Merge branch 'main' into beginner-dcgan-face-typo-fix

Author: Soooda

README.md

@@ -24,8 +24,8 @@ Here is how you can create a new tutorial (for a detailed description, see [CONT
 
 1. Create a Python file. If you want it executed while inserted into documentation, save the file with the suffix `tutorial` so that the file name is `your_tutorial.py`.
 2. Put it in one of the `beginner_source`, `intermediate_source`, `advanced_source` directory based on the level of difficulty. If it is a recipe, add it to `recipes_source`. For tutorials demonstrating unstable prototype features, add to the `prototype_source`.
-2. For Tutorials (except if it is a prototype feature), include it in the `toctree` directive and create a `customcarditem` in [index.rst](./index.rst).
-3. For Tutorials (except if it is a prototype feature), create a thumbnail in the [index.rst file](https://github.com/pytorch/tutorials/blob/main/index.rst) using a command like `.. customcarditem:: beginner/your_tutorial.html`. For Recipes, create a thumbnail in the [recipes_index.rst](https://github.com/pytorch/tutorials/blob/main/recipes_source/recipes_index.rst)
+3. For Tutorials (except if it is a prototype feature), include it in the `toctree` directive and create a `customcarditem` in [index.rst](./index.rst).
+4. For Tutorials (except if it is a prototype feature), create a thumbnail in the [index.rst file](https://github.com/pytorch/tutorials/blob/main/index.rst) using a command like `.. customcarditem:: beginner/your_tutorial.html`. For Recipes, create a thumbnail in the [recipes_index.rst](https://github.com/pytorch/tutorials/blob/main/recipes_source/recipes_index.rst)
 
 If you are starting off with a Jupyter notebook, you can use [this script](https://gist.github.com/chsasank/7218ca16f8d022e02a9c0deb94a310fe) to convert the notebook to Python file. After conversion and addition to the project, please make sure that section headings and other things are in logical order.
 

beginner_source/introyt/modelsyt_tutorial.py

@@ -342,7 +342,7 @@ def forward(self, sentence):
 # the 6x6 input.
 # 
 # **Normalization layers** re-center and normalize the output of one layer
-# before feeding it to another. Centering the and scaling the intermediate
+# before feeding it to another. Centering and scaling the intermediate
 # tensors has a number of beneficial effects, such as letting you use
 # higher learning rates without exploding/vanishing gradients.
 # 

prototype_source/pt2e_quant_ptq_x86_inductor.rst

@@ -8,6 +8,7 @@ Prerequisites
 
 -  `PyTorch 2 Export Post Training Quantization <https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html>`_
 -  `TorchInductor and torch.compile concepts in PyTorch <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
+-  `Inductor C++ Wrapper concepts <https://pytorch.org/tutorials/prototype/inductor_cpp_wrapper_tutorial.html>`_
 
 Introduction
 ^^^^^^^^^^^^^^
@@ -161,7 +162,18 @@ After these steps, we finished running the quantization flow and we will get the
 3. Lower into Inductor
 ------------------------
 
-After we get the quantized model, we will further lower it to the inductor backend.
+After we get the quantized model, we will further lower it to the inductor backend. The default Inductor wrapper
+generates Python code to invoke both generated kernels and external kernels. Additionally, Inductor supports
+C++ wrapper that generates pure C++ code. This allows seamless integration of the generated and external kernels,
+effectively reducing Python overhead. In the future, leveraging the C++ wrapper, we can extend the capability
+to achieve pure C++ deployment. For more comprehensive details about C++ Wrapper in general, please refer to the
+dedicated tutorial on `Inductor C++ Wrapper Tutorial <https://pytorch.org/tutorials/prototype/inductor_cpp_wrapper_tutorial.html>`_.
+
+::
+
+    # Optional: using the C++ wrapper instead of default Python wrapper
+    import torch._inductor.config as config
+    config.cpp_wrapper = True
 
 ::