def __init__(self, opt, shared=None):
# In general use a basic TorchAgent wherever possible
super().__init__(opt, shared)
if not shared:
# this is not a shared instance of this class, so do full initialization
# fairseq expects options to be in argparse format, instead of a dict
# We also need to do some argument postprocessing and whatnot
self.args, self.opt = _fairseq_opt_wrapper(opt)
# seed the RNG
torch.manual_seed(self.args.seed)
# Just some identifying info
self.id = "fairseq:{}".format(self.args.arch)
# construct dictionaries for parlai frontend and fairseq backend
self.dict = _FairseqDictionary(self.opt)
# We need a placeholder task for fairseq
self.task = _ParlaiTask(self.dict)
# actually construct the model and generator
model_class = models.ARCH_MODEL_REGISTRY[self.args.arch]
self.model = model_class.build_model(self.args, self.task)
self.generator = SequenceGenerator(
[self.model],
tgt_dict=self.dict,
beam_size=self.args.beam,
stop_early=(not self.args.no_early_stop),
normalize_scores=(not self.args.unnormalized),
len_penalty=self.args.lenpen,
)
# set up the grader and the trainer
# TODO: maybe support label smoothing here
self.criterion = CrossEntropyCriterion(self.args, self.task)
if self.args.fp16:
self.trainer = fp16_trainer.FP16Trainer(
self.args, self.task, self.model, self.criterion
)
else:
# TODO: we might choose to add a --no-fp16 opt in the future to
# explicitly disable fp16 instead
if torch.cuda.get_device_capability(0)[0] >= 7:
print("Heads up: using --fp16 could be a lot faster!")
self.trainer = trainer.Trainer(
self.args, self.task, self.model, self.criterion
)
# if the model already existed, let's preload it and the trainer
if self.opt.get('model_file') and os.path.isfile(self.opt['model_file']):
print('Loading existing model params from ' + self.opt['model_file'])
self.load(self.opt.get('model_file'))
# move things to the GPU if possible
if self.use_cuda:
self.model = self.model.cuda()
self.generator = self.generator.cuda()
# Start things off clean
self.reset()
def __init__(self, opt, shared=None):
# In general use a basic TorchAgent wherever possible
super().__init__(opt, shared)
if not shared:
# this is not a shared instance of this class, so do full initialization
# check early if we're going to be loading the model from a checkpoint
model_file_exists = (self.opt.get('model_file')
and os.path.isfile(self.opt['model_file']))
# fairseq expects options to be in argparse format, instead of a dict
# We also need to do some argument postprocessing and whatnot
# We'll skip pretrained embeddings if we're going to override them with
# a model checkpoint anyway
self.args, self.opt = _fairseq_opt_wrapper(opt, model_file_exists)
# seed the RNG
torch.manual_seed(self.args.seed)
# Just some identifying info
self.id = "fairseq:{}".format(self.args.arch)
# We need a placeholder task for fairseq
self.task = _ParlaiTask(self.dict)
# actually construct the model and generator
self.model = self.build_model()
# Construct the generator and scorer
self.generator = SequenceGenerator(
[self.model],
tgt_dict=self.dict,
beam_size=self.args.beam,
stop_early=(not self.args.no_early_stop),
normalize_scores=(not self.args.unnormalized),
len_penalty=self.args.lenpen,
unk_penalty=self.args.unkpen,
sampling=self.args.sampling,
sampling_topk=self.args.sampling_topk,
sampling_temperature=self.args.sampling_temperature,
)
self.scorer = SequenceScorer([self.model], self.dict)
# set up the grader and the trainer
self.criterion = criterions.build_criterion(self.args, self.task)
if getattr(self.args, 'fp16', None):
self.trainer = fp16_trainer.FP16Trainer(
self.args, self.task, self.model, self.criterion)
else:
# TODO: we might choose to add a --no-fp16 opt in the future to
# explicitly disable fp16 instead
if torch.cuda.get_device_capability(0)[0] >= 7:
print("Heads up: using --fp16 could be a lot faster!")
self.trainer = trainer.Trainer(self.args, self.task,
self.model, self.criterion)
# if the model already existed, let's preload it and the trainer
if model_file_exists:
print('Loading existing model params from ' +
self.opt['model_file'])
self.load(self.opt.get('model_file'))
# move things to the GPU if possible
if self.use_cuda:
self.model = self.model.cuda()
self.generator = self.generator.cuda()
else:
self.model = shared['model']
self.trainer = shared['trainer']
self.generator = shared['generator']
self.dict = shared['dict']
self.args = shared['args']
# Start things off clean
self.reset()
