2.1.30 docs update (#2849)

xpu/2.1.30+xpu/_sources/tutorials/contribution.md.txt

@@ -16,7 +16,7 @@ Once you implement and test your feature or bug-fix, submit a Pull Request to ht
 
 ## Developing Intel® Extension for PyTorch\* on XPU
 
-A full set of instructions on installing Intel® Extension for PyTorch\* from source is in the [Installation document](../../../index.html#installation?platform=gpu&version=v2.1.30%2Bxpu).
+A full set of instructions on installing Intel® Extension for PyTorch\* from source is in the [Installation document](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu&version=v2.1.30%2bxpu).
 
 To develop on your machine, here are some tips:
 

xpu/2.1.30+xpu/_sources/tutorials/features/DDP.md.txt

@@ -3,7 +3,7 @@ DistributedDataParallel (DDP)
 
 ## Introduction
 
-`DistributedDataParallel (DDP)` is a PyTorch\* module that implements multi-process data parallelism across multiple GPUs and machines. With DDP, the model is replicated on every process, and each model replica is fed a different set of input data samples. DDP enables overlapping between gradient communication and gradient computations to speed up training. Please refer to [DDP Tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for an introduction to DDP.
+`DistributedDataParallel (DDP)` is a PyTorch\* module that implements multi-process data parallelism across multiple GPUs and machines. With DDP, the model is replicated on every process, and each model replica is fed a different set of input data samples. Please refer to [DDP Tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for an introduction to DDP.
 
 The PyTorch `Collective Communication (c10d)` library supports communication across processes. To run DDP on GPU, we use Intel® oneCCL Bindings for Pytorch\* (formerly known as torch-ccl) to implement the PyTorch c10d ProcessGroup API (https://github.com/intel/torch-ccl). It holds PyTorch bindings maintained by Intel for the Intel® oneAPI Collective Communications Library\* (oneCCL), a library for efficient distributed deep learning training implementing such collectives as `allreduce`, `allgather`, and `alltoall`. Refer to [oneCCL Github page](https://github.com/oneapi-src/oneCCL) for more information about oneCCL.
 
@@ -14,63 +14,25 @@ To use PyTorch DDP on GPU, install Intel® oneCCL Bindings for Pytorch\* as desc
 ### Install PyTorch and Intel® Extension for PyTorch\*
 
 Make sure you have installed PyTorch and Intel® Extension for PyTorch\* successfully.
-For more detailed information, check [installation guide](../../../../index.html#installation).
+For more detailed information, check [Installation Guide](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu).
 
 ### Install Intel® oneCCL Bindings for Pytorch\*
 
-#### Install from source:
+#### [Recommended] Install from prebuilt wheels
 
-Installation for CPU:
+1. Install oneCCL package:
 
-```bash
-git clone https://github.com/intel/torch-ccl.git -b v2.1.0+cpu
-cd torch-ccl
-git submodule sync
-git submodule update --init --recursive
-python setup.py install
-```
-
-Installation for GPU:
-
-- Clone the `oneccl_bindings_for_pytorch`
-
-```bash
-git clone https://github.com/intel/torch-ccl.git -b v2.1.300+xpu
-cd torch-ccl
-git submodule sync 
-git submodule update --init --recursive
-```
-
-- Install `oneccl_bindings_for_pytorch`
-
-Option 1: build with oneCCL from third party
-
-```bash
-COMPUTE_BACKEND=dpcpp python setup.py install
-```
-
-Option 2: build without oneCCL and use oneCCL in system (Recommend)
-
-We recommend to use apt/yum/dnf to install the oneCCL package. Refer to [Base Toolkit Installation](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for adding the APT/YUM/DNF key and sources for first-time users.
+We recommend using apt/yum/dnf to install the oneCCL package. Refer to [Base Toolkit Installation](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for adding the APT/YUM/DNF key and sources for first-time users.
 
 Reference commands:
 
 ```bash
-sudo apt install intel-oneapi-ccl-devel=2021.11.1-6
-sudo yum install intel-oneapi-ccl-devel=2021.11.1-6
-sudo dnf install intel-oneapi-ccl-devel=2021.11.1-6
+sudo apt install intel-oneapi-ccl-devel=2021.12.0-309
+sudo yum install intel-oneapi-ccl-devel=2021.12.0-309
+sudo dnf install intel-oneapi-ccl-devel=2021.12.0-309
 ```
 
-Compile with commands below.
-
-```bash
-export INTELONEAPIROOT=/opt/intel/oneapi
-USE_SYSTEM_ONECCL=ON COMPUTE_BACKEND=dpcpp python setup.py install
-```
-
-#### Install from prebuilt wheel:
-
-Prebuilt wheel files for CPU, GPU with generic Python\* and GPU with Intel® Distribution for Python\* are released in separate repositories.
+2. Install `oneccl_bindings_for_pytorch`
 
 ```
 # Generic Python* for CPU
@@ -85,25 +47,19 @@ Installation from either repository shares the command below. Replace the place
 python -m pip install oneccl_bind_pt --extra-index-url <REPO_URL>
 ```
 
-### Runtime Dynamic Linking
 
-- If torch-ccl is built with oneCCL from third party or installed from prebuilt wheel:
-Dynamic link oneCCL and Intel MPI libraries: 
+#### Install from source
 
-```bash
-source $(python -c "import oneccl_bindings_for_pytorch as torch_ccl;print(torch_ccl.cwd)")/env/setvars.sh
-```
+Refer to [Installation Guide](https://github.com/intel/torch-ccl/tree/ccl_torch2.1.300+xpu?tab=readme-ov-file#install-from-source) to install Intel® oneCCL Bindings for Pytorch\* from source.
 
-Dynamic link oneCCL only (not including Intel MPI):
+### Runtime Dynamic Linking
 
-```bash
-source $(python -c "import oneccl_bindings_for_pytorch as torch_ccl;print(torch_ccl.cwd)")/env/vars.sh 
-```
 
-- If torch-ccl is built without oneCCL and use oneCCL in system, dynamic link oneCCl from oneAPI basekit:
+- dynamic link oneCCl from oneAPI basekit:
 
 ```bash
 source <ONEAPI_ROOT>/ccl/latest/env/vars.sh
+source <ONEAPI_ROOT>/mpi/latest/env/vars.sh
 ```
 
 Note: Make sure you have installed [basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html#base-kit) when using Intel® oneCCL Bindings for Pytorch\* on Intel® GPUs. If the basekit is installed with a package manager, <ONEAPI_ROOT> is `/opt/intel/oneapi`.
@@ -148,6 +104,7 @@ Dynamic link oneCCL and Intel MPI libraries:
 source $(python -c "import oneccl_bindings_for_pytorch as torch_ccl;print(torch_ccl.cwd)")/env/setvars.sh
 # Or
 source <ONEAPI_ROOT>/ccl/latest/env/vars.sh
+source <ONEAPI_ROOT>/mpi/latest/env/vars.sh
 ```
 
 `Example_DDP.py`

xpu/2.1.30+xpu/_sources/tutorials/features/ipex_log.md.txt

@@ -37,8 +37,8 @@ All the usage are defined in `utils/LogUtils.h`. Currently Intel® Extension for
 You can use `IPEX_XXX_LOG`, XXX represents the log level as mentioned above. There are four parameters defined for simple log:
 - Log component, representing which part of Intel® Extension for PyTorch\* does this log belong to.
 - Log sub component, input an empty string("") for general usages. For `SYNGRAPH` you can add any log sub componment.
-- Log message template format string.
-- Log name.
+- Log message template format string, same as fmt_string in lib fmt, `{}` is used as a place holder for format args .
+- Log args for template format string, args numbers should be aligned with size of `{}`s.
 
 Below is an example for using simple log inside abs kernel:
 
@@ -48,14 +48,14 @@ IPEX_INFO_LOG("OPS", "", "Add a log for inside ops {}", "abs");
 
 ```
 ### Event Log
-Event log is used for recording a whole event, such as an operator calculation. The whole event is identified by an unique `event_id`. You can also mark each step by using `step_id`. Use `IPEX_XXX_EVENT_END()` to complete the logging of the whole event.
+Event log is used for recording a whole event, such as an operator calculation. The whole event is identified by an unique `event_id`. You can also mark each step by using `step_id`. Use `IPEX_XXX_EVENT_END()` to complete the logging of the whole event. `XXX` represents the log level mentioned above. It will be used as the log level for all logs within one single log event.
 
 Below is an example for using event log:
 
 ```c++
-IPEX_EVENT_END("OPS", "", "record_avg_pool", "start", "Here record the time start with arg:{}", arg);
+IPEX_EVENT_LOG("OPS", "", "record_avg_pool", "start", "Here record the time start with arg:{}", arg);
 prepare_data();
-IPEX_EVENT_END("OPS", "", "record_avg_pool", "data_prepare_finish", "Here record the data_prepare_finish with arg:{}", arg);
+IPEX_EVENT_LOG("OPS", "", "record_avg_pool", "data_prepare_finish", "Here record the data_prepare_finish with arg:{}", arg);
 avg_pool();
 IPEX_INFO_EVENT_END("OPS", "", "record_avg_pool", "finish conv", "Here record the end");
 ```

xpu/2.1.30+xpu/_sources/tutorials/features/torch_compile_gpu.md.txt

@@ -14,7 +14,7 @@ Intel® Extension for PyTorch\* now empowers users to seamlessly harness graph c
 - `intel_extension_for_pytorch` : > v2.1.10
 - `triton` : [v2.1.0](https://github.com/intel/intel-xpu-backend-for-triton/releases/tag/v2.1.0) with Intel® XPU Backend for Triton* backend enabled.
 
-Follow [Intel® Extension for PyTorch\* Installation](https://intel.github.io/intel-extension-for-pytorch/xpu/2.1.30+xpu/tutorials/installation.html) to install `torch` and `intel_extension_for_pytorch` firstly.
+Follow [Intel® Extension for PyTorch\* Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu&version=v2.1.30%2bxpu) to install `torch` and `intel_extension_for_pytorch` firstly.
 
 Then install [Intel® XPU Backend for Triton\* backend](https://github.com/intel/intel-xpu-backend-for-triton) for `triton` package. You may install it via prebuilt wheel package or build it from the source. We recommend installing via prebuilt package:
 

xpu/2.1.30+xpu/_sources/tutorials/getting_started.md.txt

@@ -1,6 +1,6 @@
 # Quick Start
 
-The following instructions assume you have installed the Intel® Extension for PyTorch\*. For installation instructions, refer to [Installation](../../../index.html#installation?platform=gpu&version=v2.1.30%2Bxpu).
+The following instructions assume you have installed the Intel® Extension for PyTorch\*. For installation instructions, refer to [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu&version=v2.1.30%2bxpu).
 
 To start using the Intel® Extension for PyTorch\* in your code, you need to make the following changes:
 

xpu/2.1.30+xpu/_sources/tutorials/installation.rst.txt

@@ -1,7 +1,7 @@
 Installation
 ============
 
-Select your preferences and follow the installation instructions provided on the `Installation page <../../../index.html#installation?platform=gpu&version=v2.1.30%2Bxpu>`_.
+Select your preferences and follow the installation instructions provided on the `Installation page <https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu&version=v2.1.30%2bxpu>`_.
 
 After successful installation, refer to the `Quick Start <getting_started.md>`_ and `Examples <examples.md>`_ sections to start using the extension in your code.
 

xpu/2.1.30+xpu/_sources/tutorials/introduction.rst.txt

@@ -9,7 +9,7 @@ For the detailed list of supported features and usage instructions, refer to `Fe
 
 Get Started
 -----------
-- `Installation <../../../index.html#installation?platform=gpu&version=v2.1.30%2Bxpu>`_
+- `Installation <https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu&version=v2.1.30%2bxpu>`_
 - `Quick Start <getting_started.md>`_
 - `Examples <examples.md>`_
 

xpu/2.1.30+xpu/genindex.html

@@ -350,7 +350,7 @@ <h2 id="X">X</h2>
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xpu/2.1.30+xpu/index.html

@@ -175,7 +175,7 @@ <h2>Support<a class="headerlink" href="#support" title="Permalink to this headin
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     provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   <jinja2.runtime.BlockReference object at 0x7f5a207313d0> 
+   <jinja2.runtime.BlockReference object at 0x7f644f1f47c0> 
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xpu/2.1.30+xpu/search.html

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xpu/2.1.30+xpu/tutorials/api_doc.html

@@ -1181,7 +1181,7 @@ <h2>C++ API<a class="headerlink" href="#c-api" title="Permalink to this heading"
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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     provided by <a href="https://readthedocs.org">Read the Docs</a>.
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xpu/2.1.30+xpu/tutorials/blogs_publications.html

@@ -162,7 +162,7 @@ <h1>Blogs &amp; Publications<a class="headerlink" href="#blogs-publications" tit
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