Merge branch 'main' into test_mosaic_tutorial_2.11

Author: sekyondaMeta

intermediate_source/compiled_autograd_tutorial.rst

@@ -16,7 +16,7 @@ Compiled Autograd: Capturing a larger backward graph for ``torch.compile``
 
        * PyTorch 2.4
        * Complete the `Introduction to torch.compile <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
-       * Read through the TorchDynamo and AOTAutograd sections of `Get Started with PyTorch 2.x <https://pytorch.org/get-started/pytorch-2.0/>`_
+       * Read through the TorchDynamo and AOTAutograd sections of `Get Started with PyTorch 2.x <https://pytorch.org/get-started/pytorch-2-x/>`_
 
 Overview
 --------

intermediate_source/pinmem_nonblock.py

@@ -127,7 +127,7 @@
 #   1. The device must have at least one free DMA (Direct Memory Access) engine. Modern GPU architectures such as Volterra,
 #      Tesla, or H100 devices have more than one DMA engine.
 #
-#   2. The transfer must be done on a separate, non-default cuda stream. In PyTorch, cuda streams can be handles using
+#   2. The transfer must be done on a separate, non-default cuda stream. In PyTorch, cuda streams can be handled using
 #      :class:`~torch.cuda.Stream`.
 #
 #   3. The source data must be in pinned memory.

recipes_source/intel_neural_compressor_for_pytorch.rst

@@ -17,7 +17,7 @@ Features
 
 - **Accuracy-driven Tuning:** Intel® Neural Compressor supports accuracy-driven automatic tuning process, provides ``autotune`` API for user usage.
 
-- **Kinds of Quantization:** Intel® Neural Compressor supports a variety of quantization methods, including classic INT8 quantization, weight-only quantization and the popular FP8 quantization. Neural compressor also provides the latest research in simulation work, such as MX data type emulation quantization. For more details, please refer to `Supported Matrix <https://github.com/intel/neural-compressor/blob/master/docs/source/3x/PyTorch.md#supported-matrix>`_.
+- **Kinds of Quantization:** Intel® Neural Compressor supports a variety of quantization methods, including classic INT8 quantization, weight-only quantization and the popular FP8 quantization. Neural compressor also provides the latest research in simulation work, such as MX data type emulation quantization. For more details, please refer to `Supported Matrix <https://github.com/intel/neural-compressor/blob/master/docs/source/PyTorch.md#supported-matrix>`_.
 
 Getting Started
 ---------------
@@ -158,4 +158,4 @@ To leverage accuracy-driven automatic tuning, a specified tuning space is necess
 Tutorials
 ---------
 
-More detailed tutorials are available in the official Intel® Neural Compressor `doc <https://intel.github.io/neural-compressor/latest/docs/source/Welcome.html>`_.
+More detailed tutorials are available in the official Intel® Neural Compressor `doc <https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html>`_.