def create_agent_from_opt_file_and_model_class(opt, model_class):
model_file = opt['model_file']
optfile = model_file + '.opt'
if not PathManager.exists(optfile):
return None
opt_from_file = Opt.load(optfile)
# delete args that we do not want to copy over when loading the model
for arg in NOCOPY_ARGS:
if arg in opt_from_file:
del opt_from_file[arg]
# only override opts specified in 'override' dict
if opt.get('override'):
for k, v in opt['override'].items():
if k in opt_from_file and str(v) != str(opt_from_file.get(k)):
logging.warn(
f'Overriding opt["{k}"] to {v} (previously: {opt_from_file.get(k)})'
)
opt_from_file[k] = v
if hasattr(model_class, 'upgrade_opt'):
opt_from_file = model_class.upgrade_opt(opt_from_file)
# add model arguments to opt_from_file if they aren't in opt_from_file already
for k, v in opt.items():
if k not in opt_from_file:
opt_from_file[k] = v
# update model file path to the one set by opt
opt_from_file['model_file'] = model_file
# update init model path to the one set by opt
# NOTE: this step is necessary when for example the 'init_model' is
# set by the Train Loop (as is the case when loading from checkpoint)
if opt.get('init_model') is not None:
opt_from_file['init_model'] = opt['init_model']
# update dict file path
if not opt_from_file.get('dict_file'):
old_dict_file = None
opt_from_file['dict_file'] = model_file + '.dict'
elif opt_from_file.get('dict_file') and not PathManager.exists(
opt_from_file['dict_file']):
old_dict_file = opt_from_file['dict_file']
opt_from_file['dict_file'] = model_file + '.dict'
if not PathManager.exists(opt_from_file['dict_file']):
warn_once(
'WARNING: Neither the specified dict file ({}) nor the '
'`model_file`.dict file ({}) exists, check to make sure either '
'is correct. This may manifest as a shape mismatch later '
'on.'.format(old_dict_file, opt_from_file['dict_file']))
# if we want to load weights from --init-model, compare opts with
# loaded ones
compare_init_model_opts(opt, opt_from_file)
return model_class(opt_from_file)
def _initialize_bart(self, opt: Opt) -> Opt:
"""
Download and convert BART pre-trained models.
Additionally, convert `init-fairseq-model` if necessary.
:param opt:
ParlAI-parsed options
:return opt:
return opt with BART-specific args.
"""
if not opt.get('converting'):
download(opt['datapath'])
opt['init_model'] = os.path.join(opt['datapath'],
'models/bart/bart_large/model')
if opt.get('init_fairseq_model'):
opt = self._convert_model(opt)
opt.update(BART_ARGS)
compare_init_model_opts(opt, opt)
return opt