[doc] Format change (#1735)

* format change for doc generation

* remove old library name in error info

* remove model zoo info

Author: tye1

docs/tutorials/examples.md

@@ -592,7 +592,3 @@ with torch.no_grad():
 ## C++
 
 Intel® Extension for PyTorch\* provides its C++ dynamic library to allow users to implement custom DPC++ kernels to run on the XPU device. Refer to the [DPC++ extension](./features/DPC++_Extension.md) for the details.
-
-## Model Zoo
-
-Use cases that are already optimized by Intel engineers are available at [Model Zoo for Intel® Architecture](https://github.com/IntelAI/models). You can get performance benefits out-of-box by simply running scripts in the Model Zoo.

docs/tutorials/releases.md

@@ -13,7 +13,7 @@ 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
+  - 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
@@ -34,7 +34,7 @@ This release supports the following fusion patterns in PyTorch\* JIT mode:
 - Linear + Sigmoid
 - Linear + Div(scalar)
 - Linear + GeLu
-- Linear + GeLu_
+- Linear + GeLu\_
 - T + Addmm
 - T + Addmm + ReLu
 - T + Addmm + Sigmoid
@@ -52,8 +52,8 @@ This release supports the following fusion patterns in PyTorch\* JIT mode:
 - Dequantize + PixelShuffle + Quantize
 - Mul + Add
 - Add + ReLU
-- Conv2D + Leaky_relu
-- Conv2D + Leaky_relu_
+- Conv2D + Leaky\_relu
+- Conv2D + Leaky\_relu\_
 - Conv2D + Sigmoid
 - Conv2D + Dequantize
 - Softplus + Tanh
@@ -64,61 +64,55 @@ This release supports the following fusion patterns in PyTorch\* JIT mode:
 
 ### Known Issues
 
-#### [CRITICAL ERROR] Kernel 'XXX' removed due to usage of FP64 instructions unsupported by the targeted hardware
+- [CRITICAL 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](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/data-center-gpu/flex-series/overview.html) 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.
+    FP64 is not natively supported by the [Intel® Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/data-center-gpu/flex-series/overview.html) 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
+- symbol undefined caused by \_GLIBCXX\_USE\_CXX11\_ABI
 
-   Error info: <br>
-
-  ```bash
-  File "/root/.local/lib/python3.9/site-packages/ipex/__init__.py", line 4, in <module>
-      from . import _C
-  ImportError: /root/.local/lib/python3.9/site-packages/ipex/lib/libipex_gpu_core.so: 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. <br>
-
-#### Can't find oneMKL library when build Intel® Extension for PyTorch\* without oneMKL
+    ```bash
+    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.
 
-    Error info: <br>
+- Can't find oneMKL library when build Intel® Extension for PyTorch\* without oneMKL
 
     ```bash
-    /usr/bin/ld: cannot find -lmkl_sycl <br>
-    /usr/bin/ld: cannot find -lmkl_intel_ilp64 <br>
-    /usr/bin/ld: cannot find -lmkl_core <br>
-    /usr/bin/ld: cannot find -lmkl_tbb_thread <br>
-    dpcpp: error: linker command failed with exit code 1 (use -v to see invocation) <br>
+    /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:
-
+    
     ```bash
     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
+- 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:
-
+    
     ```bash
     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
+- 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:
-
+    
     ```bash
     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.
+    
+    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.