Source code for torch.nn.parallel.data_parallel

import torch
from ..modules import Module
from .scatter_gather import scatter_kwargs, gather
from .replicate import replicate
from .parallel_apply import parallel_apply


[docs]class DataParallel(Module): """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. 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. It should also be an integer multiple of the number of GPUs so that each chunk is the same size (so that each GPU processes the same number of samples). See also: :ref:`cuda-nn-dataparallel-instead` Arbitrary positional and keyword inputs are allowed to be passed into DataParallel EXCEPT Tensors. All variables will be scattered on dim specified (default 0). Primitive types will be broadcasted, but all other types will be a shallow copy and can be corrupted if written to in the model's forward pass. Args: module: module to be parallelized device_ids: CUDA devices (default: all devices) output_device: device location of output (default: device_ids[0]) Example:: >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2]) >>> output = net(input_var) """ # 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 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 = device_ids self.output_device = output_device if len(self.device_ids) == 1: self.module.cuda(device_ids[0]) def forward(self, *inputs, **kwargs): 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) 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) 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): """Evaluates module(input) in parallel across the GPUs given in device_ids. This is the functional version of the DataParallel module. Args: module: the module to evaluate in parallel inputs: inputs to the module device_ids: GPU ids on which to replicate module output_device: GPU location of the output Use -1 to indicate the CPU. (default: device_ids[0]) Returns: a Variable 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] inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim) if len(device_ids) == 1: return module(*inputs[0], **module_kwargs[0]) replicas = replicate(module, device_ids[:len(inputs)]) outputs = parallel_apply(replicas, inputs, module_kwargs) return gather(outputs, output_device, dim)