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"""
Organizes the model itself and its optimizer in one place
Saving the optimizer allows for easy restarting of training
"""
import logging
import os
import torch
import torch.optim as optim
import stanza.models.classifiers.data as data
import stanza.models.classifiers.cnn_classifier as cnn_classifier
from stanza.models.common.foundation_cache import load_bert, load_charlm, load_pretrain
from stanza.models.common.pretrain import Pretrain
logger = logging.getLogger('stanza')
class Trainer:
"""
Stores a constituency model and its optimizer
"""
def __init__(self, model, optimizer=None, epochs_trained=0, global_step=0, best_score=None):
self.model = model
self.optimizer = optimizer
# we keep track of position in the learning so that we can
# checkpoint & restart if needed without restarting the epoch count
self.epochs_trained = epochs_trained
self.global_step = global_step
# save the best dev score so that when reloading a checkpoint
# of a model, we know how far we got
self.best_score = best_score
def save(self, filename, epochs_trained=None, skip_modules=True, save_optimizer=True):
"""
save the current model, optimizer, and other state to filename
epochs_trained can be passed as a parameter to handle saving at the end of an epoch
"""
if epochs_trained is None:
epochs_trained = self.epochs_trained
save_dir = os.path.split(filename)[0]
os.makedirs(save_dir, exist_ok=True)
model_state = self.model.state_dict()
# skip saving modules like pretrained embeddings, because they are large and will be saved in a separate file
if skip_modules:
skipped = [k for k in model_state.keys() if k.split('.')[0] in self.model.unsaved_modules]
for k in skipped:
del model_state[k]
params = {
'model': model_state,
'config': self.model.config,
'labels': self.model.labels,
'extra_vocab': self.model.extra_vocab,
'epochs_trained': epochs_trained,
'global_step': self.global_step,
'best_score': self.best_score,
}
if save_optimizer and self.optimizer is not None:
params['optimizer_state_dict'] = self.optimizer.state_dict()
torch.save(params, filename, _use_new_zipfile_serialization=False)
logger.info("Model saved to {}".format(filename))
@staticmethod
def load(filename, args, foundation_cache=None, load_optimizer=False):
if not os.path.exists(filename):
if os.path.exists(os.path.join(args.save_dir, filename)):
filename = os.path.join(args.save_dir, filename)
else:
raise FileNotFoundError("Cannot find model in {} or in {}".format(filename, os.path.join(args.save_dir, filename)))
try:
checkpoint = torch.load(filename, lambda storage, loc: storage)
except BaseException:
logger.exception("Cannot load model from {}".format(filename))
raise
logger.debug("Loaded model {}".format(filename))
# TODO: should not be needed when all models have this value set
setattr(checkpoint['config'], 'use_elmo', getattr(checkpoint['config'], 'use_elmo', False))
setattr(checkpoint['config'], 'elmo_projection', getattr(checkpoint['config'], 'elmo_projection', False))
setattr(checkpoint['config'], 'char_lowercase', getattr(checkpoint['config'], 'char_lowercase', False))
setattr(checkpoint['config'], 'charlm_projection', getattr(checkpoint['config'], 'charlm_projection', None))
setattr(checkpoint['config'], 'bert_model', getattr(checkpoint['config'], 'bert_model', None))
setattr(checkpoint['config'], 'bilstm', getattr(checkpoint['config'], 'bilstm', False))
setattr(checkpoint['config'], 'bilstm_hidden_dim', getattr(checkpoint['config'], 'bilstm_hidden_dim', 0))
setattr(checkpoint['config'], 'maxpool_width', getattr(checkpoint['config'], 'maxpool_width', 1))
epochs_trained = checkpoint.get('epochs_trained', 0)
global_step = checkpoint.get('global_step', 0)
best_score = checkpoint.get('best_score', None)
# TODO: the getattr is not needed when all models have this baked into the config
model_type = getattr(checkpoint['config'], 'model_type', 'CNNClassifier')
pretrain = Trainer.load_pretrain(args, foundation_cache)
elmo_model = utils.load_elmo(args.elmo_model) if args.use_elmo else None
charmodel_forward = load_charlm(args.charlm_forward_file, foundation_cache)
charmodel_backward = load_charlm(args.charlm_backward_file, foundation_cache)
bert_model = checkpoint['config'].bert_model
bert_model, bert_tokenizer = load_bert(bert_model, foundation_cache)
if model_type == 'CNNClassifier':
extra_vocab = checkpoint.get('extra_vocab', None)
model = cnn_classifier.CNNClassifier(pretrain=pretrain,
extra_vocab=extra_vocab,
labels=checkpoint['labels'],
charmodel_forward=charmodel_forward,
charmodel_backward=charmodel_backward,
elmo_model=elmo_model,
bert_model=bert_model,
bert_tokenizer=bert_tokenizer,
args=checkpoint['config'])
else:
raise ValueError("Unknown model type {}".format(model_type))
model.load_state_dict(checkpoint['model'], strict=False)
if args.cuda:
model.cuda()
logger.debug("-- MODEL CONFIG --")
for k in model.config.__dict__:
logger.debug(" --{}: {}".format(k, model.config.__dict__[k]))
logger.debug("-- MODEL LABELS --")
logger.debug(" {}".format(" ".join(model.labels)))
optimizer = None
if load_optimizer:
optimizer = Trainer.build_optimizer(model, args)
if checkpoint.get('optimizer_state_dict', None) is not None:
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
logger.info("Attempted to load optimizer to resume training, but optimizer not saved. Creating new optimizer")
trainer = Trainer(model, optimizer, epochs_trained, global_step, best_score)
return trainer
def load_pretrain(args, foundation_cache):
if args.wordvec_pretrain_file:
pretrain_file = args.wordvec_pretrain_file
elif args.wordvec_type:
pretrain_file = '{}/{}.{}.pretrain.pt'.format(args.save_dir, args.shorthand, args.wordvec_type.name.lower())
else:
raise RuntimeError("TODO: need to get the wv type back from get_wordvec_file")
logger.info("Looking for pretrained vectors in {}".format(pretrain_file))
if os.path.exists(pretrain_file):
return load_pretrain(pretrain_file, foundation_cache)
elif args.wordvec_raw_file:
vec_file = args.wordvec_raw_file
logger.info("Pretrain not found. Looking in {}".format(vec_file))
else:
vec_file = utils.get_wordvec_file(args.wordvec_dir, args.shorthand, args.wordvec_type.name.lower())
logger.info("Pretrain not found. Looking in {}".format(vec_file))
pretrain = Pretrain(pretrain_file, vec_file, args.pretrain_max_vocab)
logger.info("Embedding shape: %s" % str(pretrain.emb.shape))
return pretrain
@staticmethod
def build_new_model(args, train_set):
"""
Load pretrained pieces and then build a new model
"""
if train_set is None:
raise ValueError("Must have a train set to build a new model - needed for labels and delta word vectors")
pretrain = Trainer.load_pretrain(args, foundation_cache=None)
elmo_model = utils.load_elmo(args.elmo_model) if args.use_elmo else None
charmodel_forward = load_charlm(args.charlm_forward_file)
charmodel_backward = load_charlm(args.charlm_backward_file)
labels = data.dataset_labels(train_set)
extra_vocab = data.dataset_vocab(train_set)
bert_model, bert_tokenizer = load_bert(args.bert_model)
model = cnn_classifier.CNNClassifier(pretrain=pretrain,
extra_vocab=extra_vocab,
labels=labels,
charmodel_forward=charmodel_forward,
charmodel_backward=charmodel_backward,
elmo_model=elmo_model,
bert_model=bert_model,
bert_tokenizer=bert_tokenizer,
args=args)
if args.cuda:
model.cuda()
optimizer = Trainer.build_optimizer(model, args)
return Trainer(model, optimizer)
@staticmethod
def build_optimizer(model, args):
if args.optim.lower() == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay)
elif args.optim.lower() == 'adadelta':
optimizer = optim.Adadelta(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
elif args.optim.lower() == 'madgrad':
try:
import madgrad
except ModuleNotFoundError as e:
raise ModuleNotFoundError("Could not create madgrad optimizer. Perhaps the madgrad package is not installed") from e
optimizer = madgrad.MADGRAD(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay, momentum=args.momentum)
else:
raise ValueError("Unknown optimizer: %s" % args.optim)
return optimizer
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