kernel ‘XXX’ removed due to usage of FP64 instructions unsupported by the targeted hardware

FP64 is not natively supported by the Intel® Data Center GPU Flex Series platform. If you run any AI workload on that platform and receive this error message, it means a kernel requiring FP64 instructions is removed and not executed, hence the accuracy of the whole workload is wrong.
symbol undefined caused by _GLIBCXX_USE_CXX11_ABI
```
ImportError: undefined symbol: _ZNK5torch8autograd4Node4nameB5cxx11Ev
```
DPC++ does not support _GLIBCXX_USE_CXX11_ABI=0, Intel® Extension for PyTorch* is always compiled with _GLIBCXX_USE_CXX11_ABI=1. This symbol undefined issue appears when PyTorch* is compiled with _GLIBCXX_USE_CXX11_ABI=0. Update PyTorch* CMAKE file to set _GLIBCXX_USE_CXX11_ABI=1 and compile PyTorch* with particular compiler which supports _GLIBCXX_USE_CXX11_ABI=1. We recommend to use gcc version 9.4.0 on ubuntu 20.04.

Can’t find oneMKL library when build Intel® Extension for PyTorch* without oneMKL

/usr/bin/ld: cannot find -lmkl_sycl
/usr/bin/ld: cannot find -lmkl_intel_ilp64
/usr/bin/ld: cannot find -lmkl_core
/usr/bin/ld: cannot find -lmkl_tbb_thread
dpcpp: error: linker command failed with exit code 1 (use -v to see invocation)

When PyTorch* is built with oneMKL library and Intel® Extension for PyTorch* is built without oneMKL library, this linker issue may occur. Resolve it by setting:

export USE_ONEMKL=OFF
export MKL_DPCPP_ROOT=${PATH_To_Your_oneMKL}/__release_lnx/mkl

Then clean build Intel® Extension for PyTorch*.

undefined symbol: mkl_lapack_dspevd. Intel MKL FATAL ERROR: cannot load libmkl_vml_avx512.so.2 or libmkl_vml_def.so.2

This issue may occur when Intel® Extension for PyTorch* is built with oneMKL library and PyTorch* is not build with any MKL library. The oneMKL kernel may run into CPU backend incorrectly and trigger this issue. Resolve it by installing MKL library from conda:
```
conda install mkl
conda install mkl-include
```
then clean build PyTorch*.
OSError: libmkl_intel_lp64.so.1: cannot open shared object file: No such file or directory

Wrong MKL library is used when multiple MKL libraries exist in system. Preload oneMKL by:
```
export LD_PRELOAD=${MKL_DPCPP_ROOT}/lib/intel64/libmkl_intel_lp64.so.1:${MKL_DPCPP_ROOT}/lib/intel64/libmkl_intel_ilp64.so.1:${MKL_DPCPP_ROOT}/lib/intel64/libmkl_sequential.so.1:${MKL_DPCPP_ROOT}/lib/intel64/libmkl_core.so.1:${MKL_DPCPP_ROOT}/lib/intel64/libmkl_sycl.so.1
```
If you continue seeing similar issues for other shared object files, add the corresponding files under ${MKL_DPCPP_ROOT}/lib/intel64/ by LD_PRELOAD. Note that the suffix of the libraries may change (e.g. from .1 to .2), if more than one oneMKL library is installed on the system.