- An overview of the `Distributed RPC Framework`_

- An overview of the `PyTorch Profiler`_

- How to use the profiler to profile RPC-based workloads

Requirements

- PyTorch 1.6

The instructions for installing PyTorch are

available at `pytorch.org`_.

What is the Distributed RPC Framework?

The **Distributed RPC Framework** provides mechanisms for multi-machine model

training through a set of primitives to allow for remote communication, and a

higher-level API to automatically differentiate models split across several machines.

For this recipe, it would be helpful to be familiar with the `Distributed RPC Framework`_

as well as the `RPC Tutorials`_.

What is the PyTorch Profiler?

The profiler is a context manager based API that allows for on-demand profiling of

operators in a model's workload. The profiler can be used to analyze various aspects

of a model including execution time, operators invoked, and memory consumption. For a

detailed tutorial on using the profiler to profile a single-node model, please see the

`Profiler Recipe`_.

How to use the Profiler for RPC-based workloads

The profiler supports profiling of calls made of RPC and allows the user to have a

detailed view into the operations that take place on different nodes. To demonstrate an

example of this, let's first set up the RPC framework. The below code snippet will initialize

two RPC workers on the same host, named ``worker0`` and ``worker1`` respectively. The workers will

be spawned as subprocesses, and we set some environment variables required for proper

initialization.

::

Running the above program should present you with the following output:

::

Now that we have a skeleton setup of our RPC framework, we can move on to

sending RPCs back and forth and using the profiler to obtain a view of what's

happening under the hood. Let's add to the above ``worker`` function:

::

The aformented code creates 2 RPCs, specifying ``torch.add`` and ``torch.mul``, respectively,

to be run with two random input tensors on worker 1. Since we use the ``rpc_async`` API,

we are returned a ``torch.futures.Future`` object, which must be awaited for the result

of the computation. Note that this wait must take place within the scope created by

the profiling context manager in order for the RPC to be accurately profiled. Running

the code with this new worker function should result in the following output:

::

Here we can see that the profiler has profiled our ``rpc_async`` calls made to ``worker1``

from ``worker0``. In particular, the first 2 entries in the table show details (such as

the operator name, originating worker, and destination worker) about each RPC call made

and the ``CPU total`` column indicates the end-to-end latency of the RPC call.

We also have visibility into the actual operators invoked remotely on worker 1 due RPC.

We can see operations that took place on ``worker1`` by checking the ``Node ID`` column. For

example, we can interpret the row with name ``rpc_async#aten::mul(worker0 -> worker1)#remote_op: mul``

as a ``mul`` operation taking place on the remote node, as a result of the RPC sent to ``worker1``

from ``worker0``, specifying ``worker1`` to run the builtin ``mul`` operator on the input tensors.

Note that names of remote operations are prefixed with the name of the RPC event that resulted

in them. For example, remote operations corresponding to the ``rpc.rpc_async(dst_worker_name, torch.add, args=(t1, t2))``

call are prefixed with ``rpc_async#aten::mul(worker0 -> worker1)``.

We can also use the profiler gain insight into user-defined functions that are executed over RPC.

For example, let's add the following to the above ``worker`` function:

::

The above code creates a user-defined function that sleeps for 1 second, and then executes various

operators. Similar to what we've done above, we send an RPC to the remote worker, specifying it to

run our user-defined function. Running this code should result in the following output:

::

Here we can see that the user-defined function has successfully been profiled with its name

``(rpc_async#udf_with_ops(worker0 -> worker1))``, and has the CPU total time we would roughly expect

(slightly greater than 1s given the ``sleep``). Similar to the above profiling output, we can see the

remote operators that have been executed on worker 1 as part of executing this RPC request.

Lastly, we can visualize remote execution using the tracing functionality provided by the profiler.

Let's add the following code to the above ``worker`` function:

::

Now, we can load the trace file in Chrome (``chrome://tracing``). We should see output similar to

the following:

.. image:: ../_static/img/rpc_trace_img.png

:scale: 25 %

As we can see, we have traced our RPC requests and can also visualize traces of the remote operations,

in this case, given in the trace column for ``node_id: 1``.

Putting it all together, we have the following code for this recipe:

::

Learn More

- `pytorch.org`_ for installation instructions, and more documentation

and tutorials.

- `Distributed RPC Framework`_ for RPC framework and API reference.

- `Full profiler documentation`_ for profiler documentation.

.. _pytorch.org: https://pytorch.org/

.. _Full profiler documentation: https://pytorch.org/docs/stable/autograd.html#profiler

.. _Pytorch Profiler: https://pytorch.org/docs/stable/autograd.html#profiler

.. _Distributed RPC Framework: https://pytorch.org/docs/stable/rpc.html

.. _RPC Tutorials: https://pytorch.org/tutorials/intermediate/rpc_tutorial.html

.. _Profiler Recipe: https://pytorch.org/tutorials/recipes/recipes/profiler.html