Note
Please refer to PyTorch Distributed Overview for a brief introduction to all features related to distributed training.
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 |
|
|
| |||
|---|---|---|---|---|---|---|
Device | CPU | GPU | CPU | GPU | CPU | GPU |
send | ✓ | ✘ | ✓ | ? | ✘ | ✘ |
recv | ✓ | ✘ | ✓ | ? | ✘ | ✘ |
broadcast | ✓ | ✓ | ✓ | ? | ✘ | ✓ |
all_reduce | ✓ | ✓ | ✓ | ? | ✘ | ✓ |
reduce | ✓ | ✘ | ✓ | ? | ✘ | ✓ |
all_gather | ✓ | ✘ | ✓ | ? | ✘ | ✓ |
gather | ✓ | ✘ | ✓ | ? | ✘ | ✘ |
scatter | ✓ | ✘ | ✓ | ? | ✘ | ✘ |
reduce_scatter | ✘ | ✘ | ✘ | ✘ | ✘ | ✓ |
all_to_all | ✘ | ✘ | ✓ | ? | ✘ | ✘ |
barrier | ✓ | ✘ | ✓ | ? | ✘ | ✓ |
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.)
Warning
As of PyTorch v1.7, Windows support for the distributed package only covers collective communications with Gloo backend, FileStore, and DistributedDataParallel. Therefore, the init_method argument in init_process_group() must point to a file. This works for both local and shared file systems:
init_method="file:///d:/tmp/some_file"
init_method="file://////{machine_name}/{share_folder_name}/some_file"
Similarly, if you directly pass in a store argument, it must be a FileStore instance.
In the past, we were often asked: “which backend should I use?”.
Rule of thumb
GPU hosts with InfiniBand interconnect
GPU hosts with Ethernet interconnect
CPU hosts with InfiniBand interconnect
CPU hosts with Ethernet interconnect
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):
export NCCL_SOCKET_IFNAME=eth0
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.
NCCL has also provided a number of environment variables for fine-tuning purposes.
Commonly used ones include the following for debugging purposes:
export NCCL_DEBUG=INFOexport NCCL_DEBUG_SUBSYS=ALLFor the full list of NCCL environment variables, please refer to NVIDIA NCCL’s official documentation
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():
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() [source]
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.
torch.distributed.init_process_group(backend, init_method=None, timeout=datetime.timedelta(0, 1800), world_size=-1, rank=-1, store=None, group_name='') [source]
Initializes the default distributed process group, and this will also initialize the distributed package.
store, rank, and world_size explicitly.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://”.
mpi, gloo, and nccl. 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 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.init_method or store is specified. Mutually exclusive with store.store is specified.store is specified.init_method.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 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 to run on corrupted data. Only one of these two environment variables should be set.To enable backend == Backend.MPI, PyTorch needs to be built from source on a system that supports MPI.
class torch.distributed.Backend [source]
An enum-like class of available backends: GLOO, NCCL, 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.
torch.distributed.get_backend(group=<object object>) [source]
Returns the backend of the given process group.
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.
The backend of the given process group as a lower case string.
torch.distributed.get_rank(group=<object object>) [source]
Returns the rank of current process group
Rank is a unique identifier assigned to each process within a distributed process group. They are always consecutive integers ranging from 0 to world_size.
group (ProcessGroup, optional) – The process group to work on
The rank of the process group -1, if not part of the group
torch.distributed.get_world_size(group=<object object>) [source]
Returns the number of processes in the current process group
group (ProcessGroup, optional) – The process group to work on
The world size of the process group -1, if not part of the group
torch.distributed.is_initialized() [source]
Checking if the default process group has been initialized
torch.distributed.is_mpi_available() [source]
Checks if the MPI backend is available.
torch.distributed.is_nccl_available() [source]
Checks if the NCCL backend is available.
Currently three initialization methods are supported:
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)
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)
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 0MASTER_ADDR - required (except for rank 0); address of rank 0 nodeWORLD_SIZE - required; can be set either here, or in a call to init functionRANK - required; can be set either here, or in a call to init functionThe 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://).
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 pacakge 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.
get() and wait().>>> import torch.distributed as dist
>>> server_store = dist.TCPStore("127.0.0.1", 0, true, timedelta(seconds=30))
>>> client_store = dist.TCPStore("127.0.0.1", 0, 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.
>>> 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.
>>> 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.
torch.distributed.Store.set(self: torch.distributed.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.
>>> import torch.distributed as dist
>>> store = dist.TCPStore("127.0.0.1", 0, true, timedelta(seconds=30))
>>> store.set("first_key", "first_value")
>>> # Should return "first_value"
>>> store.get("first_key")
torch.distributed.Store.get(self: torch.distributed.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.
key (str) – The function will return the value associated with this key.
Value associated with key if key is in the store.
>>> import torch.distributed as dist
>>> store = dist.TCPStore("127.0.0.1", 0, true, timedelta(seconds=30))
>>> store.set("first_key", "first_value")
>>> # Should return "first_value"
>>> store.get("first_key")
torch.distributed.Store.add(self: torch.distributed.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.
>>> import torch.distributed as dist
>>> # Using TCPStore as an example, other store types can also be used
>>> store = dist.TCPStore("127.0.0.1", 0, true, timedelta(seconds=30))
>>> store.add("first_key", 1)
>>> store.add("first_key", 6)
>>> # Should return 7
>>> store.get("first_key")
torch.distributed.Store.wait(*args, **kwargs) Overloaded function.
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.
keys (list) – List of keys on which to wait until they are set in the store.
>>> import torch.distributed as dist
>>> # Using TCPStore as an example, other store types can also be used
>>> store = dist.TCPStore("127.0.0.1", 0, true, timedelta(seconds=30))
>>> # This will throw an exception after 30 seconds
>>> store.wait(["bad_key"])
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.
>>> import torch.distributed as dist
>>> # Using TCPStore as an example, other store types can also be used
>>> store = dist.TCPStore("127.0.0.1", 0, 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.distributed.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
The num_keys API is only supported by the TCPStore. Using this API with the FileStore or HashStore will result in an exception.
The number of keys present in the store.
>>> import torch.distributed as dist
>>> store = dist.TCPStore("127.0.0.1", 0, true, timedelta(seconds=30))
>>> store.set("first_key", "first_value")
>>> # This should return 2
>>> store.num_keys()
torch.distributed.Store.delete_key(self: torch.distributed.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. Using this API with the FileStore or HashStore will result in an exception.
key (str) – The key to be deleted from the store
true if key was deleted, otherwise false.
>>> import torch.distributed as dist
>>> store = dist.TCPStore("127.0.0.1", 0, 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.distributed.Store, arg0: datetime.timedelta) → None Sets the store’s default timeout. This timeout is used during initialization and in wait() and get().
timeout (timedelta) – timeout to be set in the store.
>>> import torch.distributed as dist
>>> # Using TCPStore as an example, other store types can also be used
>>> store = dist.TCPStore("127.0.0.1", 0, true, timedelta(seconds=30))
>>> store.set_timeout(timedelta(seconds=10))
>>> # This will throw an exception after 10 seconds
>>> store.wait(["bad_key"])
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(0, 1800), backend=None) [source]
Creates a new distributed group.
This function requires that all processes in the main group (i.e. all processes that are part of the distributed job) enter this function, even if they are not going to be members of the group. Additionally, groups should be created in the same order in all processes.
None, will be set to all ranks. Default is None.gloo backend.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).A handle of distributed group that can be given to collective calls.
torch.distributed.send(tensor, dst, group=<object object>, tag=0) [source]
Sends a tensor synchronously.
torch.distributed.recv(tensor, src=None, group=<object object>, tag=0) [source]
Receives a tensor synchronously.
Sender rank -1, if not part of the group
isend() and irecv() return distributed request objects when used. In general, the type of this object is unspecified as they should never be created manually, but they are guaranteed to support two methods:
is_completed() - returns True if the operation has finishedwait() - will block the process until the operation is finished. is_completed() is guaranteed to return True once it returns.torch.distributed.isend(tensor, dst, group=<object object>, tag=0) [source]
Sends a tensor asynchronously.
torch.distributed.irecv(tensor, src, group=<object object>, tag=0) [source]
Receives a tensor asynchronously.
Every collective operation function supports the following two kinds of operations:
synchronous operation - the default mode, when async_op is set to False. when the function returns, it is guaranteed that the collective operation is performed (not necessarily completed if it’s a CUDA op since all CUDA ops are asynchronous), and any further function calls depending on the data of the collective operation can be called. In the synchronous mode, the collective function does not return anything
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() - returns True if the operation has finishedwait() - will block the process until the operation is finished.torch.distributed.broadcast(tensor, src, group=<object object>, async_op=False) [source]
Broadcasts the tensor to the whole group.
tensor must have the same number of elements in all processes participating in the collective.
Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.all_reduce(tensor, op=ReduceOp.SUM, group=<object object>, async_op=False) [source]
Reduces the tensor data across all machines in such a way that all get the final result.
After the call tensor is going to be bitwise identical in all processes.
torch.distributed.ReduceOp enum. Specifies an operation used for element-wise reductions.Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.reduce(tensor, dst, op=ReduceOp.SUM, group=<object object>, async_op=False) [source]
Reduces the tensor data across all machines.
Only the process with rank dst is going to receive the final result.
torch.distributed.ReduceOp enum. Specifies an operation used for element-wise reductions.Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.all_gather(tensor_list, tensor, group=<object object>, async_op=False) [source]
Gathers tensors from the whole group in a list.
Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.gather(tensor, gather_list=None, dst=0, group=<object object>, async_op=False) [source]
Gathers a list of tensors in a single process.
Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.scatter(tensor, scatter_list=None, src=0, group=<object object>, async_op=False) [source]
Scatters a list of tensors to all processes in a group.
Each process will receive exactly one tensor and store its data in the tensor argument.
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(output, input_list, op=ReduceOp.SUM, group=<object object>, async_op=False) [source]
Reduces, then scatters a list of tensors to all processes in a group.
Async work handle, if async_op is set to True. None, if not async_op or if not part of the group.
Note
all_to_all is a Prototype function.
torch.distributed.all_to_all(output_tensor_list, input_tensor_list, group=<object object>, async_op=False) [source]
Each process scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
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.
>>> 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
torch.distributed.barrier(group=<object object>, async_op=False) [source]
Synchronizes all processes.
This collective blocks processes until the whole group enters this function, if async_op is False, or if async work handle is called on wait().
Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
class torch.distributed.ReduceOp An enum-like class for available reduction operations: SUM, PRODUCT, MIN, MAX, BAND, BOR, and BXOR.
Note that BAND, BOR, and BXOR reductions are not available when using the NCCL backend.
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.
Members:
SUM
PRODUCT
MIN
MAX
BAND
BOR
BXOR
class torch.distributed.reduce_op [source]
Deprecated enum-like class for reduction operations: SUM, PRODUCT, MIN, and MAX.
ReduceOp is recommended to use instead.
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=<object object>, async_op=False, src_tensor=0) [source]
Broadcasts the tensor to the whole group with multiple GPU tensors per node.
tensor must have the same number of elements in all the GPUs from all processes participating in the collective. each tensor in the list must be on a different GPU
Only nccl and gloo backend are currently supported tensors should only be GPU tensors
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.tensor_list
Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.all_reduce_multigpu(tensor_list, op=ReduceOp.SUM, group=<object object>, async_op=False) [source]
Reduces the tensor data across all machines in such a way that all get the final result. This function reduces a number of tensors on every node, while each tensor resides on different GPUs. Therefore, the input tensor in the tensor list needs to be GPU tensors. Also, each tensor in the tensor list needs to reside on a different GPU.
After the call, all tensor in tensor_list is going to be bitwise identical in all processes.
Only nccl and gloo backend is currently supported tensors should only be GPU tensors
len(tensor_list) is the same for all the distributed processes calling this function.torch.distributed.ReduceOp enum. Specifies an operation used for element-wise reductions.Async work handle, if async_op is set to True. None, if not async_op or if not part of the group
torch.distributed.reduce_multigpu(tensor_list, dst, op=ReduceOp.SUM, group=<object object>, async_op=False, dst_tensor=0) [source]
Reduces the tensor data on multiple GPUs across all machines. Each tensor in tensor_list should reside on a separate GPU
Only the GPU of tensor_list[dst_tensor] on the process with rank dst is going to receive the final result.
Only nccl backend is currently supported tensors should only be GPU tensors
len(tensor_list) is the same for all the distributed processes calling this function.torch.distributed.ReduceOp enum. Specifies an operation used for element-wise reductions.tensor_list
Async work handle, if async_op is set to True. None, otherwise
torch.distributed.all_gather_multigpu(output_tensor_lists, input_tensor_list, group=<object object>, async_op=False) [source]
Gathers tensors from the whole group in a list. Each tensor in tensor_list should reside on a separate GPU
Only nccl backend is currently supported tensors should only be GPU tensors
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.
len(input_tensor_list) needs to be the same for all the distributed processes calling this function.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=ReduceOp.SUM, group=<object object>, async_op=False) [source]
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.
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.
Async work handle, if async_op is set to True. None, if not async_op or if not part of the group.
Note
This is a Prototype function.
Besides the 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.
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.
The utility can be used for single-node distributed training, in which one or more processes per node will be spawned. The utility can be used for either CPU training or GPU training. If the utility is used for GPU training, each distributed process will be operating on a single GPU. This can achieve well-improved single-node training performance. It can also be used in multi-node distributed training, by spawning up multiple processes on each node for well-improved multi-node distributed training performance as well. This will especially be benefitial for systems with multiple Infiniband interfaces that have direct-GPU support, since all of them can be utilized for aggregated communication bandwidth.
In both cases of single-node distributed training or multi-node distributed training, this utility will launch the given number of processes per node (--nproc_per_node). If used for GPU training, this number needs to be less or 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:
>>> 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)
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)
>>> 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(arg.local_rank) # before your code runs
or
>>> with torch.cuda.device(arg.local_rank): >>> # your code to run
3. In your training program, you are supposed to call the following function at the beginning to start the distributed backend. You need to make sure that the init_method uses env://, which is the only supported init_method by this module.
torch.distributed.init_process_group(backend='YOUR BACKEND',
init_method='env://')
4. In your training program, you can either use regular distributed functions or use torch.nn.parallel.DistributedDataParallel() module. If your training program uses GPUs for training and you would like to use torch.nn.parallel.DistributedDataParallel() module, here is how to configure it.
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[arg.local_rank],
output_device=arg.local_rank)
Please ensure that device_ids argument is set to be the only GPU device id that your code will be operating on. This is generally the local rank of the process. In other words, the device_ids needs to be [args.local_rank], and output_device needs to be args.local_rank in order to use this utility
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.
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.
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/distributed.html