def get_model(args):
ntokens = args.data_size
concat_pools = [args.concat_max, args.concat_min, args.concat_mean]
if args.model.lower() == 'transformer':
model = TransformerFeaturizer(False, args)
else:
model = RNNFeaturizer(args.model, ntokens, args.emsize, args.nhid, args.nlayers,
0.0, args.all_layers, concat_pools, residuals=args.residuals)
if args.cuda:
model.cuda()
if args.fp16:
model.half()
if args.load is not None and args.load != '':
# load char embedding and recurrent encoder for featurization
with open(args.load, 'rb') as f:
sd = x = torch.load(f)
if 'sd' in sd:
sd = sd['sd']
if 'lm_encoder' in sd:
sd = sd['lm_encoder']
try:
model.load_state_dict(sd)
except:
# if state dict has weight normalized parameters apply and remove weight norm to model while loading sd
if hasattr(model, 'rnn'):
apply_weight_norm(model.rnn)
else:
apply_weight_norm(model)
model.load_state_dict(sd)
remove_weight_norm(model)
return model
def get_model(args):
sd = None
model_args = args
if args.load is not None and args.load != '':
sd = torch.load(args.load)
if 'args' in sd:
model_args = sd['args']
if 'sd' in sd:
sd = sd['sd']
ntokens = model_args.data_size
concat_pools = model_args.concat_max, model_args.concat_min, model_args.concat_mean
if args.model == 'transformer':
model = SentimentClassifier(model_args.model, ntokens, None, None,
None, model_args.classifier_hidden_layers,
model_args.classifier_dropout, None,
concat_pools, False, model_args)
else:
model = SentimentClassifier(
model_args.model, ntokens, model_args.emsize, model_args.nhid,
model_args.nlayers, model_args.classifier_hidden_layers,
model_args.classifier_dropout, model_args.all_layers, concat_pools,
False, model_args)
args.heads_per_class = model_args.heads_per_class
args.use_softmax = model_args.use_softmax
try:
args.classes = list(model_args.classes)
except:
args.classes = [args.label_key]
try:
args.dual_thresh = model_args.dual_thresh and not model_args.joint_binary_train
except:
args.dual_thresh = False
if args.cuda:
model.cuda()
if args.fp16:
model.half()
if sd is not None:
try:
model.load_state_dict(sd)
except:
# if state dict has weight normalized parameters apply and remove weight norm to model while loading sd
if hasattr(model.lm_encoder, 'rnn'):
apply_weight_norm(model.lm_encoder.rnn)
else:
apply_weight_norm(model.lm_encoder)
model.lm_encoder.load_state_dict(sd)
remove_weight_norm(model)
if args.neurons > 0:
print('WARNING. Setting neurons %s' % str(args.neurons))
model.set_neurons(args.neurons)
return model
def remove_weight_norm(self): """removes weight norm from all module parameters""" remove_weight_norm(self.module) self.weight_norm = False
def get_model_and_optim(args, train_data):
if args.use_softmax:
args.report_no_thresholding = True
ntokens = args.data_size
concat_pools = args.concat_max, args.concat_min, args.concat_mean
if args.model == 'transformer':
model = M.SentimentClassifier(
model_type=args.model,
ntoken=ntokens,
ninp=None,
nhid=None,
nlayers=None,
classifier_hidden_layers=args.classifier_hidden_layers,
dropout=args.classifier_dropout,
all_layers=None,
concat_pools=concat_pools,
get_lm_out=args.aux_lm_loss,
args=args,
)
else:
model = M.SentimentClassifier(
model_type=args.model,
ntoken=ntokens,
ninp=args.emsize,
nhid=args.nhid,
nlayers=args.nlayers,
classifier_hidden_layers=args.classifier_hidden_layers,
dropout=args.classifier_dropout,
all_layers=args.all_layers,
concat_pools=concat_pools,
get_lm_out=args.aux_lm_loss,
args=args,
)
if args.cuda:
model.cuda()
if args.fp16:
model.half()
# load char embedding and recurrent encoder for featurization
if args.load is not None and args.load != '':
with open(args.load, 'rb') as f:
sd = x = torch.load(f, 'cpu')
if 'sd' in sd:
sd = sd['sd']
if not args.load_finetuned:
if 'lm_encoder' in sd:
sd = sd['lm_encoder']
try:
model.lm_encoder.load_state_dict(sd)
except:
# if state dict has weight normalized parameters apply and remove weight norm to model while loading sd
if hasattr(model.lm_encoder, 'rnn'):
apply_weight_norm(model.lm_encoder.rnn)
else:
apply_weight_norm(model.lm_encoder)
model.lm_encoder.load_state_dict(sd)
remove_weight_norm(model)
else:
model.load_state_dict(sd)
if args.thresh_test_preds:
model.set_thresholds(
pd.read_csv(args.thresh_test_preds,
header=None).values.squeeze(), args.double_thresh,
args.dual_thresh and not args.joint_binary_train)
optims = {'adam': 'Adam', 'sgd': 'SGD'}
optim = eval('torch.optim.' + optims[args.optim.lower()])(
model.parameters(), lr=args.lr)
iters_per_epoch = len(train_data)
num_iters = iters_per_epoch * args.epochs
assert not (args.stlr_cut_frac and args.cos_cut_frac)
if args.stlr_cut_frac is not None:
LR = SlantedTriangularLR(optim,
max_val=args.lr,
cut_frac=args.stlr_cut_frac,
num_iters=num_iters)
elif args.cos_cut_frac is not None:
LR = AnnealingLR(optim,
start_lr=args.lr,
warmup_iter=int(args.cos_cut_frac * num_iters),
num_iters=num_iters,
decay_style='cosine')
elif args.decay_style is not None:
warmup_iters = int(args.warmup_epochs * iters_per_epoch)
if args.decay_epochs == -1:
decay_iters = int(args.epochs * iters_per_epoch)
else:
decay_iters = int(args.decay_epochs * iters_per_epoch)
if args.decay_style == 'constant':
#TODO: implement
LR = AnnealingLR(optim,
start_lr=args.lr,
warmup_iter=warmup_iters,
num_iters=decay_iters + warmup_iters,
decay_style=args.decay_style)
elif args.decay_style == 'linear':
#TODO: implement
LR = AnnealingLR(optim,
start_lr=args.lr,
warmup_iter=warmup_iters,
num_iters=decay_iters + warmup_iters,
decay_style=args.decay_style)
elif args.decay_style == 'cosine':
LR = AnnealingLR(optim,
start_lr=args.lr,
warmup_iter=warmup_iters,
num_iters=decay_iters + warmup_iters,
decay_style=args.decay_style)
elif args.decay_style == 'exponential':
#TODO: implement
LR = ConstantLR(optim, lr=args.lr)
else:
LR = ConstantLR(optim, lr=args.lr)
else:
LR = ConstantLR(optim, lr=args.lr)
return model, optim, LR
def setup_model_and_optim(args, train_data, tokenizer):
ntokens = args.data_size
if args.model.lower() == 'transformer':
embed_tokens = m.Embedding(
ntokens,
args.decoder_embed_dim,
padding_idx=tokenizer.command_name_map['pad'].Id)
model = m.TransformerModel(m.DecoderPreprocessor(args, embed_tokens),
m.TransformerDecoder(args, embed_tokens))
else:
model = m.RNNModel(args.model, ntokens, args.emsize, args.nhid,
args.nlayers, args.dropout, args.tied)
global rnn_model
rnn_model = model
LR_Warmer = None
print('* number of parameters: %d' %
sum([p.nelement() for p in model.parameters()]))
if args.cuda:
model.cuda()
optim = None
if args.load is not None and args.load != '':
sd = torch.load(args.load, map_location='cpu')
if args.load_optim:
#optim_sd = torch.load(os.path.join(os.path.dirname(args.load), 'optim.pt'), map_location='cpu')
rng = torch.load(os.path.join(os.path.dirname(args.load),
'rng.pt'))
torch.cuda.set_rng_state(rng[0])
torch.set_rng_state(rng[1])
try:
model.load_state_dict(sd)
except:
if hasattr(model, 'rnn'):
apply_weight_norm(model.rnn, hook_child=False)
else:
apply_weight_norm(model, hook_child=False)
model.load_state_dict(sd)
remove_weight_norm(model)
if not args.no_weight_norm:
if hasattr(model, 'rnn'):
apply_weight_norm(model.rnn, hook_child=False)
else:
apply_weight_norm(model, hook_child=False)
if optim is None:
optim_choice = 'Adam' if args.stlr_cut_frac else args.optim
if args.fp16:
model = FP16_Module(model)
optim = eval('torch.optim.' + args.optim)(model.parameters(),
lr=args.lr)
optim = FP16_Optimizer(optim,
static_loss_scale=args.loss_scale,
dynamic_loss_scale=args.dynamic_loss_scale)
else:
optim = eval('torch.optim.' + args.optim)(model.parameters(),
lr=args.lr)
if args.load_optim:
optim.load_state_dict(optim_sd)
# add linear learning rate scheduler
if train_data is not None:
if args.constant_decay:
num_iters = args.constant_decay
else:
num_iters = args.train_iters * args.epochs
init_step = -1
if args.load_optim:
#TODO: this no longer makes sense given the new data loaders
init_step = optim_sd['iter'] - optim_sd['skipped_iter']
train_data.batch_sampler.start_iter = (optim_sd['iter'] %
len(train_data)) + 1
warmup_iter = args.warmup * num_iters
if args.stlr_cut_frac is not None:
LR = SlantedTriangularLR(optim,
cut_frac=args.stlr_cut_frac,
num_iters=num_iters)
else:
LR = AnnealingLR(optim,
start_lr=args.lr,
warmup_iter=warmup_iter,
num_iters=num_iters,
decay_style=args.decay_style)
if args.warmup != 0:
LR_Warmer = WarmupLR(optim, warmup_iter, last_iter=init_step)
# wrap model for distributed training
if args.world_size > 1:
model = DDP(model)
criterion = nn.CrossEntropyLoss(reduce=False)
return model, optim, LR, LR_Warmer, criterion