import os from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F from torch import optim from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from torchvision import transforms from torchvision.datasets import MNIST try: from test_tube import HyperOptArgumentParser except ImportError: # TODO: this should be discussed and moved out of this package raise ImportError('Missing test-tube package.') from pytorch_lightning.core.decorators import data_loader from pytorch_lightning.core.lightning import LightningModule class TestingMNIST(MNIST): def __init__(self, root, train=True, transform=None, target_transform=None, download=False, num_samples=8000): super(TestingMNIST, self).__init__( root, train=train, transform=transform, target_transform=target_transform, download=download ) # take just a subset of MNIST dataset self.data = self.data[:num_samples] self.targets = self.targets[:num_samples] class LightningTestModelBase(LightningModule): """ Base LightningModule for testing. Implements only the required interface """ def __init__(self, hparams, force_remove_distributed_sampler=False): """ Pass in parsed HyperOptArgumentParser to the model :param hparams: """ # init superclass super(LightningTestModelBase, self).__init__() self.hparams = hparams self.batch_size = hparams.batch_size # if you specify an example input, the summary will show input/output for each layer self.example_input_array = torch.rand(5, 28 * 28) # remove to test warning for dist sampler self.force_remove_distributed_sampler = force_remove_distributed_sampler # build model self.__build_model() # --------------------- # MODEL SETUP # --------------------- def __build_model(self): """ Layout model :return: """ self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim) self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim) self.c_d1_drop = nn.Dropout(self.hparams.drop_prob) self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features) # --------------------- # TRAINING # --------------------- def forward(self, x): """ No special modification required for lightning, define as you normally would :param x: :return: """ x = self.c_d1(x) x = torch.tanh(x) x = self.c_d1_bn(x) x = self.c_d1_drop(x) x = self.c_d2(x) logits = F.log_softmax(x, dim=1) return logits def loss(self, labels, logits): nll = F.nll_loss(logits, labels) return nll def training_step(self, batch, batch_idx): """ Lightning calls this inside the training loop :param batch: :return: """ # forward pass x, y = batch x = x.view(x.size(0), -1) y_hat = self.forward(x) # calculate loss loss_val = self.loss(y, y_hat) # in DP mode (default) make sure if result is scalar, there's another dim in the beginning if self.trainer.use_dp: loss_val = loss_val.unsqueeze(0) # alternate possible outputs to test if self.trainer.batch_idx % 1 == 0: output = OrderedDict({ 'loss': loss_val, 'progress_bar': {'some_val': loss_val * loss_val}, 'log': {'train_some_val': loss_val * loss_val}, }) return output if self.trainer.batch_idx % 2 == 0: return loss_val # --------------------- # TRAINING SETUP # --------------------- def configure_optimizers(self): """ return whatever optimizers we want here. :return: list of optimizers """ # try no scheduler for this model (testing purposes) if self.hparams.optimizer_name == 'lbfgs': optimizer = optim.LBFGS(self.parameters(), lr=self.hparams.learning_rate) else: optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate) # test returning only 1 list instead of 2 return optimizer def _dataloader(self, train): # init data generators transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))]) dataset = TestingMNIST(root=self.hparams.data_root, train=train, transform=transform, download=True, num_samples=2000) # when using multi-node we need to add the datasampler train_sampler = None batch_size = self.hparams.batch_size try: if self.use_ddp and not self.force_remove_distributed_sampler: train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank) batch_size = batch_size // self.trainer.world_size # scale batch size except Exception: pass should_shuffle = train_sampler is None loader = DataLoader( dataset=dataset, batch_size=batch_size, shuffle=should_shuffle, sampler=train_sampler ) return loader @data_loader def train_dataloader(self): return self._dataloader(train=True) @staticmethod def add_model_specific_args(parent_parser, root_dir): # pragma: no cover """ Parameters you define here will be available to your model through self.hparams :param parent_parser: :param root_dir: :return: """ parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser]) # param overwrites # parser.set_defaults(gradient_clip_val=5.0) # network params parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False) parser.add_argument('--in_features', default=28 * 28, type=int) parser.add_argument('--out_features', default=10, type=int) # use 500 for CPU, 50000 for GPU to see speed difference parser.add_argument('--hidden_dim', default=50000, type=int) # data parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str) # training params (opt) parser.opt_list('--learning_rate', default=0.001 * 8, type=float, options=[0.0001, 0.0005, 0.001, 0.005], tunable=False) parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False) # if using 2 nodes with 4 gpus each the batch size here # (256) will be 256 / (2*8) = 16 per gpu parser.opt_list('--batch_size', default=256 * 8, type=int, options=[32, 64, 128, 256], tunable=False, help='batch size will be divided over all gpus being used across all nodes') return parser