Merge branch 'main' into master

advanced_source/extend_dispatcher.rst

@@ -17,7 +17,7 @@ to `register a dispatched operator in C++ <dispatcher>`_ and how to write a
 What's a new backend?
 ---------------------
 
-Adding a new backend to PyTorch requires a lot of developement and maintainence from backend extenders.
+Adding a new backend to PyTorch requires a lot of development and maintenance from backend extenders.
 Before adding a new backend, let's first consider a few common use cases and recommended solutions for them:
 
 * If you have new algorithms for an existing PyTorch operator, send a PR to PyTorch.
@@ -30,7 +30,7 @@ Before adding a new backend, let's first consider a few common use cases and rec
 
 In this tutorial we'll mainly focus on adding a new out-of-tree device below.  Adding out-of-tree support
 for a different tensor layout might share many common steps with devices, but we haven't seen an example of
-such integrations yet so it might require addtional work from PyTorch to support it.
+such integrations yet so it might require additional work from PyTorch to support it.
 
 Get a dispatch key for your backend
 -----------------------------------
@@ -67,12 +67,12 @@ To create a Tensor on ``PrivateUse1`` backend, you need to set dispatch key in `
 Note that ``TensorImpl`` class above assumes your Tensor is backed by a storage like CPU/CUDA. We also
 provide ``OpaqueTensorImpl`` for backends without a storage. And you might need to tweak/override certain
 methods to fit your customized hardware.
-One example in pytorch repo is `Vulkan TensorImpl <https://github.com/pytorch/pytorch/blob/1.7/aten/src/ATen/native/vulkan/VulkanOpaqueTensorImpl.h>`_.
+One example in pytorch repo is `Vulkan TensorImpl <https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/vulkan/VulkanOpaqueTensorImpl.h>`_.
 
 
 .. note::
    Once the prototype is done and you plan to do regular releases for your backend extension,  please feel free to
-   submit a PR to ``pytorch/pytorch`` to reserve a dedicated dispath key for your backend.
+   submit a PR to ``pytorch/pytorch`` to reserve a dedicated dispatch key for your backend.
 
 
 Get the full list of PyTorch operators
@@ -361,7 +361,7 @@ actively working on might improve the experience in the future:
 
 * Improve test coverage of generic testing framework.
 * Improve ``Math`` kernel coverage and more comprehensive tests to make sure ``Math``
-  kernel bahavior matches other backends like ``CPU/CUDA``.
+  kernel behavior matches other backends like ``CPU/CUDA``.
 * Refactor ``RegistrationDeclarations.h`` to carry the minimal information and reuse
   PyTorch's codegen as much as possible.
 * Support a backend fallback kernel to automatic convert inputs to CPU and convert the

beginner_source/dist_overview.rst

@@ -70,7 +70,7 @@ When deciding what parallelism techniques to choose for your model, use these co
 #. Use `DistributedDataParallel (DDP) <https://pytorch.org/docs/stable/notes/ddp.html>`__,
    if your model fits in a single GPU but you want to easily scale up training using multiple GPUs.
 
-   * Use `torchrun <https://pytorch.org/docs/stable/elastic/run.html>`__, to launch multiple pytorch processes if you are you using more than one node.
+   * Use `torchrun <https://pytorch.org/docs/stable/elastic/run.html>`__, to launch multiple pytorch processes if you are using more than one node.
 
    * See also: `Getting Started with Distributed Data Parallel <../intermediate/ddp_tutorial.html>`__
 

prototype_source/flight_recorder_tutorial.rst

@@ -46,13 +46,15 @@ Flight Recorder consists of two core parts:
 
 Enabling Flight Recorder
 ------------------------
-There are two required environment variables to get the initial version of Flight Recorder working.
+There are three required environment variables to get the initial version of Flight Recorder working.
 
 - ``TORCH_NCCL_TRACE_BUFFER_SIZE = (0, N)``: Setting ``N`` to a positive number enables collection.
   ``N`` represents the number of entries that will be kept internally in a circular buffer.
-  We recommended to set this value at *2000*.
+  We recommended to set this value at *2000*. The default value is ``2000``.
 - ``TORCH_NCCL_DUMP_ON_TIMEOUT = (true, false)``: Setting this to ``true`` will write out diagnostic files to disk on job timeout.
-  If enabled, there will be one file per rank output in the job's running directory.
+  If enabled, there will be one file per rank output in the job's running directory. The default value is ``false``.
+- ``TORCH_NCCL_DEBUG_INFO_TEMP_FILE``: Setting the path where the flight recorder will be dumped with file prefix. One file per
+  rank. The default value is ``/tmp/nccl_trace_rank_``.
 
 **Optional settings:**
 
@@ -71,6 +73,10 @@ Additional Settings
 
      ``fast`` is a new experimental mode that is shown to be much faster than the traditional ``addr2line``.
      Use this setting in conjunction with ``TORCH_NCCL_TRACE_CPP_STACK`` to collect C++ traces in the Flight Recorder data.
+- If you prefer not to have the flight recorder data dumped into the local disk but rather onto your own storage, you can define your own writer class.
+  This class should inherit from class ``::c10d::DebugInfoWriter`` `(code) <https://github.com/pytorch/pytorch/blob/release/2.5/torch/csrc/distributed/c10d/NCCLUtils.hpp#L237>`__
+  and then register the new writer using ``::c10d::DebugInfoWriter::registerWriter`` `(code) <https://github.com/pytorch/pytorch/blob/release/2.5/torch/csrc/distributed/c10d/NCCLUtils.hpp#L242>`__
+  before we initiate PyTorch distributed.
 
 Retrieving Flight Recorder Data via an API
 ------------------------------------------
@@ -169,9 +175,29 @@ To run the convenience script, follow these steps:
 
 2. To run the script, use this command:
 
-.. code:: python
+.. code:: shell
+
+  python fr_trace.py <dump dir containing trace files> [-o <output file>]
+
+If you install the PyTorch nightly build or build from scratch with ``USE_DISTRIBUTED=1``, you can directly use the following
+command directly:
+
+.. code:: shell
+
+  torchfrtrace <dump dir containing trace files> [-o <output file>]
+
+
+Currently, we support two modes for the analyzer script. The first mode allows the script to apply some heuristics to the parsed flight
+recorder dumps to generate a report identifying potential culprits for the timeout. The second mode is simply outputs the raw dumps.
+By default, the script prints flight recoder dumps for all ranks and all ``ProcessGroups``(PGs). This can be narrowed down to certain
+ranks and PGs using the *--selected-ranks* argument. An example command is:
+
+Caveat: tabulate module is needed, so you might need pip install it first.
+
+.. code:: shell
 
-  python fr_trace.py -d <dump dir containing trace files> [-o <output file>]
+  python fr_trace.py <dump dir containing trace files> -j [--selected-ranks i j k ...]
+  torchfrtrace <dump dir containing trace files> -j [--selected-ranks i j k ...]
 
 Conclusion
 ----------