Fix pt2e quant ptq x86 title issue

Author: leslie-fang-intel

prototype_source/pt2e_quant_ptq_x86_inductor.rst

@@ -4,14 +4,14 @@ PyTorch 2 Export Post Training Quantization with X86 Backend through Inductor
 **Author**: `Leslie Fang <https://github.com/leslie-fang-intel>`_, `Weiwen Xia <https://github.com/Xia-Weiwen>`_, `Jiong Gong <https://github.com/jgong5>`_, `Jerry Zhang <https://github.com/jerryzh168>`_
 
 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
-^^^^^^^^^^^^^^
+------------
 
 This tutorial introduces the steps for utilizing the PyTorch 2 Export Quantization flow to generate a quantized model customized
 for the x86 inductor backend and explains how to lower the quantized model into the inductor.
@@ -63,8 +63,8 @@ further boost the models' performance by leveraging the
 
 Now, we will walk you through a step-by-step tutorial for how to use it with `torchvision resnet18 model <https://download.pytorch.org/models/resnet18-f37072fd.pth>`_.
 
-1. Capture FX Graph
----------------------
+Capture FX Graph
+----------------
 
 We will start by performing the necessary imports, capturing the FX Graph from the eager module.
 
@@ -110,8 +110,8 @@ We will start by performing the necessary imports, capturing the FX Graph from t
 
 Next, we will have the FX Module to be quantized.
 
-2. Apply Quantization
-----------------------------
+Apply Quantization
+------------------
 
 After we capture the FX Module to be quantized, we will import the Backend Quantizer for X86 CPU and configure how to
 quantize the model.
@@ -159,8 +159,8 @@ Finally, we will convert the calibrated Model to a quantized Model. ``convert_pt
 After these steps, we finished running the quantization flow and we will get the quantized model.
 
 
-3. Lower into Inductor
-------------------------
+Lower into Inductor
+-------------------
 
 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
@@ -222,8 +222,8 @@ With PyTorch 2.1 release, all CNN models from TorchBench test suite have been me
 to `this document <https://dev-discuss.pytorch.org/t/torchinductor-update-6-cpu-backend-performance-update-and-new-features-in-pytorch-2-1/1514#int8-inference-with-post-training-static-quantization-3>`_
 for detail benchmark number.
 
-4. Conclusion
----------------
+Conclusion
+----------
 
 With this tutorial, we introduce how to use Inductor with X86 CPU in PyTorch 2 Quantization. Users can learn about
 how to use ``X86InductorQuantizer`` to quantize a model and lower it into the inductor with X86 CPU devices.