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Path: blob/main/intermediate_source/dist_tuto.rst
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Writing Distributed Applications with PyTorch
=============================================
**Author**: `Séb Arnold <https://seba1511.com>`_
.. note::
|edit| View and edit this tutorial in `github <https://github.com/pytorch/tutorials/blob/main/intermediate_source/dist_tuto.rst>`__.
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 of the internals of
the package.
Setup
-----
.. raw:: html
<!--
* Processes & machines
* variables and init_process_group
-->
The distributed package included in PyTorch (i.e.,
``torch.distributed``) enables researchers and practitioners to easily
parallelize their computations across processes and clusters of
machines. To do so, it leverages message passing semantics
allowing each process to communicate data to any of the other processes.
As opposed to the multiprocessing (``torch.multiprocessing``) package,
processes can use different communication backends and are not
restricted to being executed on the same machine.
In order to get started we need the ability to run multiple processes
simultaneously. If you have access to compute cluster you should check
with your local sysadmin or use your favorite coordination tool (e.g.,
`pdsh <https://linux.die.net/man/1/pdsh>`__,
`clustershell <https://cea-hpc.github.io/clustershell/>`__, or
`others <https://slurm.schedmd.com/>`__). For the purpose of this
tutorial, we will use a single machine and spawn multiple processes using
the following template.
.. code:: python
"""run.py:"""
#!/usr/bin/env python
import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
def run(rank, size):
""" Distributed function to be implemented later. """
pass
def init_process(rank, size, fn, backend='gloo'):
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29500'
dist.init_process_group(backend, rank=rank, world_size=size)
fn(rank, size)
if __name__ == "__main__":
size = 2
processes = []
mp.set_start_method("spawn")
for rank in range(size):
p = mp.Process(target=init_process, args=(rank, size, run))
p.start()
processes.append(p)
for p in processes:
p.join()
The above script spawns two processes who will each setup the
distributed environment, initialize the process group
(``dist.init_process_group``), and finally execute the given ``run``
function.
Let's have a look at the ``init_process`` function. It ensures that
every process will be able to coordinate through a master, using the
same ip address and port. Note that we used the ``gloo`` backend but
other backends are available. (c.f.
`Section 5.1 <#communication-backends>`__) We will go over the magic
happening in ``dist.init_process_group`` at the end of this tutorial,
but it essentially allows processes to communicate with each other by
sharing their locations.
Point-to-Point Communication
----------------------------
.. figure:: /_static/img/distributed/send_recv.png
:width: 100%
:align: center
:alt: Send and Recv
Send and Recv
A transfer of data from one process to another is called a
point-to-point communication. These are achieved through the ``send``
and ``recv`` functions or their *immediate* counter-parts, ``isend`` and
``irecv``.
.. code:: python
"""Blocking point-to-point communication."""
def run(rank, size):
tensor = torch.zeros(1)
if rank == 0:
tensor += 1
# Send the tensor to process 1
dist.send(tensor=tensor, dst=1)
else:
# Receive tensor from process 0
dist.recv(tensor=tensor, src=0)
print('Rank ', rank, ' has data ', tensor[0])
In the above example, both processes start with a zero tensor, then
process 0 increments the tensor and sends it to process 1 so that they
both end up with 1.0. Notice that process 1 needs to allocate memory in
order to store the data it will receive.
Also notice that ``send``/``recv`` are **blocking**: both processes stop
until the communication is completed. On the other hand immediates are
**non-blocking**; the script continues its execution and the methods
return a ``Work`` object upon which we can choose to
``wait()``.
.. code:: python
"""Non-blocking point-to-point communication."""
def run(rank, size):
tensor = torch.zeros(1)
req = None
if rank == 0:
tensor += 1
# Send the tensor to process 1
req = dist.isend(tensor=tensor, dst=1)
print('Rank 0 started sending')
else:
# Receive tensor from process 0
req = dist.irecv(tensor=tensor, src=0)
print('Rank 1 started receiving')
req.wait()
print('Rank ', rank, ' has data ', tensor[0])
When using immediates we have to be careful about how we use the sent and received tensors.
Since we do not know when the data will be communicated to the other process,
we should not modify the sent tensor nor access the received tensor before ``req.wait()`` has completed.
In other words,
- writing to ``tensor`` after ``dist.isend()`` will result in undefined behaviour.
- reading from ``tensor`` after ``dist.irecv()`` will result in undefined behaviour.
However, after ``req.wait()``
has been executed we are guaranteed that the communication took place,
and that the value stored in ``tensor[0]`` is 1.0.
Point-to-point communication is useful when we want more fine-grained
control over the communication of our processes. They can be used to
implement fancy algorithms, such as the one used in `Baidu's
DeepSpeech <https://github.com/baidu-research/baidu-allreduce>`__ or
`Facebook's large-scale
experiments <https://research.fb.com/publications/imagenet1kin1h/>`__.(c.f.
`Section 4.1 <#our-own-ring-allreduce>`__)
Collective Communication
------------------------
+----------------------------------------------------+-----------------------------------------------------+
| .. figure:: /_static/img/distributed/scatter.png | .. figure:: /_static/img/distributed/gather.png |
| :alt: Scatter | :alt: Gather |
| :width: 100% | :width: 100% |
| :align: center | :align: center |
| | |
| Scatter | Gather |
+----------------------------------------------------+-----------------------------------------------------+
| .. figure:: /_static/img/distributed/reduce.png | .. figure:: /_static/img/distributed/all_reduce.png |
| :alt: Reduce | :alt: All-Reduce |
| :width: 100% | :width: 100% |
| :align: center | :align: center |
| | |
| Reduce | All-Reduce |
+----------------------------------------------------+-----------------------------------------------------+
| .. figure:: /_static/img/distributed/broadcast.png | .. figure:: /_static/img/distributed/all_gather.png |
| :alt: Broadcast | :alt: All-Gather |
| :width: 100% | :width: 100% |
| :align: center | :align: center |
| | |
| Broadcast | All-Gather |
+----------------------------------------------------+-----------------------------------------------------+
As opposed to point-to-point communcation, collectives allow for
communication patterns across all processes in a **group**. A group is a
subset of all our processes. To create a group, we can pass a list of
ranks to ``dist.new_group(group)``. By default, collectives are executed
on all processes, also known as the **world**. For example, in order
to obtain the sum of all tensors on all processes, we can use the
``dist.all_reduce(tensor, op, group)`` collective.
.. code:: python
""" All-Reduce example."""
def run(rank, size):
""" Simple collective communication. """
group = dist.new_group([0, 1])
tensor = torch.ones(1)
dist.all_reduce(tensor, op=dist.ReduceOp.SUM, group=group)
print('Rank ', rank, ' has data ', tensor[0])
Since we want the sum of all tensors in the group, we use
``dist.ReduceOp.SUM`` as the reduce operator. Generally speaking, any
commutative mathematical operation can be used as an operator.
Out-of-the-box, PyTorch comes with 4 such operators, all working at the
element-wise level:
- ``dist.ReduceOp.SUM``,
- ``dist.ReduceOp.PRODUCT``,
- ``dist.ReduceOp.MAX``,
- ``dist.ReduceOp.MIN``.
In addition to ``dist.all_reduce(tensor, op, group)``, there are a total
of 6 collectives currently implemented in PyTorch.
- ``dist.broadcast(tensor, src, group)``: Copies ``tensor`` from
``src`` to all other processes.
- ``dist.reduce(tensor, dst, op, group)``: Applies ``op`` to every
``tensor`` and stores the result in ``dst``.
- ``dist.all_reduce(tensor, op, group)``: Same as reduce, but the
result is stored in all processes.
- ``dist.scatter(tensor, scatter_list, src, group)``: Copies the
:math:`i^{\text{th}}` tensor ``scatter_list[i]`` to the
:math:`i^{\text{th}}` process.
- ``dist.gather(tensor, gather_list, dst, group)``: Copies ``tensor``
from all processes in ``dst``.
- ``dist.all_gather(tensor_list, tensor, group)``: Copies ``tensor``
from all processes to ``tensor_list``, on all processes.
- ``dist.barrier(group)``: Blocks all processes in `group` until each one has entered this function.
Distributed Training
--------------------
.. raw:: html
<!--
* Gloo Backend
* Simple all_reduce on the gradients
* Point to optimized DistributedDataParallel
TODO: Custom ring-allreduce
-->
**Note:** You can find the example script of this section in `this
GitHub repository <https://github.com/seba-1511/dist_tuto.pth/>`__.
Now that we understand how the distributed module works, let us write
something useful with it. Our goal will be to replicate the
functionality of
`DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
Of course, this will be a didactic example and in a real-world
situation you should use the official, well-tested and well-optimized
version linked above.
Quite simply we want to implement a distributed version of stochastic
gradient descent. Our script will let all processes compute the
gradients of their model on their batch of data and then average their
gradients. In order to ensure similar convergence results when changing
the number of processes, we will first have to partition our dataset.
(You could also use
`tnt.dataset.SplitDataset <https://github.com/pytorch/tnt/blob/master/torchnet/dataset/splitdataset.py#L4>`__,
instead of the snippet below.)
.. code:: python
""" Dataset partitioning helper """
class Partition(object):
def __init__(self, data, index):
self.data = data
self.index = index
def __len__(self):
return len(self.index)
def __getitem__(self, index):
data_idx = self.index[index]
return self.data[data_idx]
class DataPartitioner(object):
def __init__(self, data, sizes=[0.7, 0.2, 0.1], seed=1234):
self.data = data
self.partitions = []
rng = Random() # from random import Random
rng.seed(seed)
data_len = len(data)
indexes = [x for x in range(0, data_len)]
rng.shuffle(indexes)
for frac in sizes:
part_len = int(frac * data_len)
self.partitions.append(indexes[0:part_len])
indexes = indexes[part_len:]
def use(self, partition):
return Partition(self.data, self.partitions[partition])
With the above snippet, we can now simply partition any dataset using
the following few lines:
.. code:: python
""" Partitioning MNIST """
def partition_dataset():
dataset = datasets.MNIST('./data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
size = dist.get_world_size()
bsz = 128 // size
partition_sizes = [1.0 / size for _ in range(size)]
partition = DataPartitioner(dataset, partition_sizes)
partition = partition.use(dist.get_rank())
train_set = torch.utils.data.DataLoader(partition,
batch_size=bsz,
shuffle=True)
return train_set, bsz
Assuming we have 2 replicas, then each process will have a ``train_set``
of 60000 / 2 = 30000 samples. We also divide the batch size by the
number of replicas in order to maintain the *overall* batch size of 128.
We can now write our usual forward-backward-optimize training code, and
add a function call to average the gradients of our models. (The
following is largely inspired by the official `PyTorch MNIST
example <https://github.com/pytorch/examples/blob/master/mnist/main.py>`__.)
.. code:: python
""" Distributed Synchronous SGD Example """
def run(rank, size):
torch.manual_seed(1234)
train_set, bsz = partition_dataset()
model = Net()
optimizer = optim.SGD(model.parameters(),
lr=0.01, momentum=0.5)
num_batches = ceil(len(train_set.dataset) / float(bsz))
for epoch in range(10):
epoch_loss = 0.0
for data, target in train_set:
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
epoch_loss += loss.item()
loss.backward()
average_gradients(model)
optimizer.step()
print('Rank ', dist.get_rank(), ', epoch ',
epoch, ': ', epoch_loss / num_batches)
It remains to implement the ``average_gradients(model)`` function, which
simply takes in a model and averages its gradients across the whole
world.
.. code:: python
""" Gradient averaging. """
def average_gradients(model):
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
param.grad.data /= size
*Et voilà*! We successfully implemented distributed synchronous SGD and
could train any model on a large computer cluster.
**Note:** While the last sentence is *technically* true, there are `a
lot more tricks <https://seba-1511.github.io/dist_blog>`__ required to
implement a production-level implementation of synchronous SGD. Again,
use what `has been tested and
optimized <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
Our Own Ring-Allreduce
~~~~~~~~~~~~~~~~~~~~~~
As an additional challenge, imagine that we wanted to implement
DeepSpeech's efficient ring allreduce. This is fairly easy to implement
using point-to-point collectives.
.. code:: python
""" Implementation of a ring-reduce with addition. """
def allreduce(send, recv):
rank = dist.get_rank()
size = dist.get_world_size()
send_buff = send.clone()
recv_buff = send.clone()
accum = send.clone()
left = ((rank - 1) + size) % size
right = (rank + 1) % size
for i in range(size - 1):
if i % 2 == 0:
# Send send_buff
send_req = dist.isend(send_buff, right)
dist.recv(recv_buff, left)
accum[:] += recv_buff[:]
else:
# Send recv_buff
send_req = dist.isend(recv_buff, right)
dist.recv(send_buff, left)
accum[:] += send_buff[:]
send_req.wait()
recv[:] = accum[:]
In the above script, the ``allreduce(send, recv)`` function has a
slightly different signature than the ones in PyTorch. It takes a
``recv`` tensor and will store the sum of all ``send`` tensors in it. As
an exercise left to the reader, there is still one difference between
our version and the one in DeepSpeech: their implementation divides the
gradient tensor into *chunks*, so as to optimally utilize the
communication bandwidth. (Hint:
`torch.chunk <https://pytorch.org/docs/stable/torch.html#torch.chunk>`__)
Advanced Topics
---------------
We are now ready to discover some of the more advanced functionalities
of ``torch.distributed``. Since there is a lot to cover, this section is
divided into two subsections:
1. Communication Backends: where we learn how to use MPI and Gloo for
GPU-GPU communication.
2. Initialization Methods: where we understand how to best set up the
initial coordination phase in ``dist.init_process_group()``.
Communication Backends
~~~~~~~~~~~~~~~~~~~~~~
One of the most elegant aspects of ``torch.distributed`` is its ability
to abstract and build on top of different backends. As mentioned before,
there are currently three backends implemented in PyTorch: Gloo, NCCL, and
MPI. They each have different specifications and tradeoffs, depending
on the desired use case. A comparative table of supported functions can
be found
`here <https://pytorch.org/docs/stable/distributed.html#module-torch.distributed>`__.
**Gloo Backend**
So far we have made extensive usage of the `Gloo backend <https://github.com/facebookincubator/gloo>`__.
It is quite handy as a development platform, as it is included in
the pre-compiled PyTorch binaries and works on both Linux (since 0.2)
and macOS (since 1.3). It supports all point-to-point and collective
operations on CPU, and all collective operations on GPU. The
implementation of the collective operations for CUDA tensors is not as
optimized as the ones provided by the NCCL backend.
As you have surely noticed, our
distributed SGD example does not work if you put ``model`` on the GPU.
In order to use multiple GPUs, let us also make the following
modifications:
1. Use ``device = torch.device("cuda:{}".format(rank))``
2. ``model = Net()`` :math:`\rightarrow` ``model = Net().to(device)``
3. Use ``data, target = data.to(device), target.to(device)``
With the above modifications, our model is now training on two GPUs and
you can monitor their utilization with ``watch nvidia-smi``.
**MPI Backend**
The Message Passing Interface (MPI) is a standardized tool from the
field of high-performance computing. It allows to do point-to-point and
collective communications and was the main inspiration for the API of
``torch.distributed``. Several implementations of MPI exist (e.g.
`Open-MPI <https://www.open-mpi.org/>`__,
`MVAPICH2 <http://mvapich.cse.ohio-state.edu/>`__, `Intel
MPI <https://software.intel.com/en-us/intel-mpi-library>`__) each
optimized for different purposes. The advantage of using the MPI backend
lies in MPI's wide availability - and high-level of optimization - on
large computer clusters. `Some <https://developer.nvidia.com/mvapich>`__
`recent <https://developer.nvidia.com/ibm-spectrum-mpi>`__
`implementations <https://www.open-mpi.org/>`__ are also able to take
advantage of CUDA IPC and GPU Direct technologies in order to avoid
memory copies through the CPU.
Unfortunately, PyTorch's binaries cannot include an MPI implementation
and we'll have to recompile it by hand. Fortunately, this process is
fairly simple given that upon compilation, PyTorch will look *by itself*
for an available MPI implementation. The following steps install the MPI
backend, by installing PyTorch `from
source <https://github.com/pytorch/pytorch#from-source>`__.
1. Create and activate your Anaconda environment, install all the
pre-requisites following `the
guide <https://github.com/pytorch/pytorch#from-source>`__, but do
**not** run ``python setup.py install`` yet.
2. Choose and install your favorite MPI implementation. Note that
enabling CUDA-aware MPI might require some additional steps. In our
case, we'll stick to Open-MPI *without* GPU support:
``conda install -c conda-forge openmpi``
3. Now, go to your cloned PyTorch repo and execute
``python setup.py install``.
In order to test our newly installed backend, a few modifications are
required.
1. Replace the content under ``if __name__ == '__main__':`` with
``init_process(0, 0, run, backend='mpi')``.
2. Run ``mpirun -n 4 python myscript.py``.
The reason for these changes is that MPI needs to create its own
environment before spawning the processes. MPI will also spawn its own
processes and perform the handshake described in `Initialization
Methods <#initialization-methods>`__, making the ``rank``\ and ``size``
arguments of ``init_process_group`` superfluous. This is actually quite
powerful as you can pass additional arguments to ``mpirun`` in order to
tailor computational resources for each process. (Things like number of
cores per process, hand-assigning machines to specific ranks, and `some
more <https://www.open-mpi.org/faq/?category=running#mpirun-hostfile>`__)
Doing so, you should obtain the same familiar output as with the other
communication backends.
**NCCL Backend**
The `NCCL backend <https://github.com/nvidia/nccl>`__ provides an
optimized implementation of collective operations against CUDA
tensors. If you only use CUDA tensors for your collective operations,
consider using this backend for the best in class performance. The
NCCL backend is included in the pre-built binaries with CUDA support.
Initialization Methods
~~~~~~~~~~~~~~~~~~~~~~
To finish this tutorial, let's talk about the very first function we
called: ``dist.init_process_group(backend, init_method)``. In
particular, we will go over the different initialization methods which
are responsible for the initial coordination step between each process.
Those methods allow you to define how this coordination is done.
Depending on your hardware setup, one of these methods should be
naturally more suitable than the others. In addition to the following
sections, you should also have a look at the `official
documentation <https://pytorch.org/docs/stable/distributed.html#initialization>`__.
**Environment Variable**
We have been using the environment variable initialization method
throughout this tutorial. By setting the following four environment
variables on all machines, all processes will be able to properly
connect to the master, obtain information about the other processes, and
finally handshake with them.
- ``MASTER_PORT``: A free port on the machine that will host the
process with rank 0.
- ``MASTER_ADDR``: IP address of the machine that will host the process
with rank 0.
- ``WORLD_SIZE``: The total number of processes, so that the master
knows how many workers to wait for.
- ``RANK``: Rank of each process, so they will know whether it is the
master or a worker.
**Shared File System**
The shared filesystem requires all processes to have access to a shared
file system, and will coordinate them through a shared file. This means
that each process will open the file, write its information, and wait
until everybody did so. After that all required information will be
readily available to all processes. In order to avoid race conditions,
the file system must support locking through
`fcntl <http://man7.org/linux/man-pages/man2/fcntl.2.html>`__.
.. code:: python
dist.init_process_group(
init_method='file:///mnt/nfs/sharedfile',
rank=args.rank,
world_size=4)
**TCP**
Initializing via TCP can be achieved by providing the IP address of the process with rank 0 and a reachable port number.
Here, all workers will be able to connect to the process
with rank 0 and exchange information on how to reach each other.
.. code:: python
dist.init_process_group(
init_method='tcp://10.1.1.20:23456',
rank=args.rank,
world_size=4)
.. raw:: html
<!--
## Internals
* The magic behind init_process_group:
1. validate and parse the arguments
2. resolve the backend: name2channel.at()
3. Drop GIL & THDProcessGroupInit: instantiate the channel and add address of master from config
4. rank 0 inits master, others workers
5. master: create sockets for all workers -> wait for all workers to connect -> send them each the info about location of other processes
6. worker: create socket to master, send own info, receive info about each worker, and then handshake with each of them
7. By this time everyone has handshake with everyone.
-->
.. raw:: html
<center>
**Acknowledgements**
.. raw:: html
</center>
I'd like to thank the PyTorch developers for doing such a good job on
their implementation, documentation, and tests. When the code was
unclear, I could always count on the
`docs <https://pytorch.org/docs/stable/distributed.html>`__ or the
`tests <https://github.com/pytorch/pytorch/tree/master/test/distributed>`__
to find an answer. In particular, I'd like to thank Soumith Chintala,
Adam Paszke, and Natalia Gimelshein for providing insightful comments
and answering questions on early drafts.