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# Distributed communication package - torch.distributed

Note

Please refer to PyTorch Distributed Overview for a brief introduction to all features related to distributed training.

## Backends

`torch.distributed` supports three built-in 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 | ✓ | ✘ | ✓ | ? | ✘ | ✓
broadcast | ✓ | ✓ | ✓ | ? | ✘ | ✓
all_reduce | ✓ | ✓ | ✓ | ? | ✘ | ✓
reduce | ✓ | ✘ | ✓ | ? | ✘ | ✓
all_gather | ✓ | ✘ | ✓ | ? | ✘ | ✓
gather | ✓ | ✘ | ✓ | ? | ✘ | ✓
scatter | ✓ | ✘ | ✓ | ? | ✘ | ✓
reduce_scatter | ✘ | ✘ | ✘ | ✘ | ✘ | ✓
all_to_all | ✘ | ✘ | ✓ | ? | ✘ | ✓
barrier | ✓ | ✘ | ✓ | ? | ✘ | ✓

### Backends that come with PyTorch

PyTorch distributed package supports Linux (stable), MacOS (stable), and Windows (prototype). By default for Linux, the Gloo and NCCL backends are built and included in PyTorch distributed (NCCL only when building with CUDA). MPI is an optional backend that can only be included if you build PyTorch from source. (e.g. building PyTorch on a host that has MPI installed.)

Note

As of PyTorch v1.8, Windows supports all collective communications backend but NCCL, If the init_method argument of `init_process_group()` points to a file it must adhere to the following schema:

- Local file system, `init_method="file:///d:/tmp/some_file"`

- Shared file system, `init_method="file://////{machine_name}/{share_folder_name}/some_file"`

Same as on Linux platform, you can enable TcpStore by setting environment variables, MASTER_ADDR and MASTER_PORT.

### Which backend to use?

In the past, we were often asked: “which backend should I use?”.

- Rule of thumb

  - Use the NCCL backend for distributed GPU training

  - Use the Gloo backend for distributed CPU training.

- GPU hosts with InfiniBand interconnect

  - Use NCCL, since it’s the only backend that currently supports InfiniBand and GPUDirect.

- GPU hosts with Ethernet interconnect

  - Use NCCL, since it currently provides the best distributed GPU training performance, especially for multiprocess single-node or multi-node distributed training. If you encounter any problem with NCCL, use Gloo as the fallback option. (Note that Gloo currently runs slower than NCCL for GPUs.)

- CPU hosts with InfiniBand interconnect

  - If your InfiniBand has enabled IP over IB, use Gloo, otherwise, use MPI instead. We are planning on adding InfiniBand support for Gloo in the upcoming releases.

- CPU hosts with Ethernet interconnect

  - Use Gloo, unless you have specific reasons to use MPI.

### Common environment variables

#### Choosing the network interface to use

By default, both the NCCL and Gloo backends will try to find the right network interface to use. If the automatically detected interface is not correct, you can override it using the following environment variables (applicable to the respective backend):

- NCCL_SOCKET_IFNAME, for example `export NCCL_SOCKET_IFNAME=eth0`

- GLOO_SOCKET_IFNAME, for example `export GLOO_SOCKET_IFNAME=eth0`

If you’re using the Gloo backend, you can specify multiple interfaces by separating them by a comma, like this: `export GLOO_SOCKET_IFNAME=eth0,eth1,eth2,eth3`. The backend will dispatch operations in a round-robin fashion across these interfaces. It is imperative that all processes specify the same number of interfaces in this variable.

#### Other NCCL environment variables

Debugging - in case of NCCL failure, you can set `NCCL_DEBUG=INFO` to print an explicit warning message as well as basic NCCL initialization information.

You may also use `NCCL_DEBUG_SUBSYS` to get more details about a specific aspect of NCCL. For example, `NCCL_DEBUG_SUBSYS=COLL` would print logs of collective calls, which may be helpful when debugging hangs, especially those caused by collective type or message size mismatch. In case of topology detection failure, it would be helpful to set `NCCL_DEBUG_SUBSYS=GRAPH` to inspect the detailed detection result and save as reference if further help from NCCL team is needed.

Performance tuning - NCCL performs automatic tuning based on its topology detection to save users’ tuning effort. On some socket-based systems, users may still try tuning `NCCL_SOCKET_NTHREADS` and `NCCL_NSOCKS_PERTHREAD` to increase socket network bandwidth. These two environment variables have been pre-tuned by NCCL for some cloud providers, such as AWS or GCP.

For a full list of NCCL environment variables, please refer to NVIDIA NCCL’s official documentation

## 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.is_available()

  Returns `True` if the distributed package is available. Otherwise, `torch.distributed` does not expose any other APIs. Currently, `torch.distributed` is available on Linux, MacOS and Windows. Set `USE_DISTRIBUTED=1` to enable it when building PyTorch from source. Currently, the default value is `USE_DISTRIBUTED=1` for Linux and Windows, `USE_DISTRIBUTED=0` for MacOS.

  Return type:

    bool

torch.distributed.init_process_group(backend=None, init_method=None, timeout=datetime.timedelta(seconds=1800), world_size=- 1, rank=- 1, store=None, group_name='', pg_options=None)

  Initializes the default distributed process group, and this will also initialize the distributed package.

  There are 2 main ways to initialize a process group:

    1. Specify `store`, `rank`, and `world_size` explicitly.

    2. Specify `init_method` (a URL string) which indicates where/how to discover peers. Optionally specify `rank` and `world_size`, or encode all required parameters in the URL and omit them.

  If neither is specified, `init_method` is assumed to be “env://”.

  Parameters:

    - backend (str or Backend, optional) – The backend to use. Depending on build-time configurations, valid values include `mpi`, `gloo`, `nccl`, and `ucc`. If the backend is not provided, then both a `gloo` and `nccl` backend will be created, see notes below for how multiple backends are managed. This field can be given as a lowercase string (e.g., `"gloo"`), which can also be accessed via `Backend` attributes (e.g., `Backend.GLOO`). If using multiple processes per machine with `nccl` backend, each process must have exclusive access to every GPU it uses, as sharing GPUs between processes can result in deadlocks. `ucc` backend is experimental.

    - init_method (str, optional) – URL specifying how to initialize the process group. Default is “env://” if no `init_method` or `store` is specified. Mutually exclusive with `store`.

    - world_size (int, optional) – Number of processes participating in the job. Required if `store` is specified.

    - rank (int, optional) – Rank of the current process (it should be a number between 0 and `world_size`-1). Required if `store` is specified.

    - store (Store, optional) – Key/value store accessible to all workers, used to exchange connection/address information. Mutually exclusive with `init_method`.

    - timeout (timedelta, optional) – Timeout for operations executed against the process group. Default value equals 30 minutes. This is applicable for the `gloo` backend. For `nccl`, this is applicable only if the environment variable `NCCL_BLOCKING_WAIT` or `NCCL_ASYNC_ERROR_HANDLING` is set to 1. When `NCCL_BLOCKING_WAIT` is set, this is the duration for which the process will block and wait for collectives to complete before throwing an exception. When `NCCL_ASYNC_ERROR_HANDLING` is set, this is the duration after which collectives will be aborted asynchronously and the process will crash. `NCCL_BLOCKING_WAIT` will provide errors to the user which can be caught and handled, but due to its blocking nature, it has a performance overhead. On the other hand, `NCCL_ASYNC_ERROR_HANDLING` has very little performance overhead, but crashes the process on errors. This is done since CUDA execution is async and it is no longer safe to continue executing user code since failed async NCCL operations might result in subsequent CUDA operations running on corrupted data. Only one of these two environment variables should be set. For `ucc`, blocking wait is supported similar to NCCL. However, async error handling is done differently since with UCC we have progress thread and not watch-dog thread.

    - group_name (str, optional, deprecated) – Group name. This argument is ignored

    - pg_options (ProcessGroupOptions, optional) – process group options specifying what additional options need to be passed in during the construction of specific process groups. As of now, the only options we support is `ProcessGroupNCCL.Options` for the `nccl` backend, `is_high_priority_stream` can be specified so that the nccl backend can pick up high priority cuda streams when there’re compute kernels waiting.

  Note

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

  Note

  Support for multiple backends is experimental. Currently when no backend is specified, both `gloo` and `nccl` backends will be created. The `gloo` backend will be used for collectives with CPU tensors and the `nccl` backend will be used for collectives with CUDA tensors. A custom backend can be specified by passing in a string with format “<device_type>:<backend_name>,<device_type>:<backend_name>”, e.g. “cpu:gloo,cuda:custom_backend”.

torch.distributed.is_initialized()

  Checking if the default process group has been initialized

  Return type:

    bool

torch.distributed.is_mpi_available()

  Checks if the MPI backend is available.

  Return type:

    bool

torch.distributed.is_nccl_available()

  Checks if the NCCL backend is available.

  Return type:

    bool

torch.distributed.is_gloo_available()

  Checks if the Gloo backend is available.

  Return type:

    bool

torch.distributed.is_torchelastic_launched()

  Checks whether this process was launched with `torch.distributed.elastic` (aka torchelastic). The existence of `TORCHELASTIC_RUN_ID` environment variable is used as a proxy to determine whether the current process was launched with torchelastic. This is a reasonable proxy since `TORCHELASTIC_RUN_ID` maps to the rendezvous id which is always a non-null value indicating the job id for peer discovery purposes..

  Return type:

    bool

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 will always create the file and try its best to clean up and remove the file at the end of the program. In other words, each initialization with the file init method will need a brand new empty file in order for the initialization to succeed. If the same file used by the previous initialization (which happens not to get cleaned up) is used again, this is unexpected behavior and can often cause deadlocks and failures. Therefore, even though this method will try its best to clean up the file, if the auto-delete happens to be unsuccessful, it is your responsibility to ensure that the file is removed at the end of the training to prevent the same file to be reused again during the next time. 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, failures are expected. The rule of thumb here is that, make sure that the file is non-existent or empty every time `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://`).

## Post-Initialization

Once `torch.distributed.init_process_group()` was run, the following functions can be used. To check whether the process group has already been initialized use `torch.distributed.is_initialized()`.

class torch.distributed.Backend(name)

  An enum-like class of available backends: GLOO, NCCL, UCC, MPI, and other registered backends.

  The values of this class are lowercase strings, e.g., `"gloo"`. They can be accessed as attributes, e.g., `Backend.NCCL`.

  This class can be directly called to parse the string, e.g., `Backend(backend_str)` will check if `backend_str` is valid, and return the parsed lowercase string if so. It also accepts uppercase strings, e.g., `Backend("GLOO")` returns `"gloo"`.

  Note

  The entry `Backend.UNDEFINED` is present but only used as initial value of some fields. Users should neither use it directly nor assume its existence.

  classmethod register_backend(name, func, extended_api=False, devices=None)

    Registers a new backend with the given name and instantiating function.

    This class method is used by 3rd party `ProcessGroup` extension to register new backends.

    Parameters:

      - name (str) – Backend name of the `ProcessGroup` extension. It should match the one in `init_process_group()`.

      - func (function) – Function handler that instantiates the backend. The function should be implemented in the backend extension and takes four arguments, including `store`, `rank`, `world_size`, and `timeout`.

      - extended_api (bool, optional) – Whether the backend supports extended argument structure. Default: `False`. If set to `True`, the backend will get an instance of `c10d::DistributedBackendOptions`, and a process group options object as defined by the backend implementation.

      - device (str or list of str, optional) – device type this backend supports, e.g. “cpu”, “cuda”, etc. If None, assuming both “cpu” and “cuda”

    Note

    This support of 3rd party backend is experimental and subject to change.

torch.distributed.get_backend(group=None)

  Returns the backend of the given process group.

  Parameters:

    group (ProcessGroup, optional) – The process group to work on. The default is the general main process group. If another specific group is specified, the calling process must be part of `group`.

  Returns:

    The backend of the given process group as a lower case string.

  Return type:

    str

torch.distributed.get_rank(group=None)

  Returns the rank of the current process in the provided `group` or the default group if none was provided.

  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. If None, the default process group will be used.

  Returns:

    The rank of the process group -1, if not part of the group

  Return type:

    int

torch.distributed.get_world_size(group=None)

  Returns the number of processes in the current process group

  Parameters:

    group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

  Returns:

    The world size of the process group -1, if not part of the group

  Return type:

    int

## Distributed Key-Value Store

The distributed package comes with a distributed key-value store, which can be used to share information between processes in the group as well as to initialize the distributed package in `torch.distributed.init_process_group()` (by explicitly creating the store as an alternative to specifying `init_method`.) There are 3 choices for Key-Value Stores: `TCPStore`, `FileStore`, and `HashStore`.

class torch.distributed.Store

  Base class for all store implementations, such as the 3 provided by PyTorch distributed: (`TCPStore`, `FileStore`, and `HashStore`).

class torch.distributed.TCPStore

  A TCP-based distributed key-value store implementation. The server store holds the data, while the client stores can connect to the server store over TCP and perform actions such as `set()` to insert a key-value pair, `get()` to retrieve a key-value pair, etc. There should always be one server store initialized because the client store(s) will wait for the server to establish a connection.

  Parameters:

    - host_name (str) – The hostname or IP Address the server store should run on.

    - port (int) – The port on which the server store should listen for incoming requests.

    - world_size (int, optional) – The total number of store users (number of clients + 1 for the server). Default is None (None indicates a non-fixed number of store users).

    - is_master (bool, optional) – True when initializing the server store and False for client stores. Default is False.

    - timeout (timedelta, optional) – Timeout used by the store during initialization and for methods such as `get()` and `wait()`. Default is timedelta(seconds=300)

    - wait_for_worker (bool, optional) – Whether to wait for all the workers to connect with the server store. This is only applicable when world_size is a fixed value. Default is True.

    - multi_tenant (bool, optional) – If True, all `TCPStore` instances in the current process with the same host/port will use the same underlying `TCPServer`. Default is False.

    - master_listen_fd (int, optional) – If specified, the underlying `TCPServer` will listen on this file descriptor, which must be a socket already bound to `port`. Useful to avoid port assignment races in some scenarios. Default is None (meaning the server creates a new socket and attempts to bind it to `port`).

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> # Run on process 1 (server)
        >>> server_store = dist.TCPStore("127.0.0.1", 1234, 2, True, timedelta(seconds=30))
        >>> # Run on process 2 (client)
        >>> client_store = dist.TCPStore("127.0.0.1", 1234, 2, False)
        >>> # Use any of the store methods from either the client or server after initialization
        >>> server_store.set("first_key", "first_value")
        >>> client_store.get("first_key")

class torch.distributed.HashStore

  A thread-safe store implementation based on an underlying hashmap. This store can be used within the same process (for example, by other threads), but cannot be used across processes.

  Example::

        >>> import torch.distributed as dist
        >>> store = dist.HashStore()
        >>> # store can be used from other threads
        >>> # Use any of the store methods after initialization
        >>> store.set("first_key", "first_value")

class torch.distributed.FileStore

  A store implementation that uses a file to store the underlying key-value pairs.

  Parameters:

    - file_name (str) – path of the file in which to store the key-value pairs

    - world_size (int, optional) – The total number of processes using the store. Default is -1 (a negative value indicates a non-fixed number of store users).

  Example::

        >>> import torch.distributed as dist
        >>> store1 = dist.FileStore("/tmp/filestore", 2)
        >>> store2 = dist.FileStore("/tmp/filestore", 2)
        >>> # Use any of the store methods from either the client or server after initialization
        >>> store1.set("first_key", "first_value")
        >>> store2.get("first_key")

class torch.distributed.PrefixStore

  A wrapper around any of the 3 key-value stores (`TCPStore`, `FileStore`, and `HashStore`) that adds a prefix to each key inserted to the store.

  Parameters:

    - prefix (str) – The prefix string that is prepended to each key before being inserted into the store.

    - store (torch.distributed.store) – A store object that forms the underlying key-value store.

torch.distributed.Store.set(self: torch._C._distributed_c10d.Store, arg0: str, arg1: str) → None

  Inserts the key-value pair into the store based on the supplied `key` and `value`. If `key` already exists in the store, it will overwrite the old value with the new supplied `value`.

  Parameters:

    - key (str) – The key to be added to the store.

    - value (str) – The value associated with `key` to be added to the store.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> store.set("first_key", "first_value")
        >>> # Should return "first_value"
        >>> store.get("first_key")

torch.distributed.Store.get(self: torch._C._distributed_c10d.Store, arg0: str) → bytes

  Retrieves the value associated with the given `key` in the store. If `key` is not present in the store, the function will wait for `timeout`, which is defined when initializing the store, before throwing an exception.

  Parameters:

    key (str) – The function will return the value associated with this key.

  Returns:

    Value associated with `key` if `key` is in the store.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> store.set("first_key", "first_value")
        >>> # Should return "first_value"
        >>> store.get("first_key")

torch.distributed.Store.add(self: torch._C._distributed_c10d.Store, arg0: str, arg1: int) → int

  The first call to add for a given `key` creates a counter associated with `key` in the store, initialized to `amount`. Subsequent calls to add with the same `key` increment the counter by the specified `amount`. Calling `add()` with a key that has already been set in the store by `set()` will result in an exception.

  Parameters:

    - key (str) – The key in the store whose counter will be incremented.

    - amount (int) – The quantity by which the counter will be incremented.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> # Using TCPStore as an example, other store types can also be used
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> store.add("first_key", 1)
        >>> store.add("first_key", 6)
        >>> # Should return 7
        >>> store.get("first_key")

torch.distributed.Store.compare_set(self: torch._C._distributed_c10d.Store, arg0: str, arg1: str, arg2: str) → bytes

  Inserts the key-value pair into the store based on the supplied `key` and performs comparison between `expected_value` and `desired_value` before inserting. `desired_value` will only be set if `expected_value` for the `key` already exists in the store or if `expected_value` is an empty string.

  Parameters:

    - key (str) – The key to be checked in the store.

    - expected_value (str) – The value associated with `key` to be checked before insertion.

    - desired_value (str) – The value associated with `key` to be added to the store.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> store.set("key", "first_value")
        >>> store.compare_set("key", "first_value", "second_value")
        >>> # Should return "second_value"
        >>> store.get("key")

torch.distributed.Store.wait(*args, **kwargs)

  Overloaded function.

  1. wait(self: torch._C._distributed_c10d.Store, arg0: List[str]) -> None

  Waits for each key in `keys` to be added to the store. If not all keys are set before the `timeout` (set during store initialization), then `wait` will throw an exception.

  Parameters:

    keys (list) – List of keys on which to wait until they are set in the store.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> # Using TCPStore as an example, other store types can also be used
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> # This will throw an exception after 30 seconds
        >>> store.wait(["bad_key"])

  2. wait(self: torch._C._distributed_c10d.Store, arg0: List[str], arg1: datetime.timedelta) -> None

  Waits for each key in `keys` to be added to the store, and throws an exception if the keys have not been set by the supplied `timeout`.

  Parameters:

    - keys (list) – List of keys on which to wait until they are set in the store.

    - timeout (timedelta) – Time to wait for the keys to be added before throwing an exception.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> # Using TCPStore as an example, other store types can also be used
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> # This will throw an exception after 10 seconds
        >>> store.wait(["bad_key"], timedelta(seconds=10))

torch.distributed.Store.num_keys(self: torch._C._distributed_c10d.Store) → int

  Returns the number of keys set in the store. Note that this number will typically be one greater than the number of keys added by `set()` and `add()` since one key is used to coordinate all the workers using the store.

  Warning

  When used with the `TCPStore`, `num_keys` returns the number of keys written to the underlying file. If the store is destructed and another store is created with the same file, the original keys will be retained.

  Returns:

    The number of keys present in the store.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> # Using TCPStore as an example, other store types can also be used
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> store.set("first_key", "first_value")
        >>> # This should return 2
        >>> store.num_keys()

torch.distributed.Store.delete_key(self: torch._C._distributed_c10d.Store, arg0: str) → bool

  Deletes the key-value pair associated with `key` from the store. Returns true if the key was successfully deleted, and false if it was not.

  Warning

  The `delete_key` API is only supported by the `TCPStore` and `HashStore`. Using this API with the `FileStore` will result in an exception.

  Parameters:

    key (str) – The key to be deleted from the store

  Returns:

    True if `key` was deleted, otherwise False.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> # Using TCPStore as an example, HashStore can also be used
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> store.set("first_key")
        >>> # This should return true
        >>> store.delete_key("first_key")
        >>> # This should return false
        >>> store.delete_key("bad_key")

torch.distributed.Store.set_timeout(self: torch._C._distributed_c10d.Store, arg0: datetime.timedelta) → None

  Sets the store’s default timeout. This timeout is used during initialization and in `wait()` and `get()`.

  Parameters:

    timeout (timedelta) – timeout to be set in the store.

  Example::

        >>> import torch.distributed as dist
        >>> from datetime import timedelta
        >>> # Using TCPStore as an example, other store types can also be used
        >>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
        >>> store.set_timeout(timedelta(seconds=10))
        >>> # This will throw an exception after 10 seconds
        >>> store.wait(["bad_key"])

## 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, timeout=datetime.timedelta(seconds=1800), backend=None, pg_options=None, use_local_synchronization=False)

  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.

  Warning

  Using multiple process groups with the `NCCL` backend concurrently is not safe and the user should perform explicit synchronization in their application to ensure only one process group is used at a time. This means collectives from one process group should have completed execution on the device (not just enqueued since CUDA execution is async) before collectives from another process group are enqueued. See Using multiple NCCL communicators concurrently for more details.

  Parameters:

    - ranks (list[int]) – List of ranks of group members. If `None`, will be set to all ranks. Default is `None`.

    - timeout (timedelta, optional) – Timeout for operations executed against the process group. Default value equals 30 minutes. This is applicable for the `gloo` backend. For `nccl`, this is applicable only if the environment variable `NCCL_BLOCKING_WAIT` or `NCCL_ASYNC_ERROR_HANDLING` is set to 1. When `NCCL_BLOCKING_WAIT` is set, this is the duration for which the process will block and wait for collectives to complete before throwing an exception. When `NCCL_ASYNC_ERROR_HANDLING` is set, this is the duration after which collectives will be aborted asynchronously and the process will crash. `NCCL_BLOCKING_WAIT` will provide errors to the user which can be caught and handled, but due to its blocking nature, it has a performance overhead. On the other hand, `NCCL_ASYNC_ERROR_HANDLING` has very little performance overhead, but crashes the process on errors. This is done since CUDA execution is async and it is no longer safe to continue executing user code since failed async NCCL operations might result in subsequent CUDA operations running on corrupted data. Only one of these two environment variables should be set.

    - backend (str or Backend, optional) – The backend to use. Depending on build-time configurations, valid values are `gloo` and `nccl`. By default uses the same backend as the global group. This field should be given as a lowercase string (e.g., `"gloo"`), which can also be accessed via `Backend` attributes (e.g., `Backend.GLOO`). If `None` is passed in, the backend corresponding to the default process group will be used. Default is `None`.

    - pg_options (ProcessGroupOptions, optional) – process group options specifying what additional options need to be passed in during the construction of specific process groups. i.e. for the `nccl` backend, `is_high_priority_stream` can be specified so that process group can pick up high priority cuda streams.

    - use_local_synchronization (bool, optional) – perform a group-local barrier at the end of the process group creation. This is different in that non-member ranks don’t need to call into API and don’t join the barrier.

  Returns:

    A handle of distributed group that can be given to collective calls or None if the rank is not part of `ranks`.

  N.B. use_local_synchronization doesn’t work with MPI.

  N.B. While use_local_synchronization=True can be significantly faster with larger clusters and small process groups, care must be taken since it changes cluster behavior as non-member ranks don’t join the group barrier().

  N.B. use_local_synchronization=True can lead to deadlocks when each rank creates multiple overlaping process groups. To avoid that, make sure all ranks follow the same global creation order.

torch.distributed.get_group_rank(group, global_rank)

  Translate a global rank into a group rank.

  `global_rank` must be part of `group` otherwise this raises RuntimeError.

  Parameters:

    - group (ProcessGroup) – ProcessGroup to find the relative rank.

    - global_rank (int) – Global rank to query.

  Returns:

    Group rank of `global_rank` relative to `group`

  Return type:

    int

  N.B. calling this function on the default process group returns identity

torch.distributed.get_global_rank(group, group_rank)

  Translate a group rank into a global rank.

  `group_rank` must be part of group otherwise this raises RuntimeError.

  Parameters:

    - group (ProcessGroup) – ProcessGroup to find the global rank from.

    - group_rank (int) – Group rank to query.

  Returns:

    Global rank of `group_rank` relative to `group`

  Return type:

    int

  N.B. calling this function on the default process group returns identity

torch.distributed.get_process_group_ranks(group)

  Get all ranks associated with `group`.

  Parameters:

    group (ProcessGroup) – ProcessGroup to get all ranks from.

  Returns:

    List of global ranks ordered by group rank.

## Point-to-point communication

torch.distributed.send(tensor, dst, group=None, tag=0)

  Sends a tensor synchronously.

  Parameters:

    - tensor (Tensor) – Tensor to send.

    - dst (int) – Destination rank. Destination rank should not be the same

    - process. (as the rank of the current) –

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - tag (int, optional) – Tag to match send with remote recv

torch.distributed.recv(tensor, src=None, group=None, tag=0)

  Receives a tensor synchronously.

  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. If None, the default process group will be used.

    - tag (int, optional) – Tag to match recv with remote send

  Returns:

    Sender rank -1, if not part of the group

  Return type:

    int

`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=None, tag=0)

  Sends a tensor asynchronously.

  Warning

  Modifying `tensor` before the request completes causes undefined behavior.

  Warning

  `tag` is not supported with the NCCL backend.

  Parameters:

    - tensor (Tensor) – Tensor to send.

    - dst (int) – Destination rank.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - tag (int, optional) – Tag to match send with remote recv

  Returns:

    A distributed request object. None, if not part of the group

  Return type:

    Work

torch.distributed.irecv(tensor, src=None, group=None, tag=0)

  Receives a tensor asynchronously.

  Warning

  `tag` is not supported with the NCCL backend.

  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. If None, the default process group will be used.

    - tag (int, optional) – Tag to match recv with remote send

  Returns:

    A distributed request object. None, if not part of the group

  Return type:

    Work

torch.distributed.batch_isend_irecv(p2p_op_list)

  Send or Receive a batch of tensors asynchronously and return a list of requests.

  Process each of the operations in `p2p_op_list` and return the corresponding requests. NCCL, Gloo, and UCC backend are currently supported.

  Parameters:

    p2p_op_list – A list of point-to-point operations(type of each operator is `torch.distributed.P2POp`). The order of the isend/irecv in the list matters and it needs to match with corresponding isend/irecv on the remote end.

  Returns:

    A list of distributed request objects returned by calling the corresponding op in the op_list.

  Examples

      >>> send_tensor = torch.arange(2) + 2 * rank
      >>> recv_tensor = torch.randn(2)
      >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1)%world_size)
      >>> recv_op = dist.P2POp(dist.irecv, recv_tensor, (rank - 1 + world_size)%world_size)
      >>> reqs = batch_isend_irecv([send_op, recv_op])
      >>> for req in reqs:
      >>>     req.wait()
      >>> recv_tensor
      tensor([2, 3])     # Rank 0
      tensor([0, 1])     # Rank 1

  Note

  Note that when this API is used with the NCCL PG backend, users must set the current GPU device with torch.cuda.set_device, otherwise it will lead to unexpected hang issues.

  In addition, if this API is the first collective call in the `group` passed to `dist.P2POp`, all ranks of the `group` must participate in this API call; otherwise, the behavior is undefined. If this API call is not the first collective call in the `group`, batched P2P operations involving only a subset of ranks of the `group` are allowed.

class torch.distributed.P2POp(op, tensor, peer, group=None, tag=0)

  A class to build point-to-point operations for `batch_isend_irecv`.

  This class builds the type of P2P operation, communication buffer, peer rank, Process Group, and tag. Instances of this class will be passed to `batch_isend_irecv` for point-to-point communications.

  Parameters:

    - op (Callable) – A function to send data to or receive data from a peer process. The type of `op` is either `torch.distributed.isend` or `torch.distributed.irecv`.

    - tensor (Tensor) – Tensor to send or receive.

    - peer (int) – Destination or source rank.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - tag (int, optional) – Tag to match send with recv.

## Synchronous and asynchronous collective operations

Every collective operation function supports the following two kinds of operations, depending on the setting of the `async_op` flag passed into the collective:

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. In the case of CUDA operations, it is not guaranteed that the CUDA operation is completed, since CUDA operations are asynchronous. For CPU collectives, any further function calls utilizing the output of the collective call will behave as expected. For CUDA collectives, function calls utilizing the output on the same CUDA stream will behave as expected. Users must take care of synchronization under the scenario of running under different streams. For details on CUDA semantics such as stream synchronization, see CUDA Semantics. See the below script to see examples of differences in these semantics for CPU and CUDA operations.

Asynchronous 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()` - in the case of CPU collectives, returns `True` if completed. In the case of CUDA operations, returns `True` if the operation has been successfully enqueued onto a CUDA stream and the output can be utilized on the default stream without further synchronization.

- `wait()` - in the case of CPU collectives, will block the process until the operation is completed. In the case of CUDA collectives, will block until the operation has been successfully enqueued onto a CUDA stream and the output can be utilized on the default stream without further synchronization.

- `get_future()` - returns `torch._C.Future` object. Supported for NCCL, also supported for most operations on GLOO and MPI, except for peer to peer operations. Note: as we continue adopting Futures and merging APIs, `get_future()` call might become redundant.

Example

The following code can serve as a reference regarding semantics for CUDA operations when using distributed collectives. It shows the explicit need to synchronize when using collective outputs on different CUDA streams:

    # Code runs on each rank.
    dist.init_process_group("nccl", rank=rank, world_size=2)
    output = torch.tensor([rank]).cuda(rank)
    s = torch.cuda.Stream()
    handle = dist.all_reduce(output, async_op=True)
    # Wait ensures the operation is enqueued, but not necessarily complete.
    handle.wait()
    # Using result on non-default stream.
    with torch.cuda.stream(s):
        s.wait_stream(torch.cuda.default_stream())
        output.add_(100)
    if rank == 0:
        # if the explicit call to wait_stream was omitted, the output below will be
        # non-deterministically 1 or 101, depending on whether the allreduce overwrote
        # the value after the add completed.
        print(output)

## Collective functions

torch.distributed.broadcast(tensor, src, group=None, async_op=False)

  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. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

torch.distributed.broadcast_object_list(object_list, src=0, group=None, device=None)

  Broadcasts picklable objects in `object_list` to the whole group. Similar to `broadcast()`, but Python objects can be passed in. Note that all objects in `object_list` must be picklable in order to be broadcasted.

  Parameters:

    - object_list (List[Any]) – List of input objects to broadcast. Each object must be picklable. Only objects on the `src` rank will be broadcast, but each rank must provide lists of equal sizes.

    - src (int) – Source rank from which to broadcast `object_list`.

    - group – (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is `None`.

    - device (`torch.device`, optional) – If not None, the objects are serialized and converted to tensors which are moved to the `device` before broadcasting. Default is `None`.

  Returns:

    `None`. If rank is part of the group, `object_list` will contain the broadcasted objects from `src` rank.

  Note

  For NCCL-based process groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by `torch.cuda.current_device()` and it is the user’s responsibility to ensure that this is set so that each rank has an individual GPU, via `torch.cuda.set_device()`.

  Note

  Note that this API differs slightly from the `all_gather()` collective since it does not provide an `async_op` handle and thus will be a blocking call.

  Warning

  `broadcast_object_list()` uses `pickle` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust.

  Warning

  Calling `broadcast_object_list()` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using `broadcast()` instead.

  Example::

        >>> # Note: Process group initialization omitted on each rank.
        >>> import torch.distributed as dist
        >>> if dist.get_rank() == 0:
        >>>     # Assumes world_size of 3.
        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
        >>> else:
        >>>     objects = [None, None, None]
        >>> # Assumes backend is not NCCL
        >>> device = torch.device("cpu")
        >>> dist.broadcast_object_list(objects, src=0, device=device)
        >>> objects
        ['foo', 12, {1: 2}]

torch.distributed.all_reduce(tensor, op=<RedOpType.SUM: 0>, group=None, async_op=False)

  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.

  Complex tensors are supported.

  Parameters:

    - tensor (Tensor) – Input and output of the collective. The function operates in-place.

    - op (optional) – One of the values from `torch.distributed.ReduceOp` enum. Specifies an operation used for element-wise reductions.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

  Examples

      >>> # All tensors below are of torch.int64 type.
      >>> # We have 2 process groups, 2 ranks.
      >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
      >>> tensor
      tensor([1, 2]) # Rank 0
      tensor([3, 4]) # Rank 1
      >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
      >>> tensor
      tensor([4, 6]) # Rank 0
      tensor([4, 6]) # Rank 1

      >>> # All tensors below are of torch.cfloat type.
      >>> # We have 2 process groups, 2 ranks.
      >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j)
      >>> tensor
      tensor([1.+1.j, 2.+2.j]) # Rank 0
      tensor([3.+3.j, 4.+4.j]) # Rank 1
      >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
      >>> tensor
      tensor([4.+4.j, 6.+6.j]) # Rank 0
      tensor([4.+4.j, 6.+6.j]) # Rank 1

torch.distributed.reduce(tensor, dst, op=<RedOpType.SUM: 0>, group=None, async_op=False)

  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.ReduceOp` enum. Specifies an operation used for element-wise reductions.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    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=None, async_op=False)

  Gathers tensors from the whole group in a list.

  Complex tensors are supported.

  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. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

  Examples

      >>> # All tensors below are of torch.int64 dtype.
      >>> # We have 2 process groups, 2 ranks.
      >>> tensor_list = [torch.zeros(2, dtype=torch.int64) for _ in range(2)]
      >>> tensor_list
      [tensor([0, 0]), tensor([0, 0])] # Rank 0 and 1
      >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
      >>> tensor
      tensor([1, 2]) # Rank 0
      tensor([3, 4]) # Rank 1
      >>> dist.all_gather(tensor_list, tensor)
      >>> tensor_list
      [tensor([1, 2]), tensor([3, 4])] # Rank 0
      [tensor([1, 2]), tensor([3, 4])] # Rank 1

      >>> # All tensors below are of torch.cfloat dtype.
      >>> # We have 2 process groups, 2 ranks.
      >>> tensor_list = [torch.zeros(2, dtype=torch.cfloat) for _ in range(2)]
      >>> tensor_list
      [tensor([0.+0.j, 0.+0.j]), tensor([0.+0.j, 0.+0.j])] # Rank 0 and 1
      >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j)
      >>> tensor
      tensor([1.+1.j, 2.+2.j]) # Rank 0
      tensor([3.+3.j, 4.+4.j]) # Rank 1
      >>> dist.all_gather(tensor_list, tensor)
      >>> tensor_list
      [tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # Rank 0
      [tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # Rank 1

torch.distributed.all_gather_into_tensor(output_tensor, input_tensor, group=None, async_op=False)

  Gather tensors from all ranks and put them in a single output tensor.

  Parameters:

    - output_tensor (Tensor) – Output tensor to accommodate tensor elements from all ranks. It must be correctly sized to have one of the following forms: (i) a concatenation of all the input tensors along the primary dimension; for definition of “concatenation”, see `torch.cat()`; (ii) a stack of all the input tensors along the primary dimension; for definition of “stack”, see `torch.stack()`. Examples below may better explain the supported output forms.

    - input_tensor (Tensor) – Tensor to be gathered from current rank. Different from the `all_gather` API, the input tensors in this API must have the same size across all ranks.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

  Examples

      >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
      >>> # We have two ranks.
      >>> device = torch.device(f'cuda:{rank}')
      >>> tensor_in = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
      >>> tensor_in
      tensor([1, 2], device='cuda:0') # Rank 0
      tensor([3, 4], device='cuda:1') # Rank 1
      >>> # Output in concatenation form
      >>> tensor_out = torch.zeros(world_size * 2, dtype=torch.int64, device=device)
      >>> dist.all_gather_into_tensor(tensor_out, tensor_in)
      >>> tensor_out
      tensor([1, 2, 3, 4], device='cuda:0') # Rank 0
      tensor([1, 2, 3, 4], device='cuda:1') # Rank 1
      >>> # Output in stack form
      >>> tensor_out2 = torch.zeros(world_size, 2, dtype=torch.int64, device=device)
      >>> dist.all_gather_into_tensor(tensor_out2, tensor_in)
      >>> tensor_out2
      tensor([[1, 2],
              [3, 4]], device='cuda:0') # Rank 0
      tensor([[1, 2],
              [3, 4]], device='cuda:1') # Rank 1

  Warning

  The Gloo backend does not support this API.

torch.distributed.all_gather_object(object_list, obj, group=None)

  Gathers picklable objects from the whole group into a list. Similar to `all_gather()`, but Python objects can be passed in. Note that the object must be picklable in order to be gathered.

  Parameters:

    - object_list (list[Any]) – Output list. It should be correctly sized as the size of the group for this collective and will contain the output.

    - obj (Any) – Pickable Python object to be broadcast from current process.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used. Default is `None`.

  Returns:

    None. If the calling rank is part of this group, the output of the collective will be populated into the input `object_list`. If the calling rank is not part of the group, the passed in `object_list` will be unmodified.

  Note

  Note that this API differs slightly from the `all_gather()` collective since it does not provide an `async_op` handle and thus will be a blocking call.

  Note

  For NCCL-based processed groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by `torch.cuda.current_device()` and it is the user’s responsiblity to ensure that this is set so that each rank has an individual GPU, via `torch.cuda.set_device()`.

  Warning

  `all_gather_object()` uses `pickle` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust.

  Warning

  Calling `all_gather_object()` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using `all_gather()` instead.

  Example::

        >>> # Note: Process group initialization omitted on each rank.
        >>> import torch.distributed as dist
        >>> # Assumes world_size of 3.
        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
        >>> output = [None for _ in gather_objects]
        >>> dist.all_gather_object(output, gather_objects[dist.get_rank()])
        >>> output
        ['foo', 12, {1: 2}]

torch.distributed.gather(tensor, gather_list=None, dst=0, group=None, async_op=False)

  Gathers a list of tensors in a single process.

  Parameters:

    - tensor (Tensor) – Input tensor.

    - gather_list (list[Tensor], optional) – List of appropriately-sized tensors to use for gathered data (default is None, must be specified on the destination rank)

    - dst (int, optional) – Destination rank (default is 0)

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    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_object(obj, object_gather_list=None, dst=0, group=None)

  Gathers picklable objects from the whole group in a single process. Similar to `gather()`, but Python objects can be passed in. Note that the object must be picklable in order to be gathered.

  Parameters:

    - obj (Any) – Input object. Must be picklable.

    - object_gather_list (list[Any]) – Output list. On the `dst` rank, it should be correctly sized as the size of the group for this collective and will contain the output. Must be `None` on non-dst ranks. (default is `None`)

    - dst (int, optional) – Destination rank. (default is 0)

    - group – (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is `None`.

  Returns:

    None. On the `dst` rank, `object_gather_list` will contain the output of the collective.

  Note

  Note that this API differs slightly from the gather collective since it does not provide an async_op handle and thus will be a blocking call.

  Note

  For NCCL-based processed groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by `torch.cuda.current_device()` and it is the user’s responsiblity to ensure that this is set so that each rank has an individual GPU, via `torch.cuda.set_device()`.

  Warning

  `gather_object()` uses `pickle` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust.

  Warning

  Calling `gather_object()` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using `gather()` instead.

  Example::

        >>> # Note: Process group initialization omitted on each rank.
        >>> import torch.distributed as dist
        >>> # Assumes world_size of 3.
        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
        >>> output = [None for _ in gather_objects]
        >>> dist.gather_object(
        ...     gather_objects[dist.get_rank()],
        ...     output if dist.get_rank() == 0 else None,
        ...     dst=0
        ... )
        >>> # On rank 0
        >>> output
        ['foo', 12, {1: 2}]

torch.distributed.scatter(tensor, scatter_list=None, src=0, group=None, async_op=False)

  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.

  Complex tensors are supported.

  Parameters:

    - tensor (Tensor) – Output tensor.

    - scatter_list (list[Tensor]) – List of tensors to scatter (default is None, must be specified on the source rank)

    - src (int) – Source rank (default is 0)

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

  Note

  Note that all Tensors in scatter_list must have the same size.

  Example::

        >>> # Note: Process group initialization omitted on each rank.
        >>> import torch.distributed as dist
        >>> tensor_size = 2
        >>> t_ones = torch.ones(tensor_size)
        >>> t_fives = torch.ones(tensor_size) * 5
        >>> output_tensor = torch.zeros(tensor_size)
        >>> if dist.get_rank() == 0:
        >>>     # Assumes world_size of 2.
        >>>     # Only tensors, all of which must be the same size.
        >>>     scatter_list = [t_ones, t_fives]
        >>> else:
        >>>     scatter_list = None
        >>> dist.scatter(output_tensor, scatter_list, src=0)
        >>> # Rank i gets scatter_list[i]. For example, on rank 1:
        >>> output_tensor
        tensor([5., 5.])

torch.distributed.scatter_object_list(scatter_object_output_list, scatter_object_input_list, src=0, group=None)

  Scatters picklable objects in `scatter_object_input_list` to the whole group. Similar to `scatter()`, but Python objects can be passed in. On each rank, the scattered object will be stored as the first element of `scatter_object_output_list`. Note that all objects in `scatter_object_input_list` must be picklable in order to be scattered.

  Parameters:

    - scatter_object_output_list (List[Any]) – Non-empty list whose first element will store the object scattered to this rank.

    - scatter_object_input_list (List[Any]) – List of input objects to scatter. Each object must be picklable. Only objects on the `src` rank will be scattered, and the argument can be `None` for non-src ranks.

    - src (int) – Source rank from which to scatter `scatter_object_input_list`.

    - group – (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is `None`.

  Returns:

    `None`. If rank is part of the group, `scatter_object_output_list` will have its first element set to the scattered object for this rank.

  Note

  Note that this API differs slightly from the scatter collective since it does not provide an `async_op` handle and thus will be a blocking call.

  Warning

  `scatter_object_list()` uses `pickle` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust.

  Warning

  Calling `scatter_object_list()` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using `scatter()` instead.

  Example::

        >>> # Note: Process group initialization omitted on each rank.
        >>> import torch.distributed as dist
        >>> if dist.get_rank() == 0:
        >>>     # Assumes world_size of 3.
        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
        >>> else:
        >>>     # Can be any list on non-src ranks, elements are not used.
        >>>     objects = [None, None, None]
        >>> output_list = [None]
        >>> dist.scatter_object_list(output_list, objects, src=0)
        >>> # Rank i gets objects[i]. For example, on rank 2:
        >>> output_list
        [{1: 2}]

torch.distributed.reduce_scatter(output, input_list, op=<RedOpType.SUM: 0>, group=None, async_op=False)

  Reduces, then scatters a list of tensors to all processes in a group.

  Parameters:

    - output (Tensor) – Output tensor.

    - input_list (list[Tensor]) – List of tensors to reduce and scatter.

    - op (optional) – One of the values from `torch.distributed.ReduceOp` enum. Specifies an operation used for element-wise reductions.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op.

  Returns:

    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_scatter_tensor(output, input, op=<RedOpType.SUM: 0>, group=None, async_op=False)

  Reduces, then scatters a tensor to all ranks in a group.

  Parameters:

    - output (Tensor) – Output tensor. It should have the same size across all ranks.

    - input (Tensor) – Input tensor to be reduced and scattered. Its size should be output tensor size times the world size. The input tensor can have one of the following shapes: (i) a concatenation of the output tensors along the primary dimension, or (ii) a stack of the output tensors along the primary dimension. For definition of “concatenation”, see `torch.cat()`. For definition of “stack”, see `torch.stack()`.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op.

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group.

  Examples

      >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
      >>> # We have two ranks.
      >>> device = torch.device(f'cuda:{rank}')
      >>> tensor_out = torch.zeros(2, dtype=torch.int64, device=device)
      >>> # Input in concatenation form
      >>> tensor_in = torch.arange(world_size * 2, dtype=torch.int64, device=device)
      >>> tensor_in
      tensor([0, 1, 2, 3], device='cuda:0') # Rank 0
      tensor([0, 1, 2, 3], device='cuda:1') # Rank 1
      >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
      >>> tensor_out
      tensor([0, 2], device='cuda:0') # Rank 0
      tensor([4, 6], device='cuda:1') # Rank 1
      >>> # Input in stack form
      >>> tensor_in = torch.reshape(tensor_in, (world_size, 2))
      >>> tensor_in
      tensor([[0, 1],
              [2, 3]], device='cuda:0') # Rank 0
      tensor([[0, 1],
              [2, 3]], device='cuda:1') # Rank 1
      >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
      >>> tensor_out
      tensor([0, 2], device='cuda:0') # Rank 0
      tensor([4, 6], device='cuda:1') # Rank 1

  Warning

  The Gloo backend does not support this API.

torch.distributed.all_to_all_single(output, input, output_split_sizes=None, input_split_sizes=None, group=None, async_op=False)

  Each process splits input tensor and then scatters the split list to all processes in a group. Then concatenate the received tensors from all the processes in the group and return single output tensor.

  Complex tensors are supported.

  Parameters:

    - output (Tensor) – Gathered concatenated output tensor.

    - input (Tensor) – Input tensor to scatter.

    - output_split_sizes – (list[Int], optional): Output split sizes for dim 0 if specified None or empty, dim 0 of `output` tensor must divide equally by `world_size`.

    - input_split_sizes – (list[Int], optional): Input split sizes for dim 0 if specified None or empty, dim 0 of `input` tensor must divide equally by `world_size`.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op.

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group.

  Warning

  all_to_all_single is experimental and subject to change.

  Examples

      >>> input = torch.arange(4) + rank * 4
      >>> input
      tensor([0, 1, 2, 3])     # Rank 0
      tensor([4, 5, 6, 7])     # Rank 1
      tensor([8, 9, 10, 11])   # Rank 2
      tensor([12, 13, 14, 15]) # Rank 3
      >>> output = torch.empty([4], dtype=torch.int64)
      >>> dist.all_to_all_single(output, input)
      >>> output
      tensor([0, 4, 8, 12])    # Rank 0
      tensor([1, 5, 9, 13])    # Rank 1
      tensor([2, 6, 10, 14])   # Rank 2
      tensor([3, 7, 11, 15])   # Rank 3

      >>> # Essentially, it is similar to following operation:
      >>> scatter_list = list(input.chunk(world_size))
      >>> gather_list  = list(output.chunk(world_size))
      >>> for i in range(world_size):
      >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)

      >>> # Another example with uneven split
      >>> input
      tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
      tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
      tensor([20, 21, 22, 23, 24])                                     # Rank 2
      tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
      >>> input_splits
      [2, 2, 1, 1]                                                     # Rank 0
      [3, 2, 2, 2]                                                     # Rank 1
      [2, 1, 1, 1]                                                     # Rank 2
      [2, 2, 2, 1]                                                     # Rank 3
      >>> output_splits
      [2, 3, 2, 2]                                                     # Rank 0
      [2, 2, 1, 2]                                                     # Rank 1
      [1, 2, 1, 2]                                                     # Rank 2
      [1, 2, 1, 1]                                                     # Rank 3
      >>> output = ...
      >>> dist.all_to_all_single(output, input, output_splits, input_splits)
      >>> output
      tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
      tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
      tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
      tensor([ 5, 17, 18, 24, 36])                                     # Rank 3

      >>> # Another example with tensors of torch.cfloat type.
      >>> input = torch.tensor([1+1j, 2+2j, 3+3j, 4+4j], dtype=torch.cfloat) + 4 * rank * (1+1j)
      >>> input
      tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
      tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
      tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
      tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
      >>> output = torch.empty([4], dtype=torch.int64)
      >>> dist.all_to_all_single(output, input)
      >>> output
      tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
      tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
      tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
      tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3

torch.distributed.all_to_all(output_tensor_list, input_tensor_list, group=None, async_op=False)

  Each process scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.

  Complex tensors are supported.

  Parameters:

    - output_tensor_list (list[Tensor]) – List of tensors to be gathered one per rank.

    - input_tensor_list (list[Tensor]) – List of tensors to scatter one per rank.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op.

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group.

  Warning

  all_to_all is experimental and subject to change.

  Examples

      >>> input = torch.arange(4) + rank * 4
      >>> input = list(input.chunk(4))
      >>> input
      [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
      [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
      [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
      [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
      >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
      >>> dist.all_to_all(output, input)
      >>> output
      [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
      [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
      [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
      [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3

      >>> # Essentially, it is similar to following operation:
      >>> scatter_list = input
      >>> gather_list  = output
      >>> for i in range(world_size):
      >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)

      >>> input
      tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
      tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
      tensor([20, 21, 22, 23, 24])                                     # Rank 2
      tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
      >>> input_splits
      [2, 2, 1, 1]                                                     # Rank 0
      [3, 2, 2, 2]                                                     # Rank 1
      [2, 1, 1, 1]                                                     # Rank 2
      [2, 2, 2, 1]                                                     # Rank 3
      >>> output_splits
      [2, 3, 2, 2]                                                     # Rank 0
      [2, 2, 1, 2]                                                     # Rank 1
      [1, 2, 1, 2]                                                     # Rank 2
      [1, 2, 1, 1]                                                     # Rank 3
      >>> input = list(input.split(input_splits))
      >>> input
      [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
      [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
      [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
      [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
      >>> output = ...
      >>> dist.all_to_all(output, input)
      >>> output
      [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
      [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
      [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
      [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3

      >>> # Another example with tensors of torch.cfloat type.
      >>> input = torch.tensor([1+1j, 2+2j, 3+3j, 4+4j], dtype=torch.cfloat) + 4 * rank * (1+1j)
      >>> input = list(input.chunk(4))
      >>> input
      [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
      [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
      [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
      [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
      >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
      >>> dist.all_to_all(output, input)
      >>> output
      [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
      [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
      [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
      [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3

torch.distributed.barrier(group=None, async_op=False, device_ids=None)

  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. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

    - device_ids ([int], optional) – List of device/GPU ids.

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group

torch.distributed.monitored_barrier(group=None, timeout=None, wait_all_ranks=False)

  Synchronizes all processes similar to `torch.distributed.barrier`, but takes a configurable timeout and is able to report ranks that did not pass this barrier within that timeout. Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0. Rank 0 will block until all send /recv from other ranks are processed, and will report failures for ranks that failed to respond in time. Note that if one rank does not reach the monitored_barrier (for example due to a hang), all other ranks would fail in monitored_barrier.

  This collective will block all processes/ranks in the group, until the whole group exits the function successfully, making it useful for debugging and synchronizing. However, it can have a performance impact and should only be used for debugging or scenarios that require full synchronization points on the host-side. For debugging purposes, this barrier can be inserted before the application’s collective calls to check if any ranks are desynchronized.

  Note

  Note that this collective is only supported with the GLOO backend.

  Parameters:

    - group (ProcessGroup, optional) – The process group to work on. If `None`, the default process group will be used.

    - timeout (datetime.timedelta, optional) – Timeout for monitored_barrier. If `None`, the default process group timeout will be used.

    - wait_all_ranks (bool, optional) – Whether to collect all failed ranks or not. By default, this is `False` and `monitored_barrier` on rank 0 will throw on the first failed rank it encounters in order to fail fast. By setting `wait_all_ranks=True` `monitored_barrier` will collect all failed ranks and throw an error containing information about all failed ranks.

  Returns:

    `None`.

  Example::

        >>> # Note: Process group initialization omitted on each rank.
        >>> import torch.distributed as dist
        >>> if dist.get_rank() != 1:
        >>>     dist.monitored_barrier() # Raises exception indicating that
        >>> # rank 1 did not call into monitored_barrier.
        >>> # Example with wait_all_ranks=True
        >>> if dist.get_rank() == 0:
        >>>     dist.monitored_barrier(wait_all_ranks=True) # Raises exception
        >>> # indicating that ranks 1, 2, ... world_size - 1 did not call into
        >>> # monitored_barrier.

class torch.distributed.ReduceOp

  An enum-like class for available reduction operations: `SUM`, `PRODUCT`, `MIN`, `MAX`, `BAND`, `BOR`, `BXOR`, and `PREMUL_SUM`.

  `BAND`, `BOR`, and `BXOR` reductions are not available when using the `NCCL` backend.

  `AVG` divides values by the world size before summing across ranks. `AVG` is only available with the `NCCL` backend, and only for NCCL versions 2.10 or later.

  `PREMUL_SUM` multiplies inputs by a given scalar locally before reduction. `PREMUL_SUM` is only available with the `NCCL` backend, and only available for NCCL versions 2.11 or later. Users are supposed to use `torch.distributed._make_nccl_premul_sum`.

  Additionally, `MAX`, `MIN` and `PRODUCT` are not supported for complex tensors.

  The values of this class can be accessed as attributes, e.g., `ReduceOp.SUM`. They are used in specifying strategies for reduction collectives, e.g., `reduce()`, `all_reduce_multigpu()`, etc.

  This class does not support `__members__` property.

class torch.distributed.reduce_op

  Deprecated enum-like class for reduction operations: `SUM`, `PRODUCT`, `MIN`, and `MAX`.

  `ReduceOp` is recommended to use instead.

## Profiling Collective Communication

Note that you can use `torch.profiler` (recommended, only available after 1.8.1) or `torch.autograd.profiler` to profile collective communication and point-to-point communication APIs mentioned here. All out-of-the-box backends (`gloo`, `nccl`, `mpi`) are supported and collective communication usage will be rendered as expected in profiling output/traces. Profiling your code is the same as any regular torch operator:

    import torch
    import torch.distributed as dist
    with torch.profiler():
        tensor = torch.randn(20, 10)
        dist.all_reduce(tensor)

Please refer to the profiler documentation for a full overview of profiler features.

## Multi-GPU collective functions

Warning

The multi-GPU functions will be deprecated. If you must use them, please revisit our documentation later.

If you have more than one GPU on each node, when using the NCCL and Gloo backend, `broadcast_multigpu()` `all_reduce_multigpu()` `reduce_multigpu()` `all_gather_multigpu()` and `reduce_scatter_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=None, async_op=False, src_tensor=0)

  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 the specified `src_tensor` element of `tensor_list` (`tensor_list[src_tensor]`) will be broadcast 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. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

    - src_tensor (int, optional) – Source tensor rank within `tensor_list`

  Returns:

    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=<RedOpType.SUM: 0>, group=None, async_op=False)

  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.

  Complex tensors are supported.

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

  Parameters:

    - tensor_list (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.ReduceOp` enum. Specifies an operation used for element-wise reductions.

    - group (ProcessGroup, optional) – The process group to work on. If `None`, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    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=<RedOpType.SUM: 0>, group=None, async_op=False, dst_tensor=0)

  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.ReduceOp` enum. Specifies an operation used for element-wise reductions.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

    - dst_tensor (int, optional) – Destination tensor rank within `tensor_list`

  Returns:

    Async work handle, if async_op is set to True. None, otherwise

torch.distributed.all_gather_multigpu(output_tensor_lists, input_tensor_list, group=None, async_op=False)

  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

  Complex tensors are supported.

  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` 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_lists[i]`, note that `input_tensor_list[j]` of rank k will be appear in `output_tensor_lists[i][k * 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. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op

  Returns:

    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_scatter_multigpu(output_tensor_list, input_tensor_lists, op=<RedOpType.SUM: 0>, group=None, async_op=False)

  Reduce and scatter a list of tensors to the whole group. Only nccl backend is currently supported.

  Each tensor in `output_tensor_list` should reside on a separate GPU, as should each list of tensors in `input_tensor_lists`.

  Parameters:

    - output_tensor_list (List[Tensor]) –

      Output tensors (on different GPUs) to receive the result of the operation.

      Note that `len(output_tensor_list)` needs to be the same for all the distributed processes calling this function.

    - input_tensor_lists (List[List[Tensor]]) –

      Input lists. It should contain correctly-sized tensors on each GPU to be used for input of the collective, e.g. `input_tensor_lists[i]` contains the reduce_scatter input that resides on the GPU of `output_tensor_list[i]`.

      Note that each element of `input_tensor_lists` has the size of `world_size * len(output_tensor_list)`, since the function scatters the result from every single GPU in the group. To interpret each element of `input_tensor_lists[i]`, note that `output_tensor_list[j]` of rank k receives the reduce-scattered result from `input_tensor_lists[i][k * world_size + j]`

      Also note that `len(input_tensor_lists)`, and the size of each element in `input_tensor_lists` (each element is a list, therefore `len(input_tensor_lists[i])`) need to be the same for all the distributed processes calling this function.

    - group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.

    - async_op (bool, optional) – Whether this op should be an async op.

  Returns:

    Async work handle, if async_op is set to True. None, if not async_op or if not part of the group.

## Third-party backends

Besides the builtin GLOO/MPI/NCCL backends, PyTorch distributed supports third-party backends through a run-time register mechanism. For references on how to develop a third-party backend through C++ Extension, please refer to Tutorials - Custom C++ and CUDA Extensions and `test/cpp_extensions/cpp_c10d_extension.cpp`. The capability of third-party backends are decided by their own implementations.

The new backend derives from `c10d::ProcessGroup` and registers the backend name and the instantiating interface through `torch.distributed.Backend.register_backend()` when imported.

When manually importing this backend and invoking `torch.distributed.init_process_group()` with the corresponding backend name, the `torch.distributed` package runs on the new backend.

Warning

The support of third-party backend is experimental and subject to change.

## Launch utility

The torch.distributed package also provides a launch utility in torch.distributed.launch. This helper utility can be used to launch multiple processes per node for distributed training.

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

Warning

This module is going to be deprecated in favor of torchrun.

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 beneficial 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 equal 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
               arguments of your training script)

2. 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
               --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
               --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
               --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
               --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
               and all other arguments of your training script)

3. To look up what optional arguments this module offers:

    python -m torch.distributed.launch --help

Important Notices:

1. This utility 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()
    >>> parser.add_argument("--local-rank", type=int)
    >>> args = parser.parse_args()

Set your device to local rank using either

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

or

    >>> with torch.cuda.device(args.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. It is strongly recommended that `init_method=env://`. Other init methods (e.g. `tcp://`) may work, but `env://` is the one that is officially supported 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=[args.local_rank],
    >>>                                                   output_device=args.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

5. Another way to pass `local_rank` to the subprocesses via environment variable `LOCAL_RANK`. This behavior is enabled when you launch the script with `--use-env=True`. You must adjust the subprocess example above to replace `args.local_rank` with `os.environ['LOCAL_RANK']`; the launcher will not pass `--local-rank` when you specify this flag.

Warning

`local_rank` is NOT globally unique: it is only unique per process on a machine. Thus, don’t use it to decide if you should, e.g., write to a networked filesystem. See https://github.com/pytorch/pytorch/issues/12042 for an example of how things can go wrong if you don’t do this correctly.

## Spawn utility

The Multiprocessing package - torch.multiprocessing package also provides a `spawn` function in `torch.multiprocessing.spawn()`. This helper function can be used to spawn multiple processes. It works by passing in the function that you want to run and spawns N processes to run it. This can be used for multiprocess distributed training as well.

For references on how to use it, please refer to PyTorch example - ImageNet implementation

Note that this function requires Python 3.4 or higher.

## Debugging `torch.distributed` applications

Debugging distributed applications can be challenging due to hard to understand hangs, crashes, or inconsistent behavior across ranks. `torch.distributed` provides a suite of tools to help debug training applications in a self-serve fashion:

### Monitored Barrier

As of v1.10, `torch.distributed.monitored_barrier()` exists as an alternative to `torch.distributed.barrier()` which fails with helpful information about which rank may be faulty when crashing, i.e. not all ranks calling into `torch.distributed.monitored_barrier()` within the provided timeout. `torch.distributed.monitored_barrier()` implements a host-side barrier using `send`/`recv` communication primitives in a process similar to acknowledgements, allowing rank 0 to report which rank(s) failed to acknowledge the barrier in time. As an example, consider the following function where rank 1 fails to call into `torch.distributed.monitored_barrier()` (in practice this could be due to an application bug or hang in a previous collective):

    import os
    from datetime import timedelta

    import torch
    import torch.distributed as dist
    import torch.multiprocessing as mp

    def worker(rank):
        dist.init_process_group("nccl", rank=rank, world_size=2)
        # monitored barrier requires gloo process group to perform host-side sync.
        group_gloo = dist.new_group(backend="gloo")
        if rank not in [1]:
            dist.monitored_barrier(group=group_gloo, timeout=timedelta(seconds=2))

    if __name__ == "__main__":
        os.environ["MASTER_ADDR"] = "localhost"
        os.environ["MASTER_PORT"] = "29501"
        mp.spawn(worker, nprocs=2, args=())

The following error message is produced on rank 0, allowing the user to determine which rank(s) may be faulty and investigate further:

    RuntimeError: Rank 1 failed to pass monitoredBarrier in 2000 ms
     Original exception:
    [gloo/transport/tcp/pair.cc:598] Connection closed by peer [2401:db00:eef0:1100:3560:0:1c05:25d]:8594

### `TORCH_DISTRIBUTED_DEBUG`

With `TORCH_CPP_LOG_LEVEL=INFO`, the environment variable `TORCH_DISTRIBUTED_DEBUG` can be used to trigger additional useful logging and collective synchronization checks to ensure all ranks are synchronized appropriately. `TORCH_DISTRIBUTED_DEBUG` can be set to either `OFF` (default), `INFO`, or `DETAIL` depending on the debugging level required. Please note that the most verbose option, `DETAIL` may impact the application performance and thus should only be used when debugging issues.

Setting `TORCH_DISTRIBUTED_DEBUG=INFO` will result in additional debug logging when models trained with `torch.nn.parallel.DistributedDataParallel()` are initialized, and `TORCH_DISTRIBUTED_DEBUG=DETAIL` will additionally log runtime performance statistics a select number of iterations. These runtime statistics include data such as forward time, backward time, gradient communication time, etc. As an example, given the following application:

    import os

    import torch
    import torch.distributed as dist
    import torch.multiprocessing as mp

    class TwoLinLayerNet(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.a = torch.nn.Linear(10, 10, bias=False)
            self.b = torch.nn.Linear(10, 1, bias=False)

        def forward(self, x):
            a = self.a(x)
            b = self.b(x)
            return (a, b)

    def worker(rank):
        dist.init_process_group("nccl", rank=rank, world_size=2)
        torch.cuda.set_device(rank)
        print("init model")
        model = TwoLinLayerNet().cuda()
        print("init ddp")
        ddp_model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[rank])

        inp = torch.randn(10, 10).cuda()
        print("train")

        for _ in range(20):
            output = ddp_model(inp)
            loss = output[0] + output[1]
            loss.sum().backward()

    if __name__ == "__main__":
        os.environ["MASTER_ADDR"] = "localhost"
        os.environ["MASTER_PORT"] = "29501"
        os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
        os.environ[
            "TORCH_DISTRIBUTED_DEBUG"
        ] = "DETAIL"  # set to DETAIL for runtime logging.
        mp.spawn(worker, nprocs=2, args=())

The following logs are rendered at initialization time:

    I0607 16:10:35.739390 515217 logger.cpp:173] [Rank 0]: DDP Initialized with:
    broadcast_buffers: 1
    bucket_cap_bytes: 26214400
    find_unused_parameters: 0
    gradient_as_bucket_view: 0
    is_multi_device_module: 0
    iteration: 0
    num_parameter_tensors: 2
    output_device: 0
    rank: 0
    total_parameter_size_bytes: 440
    world_size: 2
    backend_name: nccl
    bucket_sizes: 440
    cuda_visible_devices: N/A
    device_ids: 0
    dtypes: float
    master_addr: localhost
    master_port: 29501
    module_name: TwoLinLayerNet
    nccl_async_error_handling: N/A
    nccl_blocking_wait: N/A
    nccl_debug: WARN
    nccl_ib_timeout: N/A
    nccl_nthreads: N/A
    nccl_socket_ifname: N/A
    torch_distributed_debug: INFO

The following logs are rendered during runtime (when `TORCH_DISTRIBUTED_DEBUG=DETAIL` is set):

    I0607 16:18:58.085681 544067 logger.cpp:344] [Rank 1 / 2] Training TwoLinLayerNet unused_parameter_size=0
     Avg forward compute time: 40838608
     Avg backward compute time: 5983335
    Avg backward comm. time: 4326421
     Avg backward comm/comp overlap time: 4207652
    I0607 16:18:58.085693 544066 logger.cpp:344] [Rank 0 / 2] Training TwoLinLayerNet unused_parameter_size=0
     Avg forward compute time: 42850427
     Avg backward compute time: 3885553
    Avg backward comm. time: 2357981
     Avg backward comm/comp overlap time: 2234674

In addition, `TORCH_DISTRIBUTED_DEBUG=INFO` enhances crash logging in `torch.nn.parallel.DistributedDataParallel()` due to unused parameters in the model. Currently, `find_unused_parameters=True` must be passed into `torch.nn.parallel.DistributedDataParallel()` initialization if there are parameters that may be unused in the forward pass, and as of v1.10, all model outputs are required to be used in loss computation as `torch.nn.parallel.DistributedDataParallel()` does not support unused parameters in the backwards pass. These constraints are challenging especially for larger models, thus when crashing with an error, `torch.nn.parallel.DistributedDataParallel()` will log the fully qualified name of all parameters that went unused. For example, in the above application, if we modify `loss` to be instead computed as `loss = output[1]`, then `TwoLinLayerNet.a` does not receive a gradient in the backwards pass, and thus results in `DDP` failing. On a crash, the user is passed information about parameters which went unused, which may be challenging to manually find for large models:

    RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing
     the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by
    making sure all `forward` function outputs participate in calculating loss.
    If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return va
    lue of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
    Parameters which did not receive grad for rank 0: a.weight
    Parameter indices which did not receive grad for rank 0: 0

Setting `TORCH_DISTRIBUTED_DEBUG=DETAIL` will trigger additional consistency and synchronization checks on every collective call issued by the user either directly or indirectly (such as DDP `allreduce`). This is done by creating a wrapper process group that wraps all process groups returned by `torch.distributed.init_process_group()` and `torch.distributed.new_group()` APIs. As a result, these APIs will return a wrapper process group that can be used exactly like a regular process group, but performs consistency checks before dispatching the collective to an underlying process group. Currently, these checks include a `torch.distributed.monitored_barrier()`, which ensures all ranks complete their outstanding collective calls and reports ranks which are stuck. Next, the collective itself is checked for consistency by ensuring all collective functions match and are called with consistent tensor shapes. If this is not the case, a detailed error report is included when the application crashes, rather than a hang or uninformative error message. As an example, consider the following function which has mismatched input shapes into `torch.distributed.all_reduce()`:

    import torch
    import torch.distributed as dist
    import torch.multiprocessing as mp

    def worker(rank):
        dist.init_process_group("nccl", rank=rank, world_size=2)
        torch.cuda.set_device(rank)
        tensor = torch.randn(10 if rank == 0 else 20).cuda()
        dist.all_reduce(tensor)
        torch.cuda.synchronize(device=rank)

    if __name__ == "__main__":
        os.environ["MASTER_ADDR"] = "localhost"
        os.environ["MASTER_PORT"] = "29501"
        os.environ["TORCH_CPP_LOG_LEVEL"]="INFO"
        os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
        mp.spawn(worker, nprocs=2, args=())

With the `NCCL` backend, such an application would likely result in a hang which can be challenging to root-cause in nontrivial scenarios. If the user enables `TORCH_DISTRIBUTED_DEBUG=DETAIL` and reruns the application, the following error message reveals the root cause:

    work = default_pg.allreduce([tensor], opts)
    RuntimeError: Error when verifying shape tensors for collective ALLREDUCE on rank 0. This likely indicates that input shapes into the collective are mismatched across ranks. Got shapes:  10
    [ torch.LongTensor{1} ]

Note

For fine-grained control of the debug level during runtime the functions `torch.distributed.set_debug_level()`, `torch.distributed.set_debug_level_from_env()`, and `torch.distributed.get_debug_level()` can also be used.

In addition, TORCH_DISTRIBUTED_DEBUG=DETAIL can be used in conjunction with TORCH_SHOW_CPP_STACKTRACES=1 to log the entire callstack when a collective desynchronization is detected. These collective desynchronization checks will work for all applications that use `c10d` collective calls backed by process groups created with the `torch.distributed.init_process_group()` and `torch.distributed.new_group()` APIs.

## Logging

In addition to explicit debugging support via `torch.distributed.monitored_barrier()` and `TORCH_DISTRIBUTED_DEBUG`, the underlying C++ library of `torch.distributed` also outputs log messages at various levels. These messages can be helpful to understand the execution state of a distributed training job and to troubleshoot problems such as network connection failures. The following matrix shows how the log level can be adjusted via the combination of `TORCH_CPP_LOG_LEVEL` and `TORCH_DISTRIBUTED_DEBUG` environment variables.

`TORCH_CPP_LOG_LEVEL` | `TORCH_DISTRIBUTED_DEBUG` | Effective Log Level
--- | --- | ---
`ERROR` | ignored | Error
`WARNING` | ignored | Warning
`INFO` | ignored | Info
`INFO` | `INFO` | Debug
`INFO` | `DETAIL` | Trace (a.k.a. All)

Distributed has a custom Exception type derived from RuntimeError called torch.distributed.DistBackendError. This exception is thrown when a backend-specific error occurs. For example, if the NCCL backend is used and the user attempts to use a GPU that is not available to the NCCL library.

class torch.distributed.DistBackendError

  Exception raised when a backend error occurs in distributed

Warning

The DistBackendError exception type is an experimental feature is subject to change.

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