Adding an Overview Page for PyTorch Distributed

beginner_source/dist_overview.rst

@@ -0,0 +1,197 @@
+PyTorch Distributed Overview
+============================
+**Author**: `Shen Li <https://mrshenli.github.io/>`_
+
+
+This is the overview page for the ``torch.distributed`` package. As there are
+more and more documents, examples and tutorials added at different locations,
+it becomes unclear which document or tutorial to consult for a specific problem
+or what is the best order to read these contents. The goal of this page is to
+address this problem by categorizing documents into different topics and briefly
+describe each of them. If this is your first time building distributed training
+applications using PyTorch, it is recommended to use this document to navigate
+to the technology that can best serve your use case.
+
+
+Introduction
+------------
+
+As of PyTorch v1.6.0, features in ``torch.distributed`` can be categorized into
+three main components:
+
+* `Distributed Data-Parallel Training <https://pytorch.org/docs/master/generated/torch.nn.parallel.DistributedDataParallel.html>`__
+  (DDP) is a widely adopted single-program multiple-data training paradigm. With
+  DDP, the model is replicated on every process, and every model replica will be
+  fed with a different set of input data samples. DDP takes care of gradient
+  communications to keep model replicas synchronized and overlaps it with the
+  gradient computations to speed up training.
+* `RPC-Based Distributed Training <https://pytorch.org/docs/master/rpc.html>`__
+  (RPC) is developed to support general training structures that cannot fit into
+  data-parallel training, such as distributed pipeline parallelism, parameter
+  server paradigm, and combination of DDP with other training paradigms. It
+  helps manage remote object lifetime and extend autograd engine to beyond
+  machine boundaries.
+* `Collective Communication <https://pytorch.org/docs/stable/distributed.html>`__
+  (c10d) library support sending tensors across processes within a group. It
+  offers both collective communication APIs (e.g.,
+  `all_reduce <https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_reduce>`__
+  and `all_gather <https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_gather>`__)
+  and P2P communication APIs (e.g.,
+  `send <https://pytorch.org/docs/stable/distributed.html#torch.distributed.send>`__
+  and `isend <https://pytorch.org/docs/stable/distributed.html#torch.distributed.isend>`__).
+  DDP and RPC (`ProcessGroup Backend <https://pytorch.org/docs/master/rpc.html#process-group-backend>`__)
+  are built on c10d as of v1.6.0, where the former uses collective communications
+  and the latter uses P2P communications. Usually, developers do not need to
+  directly use this raw communication API, as DDP and RPC features above can serve
+  many distributed training scenarios. However, there are use cases where this API
+  is still helpful. One example would be distributed parameter averaging, where
+  applications would like to compute the average values of all model parameters
+  after the backward pass instead of using DDP to communicate gradients. This can
+  decouple communications from computations and allow finer-grain control over
+  what to communicate, but on the other hand, it also gives up the performance
+  optimizations offered by DDP. The
+  `Writing Distributed Applications with PyTorch <https://pytorch.org/tutorials/intermediate/dist_tuto.html>`__
+  shows examples of using c10d communication APIs.
+
+
+Most of the existing documents are written for either DDP or RPC, the remainder
+of this page will elaborate materials for these two components.
+
+
+Data Parallel Training
+----------------------
+
+PyTorch provides several options for data-parallel training. For applications
+that gradually grow from simple to complex and from prototype to production, the
+common development trajectory would be:
+
+1. Use single-device training, if the data and model can fit in one GPU, and the
+   training speed is not a concern.
+2. Use single-machine multi-GPU
+   `DataParallel <https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html>`__,
+   if there are multiple GPUs on the server, and you would like to speed up
+   training with the minimum code change.
+3. Use single-machine multi-GPU
+   `DistributedDataParallel <https://pytorch.org/docs/master/generated/torch.nn.parallel.DistributedDataParallel.html>`__,
+   if you would like to further speed up training and are willing to write a
+   little more code to set it up.
+4. Use multi-machine `DistributedDataParallel <https://pytorch.org/docs/master/generated/torch.nn.parallel.DistributedDataParallel.html>`__
+   and the `launching script <https://github.com/pytorch/examples/blob/master/distributed/ddp/README.md>`__,
+   if the application needs to scale across machine boundaries.
+5. Use `torchelastic <https://pytorch.org/elastic>`__ to launch distributed
+   training, if errors (e.g., OOM) are expected or if the resources can join and
+   leave dynamically during the training.
+
+
+.. note:: Data-parallel training also works with `Automatic Mixed Precision (AMP) <https://pytorch.org/docs/master/notes/amp_examples.html#working-with-multiple-gpus>`__.
+
+
+``torch.nn.DataParallel``
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The `DataParallel <https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html>`__
+package enables single-machine multi-GPU parallelism with the lowest coding
+hurdle. It only requires a one-line change to the application code. The tutorial
+`Optional: Data Parallelism <https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html>`__
+shows an example. The caveat is that, although ``DataParallel`` is very easy to
+use, it usually does not offer the best performance. This is because the
+implementation of ``DataParallel`` replicates the model in every forward pass,
+and its single-process multi-thread parallelism naturally suffers from GIL
+contentions. To get better performance, please consider using
+`DistributedDataParallel <https://pytorch.org/docs/master/generated/torch.nn.parallel.DistributedDataParallel.html>`__.
+
+
+``torch.nn.parallel.DistributedDataParallel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Compared to `DataParallel <https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html>`__,
+`DistributedDataParallel <https://pytorch.org/docs/master/generated/torch.nn.parallel.DistributedDataParallel.html>`__
+requires one more step to set up, i.e., calling
+`init_process_group <https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group>`__.
+DDP uses multi-process parallelism, and hence there is no GIL contention across
+model replicas. Moreover, the model is broadcast at DDP construction time instead
+of in every forward pass, which also helps to speed up training. DDP is shipped
+with several performance optimization technologies. For a more in-depth
+explanation, please refer to this
+`DDP paper <https://arxiv.org/abs/2006.15704>`__ (VLDB'20).
+
+
+DDP materials are listed below:
+
+1. `DDP notes <https://pytorch.org/docs/stable/notes/ddp.html>`__
+   offer a starter example and some brief descriptions of its design and
+   implementation. If this is your first time using DDP, please start from this
+   document.
+2. `Getting Started with Distributed Data Parallel <../intermediate/ddp_tutorial.html>`__
+   explains some common problems with DDP training, including unbalanced
+   workload, checkpointing, and multi-device models. Note that, DDP can be
+   easily combined with single-machine multi-device model parallelism which is
+   described in the
+   `Single-Machine Model Parallel Best Practices <../intermediate/model_parallel_tutorial.html>`__
+   tutorial.
+3. The `Launching and configuring distributed data parallel applications <https://github.com/pytorch/examples/blob/master/distributed/ddp/README.md>`__
+   document shows how to use the DDP launching script.
+4. `PyTorch Distributed Trainer with Amazon AWS <aws_distributed_training_tutorial.html>`__
+   demonstrates how to use DDP on AWS.
+
+TorchElastic
+~~~~~~~~~~~~
+
+With the growth of the application complexity and scale, failure recovery
+becomes an imperative requirement. Sometimes, it is inevitable to hit errors
+like OOM when using DDP, but DDP itself cannot recover from those errors nor
+does basic ``try-except`` block work. This is because DDP requires all processes
+to operate in a closely synchronized manner and all ``AllReduce`` communications
+launched in different processes must match. If one of the processes in the group
+throws an OOM exception, it is likely to lead to desynchronization (mismatched
+``AllReduce`` operations) which would then cause a crash or hang. If you expect
+failures to occur during training or if resources might leave and join
+dynamically, please launch distributed data-parallel training using
+`torchelastic <https://pytorch.org/elastic>`__.
+
+
+General Distributed Training
+----------------------------
+
+Many training paradigms do not fit into data parallelism, e.g.,
+parameter server paradigm, distributed pipeline parallelism, reinforcement
+learning applications with multiple observers or agents, etc. The
+`torch.distributed.rpc <https://pytorch.org/docs/master/rpc.html>`__ aims at
+supporting general distributed training scenarios.
+
+The `torch.distributed.rpc <https://pytorch.org/docs/master/rpc.html>`__ package
+has four main pillars:
+
+* `RPC <https://pytorch.org/docs/master/rpc.html#rpc>`__ supports running
+  a given function on a remote worker.
+* `RRef <https://pytorch.org/docs/master/rpc.html#rref>`__ helps to manage the
+  lifetime of a remote object. The reference counting protocol is presented in the
+  `RRef notes <https://pytorch.org/docs/master/rpc/rref.html#remote-reference-protocol>`__.
+* `Distributed Autograd <https://pytorch.org/docs/master/rpc.html#distributed-autograd-framework>`__
+  extends the autograd engine beyond machine boundaries. Please refer to
+  `Distributed Autograd Design <https://pytorch.org/docs/master/rpc/distributed_autograd.html#distributed-autograd-design>`__
+  for more details.
+* `Distributed Optimizer <https://pytorch.org/docs/master/rpc.html#module-torch.distributed.optim>`__
+  that automatically reaches out to all participating workers to update
+  parameters using gradients computed by the distributed autograd engine.
+
+RPC Tutorials are listed below:
+
+1. The `Getting Started with Distributed RPC Framework <../intermediate/rpc_tutorial.html>`__
+   tutorial first uses a simple Reinforcement Learning (RL) example to
+   demonstrate RPC and RRef. Then, it applies a basic distributed model
+   parallelism to an RNN example to show how to use distributed autograd and
+   distributed optimizer.
+2. The `Implementing a Parameter Server Using Distributed RPC Framework <../intermediate/rpc_param_server_tutorial.html>`__
+   tutorial borrows the spirit of
+   `HogWild! training <https://people.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf>`__
+   and applies it to an asynchronous parameter server (PS) training application.
+3. The `Distributed Pipeline Parallelism Using RPC <../intermediate/dist_pipeline_parallel_tutorial.html>`__
+   tutorial extends the single-machine pipeline parallel example (presented in
+   `Single-Machine Model Parallel Best Practices <../intermediate/model_parallel_tutorial.html>`__)
+   to a distributed environment and shows how to implement it using RPC.
+4. The `Implementing Batch RPC Processing Using Asynchronous Executions <../intermediate/rpc_async_execution.html>`__
+   tutorial demonstrates how to implement RPC batch processing using the
+   `@rpc.functions.async_execution <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.functions.async_execution>`__
+   decorator, which can help speed up inference and training. It uses similar
+   RL and PS examples employed in the above tutorials 1 and 2.

index.rst

@@ -297,6 +297,13 @@ Welcome to PyTorch Tutorials
 
 .. Parallel-and-Distributed-Training
 
+.. customcarditem::
+   :header: PyTorch Distributed Overview
+   :card_description: Briefly go over all concepts and features in the distributed package. Use this document to find the distributed training technology that can best serve your application.
+   :image: _static/img/thumbnails/cropped/PyTorch-Distributed-Overview.png
+   :link: beginner/dist_overview.html
+   :tags: Parallel-and-Distributed-Training
+
 .. customcarditem::
    :header: Single-Machine Model Parallel Best Practices
    :card_description:  Learn how to implement model parallel, a distributed training technique which splits a single model onto different GPUs, rather than replicating the entire model on each GPU
@@ -311,6 +318,13 @@ Welcome to PyTorch Tutorials
    :link: intermediate/ddp_tutorial.html
    :tags: Parallel-and-Distributed-Training
 
+.. customcarditem::
+   :header: (advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS
+   :card_description: Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package.
+   :image: _static/img/thumbnails/cropped/advanced-PyTorch-1point0-Distributed-Trainer-with-Amazon-AWS.png
+   :link: beginner/aws_distributed_training_tutorial.html
+   :tags: Parallel-and-Distributed-Training
+
 .. customcarditem::
    :header: Writing Distributed Applications with PyTorch
    :card_description: Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package.
@@ -325,13 +339,6 @@ Welcome to PyTorch Tutorials
    :link: intermediate/rpc_tutorial.html
    :tags: Parallel-and-Distributed-Training
 
-.. customcarditem::
-   :header: (advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS
-   :card_description: Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package.
-   :image: _static/img/thumbnails/cropped/advanced-PyTorch-1point0-Distributed-Trainer-with-Amazon-AWS.png
-   :link: beginner/aws_distributed_training_tutorial.html
-   :tags: Parallel-and-Distributed-Training
-
 .. customcarditem::
    :header: Implementing a Parameter Server Using Distributed RPC Framework
    :card_description: Walk through a through a simple example of implementing a parameter server using PyTorch’s Distributed RPC framework.
@@ -513,6 +520,7 @@ Additional Resources
    :hidden:
    :caption: Parallel and Distributed Training
 
+   beginner/dist_overview
    intermediate/model_parallel_tutorial
    intermediate/ddp_tutorial
    intermediate/dist_tuto

intermediate_source/ddp_tutorial.rst

@@ -2,6 +2,13 @@ Getting Started with Distributed Data Parallel
 =================================================
 **Author**: `Shen Li <https://mrshenli.github.io/>`_
 
+Prerequisites:
+
+-  `PyTorch Distributed Overview <../beginner/dist_overview.html>`__
+-  `DistributedDataParallel API documents <https://pytorch.org/docs/master/generated/torch.nn.parallel.DistributedDataParallel.html>`__
+-  `DistributedDataParallel notes <https://pytorch.org/docs/master/notes/ddp.html>`__
+
+
 `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__
 (DDP) implements data parallelism at the module level which can run across
 multiple machines. Applications using DDP should spawn multiple processes and
@@ -202,9 +209,9 @@ and elasticity support, please refer to `TorchElastic <https://pytorch.org/elast
         loss_fn(outputs, labels).backward()
         optimizer.step()
 
-        # Use a barrier() to make sure that all processes have finished reading the
-        # checkpoint
-        dist.barrier()
+        # Not necessary to use a dist.barrier() to guard the file deletion below
+        # as the AllReduce ops in the backward pass of DDP already served as
+        # a synchronization.
 
         if rank == 0:
             os.remove(CHECKPOINT_PATH)

intermediate_source/dist_pipeline_parallel_tutorial.rst

@@ -4,6 +4,7 @@ Distributed Pipeline Parallelism Using RPC
 
 Prerequisites:
 
+-  `PyTorch Distributed Overview <../beginner/dist_overview.html>`__
 -  `Single-Machine Model Parallel Best Practices <https://pytorch.org/tutorials/intermediate/model_parallel_tutorial.html>`__
 -  `Getting started with Distributed RPC Framework <https://pytorch.org/tutorials/intermediate/rpc_tutorial.html>`__
 -  RRef helper functions:

intermediate_source/dist_tuto.rst

@@ -2,6 +2,10 @@ Writing Distributed Applications with PyTorch
 =============================================
 **Author**: `Séb Arnold <https://seba1511.com>`_
 
+Prerequisites:
+
+-  `PyTorch Distributed Overview <../beginner/dist_overview.html>`__
+
 In this short tutorial, we will be going over the distributed package
 of PyTorch. We'll see how to set up the distributed setting, use the
 different communication strategies, and go over some the internals of

intermediate_source/rpc_async_execution.rst

@@ -5,8 +5,9 @@ Implementing Batch RPC Processing Using Asynchronous Executions
 
 Prerequisites:
 
--  `Getting started with Distributed RPC Framework <https://pytorch.org/tutorials/intermediate/rpc_tutorial.html>`__
--  `Implementing a Parameter Server using Distributed RPC Framework <https://pytorch.org/tutorials/intermediate/rpc_param_server_tutorial.html>`__
+-  `PyTorch Distributed Overview <../beginner/dist_overview.html>`__
+-  `Getting started with Distributed RPC Framework <rpc_tutorial.html>`__
+-  `Implementing a Parameter Server using Distributed RPC Framework <rpc_param_server_tutorial.html>`__
 -  `RPC Asynchronous Execution Decorator <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.functions.async_execution>`__
 
 This tutorial demonstrates how to build batch-processing RPC applications with