import operator
import torch
import warnings
from itertools import chain
from ..modules import Module
from .scatter_gather import scatter_kwargs, gather
from .replicate import replicate
from .parallel_apply import parallel_apply
from torch.cuda._utils import _get_device_index
def _check_balance(device_ids):
imbalance_warn = """
There is an imbalance between your GPUs. You may want to exclude GPU {} which
has less than 75% of the memory or cores of GPU {}. You can do so by setting
the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES
environment variable."""
device_ids = list(map(lambda x: _get_device_index(x, True), device_ids))
dev_props = [torch.cuda.get_device_properties(i) for i in device_ids]
def warn_imbalance(get_prop):
values = [get_prop(props) for props in dev_props]
min_pos, min_val = min(enumerate(values), key=operator.itemgetter(1))
max_pos, max_val = max(enumerate(values), key=operator.itemgetter(1))
if min_val / max_val < 0.75:
warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))
return True
return False
if warn_imbalance(lambda props: props.total_memory):
return
if warn_imbalance(lambda props: props.multi_processor_count):
return
class DataParallel(Module):
r"""Implements data parallelism at the module level.
This container parallelizes the application of the given :attr:`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 :class:`~torch.nn.parallel.DistributedDataParallel`,
instead of this class, to do multi-GPU training, even if there is only a single
node. See: :ref:`cuda-nn-ddp-instead` and :ref:`ddp`.
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 :attr:`module` must have its parameters and buffers on
``device_ids[0]`` before running this :class:`~torch.nn.DataParallel`
module.
.. warning::
In each forward, :attr:`module` is **replicated** on each device, so any
updates to the running module in ``forward`` will be lost. For example,
if :attr:`module` has a counter attribute that is incremented in each
``forward``, it will always stay at the initial value because the update
is done on the replicas which are destroyed after ``forward``. However,
:class:`~torch.nn.DataParallel` guarantees that the replica on
``device[0]`` will have its parameters and buffers sharing storage with
the base parallelized :attr:`module`. So **in-place** updates to the
parameters or buffers on ``device[0]`` will be recorded. E.g.,
:class:`~torch.nn.BatchNorm2d` and :func:`~torch.nn.utils.spectral_norm`
rely on this behavior to update the buffers.
.. warning::
Forward and backward hooks defined on :attr:`module` and its submodules
will be invoked ``len(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 via
:meth:`~torch.nn.Module.register_forward_pre_hook` be executed before
`all` ``len(device_ids)`` :meth:`~torch.nn.Module.forward` calls, but
that each such hook be executed before the corresponding
:meth:`~torch.nn.Module.forward` call of that device.
.. warning::
When :attr:`module` returns a scalar (i.e., 0-dimensional tensor) in
:func:`forward`, 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 a
:class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`.
See :ref:`pack-rnn-unpack-with-data-parallelism` section in FAQ for
details.
Args:
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])
Attributes:
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
"""
# TODO: update notes/cuda.rst when this class handles 8+ GPUs well
def __init__(self, module, device_ids=None, output_device=None, dim=0):
super(DataParallel, self).__init__()
if not torch.cuda.is_available():
self.module = module
self.device_ids = []
return
if device_ids is None:
device_ids = list(range(torch.cuda.device_count()))
if output_device is None:
output_device = device_ids[0]
self.dim = dim
self.module = module
self.device_ids = list(map(lambda x: _get_device_index(x, True), device_ids))
self.output_device = _get_device_index(output_device, True)
self.src_device_obj = torch.device("cuda:{}".format(self.device_ids[0]))
_check_balance(self.device_ids)
if len(self.device_ids) == 1:
self.module.cuda(device_ids[0])
def forward(self, *inputs, **kwargs):
if not self.device_ids:
return self.module(*inputs, **kwargs)
for t in chain(self.module.parameters(), self.module.buffers()):
if t.device != self.src_device_obj:
raise RuntimeError("module must have its parameters and buffers "
"on device {} (device_ids[0]) but found one of "
"them on device: {}".format(self.src_device_obj, t.device))
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
if len(self.device_ids) == 1:
return self.module(*inputs[0], **kwargs[0])
replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
outputs = self.parallel_apply(replicas, inputs, kwargs)
return self.gather(outputs, self.output_device)
def replicate(self, module, device_ids):
return replicate(module, device_ids, not torch.is_grad_enabled())
def scatter(self, inputs, kwargs, device_ids):
return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
def parallel_apply(self, replicas, inputs, kwargs):
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
def gather(self, outputs, output_device):
return gather(outputs, output_device, dim=self.dim)
def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):
r"""Evaluates module(input) in parallel across the GPUs given in device_ids.
This is the functional version of the DataParallel module.
Args:
module (Module): the module to evaluate in parallel
inputs (Tensor): inputs to the module
device_ids (list of int or torch.device): GPU ids on which to replicate module
output_device (list of int or torch.device): GPU location of the output Use -1 to indicate the CPU.
(default: device_ids[0])
Returns:
a Tensor containing the result of module(input) located on
output_device
"""
if not isinstance(inputs, tuple):
inputs = (inputs,)
if device_ids is None:
device_ids = list(range(torch.cuda.device_count()))
if output_device is None:
output_device = device_ids[0]
device_ids = list(map(lambda x: _get_device_index(x, True), device_ids))
output_device = _get_device_index(output_device, True)
src_device_obj = torch.device("cuda:{}".format(device_ids[0]))
for t in chain(module.parameters(), module.buffers()):
if t.device != src_device_obj:
raise RuntimeError("module must have its parameters and buffers "
"on device {} (device_ids[0]) but found one of "
"them on device: {}".format(src_device_obj, t.device))
inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)
if len(device_ids) == 1:
return module(*inputs[0], **module_kwargs[0])
used_device_ids = device_ids[:len(inputs)]
replicas = replicate(module, used_device_ids)
outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)
return gather(outputs, output_device, dim)
