Source code for torch.nn.parallel.data_parallel

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._utils import (

__all__ = ['DataParallel', 'data_parallel']

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 = [_get_device_index(x, True) for x in device_ids]
    dev_props = _get_devices_properties(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):
    if warn_imbalance(lambda props: props.multi_processor_count):

[docs]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:: >>> # xdoctest: +SKIP >>> 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().__init__() torch._C._log_api_usage_once("torch.nn.parallel.DataParallel") device_type = _get_available_device_type() if device_type is None: self.module = module self.device_ids = [] return if device_ids is None: device_ids = _get_all_device_indices() if output_device is None: output_device = device_ids[0] self.dim = dim self.module = module self.device_ids = [_get_device_index(x, True) for x in device_ids] self.output_device = _get_device_index(output_device, True) self.src_device_obj = torch.device(device_type, self.device_ids[0]) _check_balance(self.device_ids) if len(self.device_ids) == 1: def forward(self, *inputs, **kwargs): with torch.autograd.profiler.record_function("DataParallel.forward"): 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) # for forward function without any inputs, empty list and dict will be created # so the module can be executed on one device which is the first one in device_ids if not inputs and not kwargs: inputs = ((),) kwargs = ({},) 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)
[docs]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 inputs is not None else () device_type = _get_available_device_type() if device_ids is None: device_ids = _get_all_device_indices() if output_device is None: output_device = device_ids[0] device_ids = [_get_device_index(x, True) for x in device_ids] output_device = _get_device_index(output_device, True) src_device_obj = torch.device(device_type, 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) # for module without any inputs, empty list and dict will be created # so the module can be executed on one device which is the first one in device_ids if not inputs and not module_kwargs: inputs = ((),) module_kwargs = ({},) 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)


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