mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-09 11:32:07 +08:00
added gradient clipping
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William Falcon
+144
-204
@@ -1,231 +1,171 @@
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#### Template model definition
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In 99% of cases you want to just copy this template to start a new lightningModule and change the core of what your model is actually trying to do.
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### Template model definition
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In 99% of cases you want to just copy [this template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py) to start a new lightningModule and change the core of what your model is actually trying to do.
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``` {.python}
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import os
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from collections import OrderedDict
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import torch.nn as nn
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from torchvision.datasets import MNIST
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import torchvision.transforms as transforms
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import torch
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import torch.nn.functional as F
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```bash
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# get a copy of the module template
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wget https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py
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```
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---
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### Trainer Example
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** \_\_main__ function**
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Normally, we want to let the \_\_main__ function start the training.
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Inside the main we parse training arguments with whatever hyperparameters we want. Your LightningModule will have a
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chance to add hyperparameters.
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```{.python}
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from test_tube import HyperOptArgumentParser
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from torch import optim
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from pytorch_lightning.root_module.root_module import LightningModule
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if __name__ == '__main__':
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# use default args given by lightning
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root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
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parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
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add_default_args(parent_parser, root_dir)
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class LightningTemplateModel(LightningModule):
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# allow model to overwrite or extend args
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parser = ExampleModel.add_model_specific_args(parent_parser)
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hyperparams = parser.parse_args()
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# train model
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main(hyperparams)
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```
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**Main Function**
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The main function is your entry into the program. This is where you init your model, checkpoint directory, and launch the training.
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The main function should have 3 arguments:
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- hparams: a configuration of hyperparameters.
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- slurm_manager: Slurm cluster manager object (can be None)
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- dict: for you to return any values you want (useful in meta-learning, otherwise set to _)
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```{}
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def main(hparams, cluster, results_dict):
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"""
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Sample model to show how to define a template
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Main training routine specific for this project
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:param hparams:
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:return:
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"""
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# init experiment
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log_dir = os.path.dirname(os.path.realpath(__file__))
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exp = Experiment(
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name='test_tube_exp',
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debug=True,
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save_dir=log_dir,
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version=0,
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autosave=False,
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description='test demo'
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)
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# set the hparams for the experiment
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exp.argparse(hparams)
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exp.save()
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def __init__(self, hparams):
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"""
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Pass in parsed HyperOptArgumentParser to the model
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:param hparams:
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"""
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# init superclass
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super(LightningTemplateModel, self).__init__(hparams)
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# build model
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model = MyLightningModule(hparams)
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self.batch_size = hparams.batch_size
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# callbacks
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early_stop = EarlyStopping(
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monitor=hparams.early_stop_metric,
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patience=hparams.early_stop_patience,
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verbose=True,
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mode=hparams.early_stop_mode
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)
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# build model
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self.__build_model()
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model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
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checkpoint = ModelCheckpoint(
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filepath=model_save_path,
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save_function=None,
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save_best_only=True,
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verbose=True,
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monitor=hparams.model_save_monitor_value,
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mode=hparams.model_save_monitor_mode
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)
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# ---------------------
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# MODEL SETUP
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# ---------------------
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def __build_model(self):
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"""
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Layout model
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:return:
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"""
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self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
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self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
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self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
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# configure trainer
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trainer = Trainer(
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experiment=exp,
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cluster=cluster,
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checkpoint_callback=checkpoint,
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early_stop_callback=early_stop,
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)
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self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)
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# train model
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trainer.fit(model)
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```
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# ---------------------
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# TRAINING
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# ---------------------
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def forward(self, x):
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"""
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No special modification required for lightning, define as you normally would
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:param x:
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:return:
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"""
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x = self.c_d1(x)
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x = torch.tanh(x)
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x = self.c_d1_bn(x)
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x = self.c_d1_drop(x)
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x = self.c_d2(x)
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logits = F.log_softmax(x, dim=1)
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return logits
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The __main__ function will start training on your **main** function. If you use the HyperParameterOptimizer
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in hyper parameter optimization mode, this main function will get one set of hyperparameters. If you use it as a simple
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argument parser you get the default arguments in the argument parser.
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def loss(self, labels, logits):
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nll = F.nll_loss(logits, labels)
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return nll
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So, calling main(hyperparams) runs the model with the default argparse arguments.
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```{.python}
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main(hyperparams)
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```
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def training_step(self, data_batch, batch_i):
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"""
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Lightning calls this inside the training loop
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:param data_batch:
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:return:
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"""
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# forward pass
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x, y = data_batch
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x = x.view(x.size(0), -1)
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y_hat = self.forward(x)
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---
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#### CPU hyperparameter search
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# calculate loss
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loss_val = self.loss(y, y_hat)
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```{.python}
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# run a grid search over 20 hyperparameter combinations.
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hyperparams.optimize_parallel_cpu(
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main_local,
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nb_trials=20,
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nb_workers=1
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)
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```
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output = OrderedDict({
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'loss': loss_val,
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'tqdm_metrics': {}
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})
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return output
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---
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#### Hyperparameter search on a single or multiple GPUs
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```{.python}
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# run a grid search over 20 hyperparameter combinations.
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hyperparams.optimize_parallel_gpu(
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main_local,
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nb_trials=20,
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nb_workers=1,
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gpus=[0,1,2,3]
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)
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```
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def validation_step(self, data_batch, batch_i):
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"""
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Lightning calls this inside the validation loop
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:param data_batch:
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:return:
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"""
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x, y = data_batch
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x = x.view(x.size(0), -1)
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y_hat = self.forward(x)
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---
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#### Hyperparameter search on a SLURM HPC cluster
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```{.python}
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def optimize_on_cluster(hyperparams):
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# enable cluster training
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cluster = SlurmCluster(
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hyperparam_optimizer=hyperparams,
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log_path=hyperparams.tt_save_path,
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test_tube_exp_name=hyperparams.tt_name
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)
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loss_val = self.loss(y, y_hat)
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# email for cluster coms
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cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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# configure cluster
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cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
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cluster.job_time = '48:00:00'
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cluster.gpu_type = '1080ti'
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cluster.memory_mb_per_node = 48000
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': torch.tensor(val_acc),
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})
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return output
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# any modules for code to run in env
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cluster.add_command('source activate pytorch_lightning')
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def validation_end(self, outputs):
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"""
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Called at the end of validation to aggregate outputs
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:param outputs: list of individual outputs of each validation step
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:return:
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"""
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val_loss_mean = 0
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val_acc_mean = 0
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for output in outputs:
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val_loss_mean += output['val_loss']
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val_acc_mean += output['val_acc']
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# name of exp
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job_display_name = hyperparams.tt_name.split('_')[0]
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job_display_name = job_display_name[0:3]
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val_loss_mean /= len(outputs)
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val_acc_mean /= len(outputs)
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tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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return tqdm_dic
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# run hopt
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print('submitting jobs...')
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cluster.optimize_parallel_cluster_gpu(
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main,
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nb_trials=hyperparams.nb_hopt_trials,
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job_name=job_display_name
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)
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def update_tng_log_metrics(self, logs):
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return logs
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# ---------------------
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# MODEL SAVING
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# ---------------------
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def get_save_dict(self):
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checkpoint = {'state_dict': self.state_dict()}
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return checkpoint
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def load_model_specific(self, checkpoint):
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self.load_state_dict(checkpoint['state_dict'])
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pass
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# ---------------------
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# TRAINING SETUP
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# ---------------------
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def configure_optimizers(self):
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"""
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return whatever optimizers we want here
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:return: list of optimizers
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"""
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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return [optimizer]
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def __dataloader(self, train):
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# init data generators
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transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)
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loader = torch.utils.data.DataLoader(
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dataset=dataset,
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batch_size=self.hparams.batch_size,
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shuffle=True
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)
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return loader
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@property
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def tng_dataloader(self):
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if self._tng_dataloader is None:
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try:
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self._tng_dataloader = self.__dataloader(train=True)
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except Exception as e:
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print(e)
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raise e
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return self._tng_dataloader
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@property
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def val_dataloader(self):
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if self._val_dataloader is None:
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try:
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self._val_dataloader = self.__dataloader(train=False)
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except Exception as e:
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print(e)
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raise e
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return self._val_dataloader
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@property
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def test_dataloader(self):
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if self._test_dataloader is None:
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try:
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self._test_dataloader = self.__dataloader(train=False)
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except Exception as e:
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print(e)
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raise e
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return self._test_dataloader
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@staticmethod
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def add_model_specific_args(parent_parser, root_dir):
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"""
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Parameters you define here will be available to your model through self.hparams
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:param parent_parser:
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:param root_dir:
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:return:
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"""
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parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
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# param overwrites
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# parser.set_defaults(gradient_clip=5.0)
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# network params
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parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
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parser.add_argument('--in_features', default=28*28)
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parser.add_argument('--out_features', default=10)
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parser.add_argument('--hidden_dim', default=50000) # use 500 for CPU, 50000 for GPU to see speed difference
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# data
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parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
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# training params (opt)
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parser.opt_list('--learning_rate', default=0.001, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
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tunable=False)
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parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
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parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
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return parser
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```
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# run cluster hyperparameter search
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optimize_on_cluster(hyperparams)
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```
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