DataParallel¶
- class torch.nn.DataParallel(module, device_ids=None, output_device=None, dim=0)[source][source]¶
Implements data parallelism at the module level.
This container parallelizes the application of the given
module
by splitting the input across the specified devices by chunking in the batch dimension (other objects will be copied once per device). In the forward pass, the module is replicated on each device, and each replica handles a portion of the input. During the backwards pass, gradients from each replica are summed into the original module.The batch size should be larger than the number of GPUs used.
Warning
It is recommended to use
DistributedDataParallel
, instead of this class, to do multi-GPU training, even if there is only a single node. See: Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel and Distributed Data Parallel.Arbitrary positional and keyword inputs are allowed to be passed into DataParallel but some types are specially handled. tensors will be scattered on dim specified (default 0). tuple, list and dict types will be shallow copied. The other types will be shared among different threads and can be corrupted if written to in the model’s forward pass.
The parallelized
module
must have its parameters and buffers ondevice_ids[0]
before running thisDataParallel
module.Warning
In each forward,
module
is replicated on each device, so any updates to the running module inforward
will be lost. For example, ifmodule
has a counter attribute that is incremented in eachforward
, it will always stay at the initial value because the update is done on the replicas which are destroyed afterforward
. However,DataParallel
guarantees that the replica ondevice[0]
will have its parameters and buffers sharing storage with the base parallelizedmodule
. So in-place updates to the parameters or buffers ondevice[0]
will be recorded. E.g.,BatchNorm2d
andspectral_norm()
rely on this behavior to update the buffers.Warning
Forward and backward hooks defined on
module
and its submodules will be invokedlen(device_ids)
times, each with inputs located on a particular device. Particularly, the hooks are only guaranteed to be executed in correct order with respect to operations on corresponding devices. For example, it is not guaranteed that hooks set viaregister_forward_pre_hook()
be executed before alllen(device_ids)
forward()
calls, but that each such hook be executed before the correspondingforward()
call of that device.Warning
When
module
returns a scalar (i.e., 0-dimensional tensor) inforward()
, this wrapper will return a vector of length equal to number of devices used in data parallelism, containing the result from each device.Note
There is a subtlety in using the
pack sequence -> recurrent network -> unpack sequence
pattern in aModule
wrapped inDataParallel
. See My recurrent network doesn’t work with data parallelism section in FAQ for details.- Parameters
module (Module) – module to be parallelized
device_ids (list of int or torch.device) – CUDA devices (default: all devices)
output_device (int or torch.device) – device location of output (default: device_ids[0])
- Variables
module (Module) – the module to be parallelized
Example:
>>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2]) >>> output = net(input_var) # input_var can be on any device, including CPU