Merge branch 'main' into dcp_async_save

Author: LucasLLC

advanced_source/cpp_export.rst

@@ -203,7 +203,7 @@ minimal ``CMakeLists.txt`` to build it could look as simple as:
 
   add_executable(example-app example-app.cpp)
   target_link_libraries(example-app "${TORCH_LIBRARIES}")
-  set_property(TARGET example-app PROPERTY CXX_STANDARD 14)
+  set_property(TARGET example-app PROPERTY CXX_STANDARD 17)
 
 The last thing we need to build the example application is the LibTorch
 distribution. You can always grab the latest stable release from the `download

advanced_source/super_resolution_with_onnxruntime.py

@@ -9,7 +9,7 @@
     * ``torch.onnx.export`` is based on TorchScript backend and has been available since PyTorch 1.2.0.
 
 In this tutorial, we describe how to convert a model defined
-in PyTorch into the ONNX format using the TorchScript ``torch.onnx.export` ONNX exporter.
+in PyTorch into the ONNX format using the TorchScript ``torch.onnx.export`` ONNX exporter.
 
 The exported model will be executed with ONNX Runtime.
 ONNX Runtime is a performance-focused engine for ONNX models,

intermediate_source/inductor_debug_cpu.py

@@ -87,9 +87,9 @@ def neg1(x):
 # +-----------------------------+----------------------------------------------------------------+
 # | ``fx_graph_transformed.py`` | Transformed FX graph, after pattern match                      |
 # +-----------------------------+----------------------------------------------------------------+
-# | ``ir_post_fusion.txt``      | Inductor IR before fusion                                      |
+# | ``ir_pre_fusion.txt``       | Inductor IR before fusion                                      |
 # +-----------------------------+----------------------------------------------------------------+
-# | ``ir_pre_fusion.txt``       | Inductor IR after fusion                                       |
+# | ``ir_post_fusion.txt``      | Inductor IR after fusion                                       |
 # +-----------------------------+----------------------------------------------------------------+
 # | ``output_code.py``          | Generated Python code for graph, with C++/Triton kernels       |
 # +-----------------------------+----------------------------------------------------------------+

recipes_source/distributed_device_mesh.rst

@@ -156,4 +156,4 @@ they can be used to describe the layout of devices across the cluster.
 For more information, please see the following:
 
 - `2D parallel combining Tensor/Sequance Parallel with FSDP <https://github.com/pytorch/examples/blob/main/distributed/tensor_parallelism/fsdp_tp_example.py>`__
-- `Composable PyTorch Distributed with PT2 <chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://static.sched.com/hosted_files/pytorch2023/d1/%5BPTC%2023%5D%20Composable%20PyTorch%20Distributed%20with%20PT2.pdf>`__
+- `Composable PyTorch Distributed with PT2 <https://static.sched.com/hosted_files/pytorch2023/d1/%5BPTC%2023%5D%20Composable%20PyTorch%20Distributed%20with%20PT2.pdf>`__