Distributed communication package - torch.distributed¶

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 gloo mpi nccl
Device CPU GPU CPU GPU CPU GPU
send ?
recv ?
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 default distributed process group, and this will also initialize the distributed package

Parameters: backend (str or DistBackend) – The backend to use. Depending on build-time configurations, valid values include mpi, gloo, and nccl. This field should be given as a lowercase string (e.g., "gloo"), which can also be accessed via DistBackend attributes (e.g., DistBackend.GLOO). init_method (str, optional) – URL specifying how to initialize the process group. world_size (int, optional) – Number of processes participating in the job. rank (int, optional) – Rank of the current process. group_name (str, optional, deprecated) – Group name.

To enable backend == DistBackend.MPI, PyTorch needs to built from source on a system that supports MPI. The same applies to NCCL as well.

torch.distributed.get_rank(group=<object object>)[source]

Returns the rank of currrent process group

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

Parameters: group (ProcessGroup, optional) – The process group to work on The rank of the process group -1, if not part of the group
torch.distributed.get_world_size(group=<object object>)[source]

Returns the number of processes in the current process group

Parameters: group (ProcessGroup, optional) – The process group to work on The world size of the process group -1, if not part of the group
torch.distributed.is_initialized()[source]

Checking if the default process group has been initialized

torch.distributed.get_default_group()[source]

Getting the default process group created by init_process_group

torch.distributed.is_mpi_available()[source]

Checks if MPI is available

torch.distributed.is_nccl_available()[source]

Checks if NCCL is available

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 initialization method requires that all processes have manually specified ranks.

Note that multicast address is not supported anymore in the latest distributed package. group_name is deprecated as well.

import torch.distributed as dist

# Use address of one of the machines
dist.init_process_group(backend, init_method='tcp://10.1.1.20:23456', rank=args.rank, 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. File-system initialization will automatically create that file if it doesn’t exist, but will not delete the file. Therefore, it is your responsibility to make sure that the file is cleaned up before the next init_process_group call on the same file path/name.

Note that automatic rank assignment is not supported anymore in the latest distributed package and group_name is deprecated as well.

Warning

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

Warning

This method does not clean up and remove the file and it is your responsibility to remove the file at the end of the training. This is especially important if you plan to call init_process_group multiple times on the same file name. In other words, if the file is not removed/cleaned up and you call init_process_group again on that file, it is unexpected behavior and will cause failures. The rule of thumb here is that, make sure that the file is non-existent or empty everytime init_process_group is called.

import torch.distributed as dist

# rank should always be specified
dist.init_process_group(backend, init_method='file:///mnt/nfs/sharedfile',
world_size=4, rank=args.rank)


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. A handle of distributed group that can be given to collective calls.

Point-to-point communication¶

torch.distributed.send(tensor, dst, group=<object object>, tag=0)[source]

Sends a tensor synchronously.

Parameters: tensor (Tensor) – Tensor to send. dst (int) – Destination rank. group (ProcessGroup, optional) – The process group to work on tag (int, optional) – Tag to match send with remote recv
torch.distributed.recv(tensor, src=None, group=<object object>, tag=0)[source]

Parameters: tensor (Tensor) – Tensor to fill with received data. src (int, optional) – Source rank. Will receive from any process if unspecified. group (ProcessGroup, optional) – The process group to work on tag (int, optional) – Tag to match recv with remote send Sender rank -1, if not part of the group

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.
torch.distributed.isend(tensor, dst, group=<object object>, tag=0)[source]

Sends a tensor asynchronously.

Parameters: tensor (Tensor) – Tensor to send. dst (int) – Destination rank. group (ProcessGroup, optional) – The process group to work on tag (int, optional) – Tag to match send with remote recv A distributed request object. None, if not part of the group
torch.distributed.irecv(tensor, src, group=<object object>, tag=0)[source]

Parameters: tensor (Tensor) – Tensor to fill with received data. src (int) – Source rank. group (ProcessGroup, optional) – The process group to work on tag (int, optional) – Tag to match recv with remote send A distributed request object. None, if not part of the group

Synchronous and asynchornous collective operations¶

Every collective operation function supports the following two kinds of operations:

synchronous operation - the default mode, when async_op is set to False. when the function returns, it is guaranteed that the collective operation is performed (not necessarily completed if it’s a CUDA op since all CUDA ops are asynchornous), and any further function calls depending on the data of the collective operation can be called. In the synchronous mode, the collective function does not return anything

asynchornous operation - when async_op is set to True. The collective operation function returns a distributed request object. In general, you don’t need to create it manually and it is guaranteed to support two methods:

• is_completed() - returns True if the operation has finished
• wait() - will block the process until the operation is finished.

Collective functions¶

torch.distributed.broadcast(tensor, src, group=<object object>, async_op=False)[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 (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.all_reduce(tensor, op=ReduceOp.SUM, group=<object object>, async_op=False)[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.c10d.reduce_op enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.reduce(tensor, dst, op=ReduceOp.SUM, group=<object object>, async_op=False)[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. dst (int) – Destination rank op (optional) – One of the values from torch.distributed.c10d.reduce_op enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.all_gather(tensor_list, tensor, group=<object object>, async_op=False)[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 (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.gather(tensor, gather_list, dst, group=<object object>, async_op=False)[source]

Gathers a list of tensors in a single process.

Parameters: tensor (Tensor) – Input tensor. gather_list (list[Tensor]) – List of appropriately-sized tensors to use for received data. Required only in the receiving process. dst (int) – Destination rank. Required in all processes except the one that is receiveing the data. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.scatter(tensor, scatter_list, src, group=<object object>, async_op=False)[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. scatter_list (list[Tensor]) – List of tensors to scatter. Required only in the process that is sending the data. src (int) – Source rank. Required in all processes except the one that is sending the data. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.barrier(group=<object object>, async_op=False)[source]

Synchronizes all processes.

This collective blocks processes until the whole group enters this function, if async_op is False, or if async work handle is called on wait().

Parameters: group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

Multi-GPU collective functions¶

If you have more than one GPU on each node, when using the NCCL and Gloo backend, broadcast_multigpu() all_reduce_multigpu() reduce_multigpu() and all_gather_multigpu() support distributed collective operations among multiple GPUs within each node. These functions can potentially improve the overall distributed training performance and be easily used by passing a list of tensors. Each Tensor in the passed tensor list needs to be on a separate GPU device of the host where the function is called. Note that the length of the tensor list needs to be identical among all the distributed processes. Also note that currently the multi-GPU collective functions are only supported by the NCCL backend.

For example, if the system we use for distributed training has 2 nodes, each of which has 8 GPUs. On each of the 16 GPUs, there is a tensor that we would like to all-reduce. The following code can serve as a reference:

Code running on Node 0

import torch
import torch.distributed as dist

dist.init_process_group(backend="nccl",
init_method="file:///distributed_test",
world_size=2,
rank=0)
tensor_list = []
for dev_idx in range(torch.cuda.device_count()):
tensor_list.append(torch.FloatTensor([1]).cuda(dev_idx))

dist.all_reduce_multigpu(tensor_list)


Code running on Node 1

import torch
import torch.distributed as dist

dist.init_process_group(backend="nccl",
init_method="file:///distributed_test",
world_size=2,
rank=1)
tensor_list = []
for dev_idx in range(torch.cuda.device_count()):
tensor_list.append(torch.FloatTensor([1]).cuda(dev_idx))

dist.all_reduce_multigpu(tensor_list)


After the call, all 16 tensors on the two nodes will have the all-reduced value of 16

torch.distributed.broadcast_multigpu(tensor_list, src, group=<object object>, async_op=False, src_tensor=0)[source]

Broadcasts the tensor to the whole group with multiple GPU tensors per node.

tensor must have the same number of elements in all the GPUs from all processes participating in the collective. each tensor in the list must be on a different GPU

Only nccl and gloo backend are currently supported tensors should only be GPU tensors

Parameters: tensor_list (List[Tensor]) – Tensors that participate in the collective operation. if src is the rank, then src_tensorth element of tensor_list (tensor_list[src_tensor]) will be broadcasted to all other tensors (on different GPUs) in the src process and all tensors in tensor_list of other non-src processes. You also need to make sure that len(tensor_list) is the same for all the distributed processes calling this function. src (int) – Source rank. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op src_tensor (int, optional) – Source tensor rank within tensor_list Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.all_reduce_multigpu(tensor_list, op=ReduceOp.SUM, group=<object object>, async_op=False)[source]

Reduces the tensor data across all machines in such a way that all get the final result. This function reduces a number of tensors on every node, while each tensor resides on different GPUs. Therefore, the input tensor in the tensor list needs to be GPU tensors. Also, each tensor in the tensor list needs to reside on a different GPU.

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

Only nccl and gloo backend is currently supported tensors should only be GPU tensors

Parameters: list (tensor) – List of input and output tensors of the collective. The function operates in-place and requires that each tensor to be a GPU tensor on different GPUs. You also need to make sure that len(tensor_list) is the same for all the distributed processes calling this function. op (optional) – One of the values from torch.distributed.c10d.reduce_op enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.reduce_multigpu(tensor_list, dst, op=ReduceOp.SUM, group=<object object>, async_op=False, dst_tensor=0)[source]

Reduces the tensor data on multiple GPUs across all machines. Each tensor in tensor_list should reside on a separate GPU

Only the GPU of tensor_list[dst_tensor] on the process with rank dst is going to receive the final result.

Only nccl backend is currently supported tensors should only be GPU tensors

Parameters: tensor_list (List[Tensor]) – Input and output GPU tensors of the collective. The function operates in-place. You also need to make sure that len(tensor_list) is the same for all the distributed processes calling this function. dst (int) – Destination rank op (optional) – One of the values from torch.distributed.c10d.reduce_op enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op dst_tensor (int, optional) – Destination tensor rank within tensor_list Async work handle, if async_op is set to True. None, otherwise
torch.distributed.all_gather_multigpu(output_tensor_lists, input_tensor_list, group=<object object>, async_op=False)[source]

Gathers tensors from the whole group in a list. Each tensor in tensor_list should reside on a separate GPU

Only nccl backend is currently supported tensors should only be GPU tensors

Parameters: output_tensor_lists (List[List[Tensor]]) – Output lists. It should contain correctly-sized tensors on each GPU to be used for output of the collective. e.g. output_tensor_lists[i] contains the all_gather result that resides on the GPU of input_tensor_list[i]. Note that each element of output_tensor_lists[i] has the size of world_size * len(input_tensor_list), since the function all gathers the result from every single GPU in the group. To interpret each element of output_tensor_list[i], note that input_tensor_list[j] of rank k will be appear in output_tensor_list[i][rank * world_size + j] Also note that len(output_tensor_lists), and the size of each element in output_tensor_lists (each element is a list, therefore len(output_tensor_lists[i])) need to be the same for all the distributed processes calling this function. input_tensor_list (List[Tensor]) – List of tensors(on different GPUs) to be broadcast from current process. Note that len(input_tensor_list) needs to be the same for all the distributed processes calling this function. group (ProcessGroup, optional) – The process group to work on async_op (bool, optional) – Whether this op should be an async op Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

Launch utility¶

The torch.distributed package also provides a launch utility in torch.distributed.launch.

torch.distributed.launch is a module that spawns up multiple distributed training processes on each of the training nodes.

The utility can be used for single-node distributed training, in which one or more processes per node will be spawned. The utility can be used for either CPU training or GPU training. If the utility is used for GPU training, each distributed process will be operating on a single GPU. This can achieve well-improved single-node training performance. It can also be used in multi-node distributed training, by spawning up multiple processes on each node for well-improved multi-node distributed training performance as well. This will especially be benefitial for systems with multiple Infiniband interfaces that have direct-GPU support, since all of them can be utilized for aggregated communication bandwidth.

In both cases of single-node distributed training or multi-node distributed training, this utility will launch the given number of processes per node (--nproc_per_node). If used for GPU training, this number needs to be less or euqal to the number of GPUs on the current system (nproc_per_node), and each process will be operating on a single GPU from GPU 0 to GPU (nproc_per_node - 1).

How to use this module:

1. Single-Node multi-process distributed training
>>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE
YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other

1. Multi-Node multi-process distributed training: (e.g. two nodes)

Node 1: (IP: 192.168.1.1, and has a free port: 1234)

>>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE
--master_port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
and all other arguments of your training script)


Node 2:

>>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE
--master_port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
and all other arguments of your training script)

1. To look up what optional arguments this module offers:
>>> python -m torch.distributed.launch --help


Important Notices:

1. This utilty and multi-process distributed (single-node or multi-node) GPU training currently only achieves the best performance using the NCCL distributed backend. Thus NCCL backend is the recommended backend to use for GPU training.

2. In your training program, you must parse the command-line argument: --local_rank=LOCAL_PROCESS_RANK, which will be provided by this module. If your training program uses GPUs, you should ensure that your code only runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:

Parsing the local_rank argument

>>> import argparse
>>> parser = argparse.ArgumentParser()
>>> args = parser.parse_args()


Set your device to local rank using either

>>> torch.cuda.set_device(arg.local_rank)  # before your code runs


or

>>> with torch.cuda.device(arg.local_rank):
>>>    # your code to run


3. In your training program, you are supposed to call the following function at the beginning to start the distributed backend. You need to make sure that the init_method uses env://, which is the only supported init_method by this module.

torch.distributed.init_process_group(backend='YOUR BACKEND',
init_method='env://')


4. In your training program, you can either use regular distributed functions or use torch.nn.parallel.DistributedDataParallel() module. If your training program uses GPUs for training and you would like to use torch.nn.parallel.DistributedDataParallel() module, here is how to configure it.

model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[arg.local_rank],
output_device=arg.local_rank)


Please ensure that device_ids argument is set to be the only GPU device id that your code will be operating on. This is generally the local rank of the process. In other words, the device_ids needs to be [args.local_rank], and output_device needs to be args.local_rank in order to use this utility