Distributed communication package - torch.distributed

torch.distributed provides an MPI-like interface for exchanging tensor data across multi-machine networks. It supports a few different backends and initialization methods.

Currently torch.distributed supports three backends, each with different capabilities. The table below shows which functions are available for use with CPU / CUDA tensors. MPI supports cuda only if the implementation used to build PyTorch supports it.

Backend tcp gloo mpi
Device CPU GPU CPU GPU CPU GPU
send ?
recv ?
broadcast ?
all_reduce ?
reduce ?
all_gather ?
gather ?
scatter ?
barrier ?

Basics

The torch.distributed package provides PyTorch support and communication primitives for multiprocess parallelism across several computation nodes running on one or more machines. The class torch.nn.parallel.DistributedDataParallel() builds on this functionality to provide synchronous distributed training as a wrapper around any PyTorch model. This differs from the kinds of parallelism provided by Multiprocessing package - torch.multiprocessing and torch.nn.DataParallel() in that it supports multiple network-connected machines and in that the user must explicitly launch a separate copy of the main training script for each process.

In the single-machine synchronous case, torch.distributed or the torch.nn.parallel.DistributedDataParallel() wrapper may still have advantages over other approaches to data-parallelism, including torch.nn.DataParallel():

  • Each process maintains its own optimizer and performs a complete optimization step with each iteration. While this may appear redundant, since the gradients have already been gathered together and averaged across processes and are thus the same for every process, this means that no parameter broadcast step is needed, reducing time spent transferring tensors between nodes.
  • Each process contains an independent Python interpreter, eliminating the extra interpreter overhead and “GIL-thrashing” that comes from driving several execution threads, model replicas, or GPUs from a single Python process. This is especially important for models that make heavy use of the Python runtime, including models with recurrent layers or many small components.

Initialization

The package needs to be initialized using the torch.distributed.init_process_group() function before calling any other methods. This blocks until all processes have joined.

torch.distributed.init_process_group(backend, init_method='env://', **kwargs)[source]

Initializes the distributed package.

Parameters:
  • backend (str) – Name of the backend to use. Depending on build-time configuration valid values include: tcp, mpi and gloo.
  • init_method (str, optional) – URL specifying how to initialize the package.
  • world_size (int, optional) – Number of processes participating in the job.
  • rank (int, optional) – Rank of the current process.
  • group_name (str, optional) – Group name. See description of init methods.

To enable backend == mpi, PyTorch needs to built from source on a system that supports MPI.

torch.distributed.get_rank()[source]

Returns the rank of current process.

Rank is a unique identifier assigned to each process within a distributed group. They are always consecutive integers ranging from 0 to world_size.

torch.distributed.get_world_size()[source]

Returns the number of processes in the distributed group.


Currently three initialization methods are supported:

TCP initialization

There are two ways to initialize using TCP, both requiring a network address reachable from all processes and a desired world_size. The first way requires specifying an address that belongs to the rank 0 process. This first way of initialization requires that all processes have manually specified ranks.

Alternatively, the address has to be a valid IP multicast address, in which case ranks can be assigned automatically. Multicast initialization also supports a group_name argument, which allows you to use the same address for multiple jobs, as long as they use different group names.

import torch.distributed as dist

# Use address of one of the machines
dist.init_process_group(init_method='tcp://10.1.1.20:23456', rank=args.rank, world_size=4)

# or a multicast address - rank will be assigned automatically if unspecified
dist.init_process_group(init_method='tcp://[ff15:1e18:5d4c:4cf0:d02d:b659:53ba:b0a7]:23456',
                        world_size=4)

Shared file-system initialization

Another initialization method makes use of a file system that is shared and visible from all machines in a group, along with a desired world_size. The URL should start with file:// and contain a path to a non-existent file (in an existing directory) on a shared file system. This initialization method also supports a group_name argument, which allows you to use the same shared file path for multiple jobs, as long as they use different group names.

Warning

This method assumes that the file system supports locking using fcntl - most local systems and NFS support it.

import torch.distributed as dist

# Rank will be assigned automatically if unspecified
dist.init_process_group(init_method='file:///mnt/nfs/sharedfile', world_size=4,
                        group_name=args.group)

Environment variable initialization

This method will read the configuration from environment variables, allowing one to fully customize how the information is obtained. The variables to be set are:

  • MASTER_PORT - required; has to be a free port on machine with rank 0
  • MASTER_ADDR - required (except for rank 0); address of rank 0 node
  • WORLD_SIZE - required; can be set either here, or in a call to init function
  • RANK - required; can be set either here, or in a call to init function

The machine with rank 0 will be used to set up all connections.

This is the default method, meaning that init_method does not have to be specified (or can be env://).

Groups

By default collectives operate on the default group (also called the world) and require all processes to enter the distributed function call. However, some workloads can benefit from more fine-grained communication. This is where distributed groups come into play. new_group() function can be used to create new groups, with arbitrary subsets of all processes. It returns an opaque group handle that can be given as a group argument to all collectives (collectives are distributed functions to exchange information in certain well-known programming patterns).

torch.distributed.new_group(ranks=None)[source]

Creates a new distributed group.

This function requires that all processes in the main group (i.e. all processes that are part of the distributed job) enter this function, even if they are not going to be members of the group. Additionally, groups should be created in the same order in all processes.

Parameters:ranks (list[int]) – List of ranks of group members.
Returns:A handle of distributed group that can be given to collective calls.

Point-to-point communication

torch.distributed.send(tensor, dst)[source]

Sends a tensor synchronously.

Parameters:
  • tensor (Tensor) – Tensor to send.
  • dst (int) – Destination rank.
torch.distributed.recv(tensor, src=None)[source]

Receives a tensor synchronously.

Parameters:
  • tensor (Tensor) – Tensor to fill with received data.
  • src (int) – Source rank.

isend() and irecv() return distributed request objects when used. In general, the type of this object is unspecified as they should never be created manually, but they are guaranteed to support two methods:

  • is_completed() - returns True if the operation has finished
  • wait() - will block the process until the operation is finished. is_completed() is guaranteed to return True once it returns.

When using the MPI backend, isend() and irecv() support non-overtaking, which has some guarantees on supporting message order. For more detail, see http://mpi-forum.org/docs/mpi-2.2/mpi22-report/node54.htm#Node54

torch.distributed.isend(tensor, dst)[source]

Sends a tensor asynchronously.

Parameters:
  • tensor (Tensor) – Tensor to send.
  • dst (int) – Destination rank.
Returns:

A distributed request object.

torch.distributed.irecv(tensor, src)[source]

Receives a tensor asynchronously.

Parameters:
  • tensor (Tensor) – Tensor to fill with received data.
  • src (int) – Source rank.
Returns:

A distributed request object.

Collective functions

torch.distributed.broadcast(tensor, src, group=<object object>)[source]

Broadcasts the tensor to the whole group.

tensor must have the same number of elements in all processes participating in the collective.

Parameters:
  • tensor (Tensor) – Data to be sent if src is the rank of current process, and tensor to be used to save received data otherwise.
  • src (int) – Source rank.
  • group (optional) – Group of the collective.
torch.distributed.all_reduce(tensor, op=<object object>, group=<object object>)[source]

Reduces the tensor data across all machines in such a way that all get the final result.

After the call tensor is going to be bitwise identical in all processes.

Parameters:
  • tensor (Tensor) – Input and output of the collective. The function operates in-place.
  • op (optional) – One of the values from torch.distributed.reduce_op enum. Specifies an operation used for element-wise reductions.
  • group (optional) – Group of the collective.
torch.distributed.reduce(tensor, dst, op=<object object>, group=<object object>)[source]

Reduces the tensor data across all machines.

Only the process with rank dst is going to receive the final result.

Parameters:
  • tensor (Tensor) – Input and output of the collective. The function operates in-place.
  • op (optional) – One of the values from torch.distributed.reduce_op enum. Specifies an operation used for element-wise reductions.
  • group (optional) – Group of the collective.
torch.distributed.all_gather(tensor_list, tensor, group=<object object>)[source]

Gathers tensors from the whole group in a list.

Parameters:
  • tensor_list (list[Tensor]) – Output list. It should contain correctly-sized tensors to be used for output of the collective.
  • tensor (Tensor) – Tensor to be broadcast from current process.
  • group (optional) – Group of the collective.
torch.distributed.gather(tensor, **kwargs)[source]

Gathers a list of tensors in a single process.

Parameters:
  • tensor (Tensor) – Input tensor.
  • dst (int) – Destination rank. Required in all processes except the one that is receiveing the data.
  • gather_list (list[Tensor]) – List of appropriately-sized tensors to use for received data. Required only in the receiving process.
  • group (optional) – Group of the collective.
torch.distributed.scatter(tensor, **kwargs)[source]

Scatters a list of tensors to all processes in a group.

Each process will receive exactly one tensor and store its data in the tensor argument.

Parameters:
  • tensor (Tensor) – Output tensor.
  • src (int) – Source rank. Required in all processes except the one that is sending the data.
  • scatter_list (list[Tensor]) – List of tensors to scatter. Required only in the process that is sending the data.
  • group (optional) – Group of the collective.
torch.distributed.barrier(group=<object object>)[source]

Synchronizes all processes.

This collective blocks processes until the whole group enters this function.

Parameters:group (optional) – Group of the collective.