Quantization doc cleanups (#767)

Summary:

.

Differential Revision: D50107717

Author: jerryzh168

README.md

@@ -42,7 +42,6 @@ Compared to the legacy Lite Interpreter, there are some major benefits:
 - [Exporting to ExecuTorch](/docs/website/docs/tutorials/exporting_to_executorch.md)
     - [EXIR Spec](/docs/website/docs/ir_spec/00_exir.md)
     - [Exporting manual](/docs/website/docs/export/00_export_manual.md)
-    - [Quantization](/docs/website/docs/tutorials/quantization_flow.md)
     - [Delegate to a backend](/docs/website/docs/tutorials/backend_delegate.md)
     - [Profiling](/docs/website/docs/tutorials/profiling.md)
 - [ExecuTorch Google Colab](https://colab.research.google.com/drive/1m8iU4y7CRVelnnolK3ThS2l2gBo7QnAP#scrollTo=1o2t3LlYJQY5)

docs/source/index.rst

@@ -153,7 +153,7 @@ Topics in this section will help you get started with ExecuTorch.
    :caption: Quantization
    :hidden:
 
-   quantization-custom-quantization
+   quantization
 
 .. toctree::
    :glob:

docs/source/quantization-custom-quantization.md

@@ -0,0 +1,2 @@
+# Quantization
+Quantization is specific to backend, and each backend will implement their own ``Quantizer`` that has its own APIs for modeling users to configure quantization. For an example of how XNNPACK quantization flow looks like and how to implement your own ``Quantizer``, please take a look at section ``Performing Quantization`` in [this doc](./tutorials_source/export-to-executorch-tutorial.md).

docs/source/quantization.md

@@ -229,7 +229,7 @@ def f(x, y):
 #
 # Compared to
 # `FX Graph Mode Quantization <https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_static.html>`__,
-# we will need to call two new APIs: ``prepare_pt2e`` and ``compare_pt2e``
+# we will need to call two new APIs: ``prepare_pt2e`` and ``convert_pt2e``
 # instead of ``prepare_fx`` and ``convert_fx``. It differs in that
 # ``prepare_pt2e`` takes a backend-specific ``Quantizer`` as an argument, which
 # will annotate the nodes in the graph with information needed to quantize the
@@ -258,8 +258,7 @@ def f(x, y):
 ######################################################################
 # More information on how to quantize a model, and how a backend can implement a
 # ``Quantizer`` can be found
-# `here <https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html>`__ .
-
+# `here <https://pytorch.org/docs/main/quantization.html#prototype-pytorch-2-export-quantization>`__.
 ######################################################################
 # Lowering to Edge Dialect
 # ------------------------
@@ -648,7 +647,7 @@ def forward(self, x):
 # ^^^^^^^^^^^^^^^
 #
 # - `torch.export Documentation <https://pytorch.org/docs/2.1/export.html>`__
-# - `Quantization Tutorial <https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html>`__
+# - `Quantization Documentation <https://pytorch.org/docs/main/quantization.html#prototype-pytorch-2-export-quantization>`__
 # - `IR Spec <../ir-exir.html>`__
 # - `Writing Compiler Passes + Partitioner Documentation <../compiler-custom-compiler-passes.html>`__
 # - `Backend Delegation Documentation <../compiler-delegate-and-partitioner.html>`__